EM360 Podcast

The fact that OpenAI has quickly adopted Temporal, a rapidly expanding AI ecosystem, and has even entered into a partnership with Crystal Palace FC shows that its business strategy is to pursue community-first innovation.For years, tech enterprises competed against each other on the grounds of innovative features. However, the competition has changed recently. Now the winners are those who build communities.In the recent episode of the Tech Transformed podcast, host Christina Stathopoulos, Founder of Dare to Data, is joined by Melissa Herrera, Senior Developer Advocate at Temporal, and Les Jackson, Staff Developer Advocate, to discuss the pivotal role of the community in technology. They further explore how Temporal's open-source philosophy fosters developer engagement, the impact of community feedback on product development, and the significance of partnerships, such as with OpenAI and Crystal Palace. The conversation emphasises the importance of authentic community relationships and the future direction of Temporal, highlighting the need for continuous integration and collaboration with developers.TakeawaysCommunity is a central part of Temporal's growth.Temporal's philosophy is rooted in open-source software.In-person community interactions are invaluable for developers.Feedback from the community directly shapes product direction.OpenAI's adoption of Temporal led to significant scaling.Partnerships should focus on community engagement, not just transactions.Temporal's collaboration with Crystal Palace merges tech and sports communities.Investing in community fosters trust and collaboration.Continuous integration with developer tools is essential for success.Authentic community relationships drive technology innovation.Chapters00:00 Introduction to Tech Transformed Podcast02:45 The Importance of Community in Technology05:13 Feedback from Developers: Shaping Product Direction07:51 OpenAI's Adoption of Temporal: A Case Study10:02 Unique Partnerships: Temporal and Crystal Palace15:12 Looking Ahead: Future of Temporal and Community Engagement19:38 Final Thoughts: Investing in CommunityTemporal, Enterprise AI, Developer Communities, Developer Advocacy, Open Source, OpenAI, AI Agents, AI Infrastructure, Durable Execution, AI Orchestration, Enterprise Software, Developer Experience, AI Workflows, AI Adoption, Community Led Growth, Developer Led Growth, Temporal SDK, Software Engineering, AI Engineering, Enterprise Technology, Crystal Palace, Tech Transformed

In this day and age, there is probably no meeting or conversation in the tech world that doesn't concentrate on how artificial intelligence (AI) can improve the work streams of organisations worldwide. There will definitely be a moment in almost every AI strategy meeting where someone says, "We need agents." In this episode of Tech Transformed, Sara Maldon unpacks with host Christina Stathopoulos how she has heard this sentence countless times. As the head of AI and automation at Make, she's learned to treat that sentence as a starting point because most of the time, the business problem sitting underneath it doesn't actually need an agent at all.This conversation is less about hype and more about when a workflow needs autonomy, and when a simple if-this-then-that rule is doing the job just fine. Maldon's answer comes from two and a half years of running AI transformation inside a company that was automating things long before "agentic" became a buzzword.Where Agentic AI Fits on the Automation SpectrumOn the other end of the spectrum is agentic AI. Rather than following a fixed sequence of instructions, it gives an LLM a goal, the right tools, and enough context to decide for itself what needs to happen next. Maldon points to a simple example. A car-leasing company needed product images pulled automatically from manufacturers' websites. A traditional scraper did the job well enough until BMW redesigned its website. Overnight, the automation failed because the image had moved. "An agent doesn't care where the picture is on the page," she explains. "It just knows it needs to get the BMW photo."This indicates that Agentic AI isn't valuable because it's more "intelligent"; it's beneficial because it can adapt when the environment changes instead of breaking down the moment something shifts. Even Make's own AI sales agent isn't fully autonomous. It's deployed across 60 sales representatives, but, as Maldon points out, it's "95 per cent deterministic." Most of the workflow still follows predefined rules. The agent steps in only when a judgment call is needed, deciding a conversation should be escalated, followed up by email, or moved into a Slack channel. The plumbing remains deterministic because the agent simply adds decision-making where it creates the most value.Building With AIMaldon believes the people best equipped to build workflows are those who understand the business firsthand. Operations teams, customer success, marketing, and other business users see where processes slow down because they work with them every day.She knows this because she has been through the same journey herself. Before leading AI and automation at Make, she trained as a lawyer, not a developer. Today, she describes herself as one of the company's most active builders.For Maldon, technical expertise isn't the defining quality. Domain knowledge comes first, followed by an instinct for improving processes, a desire to solve problems for other people, and enough curiosity and persistence to keep experimenting until something works.This philosophy shapes how Make develops automation internally. Employees rarely begin inside the automation platform itself. Instead, they sketch ideas in Claude, using it to think through the workflow and refine the logic. If an automation proves useful beyond one or two people, the AI team then helps turn that prototype into something production-ready using AI co-worker, Maia by Make. Starting from scratch is becoming the exception rather than the rule. The first draft already exists; the job is to refine, test, and scale it.Scaling Automation Across the BusinessHere's the part leaders don't want to hear: the hard part isn't the AI. It's everything around it. Maldon points to Make's sales agent again, the one handling escalations, follow-ups, and CRM updates after every call. Building the agent itself took two days. Planning and mapping the deterministic pipeline around it took four weeks. Rolling it out to 60 people, with training, feedback loops, and adoption? Four months.Patience, she believes, is the least-discussed skill in AI transformation and maybe the most necessary. Not because the technology is slow, but because people aren't robots; they need time to trust a new process before they'll actually use it.The HR team's onboarding redesign makes the point without needing sales numbers to prove it (though the numbers help - a 10 per cent lift in AI adoption from something as small as a personalised pre-start video). Ninety-six per cent of Make's employees now run an AI agent they built themselves. Not because leadership mandated it, but the tools got low enough friction, and the culture rewarded people for trying. Maldon's advice to leaders stalling on where to start is to stop assessing tools and pick one. Run a hackathon or set aside an afternoon to experiment for a small section of your enterprise. The market moves faster than any evaluation process can keep up with anyway, so get a foot in the door, then figure out the rest as you go. If you would like to find out more, visit make.com or follow Sara Maldon on LinkedIn.TakeawaysOrganisations should start small and iterate quickly with AI projects.Building a culture of builders and curiosity accelerates AI adoption.Patience and soft skills are crucial for scaling automation.Agentic AI allows for more flexible and resilient workflows.Empowering non-technical teams to build with AI democratises innovation.Chapters00:00 Introduction to AI transformation and automation at Make00:57 Meet Sara Maldon and her role at Make01:59 Understanding the spectrum: deterministic vs agentic AI02:52 The agentic spectrum and real-world examples07:04 Building automation in the age of AI09:54 The rise of builders: no-code and operational roles12:02 Scaling automation across organisations16:13 Customer success story18:58 How Make uses AI internally to innovate22:01 Advice for leaders starting with AI and automation24:04 Conclusion and key takeaways from the episode

Every enterprise leader has heard the numbers by now as billions poured into licences, seats and tokens, with productivity gains still trailing well behind the promise. On a recent episode of Tech Transformed, host Christina Stathopoulos sat down with Jay Richman, Chief Product and Technology Officer at Multiverse, to unpack why so many organisations remain stuck in what he calls "tinker mode" and what it actually takes to move past it.Richman's view is shaped by a career spent at the sharp end of three separate technology shifts. A decade at Spotify saw him build out the company's advertising business and early subscription platform from scratch. Roughly four years at Amazon followed, working on agentic AI systems within the advertising division, effectively rebuilding the old-fashioned creative agency model using specialised generative media tools. Four months ago, he relocated from New York to London to join Multiverse, drawn by a mission he sees as almost the reverse of his previous work. Rather than using people to make machines smarter, the task now is training machines to make people smarter. That framing sits at the centre of this conversation.The AI Adoption GapRichman suggests that the widely reported gap between AI spend and AI-driven output isn't really a technology failure; it's a human one. Tools sit unused, licences go unopened, and organisations struggle to move employees from curious onlookers to confident, habitual users. Multiverse's answer is what it calls the "adoption layer", which is a diagnostic approach that pinpoints where skill gaps actually sit across a workforce, builds tailored learning paths around them, then embeds coaching directly inside the tools people already use, whether that's Claude, Gemini or ChatGPT.Crucially, Richman distinguishes between what he wryly terms "token maxing" and value creation. Blunt incentives, leaderboards, usage dashboards and mandates to switch from manual coding to AI-assisted development. All this can help people over that first hurdle, and he admits to having used some of these tactics himself. But the real measure of success has to be defined customer by customer, tied to a specific business outcome, not simply how many prompts someone fired off in a week. As he puts it, the value of any given AI initiative can only be judged against what that particular organisation set out to solve in the first place.Human Skills AI Can't ReplaceOne of the most interesting parts of the discussion explores how generative and agentic AI are reshaping team structures. Richman describes a flattening of traditional boundaries: product managers writing code, designers contributing to technical documents, and engineers drafting strategy papers. Functions that were once tightly specialised, such as a dedicated user-research team and a standalone copywriting role, are increasingly being absorbed as skills within more generalist, multi-hatted employees. At the same time, he notes, the opposite is happening at the model layer, with AI agents becoming ever more specialised. The result, in his telling, is smaller teams working with far greater autonomy and considerably less coordination overhead than in years past.Asked what he prioritises when hiring now, Richman is candid that mastery of a single discipline matters to him less than certain behavioural traits: curiosity, high agency, sound judgment. He shares an interview technique he relies on, asking candidates how they spent their time during the pandemic lockdowns, as a rough proxy for whether someone tends to seize initiative or simply wait for circumstances to dictate their next move.Moving Fast Without Cutting CornersOn responsible deployment, Richman insists culture has to start at the top. Leaders, he argues, need to be visibly hands-on and honest about their own gaps in knowledge rather than issuing mandates from a distance. At Multiverse, that meant lifting caps on AI coding tools, resetting the expectation that new work should be AI-generated by default, and investing heavily in quality assurance and evaluation frameworks so that human reviewers still hold a meaningful checkpoint before anything reaches customers. He contrasts this with the slower, more cautious approach he sees at many other organisations — one he believes is not only less efficient but also less likely to stick once the initial push fades.Measuring What Actually MattersTying this back to the earlier point about value over volume, Richman suggests the real test of an AI initiative is how closely it maps to a business's own stated objective, not adoption figures in isolation. That means starting with what a customer or team is actually trying to achieve, building learning around the specific gaps standing in the way, and only then judging success by whether the resulting behaviour change shows up in the metrics that organisation already cares about.The conversation closes on a personal note, with Richman sharing the advice he'd give his own children as they weigh careers in a fast-shifting landscape: run towards disruption rather than away from it. Having entered the workforce at the tail end of the dot-com boom, he sees clear parallels with the present moment, a period, in his words, where nobody can credibly claim expertise, which makes it as good a time as any to jump in. For organisations still puzzling over why their AI investment hasn't translated into measurable returns, Richman's message is simple: the technology was never really the difficult part. If you would like to find out more, please visit multiverse.io or connect with Richman on LinkedIn.TakeawaysSuccessful AI adoption hinges on people, not just technology.The AI adoption layer helps bridge the gap between investment and productivity.High agency and curiosity are key traits of successful AI practitioners.Leadership must lead by example and foster a culture of experimentation.Rapid technological change requires teams to be flexible and multi-skilled.Chapters00:00 Introduction to AI transformation and people-centred approach00:54 Jay Richman's background at Spotify, Amazon, and Multiverse03:47 The challenge of AI adoption and pilot mode07:04 The concept of the AI adoption layer and its purpose12:54 Distinguishing token maxing from value maxing16:03 Impact of generative AI on product and engineering teams19:09 Building teams in the AI era: blurring roles and skills21:49 Qualities and skills for future AI teams25:49 Leading responsibly with AI and fostering a culture of experimentation

Artificial intelligence has spent the last several years getting smarter on screens, recommending what to watch, drafting what to write, answering what we ask. This type of progress is now spilling out into the physical world, where machines are starting to move, sense, and adapt on their own. In this episode of Tech Transformed, host John Santaferraro, Founder and Analyst at Ferraro Consulting, talks with Prith Banerjee, Senior Vice President of Innovation at Synopsys, about what happens when AI has to obey the laws of physics instead of just the patterns in a dataset.Banerjee brings a rare vantage point to the conversation. Before Synopsys, he led HP Labs worldwide, served as group CTO at ABB and Schneider Electric, and ran engineering simulation software company ANSYS as CTO until its acquisition by Synopsys roughly a year ago - a deal that now anchors much of what he describes as Synopsys's "silicon to systems" strategy.The Road to Physical AIBanerjee traces AI's progress through distinct phases he's tracked across his career. It started with analytics, which was the correlation engines behind a Netflix recommendation, or the placement and routing improvements Synopsys has long applied inside its own chip design tools. Generative AI came next, giving machines the ability to produce original language, images, and video from a prompt rather than just surface existing content. Agentic AI followed close behind, handing off entire tasks, drafting a slide deck, prepping a sales call to systems that act more like assistants than tools.Physical AI is the phase Banerjee sees unfolding now, and it's a different kind of leap. Instead of learning from words or pixels, these systems learn from real physical measurements: pressure, temperature, stress, and strain. Training that kind of intelligence takes synthetic data generated across structural, fluid, and electromagnetic physics precisely the simulation capability ANSYS brought into Synopsys.Teaching Robots to Learn Like HumansThe shift shows up clearly in how robots are built today. A decade ago, getting a robotic arm to pick up a bottle without crushing it meant writing enormous programs, sometimes 100,000 lines of code specifying exactly how each motor should move. Physical AI throws that playbook out. Banerjee compares it to teaching a child to ride a bike: nobody narrates which pedal to push. The child watches, tries, falls, and adjusts.Robots now learn the same way, refining their behaviour through reward and penalty as they attempt a task thousands of times. Autonomous vehicles follow the identical pattern at far greater scale, learning from millions of hours of driving footage until they recognise, for instance, that a pedestrian stepping into the street means stop. Synopsys works with autonomous vehicle and robotics companies to generate the synthetic training data that makes this kind of learning possible without requiring endless real-world testing.Engineering the Intelligent Systems of TomorrowThat intelligence has to run on something, and the conversation turns to what it takes to build the silicon underneath it. Chips that once held a few hundred thousand transistors now carry tens or hundreds of billions, some approaching trillions, stacked using advanced 3D and chiplet techniques. Designing them means balancing power, performance, and thermal limits simultaneously rather than simply over-engineering for safety margin, which Banerjee calls co-design.Synopsys is tackling that complexity with what it calls agent engineers. AI systems introduced at its Converge conference that work alongside human chip designers on tasks like RTL design, test benches, and sign-off, effectively multiplying engineering capacity without multiplying headcount. The same pressure shows up at the edge, where trained AI models have to run inside a drone, car, or warehouse robot on a fraction of the power a data centre would use, with no room for cloud latency. Banerjee shares his perspective on why many AI projects struggle. He argues that the real challenge lies in balancing innovation with trust: robots operating alongside people raise questions of safety and collaboration, while autonomous systems connected to networks demand security, traceability, and clear explanations for the decisions they make.His advice to engineering leaders is to treat this shift as organisation-wide rather than a single team's problem, from legal and marketing functions already using agentic tools to engineering teams rethinking how code gets written. The goal, as he puts it, isn't replacing people but making them capable of far more than they could manage alone. If you would find out more about this, visit Synopsys or follow Prith Banerjee on LinkedIn.TakeawaysEvolution of AI from analytics to physical AI.Role of synthetic data in training physical AI.How robots learn through physical interactions.Complexity and innovation in chip design.Agentic AI and its applications in engineering.Challenges of edge AI in autonomous systems.Security, governance, and safety in physical AI.Chapters00:00 Introduction to AI's Evolution and Physical AI01:15 Prith Banerjee's Career Journey and Role at Synopsys04:19 The Phases of AI: Analytics, Generative, and Agentic07:55 What is Physical AI and How It Learns from the Physical World09:23 Robotics and Autonomous Learning Through Physical AI19:41 Impact of AI on Chip Design and Complex Systems23:29 Design Challenges of Complex, AI-Driven Chips26:57 Edge AI and Challenges in Autonomous Devices28:09 Implications for Organisations and Future Investments

Most conversations about artificial intelligence eventually return to the same concerns: is it going to replace my job, disrupt, and create an uncertain future? But what if we are looking at AI through the wrong lens?On the latest episode of Tech Transformed, host Trisha Pillay sits down with Kevin Surace, an author, AI expert and technology pioneer who has spent three decades working on technologies that helped lay the foundations for products such as Siri and Alexa. His perspective is very different from the usual debate. Rather than viewing AI primarily as a threat to human work, Surace sees it as a tool that can expand what people are capable of achieving.This shift changes the conversation from what AI might replace to what it could make possible. The discussion explores how AI can help people work faster, tackle problems that were previously out of reach and spend more time on areas where human judgment, creativity and experience still matter. For Surace, the question is not whether AI will change the way we work. That change is already happening. The more important question is what we choose to do with the capabilities it puts in our hands.Silicon Valley Author and AI ExpertSurace holds 95 patents and is often credited as the father of the AI assistant. His work goes back to General Magic, an early-1990s Apple spinout where his team built the first digital agents and a programming language called Telescript. That early assistant technology had millions of users well before anyone was talking about chatbots.His upcoming book, The Joy Success Cycle, grew out of a question people kept asking him: Why does he seem to enjoy his work so much? The answer, he told Pillay, comes down to sequencing. Most people wait for success before they let themselves feel joy. Surace argues it works the other way. Joy has to come first, and success follows from it.How AI Brings JoySurace's favourite example is presentations. He used to spend a full week getting slides right, not because the ideas took that long to develop, but because formatting, alignment, and design details ate up all his time. Now he outlines what he wants to say, hands it to an AI tool, and gets a polished deck back within the hour.The lesson he draws from that isn't about speed for its own sake. It's about separating the parts of a job that actually bring satisfaction from the parts that never did. Moving a font two pixels to the left never gave anyone joy, he says. Delivering an idea to a room full of people can. AI, in his view, quietly removes the first category and leaves more room for the second.AI and the Case for More JobsSurace pushed back hard on the idea that AI shrinks the job market. He pointed to every major shift he's lived through from the PC, email, the smartphone, and the internet. Each one triggered the same fear, and each one ended up creating more roles than it eliminated.He sees the same pattern playing out with software development right now. Coders using AI tools are shipping features and fixing bugs far faster than before, and instead of trimming teams, many companies are hiring more developers to keep pace with the new demand their own speed has created. The job itself has changed. Writing code line by line matters less than guiding the output and checking it. But the need for people hasn't gone away.Rethinking Workflows With AIOne point Surace kept returning to was how companies get AI wrong when they try to automate their existing process instead of questioning why that process exists in the first place. He used car insurance claims as an example. The old approach sends an inspector to look at damage, fills out paperwork, and routes it through several people before a check gets issued. An AI-first approach skips most of that. A customer photographs the damage, uploads it, and a payment can go out the same day. This kind of redesign, he argues, is where the real gains sit. Businesses that only bolt AI onto old workflows will see modest improvements. Businesses willing to rebuild the workflow from scratch stand to cut costs dramatically and outpace competitors who don't.Cybersecurity in the Age of AIThe conversation also turned to security, an area where Surace runs a company building biometric authentication devices. He explained that attackers rarely bother breaking into networks directly anymore, since most data sits encrypted. Instead, they use AI to generate convincing phishing emails and fake login pages designed to trick people into handing over multi-factor authentication codes. Surace believes fingerprint-based verification is where things are headed, since voices and faces can now be convincingly faked, but a fingerprint can't. He expects verified-identity badges to become common online within the next few years, giving people a way to confirm they're actually talking to the person they think they are.What Leaders Should Do NowWhen asked what businesses should do now, Surace's advice was simple: get every employee using AI tools regularly, not occasionally. He compared it to the early days of the PC, when companies had to run training sessions just to get staff comfortable with word processors and spreadsheets. Adoption took time, but the alternative was falling behind. He sees AI the same way. Leaders who wait for their teams to come around on their own risk losing ground to competitors who move faster. If you would like to learn more, visit Kevin Surace's website or follow him on Linkedln.TakeawaysAI removes repetitive work so people can focus on higher-value tasks.Joy drives success in the AI era.AI will create new jobs by accelerating innovation.An AI-first culture is key to staying competitive.Biometric security will help counter AI-powered fraudChapters00:00 Introduction to Kevin Surace and AI's Potential01:52 Kevin's Journey and the Joy Success Cycle04:14 AI as a Joy Maker in Daily Work06:07 The Cycle: Joy Leads to Success07:39 AI's Opportunity for Increased Jobs12:15 Is AI a Technology Cycle or a Transformation?13:50 The Ubiquity of Smartphones and AI's Role14:39 Workforce Transformation and AI Adoption23:06 Cybersecurity Challenges in the AI Era26:55 Advice for CEOs and Leaders in an AI World

AI is changing the conversation around legacy modernisation, but successful transformation demands far more than powerful models and automated code generation. It requires engineering discipline, governance, and a clear-eyed view of where AI genuinely adds value and where it doesn't.On a recent episode of Tech Transformed, host Christina Stathopoulos, founder of Dare to Data, sat down with Shodhan Sheth, Enterprise Modernisation Platform and Cloud Lead, and Alessio Ferri, Lead Software Engineer, both part of Thoughtworks' Global Legacy Modernisation Service Development Team, to unpack exactly that.Legacy Modernisation With AIThe conversation around AI in legacy modernisation is often muddied by marketing noise. As Sheth puts it, "value and hype can coexist". Overpromising doesn't automatically mean a technology is worthless. The real question for technology leaders is whether AI meaningfully improves the cost-time-value equation for a specific problem.This will always start with problem-solution fitness: has someone already solved a comparable challenge with AI, and does the proposed use case genuinely fit that pattern? As Sheth notes, "most things can be judged by cost, time, and value", which is a simple but effective filter for cutting through the noise.Rethinking Legacy ModernisationGenerative AI is inherently probabilistic, while enterprise software has always relied on deterministic, predictable behaviour. Ferri unpacks this tension by separating two very different use cases: using AI to build systems, and embedding AI within operational systems.When AI writes code, inconsistency is manageable; developers review, test and refine the output before it ships. Production systems are a different matter entirely, where unpredictable behaviour carries real operational risk. As Ferri explains, "AI in production requires different guardrails than AI for building."The practical answer is controlled use. AI might suggest alternative products in a marketplace, for instance, while deterministic rules still guarantee that only in-stock items are ever shown. This lets AI add value within a firm, enterprise-grade constraints.Why AI Alone Won't Modernise Legacy SystemsTechnology is only part of the story. Sheth is clear that modernisation is fundamentally about change, and change is hard, especially across large enterprises with tangled, interconnected systems. Tasks that resist automation are often the hardest, like upskilling teams, explaining complex trade-offs, and winning buy-in; these cannot be solved with code alone. These human and organisational factors are routinely underestimated. Where the impact of a change is broad, he also advises either aligning teams properly across the business or breaking the change into smaller, more manageable pieces, a strategy that reduces resistance and smooths the path to adoption.If you would like to learn more about this, visit Thoughtworks or connect with both Sheth and Ferri on LinkedIn.TakeawaysApplying AI to modernise complex enterprise systems.Distinguishing hype from practical AI applications.Balancing probabilistic AI with deterministic enterprise software.Organisational and leadership challenges in AI modernisation.Building control, traceability, and abstractions in AI workflows.Advice for CIOs and CTOs on AI adoption.Chapters00:00 Introduction to AI and Legacy Modernisation01:18 Meet the Experts: Shodhan and Alessio02:47 Distinguishing Hype from Value in AI05:51 The Tension Between Probabilistic and Deterministic Systems10:13 The Human Element in Modernisation17:12 AI's Role in Enterprise Transformation19:26 Distinguishing Hype from Value in AI22:13 Probabilistic vs Deterministic Systems27:45 People and Processes in Modernisation29:03 Lessons in AI for Legacy Modernisation

Every customer service call now produces a measurable outcome, which is exactly why customer experience has become the place where agentic AI either proves itself or falls apart. Most enterprise AI hype centres on generation and productivity. But according to Sneha Iyer, who leads AI value delivery and analytics at Observe.ai, the real test of agentic AI is happening somewhere less flashy, which is the contact centre. In a recent episode of Tech Transformed, host Ravit Jain asked Iyer why customer experience (CX) has become the industry's default stress test and what that means for enterprises still sorting isolated chatbots from genuine AI agents. Iyer has spent nearly a decade watching CX change from manual quality assurance to a blended workforce of human and AI agents. This shift, she says, has quietly rewritten the metrics, tooling, and governance decisions every enterprise leader now has to make.Why Customer Experience Became AI's Testing GroundIyer explains that CX generates more human-AI interaction data than almost any other business function. Every conversation resolves in a clear outcome, resolved or not, retained or churned, and it spans every modality a company touches, from voice to chat to email. Solve AI reliability here, and the lessons transfer everywhere else.What's actually changed, she notes, isn't that AI got smarter; it's that AI stopped just watching and started acting. Where AI agents once flagged patterns in a transcript after the fact, they now issue refunds, verify identities, and book appointments in real time. This has split the workforce in two, namely, AI agents absorb the high-volume, low-risk interactions, while human agents get pushed toward the complex, judgment-heavy calls machines shouldn't handle alone. Old metrics like containment and deflection no longer capture that split; leaders now need blended resolution measures that account for both.Fragmented AI StacksIf you ask most contact centres what their AI stack looks like, you'll find what Iyer calls a "Frankenstein stack": one tool for analytics, another for telephony, another for conversational intelligence, stitched together and barely talking to each other. That setup was tolerable when each tool did one isolated job. It breaks the moment you add agentic AI, because these types of systems depend on shared context to function.Without that context, a customer verified by an AI agent has to repeat everything to the human agent who picks up next, which turns an already frustrating support call into a worse one. Fragmented tools also make it nearly impossible to prove ROI, since every vendor tracks success differently and costs stack with each new integration.This is the case for unified CX platforms: not as a nice-to-have, but as the only way to preserve context across a customer's full journey. Iyer points to "companion agents" as a concrete example of real-time, in-context support for human agents that shows them exactly why an AI agent escalated a call and what happened before they picked up.Build vs. BuyOn the build-versus-buy question, Iyer highlights that building in-house looks cheap upfront and rarely stays that way. Models change every few weeks, which turns any custom build into a permanent maintenance commitment most teams underestimate, including in regulated industries like healthcare and finance, where leaders often assume building keeps data safer. In practice, established vendors have already solved the compliance groundwork, data residency, model governance, and audit trails that a custom build would take months to replicate.Trust, she argues, isn't really about doubting AI's competence. It's the fear of the one catastrophic interaction happening at scale. That's what simulation testing and evaluation frameworks are for, stress-testing AI agents against the hardest, most ambiguous scenarios before launch, not just the easy paths, backed by governance frameworks that can roll back a deployment fast if something breaks.Looking six to twelve months out, Iyer expects three shifts, continued consolidation around unified platforms, evaluation frameworks becoming the real competitive edge (not raw model performance), and pricing models moving from seat-based to outcome-based. Her advice to leaders is to build auditability and governance into your AI strategy now, before regulation forces the issue.Building Customer ExperienceThe throughline of Iyer's conversation with Jain is that agentic AI in customer experience succeeds or fails on infrastructure and trust, not on which model you're running. Enterprises that unify their data, rethink their success metrics, and choose vendors who can prove reliability through thorough testing are the ones positioned to scale AI-driven CX without breaking it. If you would like to find out more about this visit observe.ai or connect with Sneha Iyer on LinkedIn.TakeawaysShift from isolated AI tools to integrated agent systems.Importance of simulation testing and evaluation frameworks.Benefits of unified customer experience platforms.Build versus buy decision in enterprise AI.Future trends in AI-powered customer experience.Chapters00:00 Introduction to Tech Transform and AI Evolution02:01 Sneha Iyer's Role and Background in AI09:35 The Shift in Customer Experience with Agentic AI13:24 The Move Towards Unified Customer Experience Platforms18:55 Build vs. Buy: Navigating AI Solutions in Enterprises20:59 The Cost of AI Maintenance and Vendor Insights22:20 Navigating Compliance in Regulated Industries24:52 Building Trust in AI Adoption30:00 Use Cases Driving AI Value34:31 Future Trends in AI Customer Experience

The semiconductor industry is undergoing one of its most profound transformations in decades. Driven by the insatiable demand for compute power largely fueled by AI workloads, engineers are moving away from traditional monolithic chips and shifting toward complex multi-die designs. This shift brings a new set of challenges that conventional design and validation methods simply cannot handle.In a recent episode of the Tech Transformed podcast, host Dana Gardner sat down with Shekhar Kapoor, Executive Director of Product Line Management at Synopsys, to explore how the growing complexity of semiconductors is changing the way engineers design and validate modern systems. From thermal management to AI-driven automation, the conversation reveals why the old way of building chips is no longer good enough and what the future looks like.Multi-Die DesignKapoor explains that the transition to multi-die design is no longer a matter of preference but a necessity. He attributes this shift to the relentless demand for greater compute capacity, driven largely by the rapid growth of AI.Traditional monolithic chips are hitting hard limits. Reticle sizes are maxing out, and rising yield and cost challenges make it increasingly impractical to pack more functionality onto a single die. Multi-die designs solve this by disaggregating functionality across smaller dies, each targeting the most appropriate process technology, then integrating them into a unified, optimised package.Leading AI systems already integrate multiple compute and I/O dies alongside large high-bandwidth memory (HBM) stacks, scaling to 3x–5x reticle-class designs and beyond. The design challenge is very different. As Kapoor puts it: "You're no longer optimising a single chip, you're optimizing a system of chips."This requires system-level co-design from day one, spanning architecture, silicon, packaging, power delivery, and interconnect strategy simultaneously. Engineers must think in terms of System Technology Co-Optimisation (STCO), not just chip-level optimization. The design tools, methodologies, and team workflows all need to change. For engineers and technology leaders looking to explore these trade-offs, Synopsys has published a comprehensive eBook on accelerating multi-die design and innovation.Thermal Analysis and Multi-Physics ValidationHistorically, thermal, power, and electromagnetic analyses were performed as downstream validation steps once the core design was complete. In a multi-die world, that approach is no longer viable."Thermal management is becoming the number one issue when designing these multi-die designs. It has to be managed across a range of scales, from transistor activity to package and board level," Kapoor says.The problem with late-stage validation is timing. By the time thermal or power integrity issues surface, the most critical decisions are already locked in floorplans, interconnect topologies established, and packaging assumptions embedded.. At that point, the only options are costly ECOs, excessive margining, or a full redesign. Industry estimates suggest over-design can lead to up to 30-35 per cent wasted silicon and hundreds of millions of dollars in optimisation loss.The solution is a shift-left approach that embeds multiphysics analysis from the earliest stages of design. When thermal hotspots, voltage drop issues, and electromagnetic interactions are identified early, engineers can adjust partitioning and placement strategies before they become expensive problems.This is the methodology detailed in the Synopsys ebook on Multiphysics Fusion for multi-die design, which covers how teams can build continuous multiphysics validation into their flows to avoid late-stage surprises and protect both performance and reliability.Multiphysics Fusion and AI-Driven Chip DesignTo operationalise the shift-left methodology at scale, Synopsys has introduced the concept of Multiphysics Fusion. This is the native integration of AI-powered EDA technologies with ANSYS's gold-standard multiphysics sign-off analysis capabilities.Within the 3DIC Compiler platform, this means unifying the implementation environment with RedHawk-SC, RedHawk-SC Electrothermal, and HFSS-IC technologies. This brings IR drop, thermal, signal, and power integrity analysis directly into the design loop. The result is greater predictability, tighter correlation between in-design analysis and sign-off, and significantly fewer design iterations.The impact on design closure times has been substantial. According to Kapoor, teams using the Multiphysics Fusion solution have seen turnaround times shrink "from weeks to days, and in some cases even hours" even for large, high-performance multi-die designs.AI amplifies these gains further. Synopsys employs AI in two primary ways: assistive automation through its 3DSO.ai technology, which integrates multiphysics feedback into the optimization loop in real time, and agentic workflow orchestration, which becomes increasingly critical as system complexity scales toward designs incorporating hundreds or even thousands of GPUs. As Kapoor notes, at that scale, "agentic workflows could help engineers converge faster" and manage trade-offs that would otherwise be intractable. If you would like to find out more about this, download the full eBook: Multiphysics Fusion Technology for Multi-Die Designs Explained from Synopsys, which expands on each of these themes with real-world examples, design methodologies, and guidance for implementation teams. You can also connect with Shekhar Kapoor on LinkedIn.TakeawaysMulti-die architectures and their drivers.Challenges of traditional monolithic chips.Importance of early multi-physics analysis.Multiphysics fusion and its benefits.AI's role in design automation.Reducing time-to-market through integrated platforms.System-level co-design.Thermal management in 3D IC stacking.Shift left approach in multi-physics validation.Future trends in semiconductor design.Chapters00:00 Introduction to Semiconductor Complexity02:00 The Shift to Multi-Die Designs04:30 Challenges in Multi-Die Design08:11 The Importance of Early Multi-Physics Analysis10:05 Introducing Multiphysics Fusion12:37 AI's Role in Semiconductor Design16:37 Reducing Time to Market19:39 Applications Beyond AI21:12 Real-World Examples of Multi-Physics Validation26:20 Practical Advice for Engineers

When most people hear "digital divide," they picture communities without broadband. But in 2026, that definition is dangerously outdated. "The digital divide is no longer just about internet access." These words from Graeme Gordon, Chief Executive Officer of Converged Solutions Group, set the tone for one of the most pressing conversations in technology today.In this episode of Tech Transformed, host Trisha Pillay sits down with Gordon to unpack the changing digital divide, the massive impact of AI adoption, and what it truly takes. Gordon, whose background spans electrical engineering, oil and gas robotics, and three decades of founding and scaling tech companies, says that the new digital divide is about meaningful participation in the AI-driven economy, not just connectivity.“More people are connected than ever before,” Gordon explains. “But connection without capability is just noise.” He points to mobile internet adoption as a case in point. Billions of people now access the internet via smartphones. However, the gap between scrolling social media and using cloud-based AI tools to build products and services remains wide.This participation gap is the new frontier of digital exclusion. The implications stretch well beyond individual users. Organisations, governments, and education systems that fail to close this gap risk being locked out of the innovation economy entirely.AI Adoption Without EducationFew developments have accelerated the digital divide conversation quite like the arrival of ChatGPT in late 2022. Gordon calls it plainly: "ChatGPT has disrupted and transformed the sector," and not just for technologists. The tool put generative AI in the hands of business professionals, students, and everyday users almost overnight.Gordon says it's time to rethink our approach to AI. At a recent event he attended with 100 business leaders in the room, every hand went up when asked if they had used an AI platform in the last 24 hours. When asked who had received any formal training on how to use those tools, not a single hand was raised. This is the core paradox of AI adoption today. The tools are everywhere. The understanding of how to use them safely, strategically, and effectively is not. Without structured digital literacy and education, rapid AI adoption becomes a liability rather than an asset for individuals and organisations alike.Barriers to Digital InclusionGordon identifies several interconnected barriers preventing organisations from fully participating in the digital economy. Let's have a look:Skills gaps remain the most acute. Technology evolves faster than most training programmes, let alone formal education curricula. University degrees and annual school terms were not designed for the pace of AI-driven change.Trust and credibility are equally critical. Gordon warns of what he calls "AI slop", the growing proliferation of AI-generated content and half-built solutions that look polished but lack substance or security. Organisations that rely on AI without proper oversight risk undermining the customer trust they're trying to build.While infrastructure quality is improving globally, it still creates disparities, particularly around data sovereignty. The question of where your data sits, who can access it, and under what compliance framework is no longer just a legal concern. It is a competitive and ethical one.Sovereign AIOne of the most forward-looking concepts Gordon introduces is sovereign AI, the idea that organisations must control not just their data, but the AI infrastructure that touches it. Just as data sovereignty became a boardroom priority, AI sovereignty is now following the same path."Business leaders type sensitive information into ChatGPT or Copilot without thinking twice," Gordon cautions. The solution isn't to avoid AI, it's to build internal AI agents and platforms that interact with large language models without exposing proprietary data to the open web. This is why hyperscaler data centres are appearing in unexpected geographies: latency is secondary; sovereignty is the driver.Gordon's advice to business leaders is refreshingly direct: go experiment. "You won't break anything," he says. The AI-driven economy rewards curiosity, iteration, and speed of learning, not perfection. Leadership teams need to model responsible AI use, invest in upskilling their people, and treat education as a strategic asset. This applies as much to frontline healthcare workers as it does to C-suite executives.If you would like to find out more, connect with Graeme Gordon on LinkedIn.TakeawaysThe evolving digital divide from access to participation.Impact of AI and ChatGPT on business and society.Importance of secure and sovereign AI infrastructure.Role of education in digital literacy for all.Leadership strategies for AI adoption and trust.Barriers to digital inclusion: skills, trust, infrastructure.Practical steps for organisations to implement AI responsibly.Chapters00:00 Understanding the Digital Divide02:49 The Role of AI in Participation06:01 Barriers to Digital Adoption09:07 The Importance of Education11:45 Building a Secure AI Foundation14:51 Trust and Credibility in AI18:11 Practical Advice for Organisations

AI isn't just speeding up recruiting; it's actually forcing companies to redesign work itself, blending human judgment with agentic execution across hiring, mobility, and skills development. As a result, most conversations these days are about AI in the enterprise centre on software development and engineering. Recruiting, hiring, and talent management get far less attention, but they may be where AI's impact is most immediate.In a recent episode of Tech Transformed, host Dana Gardner spoke with Meghna Punhani, Chief People Officer at Eightfold AI, about how organisations are rethinking talent acquisition, workforce planning, and employee development in an AI-driven world. Meghna Punhani's perspective is shaped by nearly two decades at Google, a stint leading employee experience at Palo Alto Networks, and her current dual role at Eightfold AI, where she both leads the people function and helps build the product her team relies on. That vantage point gives her a practical, ground-level view of what works and what doesn't when AI meets HR.Reimagining the Talent Lifecycle with AI Punhani's central argument is that most legacy HR systems were designed for a different purpose, one that has evolved as work itself has changed and the workforce now includes AI agents alongside people. Simply bolting automation onto existing processes, she argues, isn't enough. Organisations that are succeeding are the ones re-engineering roles, workflows, and organisational structures from the ground up, treating this as an operating-model shift rather than an IT upgrade.This shift touches the entire talent lifecycle, from how companies find candidates and evaluate skills instead of just job titles to how they support internal mobility. Punhani points out that skills now have a much shorter shelf life than in the past, which means static job descriptions are giving way to dynamic, skills-based decision-making. AI, she says, helps surface pathways for employees that traditional resumes and titles would never reveal, including her own nontraditional route into HR leadership.How AI Is Reshaping Workforce Strategy Trust is the recurring theme throughout the discussion. Punhani is candid that employees often fear AI-driven decisions, especially around jobs and evaluations. Her approach is focused on transparency first. When Eightfold rolled out digital twins internally, employees were uneasy until leadership explained how the technology worked and used it themselves, which helped build organisation-wide confidence.That same principle shows up in Eightfold's own hiring practice. One example is the company's campus recruiting programme in India, where its AI interviewer conducted roughly 90 per cent of interviews. This enabled recruiting to scale from around eight or 10 university partners to more than 150, and from approximately 5,000 applications to 15,000, without pulling engineers away from their day-to-day duties.Time-to-offer dropped from around six weeks to as little as four days in some technical roles, largely because interviews could happen around the clock rather than around a recruiter's or hiring manager's schedule. Beyond recruiting, Eightfold's internal initiative, nicknamed Project Andromeda, applies the same re-engineering approach across sales and finance, reportedly reclaiming thousands of employee hours through redesigned, agent-assisted workflows.AI and the Future of TalentLooking ahead, Punhani doesn't frame AI as a threat to human contribution, but she frames it as an amplifier of it. As tools become more accessible across every function, she believes the people who will succeed won't be the ones who know the most facts, since AI can answer those questions. Instead, it will be the people who ask better questions, orchestrate multiple AI agents, and apply judgment where the right answer isn't obvious.For HR leaders specifically, Punhani's advice is to claim a seat at the table now, rather than letting AI adoption happen without a people-first lens. This means learning the technology firsthand, demonstrating its value to non-technical teams, and partnering closely with CTOs and CIOs to shape decisions jointly. Her advice for individuals entering this shifting job market is similarly grounded: focus on learning agility over any single technical skill, since the skills in demand today may look different within months.Future of AI in Talent ManagementAcross the conversation, Punhani returns to one idea, and that is AI in talent management isn't primarily a technology problem; it's a leadership and trust problem. Organisations that treat it that way, redesigning work with both humans and agents in mind, are the ones seeing measurable gains in speed, candidate experience, and internal mobility.For HR leaders exploring AI adoption, the takeaway from this episode is to start before you feel ready, build trust through transparency, and let AI handle evaluation and execution so people can focus on judgment, empathy, and connecting the dots across the organisation. If you would like to find out more, visit eightfold.ai or connect with Meghna Punhani on LinkedIn.TakeawaysAI's impact on talent acquisition and management.Reengineering work processes with AI.Building trust and transparency in AI systems.Skills-based internal mobility and workforce planning.AI-driven candidate evaluation and employee development.Chapters00:00 Introduction to AI in Talent Management02:59 Understanding AI's Role in Talent Acquisition06:07 AI's Impact on Workforce Planning and Skills Development10:02 Building Trust in AI for Hiring Processes13:04 Internal Use of AI at Eightfold AI18:58 Measuring ROI from AI in Talent Acquisition25:02 Enhancing Candidate Experience with AI29:53 Future Directions for AI in Talent Management

With enterprises now rushing to integrate AI agents into their operations and security, the most imperative focus now becomes the AI model itself. However, Eric Tschetter, Chief Architect at Imply, believes the real challenge is within the data infrastructure that supports these systems.In the recent episode of the Tech Transformed podcast, Kevin Petrie, BARC Vice President of Research, sat down with Tschetter to talk about how AI is actually increasing the current needs around scale, performance, and data access.“Agents are always running queries. They're always doing stuff,” Tschetter stated.Unlike human analysts, AI systems work continuously, producing much higher query volumes and putting more pressure on the data platforms underneath. This leads to a greater demand for observability architectures that can manage more data, more users, and more machine-to-machine interactions without losing speed.For Tschetter, the solution is not to create new observability tools, but to rethink the data layer that supports them.Key TakeawaysAI is transforming observability and security disciplines.The observability warehouse concept is gaining traction.AI agents increase the volume of queries significantly.Data silos remain a major challenge for enterprises.Collaboration between IT and security teams is essential.Observability and security teams often consume the same data.A decoupled architecture can enhance data accessibility.The semantic layer must support multiple query languages.Effective data management is crucial for AI-driven workloads.Data should be stored once and accessed from multiple platforms.Chapters00:00 Introduction to AI and Observability02:08 Challenges in Observability with AI06:44 Modernising Architecture for Observability10:49 Decoupled Observability and Semantic Layers16:31 Collaboration Between IT and Security Teams22:23 Imply's Observability Warehouse and Data LakesFor more information on AI, observability and Imply's observability warehouse and data lakes, please visit imply.io.For further information on all things B2B Tech, please visit em360tech.comImply LinkedIn: @Imply Imply X: @implydataImply YouTube: @ImplydataEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFollow: @EM360Tech on YouTube, LinkedIn and XStay connected for more expert insights, podcast episodes, and enterprise data strategy discussions

Across every industry, boards are approving AI budgets. Inside many enterprises, however, the reality is the same. Pilots never scale, tools sit unused, and transformation programmes struggle to justify their investment. In this episode of the Tech Transformed podcast, host Trisha Pillay sits down with Darin Patterson, VP of Product Advocacy and Market Strategy at Make, to find out what separates the organisations genuinely operationalising AI from those still running expensive experiments.AI Adoption GapEnterprise AI investment is accelerating. What is not accelerating at the same pace is business value. Patterson is direct about why he believes that most organisations are measuring the wrong things, assigning ownership to the wrong people, and deploying tools before they have defined the problem."The AI adoption gap is real," Patterson tells Pillay, "and it starts at the top. Leaders are approving investments without a clear framework for what success looks like."For C-suite executives, this is a critical signal. AI adoption is not primarily a technology challenge; it is an organisational one. Strategy, culture, and accountability structures determine if AI initiatives produce compounding returns or accumulate as technical debt.Ownership ModelsOne of the most instructive conversations in this episode concerns who should own AI inside an enterprise. Patterson's position is that ownership must live with the people closest to the business function being transformed."Ownership models are often unclear," he says. "And unclear ownership is where AI initiatives go to die."When AI is owned exclusively by a central IT or data science function, it becomes disconnected from the operational realities of the teams it is meant to serve. When it is owned entirely by individual business units without central governance, you get fragmented tooling, inconsistent data practices, and security exposure. The hybrid model Patterson advocates centralises governance standards, security, and infrastructure while pushing execution authority down to functional leaders. This structure creates accountability at the point of value creation rather than at a remove from it.For C-level executives building or restructuring their AI operating model, the actionable question is: do the leaders of each business unit have both the mandate and the capability to own AI outcomes in their domain?Stop Starting With the ToolA pattern Patterson sees consistently across enterprises is what he calls tool-first thinking. An organisation identifies a capable AI platform, deploys it, and then attempts to work backwards to the business problem it should solve."Focus on your business process first," he advises. "The tool is never the strategy."This is especially relevant for executives evaluating vendor proposals. The quality of an AI platform matters far less than the clarity of the problem definition sitting upstream of it. Organisations that achieve sustainable AI ROI typically begin by mapping their highest-friction processes, quantifying the cost of those inefficiencies, and only then evaluating which AI capability best addresses the root cause. The discipline of process-first thinking also prevents a common failure mode by automating a broken process rather than fixing it. AI applied to a flawed workflow does not eliminate the flaw but rather accelerates it.Culture Is the MultiplierPatterson also points to a softer but critical success indicator, which is cultural adoption. If the teams closest to an AI deployment are not using it willingly and consistently, the business case will not hold, regardless of what the pilot showed.The final, and perhaps most important, dimension Patterson raises is culture. Technical capability and strategic clarity are necessary but not sufficient conditions for AI success at scale. The organisations that are genuinely ahead are those that have invested in building an AI-literate workforce, not just an AI-enabled one."Invest in people as much as you invest in AI," Patterson says. "The technology will keep improving. Your competitive advantage comes from people who know how to use it well."For C-level leaders, this means reframing AI investment as a human capability programme as much as a technology programme. Training, change management, and psychological safety around experimentation are not soft additions to an AI strategy, but they are core to its delivery.Listen to the full conversation with Darin Patterson on the Tech Transformed podcast. Connect with Darin on LinkedIn and explore Make's automation platform at make.com.TakeawaysAI adoption challengesOrganisational culture and AIOwnership models for AIMeasuring AI successOperational AI examplesChapters00:00 The AI Adoption Landscape03:01 Bridging the ROI Gap in AI05:48 Ownership and Responsibility in AI Implementation08:57 Strategic Approaches to AI11:57 Measuring Success in AI Initiatives15:00 Cultural Transformation for AI Success18:53 Real-World AI Implementation Examples24:00 Advice for C-Level Leaders on AI Investment

For years, enterprise AI conversations have centred on chatbots, search assistants, and tools that respond when asked, but that era is ending. A new class of AI system, one that reasons, plans, and takes autonomous action, is moving from the research lab into live production environments. For C-suite leaders, the question is no longer if AI will arrive in their organisations, but whether those organisations are ready for it.In a recent episode of Tech Transformed, host Christina Stathopoulos, founder of Dare to Data, sat down with Cathal McCarthy, Chief Executive Officer of Kore.ai, and Dan Leiva, founder of CXamplify and author of Amplified, to lay out what this shift actually means in practice and why most enterprises are less prepared than they think.Have a look at Artemis, the agent platform from Kore.ai, or you can book a demo.From AI Pilot Projects to ProductionMost large organisations have run AI pilots. Far fewer have moved those pilots into meaningful production at scale. McCarthy and Leiva argue that this gap is not primarily a technology problem. It is a governance and accountability problem.Conversational AI systems, which are the kind that answer questions or generate text, operate within a relatively contained risk envelope. A poorly worded response can be corrected, and a hallucinated answer can be flagged. The stakes, whilst real, are manageable.Agentic AI operates differently. These systems do not simply respond to prompts. They assess situations, make decisions, trigger actions, and in some cases instruct other AI agents or software systems to carry out tasks on their behalf. When something goes wrong in an agentic workflow, the consequences can cascade quickly, across processes, data, customer interactions, and operational outputs.This is why the move from pilot to production represents a fundamentally different risk conversation. As McCarthy puts it, "technology is now a decision-making actor." That framing has significant implications for how enterprises structure ownership, oversight, and accountability around their AI deployments.What Agentic AI Actually Means for Your OrganisationThe term “agentic AI” is often used loosely, so it is important to clarify what it actually means. An agentic system can:Break a complex goal down into sub-tasks without human prompting at each step.Use tools, APIs, databases, and other software to execute those tasks.Adapt its approach based on intermediate results.Operate across extended time horizons without continuous human input.This is meaningfully different from a large language model that generates a report when asked, or a copilot that suggests the next line of code. Agentic systems take initiative, which means it's both their value and their risk.Leiva's book, Amplified, explores how organisations can harness this capability without losing control of it. The central argument is that autonomy is not a binary switch; it is a dial. Organisations need to be deliberate about where they set that dial across use cases, risk profiles, and stages of deployment maturity.A Framework for Smarter AI DecisionsOne of the most practical tools discussed in the episode is the three-class decision model. Rather than treating all AI decisions as equivalent, it asks leaders to classify decisions by consequence and reversibility.The first class covers routine, low-stakes decisions where agentic systems can operate with high autonomy, like scheduling, data routing, and standard customer queries. The second class covers decisions with moderate consequences, where human review should be triggered before action is taken. The third class covers high-stakes decisions where human authority must remain the final step.Mapping AI deployments to this framework is the foundation of a defensible governance structure, one that can satisfy board scrutiny and regulatory requirements simultaneously. It also forces a critical question: who owns the decision about which class a given AI action falls into? That ownership question, the guests argue, is where most enterprise AI programmes currently have a blind spot.The Leadership ImperativeWith that said, the organisations that will benefit most from the agentic era are not necessarily those with the most sophisticated technology. As Leiva writes in Amplified, they are the ones who have thought most carefully about how to deploy that technology in a way that is accountable, adaptable, and aligned with how their people actually work.Boards are already asking harder questions about AI risk. Leaders who can answer them confidently because they have built the governance frameworks and defined the accountability structures will hold a material advantage. For leaders ready to move beyond the pilot stage, McCarthy and Leiva offer grounded guidance. Listen for more insights, and if you have any questions, feel free to get in touch with them directly.Connect with the guests:Cathal McCarthy — LinkedIn | Kore.aiDan Leiva — LinkedIn | CXamplifyFurther reading: Amplified by Dan Leiva — available on AmazonHave a look at Artemis, the agent platform from Kore.ai, or you can book a demoTakeawaysThe shift from conversational to agentic AIEnterprise AI governance and accountabilityOperationalising AI at scale and risk managementBuilding trust and transparency in autonomous AI systemsTurning AI experimentation into measurable business outcomesChapters00:00 – Welcome to the Agentic Era02:33 – The Shift in AI Utilisation06:47 – From Pilots to Production: Understanding Risks10:10 – Gaps in AI Readiness13:11 – Rethinking Governance and Accountability16:50 – Operationalising Agentic Systems20:09 – Applying Agentic Workflows in Practice22:43 – Actionable Advice for Leaders

Podcast: Tech TransformedGuest: Mihir Nanavati, GM and Product Executive in MarTech and AdTechHost: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data JuiceAI might have overtaken the industry with processing data, automating workflows, and creating content. The next big thing could be a major one, says Mihir Nanavati, GM and Product Executive in MarTech and AdTech, “AI is moving from managing data to making decisions with it.”In the recent episode of the Tech Transformed podcast, host Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice, sat down with Nanavati to talk about a larger transformation in data and decision-making systems driven by AI.They particularly focus on the integration of agentic AI in marketing and customer data platforms. They explore the challenges of fragmentation in ad tech, the importance of connecting customer data to revenue outcomes, and the transformative role of AI in decision-making processes. Mihir shares insights on how companies can leverage AI to enhance their marketing strategies and the future of first-party data."This is not a cost exercise, it's about how much more you can get done and how many more ideas you can execute," said Nanavati.For years, enterprises went through waves of technological change, including cloud infrastructure, mobile platforms, and customer data platforms (CDPs). Each development helped enterprises collect, store, and manage larger amounts of data. However, Nanavati asserts that humans making most decisions will never change. Now, AI agents are introducing a new model.How AI has Moved from Data Navigation to Making DecisionsIn the past, customer data initiatives aimed to create a unified view of customers. Enterprises built warehouses, ETL pipelines, and data platforms that were designed to be reliable. However, Nanavati suggests that AI agents are changing these expectations. "Machines can reason, and that is fundamentally different."Rather than simply serving as another analytical feature in existing systems, AI agents are increasingly acting as decision-makers. They weigh trade-offs, learn from results, and execute plans based on specific goals.This change has significant implications for customer data platforms. CDPs are not just repositories for customer information now. Instead, they are becoming layers that enable intelligent actions."The role of customer data platforms is evolving into ‘how do you make meaning of this?'" While, decisions about which customer segment to target, which message to send, or which offer to present may increasingly be guided by AI-driven systems.What's the Fragmentation Problem in Modern AdTechWhile AI agents create new opportunities, Nanavati pointed out a persistent issue in the AdTech and MarTech ecosystem – fragmentation. Brands today tend to lean towards deploying multiple advertising and customer engagement platforms. These include social platforms, retail media networks, email tools, and specialised ad technologies. Each system may optimise effectively within its own space, but often fails to connect at the customer level.Nanavati calls it a "paradox of choice." "Each system is optimising locally for its own clicks and conversions, but none of that is coordinated at the consumer level."The result is a customer experience that many consumers notice, alluding to repeated retargeting for products they have already bought, irrelevant recommendations, or disconnected interactions across channels.As enterprises adopt AI agents, fragmented data environments may become an even bigger problem. AI systems can process information quickly, but they still rely heavily on context. "AI doesn't need perfect data in many cases, but it needs context."What's Next for Enterprise Tech?As AI adoption continues, Nanavati believes that successful enterprises will be recognised not by how many experiments they run, but by how fast they learn and use the results."Learn very rapidly. Then scale what you've learned." For leaders, this may require a stronger commitment than just isolated pilot programs or limited rollouts. It may also need organisational changes that place AI decision-making and customer context at the centre of growth strategies.For companies navigating the intersection of AI agents, CDPs, and customer data, the question may no longer be whether AI can automate processes. The ultimate question is about who is calling the shots.Key TakeawaysAI is fundamentally changing how decisions are made in marketing.The shift from third-party to first-party data is crucial for businesses.Fragmentation in ad tech leads to a paradox of choice for brands.Connecting customer data to revenue outcomes is essential for success.AI can help marketers make better decisions without needing perfect data.Customer data platforms are evolving to support real-time decision-making.Companies can run significantly more marketing experiments with AI.Leaders must personally drive change in their Enterprises.Successful AI implementation requires a focus on revenue outcomes.First-party data collection is becoming more sophisticated and essential.Chapters00:00 Navigating the Shift in Data and AI03:03 The Evolution of Decision-Making in Marketing05:55 Challenges of Fragmentation in Ad Tech09:00 Connecting Customer Data to Revenue Outcomes11:56 The Role of AI in Customer Data Platforms14:55 Real-World Applications of Agentic AI18:05 Blueconic's Approach to Customer Growth21:14 The Future of First-Party Data24:02 Building Habits for Successful AI ImplementationListen to the full episode of Tech Transformed for a deeper discussion on AI agents, customer data platforms (CDPs), first-party data strategies and the future of AdTech. Subscribe for upcoming episodes and join the conversation across our social channels.BlueConic LinkedIn: @BlueConicEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFor more information, please visit em360tech.com and blueconic.com.

Podcast: Tech TransformedGuests: Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal TechnologiesHost: Kevin Petrie, VP of Research at BARCArtificial Intelligence (AI) models have been breaking ground in the last three years. In the race to boost capabilities month by month among platforms like OpenAI, Anthropic, and Google's Gemini models. However, for many enterprises, the main challenge is not creating AI prototypes; it's ensuring they can reliably support real business processes.In a recent episode of the Tech Transformed podcast, Kevin Petrie, VP of Research at BARC, hosted a discussion with Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal Technologies. They talked about why enterprises find it hard to transition AI from experimentation to production and how infrastructure must change to support autonomous systems.Why AI Demos Break in the Real WorldAccording to Davis, many organisations make a common mistake: they focus on the "happy path" during experiments and overlook real-world operational challenges. “We have always ignored the non-functional requirements until we go to prod at our peril,” Davis said. “A lot of our experimentation is so focused on the models that we forget about the non-functional requirements.”This means developers often prioritise model performance but neglect reliability, scaling, and system resilience. Agent frameworks used in experiments—usually lightweight Python or TypeScript libraries—add to the issue.“What you're really building is a highly distributed system that's calling Large Language Models (LLMs) that will be rate-limited… networks are going to go down,” Davis explained. “When we move into prod, we haven't considered scale or instability.”As enterprises expand AI into their workflows, these overlooked details become imperative. A single outage, rate limit, or infrastructure failure can disrupt a complicated workflow that involves multiple AI steps.Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?What Risks are Surfacing Since the Rise of Agentic Systems?The transition from simple AI workflows to autonomous agents adds a new layer of complexity. Traditional AI applications have predictable flows—such as summarising documents, tagging data, or creating recommendations. In contrast, agentic systems choose tools and decide on actions dynamically.“When we move from non-agentic to agentic, we introduce unpredictability,” Davis said. “The tools and the order they run in are unpredictable. Whether we go through the agentic loop once or a hundred times is unpredictable.”Such unpredictability creates new governance and compliance challenges, especially in regulated industries. “Enterprises are still responsible for predictable outcomes,” Davis noted. “We need stronger audit trails to understand why the agent made the decisions it did.”For enterprises, this means AI systems must ensure traceability, accountability, and compliance, even when decision paths differ from one interaction to another.Why is Durable Execution the New Foundation for Enterprise AIFateev argues that to manage such newly surfacing risks, enterprises need a new architectural layer focused on reliability. His concept, “Durable Execution,” aims to ensure that complex workflows keep running even when infrastructure fails.“You write code as if failures don't exist,” Fateev explained. “If a process crashes, we recover all the state and continue executing.” In practical terms, Durable Execution allows long-running AI workflows to survive interruptions—from network outages to system crashes—without losing progress or data.This is essential as agents start interacting with real systems and taking real actions. “The moment agents start acting on the external world—changing files, submitting orders—you absolutely don't want those things to get lost,” Fateev said.The Temporal co-founder further emphasised that enterprise AI will not completely replace traditional software systems.“You will always have deterministic code,” he said. “You can't imagine banks dynamically deciding what a money transfer means.”Instead, the future architecture will combine deterministic software with agents that interact through controlled tools and reliable communication layers.Also Watch: How Do You Make AI Agents Reliable at Scale?Key TakeawaysAI projects fail in production when non-functional requirements are ignoredAgentic systems bring unpredictability, making governance, traceability, and auditability essential.Lightweight experimentation frameworks aren't suited for enterprise workloads.Durable execution enables reliable AI workflows, ensuring processes continue despite infrastructure failures.Enterprise AI will blend deterministic software with agents.Chapters00:00 Introduction to AI's Impact on Business03:53 Challenges in Integrating AI into Business Workflows13:00 Understanding Non-Functional Requirements in AI19:14 The Role of Orchestration in AI Systems24:26 Exploring Durable Execution in AI Workflows30:28 Future Architectures for Autonomous AI Systems36:05 Key Takeaways for Executives in AI ImplementationFor more information, please visit em360tech.com and temporal.io.To learn more about Temporal and Durable Execution, follow:Temporal LinkedIn: Temporal TechnologiesTemporal X: @TemporalioTemporal YouTube: @TemporalioEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360Tech#DurableExecution #EnterpriseAI #AIToProduction #AIOrchestration #TemporalTech #AutonomousAgents #SystemReliability #LLMs #TechTransformed #AIWorkflows

For years, data sovereignty was treated as a compliance requirement, focused mainly on keeping data within specific geographic borders. Today, that definition is no longer sufficient. True data sovereignty now encompasses control, visibility, and accountability over data wherever it resides, moves, or is processed. In an era shaped by AI adoption and increasingly fragmented cloud environments, sovereignty has become a core driver of enterprise resilience and operational autonomy rather than a regulatory checkbox. In this episode of The Security Strategist, Tim Pfaelzer, Senior Vice President and General Manager, EMEA at Veeam, explains how the meaning of data sovereignty has fundamentally changed.From Compliance Concept to Strategic PriorityA decade ago, data lived in well-defined corporate environments managed by internal IT teams. Today, it is distributed across public cloud platforms, SaaS ecosystems, edge devices, and third-party suppliers. This distribution has expanded the attack surface while making ownership and control significantly harder to define.As a result, organisations are being forced to rethink sovereignty not as a legal constraint, but as a foundation for resilience, security, and trust.Why Data Sovereignty Requires Cultural ChangeOne of the key arguments Pfaelzer makes is that data sovereignty cannot be solved through technology alone. It requires organisational alignment and executive ownership.Data is now created and consumed across every business function, which means governance must extend beyond IT. Leadership teams must treat data as a critical business asset, with clear accountability structures across its lifecycle.This shift is reinforced by regulatory pressure. Frameworks such as GDPR, the EU Data Act, and emerging AI governance rules now require organisations to demonstrate not only where data is stored, but how it is accessed, processed, and protected.The Five Dimensions of Modern Data ControlPfaelzer outlines five core dimensions that define effective data sovereignty today:Visibility: Knowing where all data exists, including backups and third-party copiesOwnership: Clear accountability for data across its lifecycleAccess governance: Controlled and regularly reviewed permissionsPortability: The ability to move data without vendor lock-inCompliance readiness: Continuous compliance rather than audit-only validationTogether, these determine how much real control an organisation has over its data estate.Data Sovereignty as the Foundation of ResilienceModern resilience is no longer defined by backup alone. It is defined by recovery speed, completeness, and operational continuity. A prolonged outage or ransomware incident can cause significant damage, but the difference between minutes and days of downtime often comes down to recovery architecture and how rigorously it has been tested under real-world conditions. In this context, sovereignty and resilience are directly linked. Without control over data, there is no predictable recovery.AI Has Raised the StakesArtificial intelligence has introduced a new layer of data risk that many organisations are still underestimating. As AI systems increasingly automate decision-making and customer interactions, the quality and integrity of training and operational data become critical. If that data is corrupted, incomplete, or outdated, the impact can spread silently across business processes before detection.Unlike infrastructure failures, AI-driven data issues are not always immediately visible. This makes governance even more important. Pfaelzer argues that AI systems should operate under the same strict data controls as human users, including lineage tracking, access controls, and continuous validation of data integrity.Why Data Sovereignty Now Defines Enterprise AutonomyUltimately, data sovereignty has changed into a measure of enterprise independence. Organisations that understand, govern, and control their data are better positioned to manage risk, comply with regulation, and adopt new technologies such as AI safely. Those who do not risk becoming dependent on opaque systems where visibility and control are limited. In 2026 and beyond, sovereignty is no longer just about where data lives. It is about who controls it, how it is used, and how quickly an organisation can recover when things go wrong.TakeawaysData sovereignty beyond geographic boundariesRisks of data fragmentation across cloud and edge environmentsStrategies for rapid data recovery and resilienceEnsuring data integrity and trust in AI systemsControl and ownership of data in a distributed landscapeChapters00:00 Introduction to Data Sovereignty and Resilience02:49 The Evolution of Data Management06:03 Control, Risk Exposure, and Accountability in Data08:57 Data Sovereignty Beyond Geography12:04 Ensuring Data Integrity in AI Systems15:05 Human Error and Data Management18:02 Case Study: University of Manchester's Data Strategy21:01 Non-Negotiables for Building a Resilient Data Strategy

Podcast: Tech Transformed podcastGuest: John Newton, Chief Innovation Strategist at HylandHost: Dana Gardner, President and Principal Analyst at Interabor SolutionsEnterprise leaders rushing to integrate artificial intelligence (AI) into their operations often think the biggest challenge is the technology itself. In reality, the issue is much closer to home. It's in the piles of unstructured enterprise data spread across documents, systems, and repositories.In the recent episode of the Tech Transformed podcast, John Newton, Chief Innovation Strategist at Hyland, sits down with host Dana Gardner, President and Principal Analyst at Interabor Solutions. They discussed how enterprises can unlock the full value of enterprise AI by addressing fragmented information and building stronger governance frameworks.Their conversation highlights that unstructured data is not an obstacle; it is the foundation for next-generation AI-driven productivity. As Newton stated, “The opportunity to truly use AI and use it effectively in your organisation really depends on that unstructured information.”For companies looking to adopt AI on a large scale, the real work is in organising and contextualising their internal knowledge.Is Unstructured Data the Hidden Fuel for Enterprise AI?Most enterprise data does not sit neatly in structured databases. Instead, it exists in contracts, reports, emails, videos, policies, and operational documents, creating a vast amount of unstructured content.The enormous amount of such unstructured data ends up creating a challenge for AI projects that rely solely on foundation models. Large language models (LLMs) may be trained on public data, but they cannot inherently access proprietary business intelligence.Newton argued that enterprise AI must therefore be built around internal knowledge systems. “Foundation models can't train on your internal information,” he explained. “What you really want is that information to be part of the AI when you're answering questions, doing research, or executing business processes.”This change requires organisations to rethink how information flows across the enterprise. Instead of isolated systems—CRM platforms, ERP databases, content repositories—companies need an interconnected information structure that connects multiple sources in real time.Such a structure enables AI systems and AI agents to find the right data at the right time. This also improves decision-making, automation, and operational intelligence.How to Reorganise Chaotic Unstructured Data?If unstructured data is the fuel, curation is the engine that drives effective AI. Newton emphasised that an enterprise data strategy must start with mapping, organising, and cleaning information assets. The aim is to reduce noise and increase clarity.“I like to look at things from a signal-to-noise perspective,” Newton says. “Curation is the key to removing uncertainty in the information.”The method could typically comprise a combination of several enterprise technologies such as content management platforms with business process management (BPM) and AI agents and LLMs.A pairing of the above strategies is aimed at helping enterprise data become more valuable. Enterprises can implement AI models to automate workflows, enhance knowledge discovery, and speed up processes across departments—from finance and manufacturing to customer operations.Importantly, Newton noted that this work also allows flexibility in the AI ecosystem. With a solid information foundation, companies can use open-source models, hyperscaler services, or internal AI deployments without tying themselves to a single vendor.In other words, an enterprise AI strategy should first focus on data readiness, not model selection.Key TakeawaysUnstructured data is the foundation for effective enterprise AI.Data curation improves AI accuracy and reduces information noise.Connecting enterprise systems enables AI to deliver real-time insights.AI guardrails help manage security, compliance, and data governance.AI automation boosts employee productivity by reducing repetitive work.Chapters00:00 Unlocking AI's Potential with Unstructured Data05:20 Signal to Noise: The Clarity Challenge11:21 Guardrails for AI: Balancing Control and Flexibility14:41 Harnessing the Enterprise Context Engine17:48 Real-World Applications: Case Studies in AI20:37 Curation: The Key to Effective Automation22:21 Future Business Value: Productivity and BeyondFor more information, please visit hyland.comTo stay updated on B2B Tech front and centre, follow EM360Tech:YouTube: @enterprisemanagement360LinkedIn: @EM360TechX: @EM360TechFollow Hyland on all its major platforms:YouTube: @HylandAILinkedIn: HylandX: @Hyland#UnstructuredData #EnterpriseAI #DataCuration #AIGuardrails #LLMs #AIAutomation #FragmentedData #InformationManagement #SignalToNoise #EnterpriseContext #TechTransformedPodcast #Hyland #B2BTech

Ecommerce no longer rewards scale alone. As customer expectations rise and margins tighten, revenue growth and conversion optimisation depend on how well organisations use their data, align their teams, and simplify their technology stack. Brands that fail to adapt are discovering that being data-rich but insight-poor is no longer a survivable position.In this episode of Tech Transformed, host Christina Stathopoulos, Founder of Dare to Data, speaks with Kailin Noivo, President and Co-Founder of Noibu, and Rohit Nathany, Chief Product and Technology Officer at Mejuri. Together, they unpack what is holding ecommerce teams back from sustained revenue and conversion growth and what actually works in practice.Ecommerce Revenue Growth in a High-Cost, High-Expectation MarketToday's ecommerce environment is shaped by rising acquisition costs, operational sprawl, and customers who expect speed, relevance, and reliability by default. Rohit points to macroeconomic pressure, tariffs, and shifting buying behaviour as forces that are squeezing margins while raising the bar for customer experience.At the same time, brands are struggling to connect the dots between marketing spend, on-site behaviour, and conversion outcomes. Personalisation is widely discussed, but execution often breaks down when teams cannot see how customer interactions move from ad click to checkout. Kailin describes this as a “perfect storm”, explaining that: “infrastructure scaled rapidly during the pandemic, and now needs consolidation, optimisation, and clearer ownership.”Customer Experience, Team Alignment, and the Practical Use of AIImproving customer experience at scale requires more than simply adopting new technology. Organisations also need the right data, processes, and operational alignment to turn those tools into meaningful customer outcomes. It requires teams to work from the same signals and trust the same data. Both Kailin and Rohit stress that AI and automation only deliver value when they remove friction from day-to-day operations rather than adding another layer of complexity.Used well, AI can support data analytics by automating routine monitoring, surfacing patterns that matter, and freeing teams to focus on higher-value work. Used poorly, it becomes just another disconnected tool. The difference comes down to team alignment and culture, like clear ownership, shared goals, and a willingness to continuously refine how decisions are made.For ecommerce leaders, this is less about digital transformation as a slogan and more about operational discipline. Simplifying the stack, aligning teams around outcomes, and treating customer experience as a measurable business driver are what sustain revenue growth when conditions are uncertain.If you would like to find out more, visit: https://www.noibu.com/TakeawaysBuilding resilience in revenue and conversion growth is crucial.Ecommerce leaders face a perfect storm of challenges.AI is central to enhancing customer experience in ecommerce.Data-rich environments often lead to insight-poor outcomes.Connecting the dots between data and decisions is essential.A strong culture of experimentation fosters innovation.Tool consolidation can streamline operations and reduce costs.Visibility in data access is critical for effective decision-making.Speed of action is influenced by organisational culture.Establishing a KPI tree helps unify team efforts.Chapters00:00 Introduction to Ecommerce Challenges06:04 Real-World Applications of Ecommerce Analytics & Monitoring11:50 The Role of AI in Ecommerce17:57 Data Utilisation and Decision Making24:13 Culture and Team Alignment in Ecommerce29:56 Practical Strategies for Ecommerce Leaders

SaaS companies moving toward usage-based and hybrid pricing models are discovering that revenue is no longer secured when the contract is signed.Instead, revenue is earned continuously through product usage, introducing new challenges for finance teams around billing accuracy, revenue visibility, forecasting, and managing increasingly complex cost structures driven by AI-powered products.In the latest episode of Tech Transformed, host Dana Gardner speaks with Lee Greene, Vice President of Sales at Vayu, about how AI and usage-based pricing are reshaping the economics of SaaS and why many companies are discovering that their pricing strategy is only as strong as the infrastructure behind it.One idea from the conversation“Pricing strategy is only as strong as the infrastructure behind it.”What you will learn in this episodeWhy usage-based pricing exposes hidden revenue leakage in many SaaS companies• How AI-driven products introduce unpredictable cost structures and margin pressure• Why disconnected CRM, product, and ERP systems break revenue visibility• What finance and revenue teams need to support scalable usage-based billing and forecastingWhy SaaS Economics Are Breaking Away From Fixed SubscriptionsGreene argues that usage-based pricing isn't simply an emerging trend. It is a response to assumptions that no longer hold true.Traditional SaaS subscription models were built around predictable costs and relatively stable product usage. AI-driven products have fundamentally changed that equation. Each interaction with an AI-powered system can create variable cost, making static pricing models increasingly difficult to sustain.This shift is also changing buyer expectations. Customers increasingly resist flat pricing structures and instead prefer models that reflect the value they actually receive. Usage-based pricing aligns economic benefit with real consumption, allowing buyers to justify spend internally while pushing vendors to be accountable for measurable outcomes rather than bundled feature sets.AI's Double RoleThe conversation also highlights how AI is introducing a structural challenge for SaaS finance and revenue teams.Usage-based pricing generates enormous volumes of data across product usage, customer behaviour, and cost inputs. Traditional billing systems were not designed to process this level of complexity.At the same time, AI is also becoming the only scalable way to manage it. Automated usage tracking, dynamic pricing logic, and real-time billing reconciliation are increasingly necessary to maintain operational accuracy and financial control.Treating AI solely as a product capability, rather than embedding it into revenue operations, can leave organizations exposed to billing errors, misaligned pricing models, and revenue leakage.Revenue Management Shifts From Contracts to OperationsOne of Greene's key observations is that usage-based pricing does not necessarily create revenue leakage. Instead, it reveals problems that already existed.The difference is visibility.In traditional SaaS models, revenue was largely secured at the moment of contract signature. In usage-based models, revenue must be earned continuously through product consumption. This means billing accuracy, system integration, and data flow directly influence financial performance.Disconnected CRM, product, and ERP systems can create gaps that lead to misbilling, delayed revenue recognition, and customer disputes. As a result, the infrastructure supporting revenue operations becomes inseparable from pricing strategy itself.What SaaS Leaders Must Build to Stay Economically ViableThe discussion concludes with a broader perspective on how SaaS companies must evolve to support this new economic model.The future belongs to organizations that design their pricing and revenue systems for variability. Pricing models must adapt to changing demand, and the systems behind them must support that flexibility without relying on heavy manual processes.Automation and no-code AI tools are increasingly enabling finance and revenue teams to adjust pricing models as usage patterns evolve. This agility is not simply about speed. It is about maintaining control in an environment where AI-driven cost structures and product usage can shift rapidly.Usage-based pricing is doing more than changing how SaaS products are sold. It is reshaping how companies think about value, risk, and revenue itself, making flexibility, intelligent automation, and data-driven decision making central to long-term success.About VayuVayu helps SaaS companies manage complex usage-based and hybrid revenue models by connecting product usage data, billing systems, and finance infrastructure.Learn more at:https://www.withvayu.com/TakeawaysThe shift from fixed subscription models to usage-based pricing driven by AI How AI is both creating and solving new pricing and billing challengesWhy revenue infrastructure plays a critical role in preventing revenue leakageThe importance of flexible pricing models that adapt to demand and usage patternsThe growing role of automation and AI in modern revenue operationsChapters00:00 – Introduction02:30 – The economic shift in SaaS: Moving toward usage-based models05:00 – The role of AI in transforming SaaS pricing and revenue streams06:47 – Buyer preferences and evolving value quantification08:38 – Infrastructure's role in supporting flexible billing models11:49 – How finance teams can shape technology to control revenue14:24 – Process reengineering and AI-driven automation17:15 – Adaptable SaaS infrastructure and market signals20:30 – Preparing for the unknown: sandboxing and scenario modeling24:49 – Opportunities in connecting SaaS apps and managing data flow28:54 – Building automated, scalable billing and integration flow

Managing product complexity has become increasingly critical as customers demand greater customisation. Manufacturers face the challenge of connecting disparate data systems effectively. In this episode of Tech Transformed, host Christina Stathopoulos and Laura Beckwith, Director of Product Management at Configit, discuss the complexities of managing product data in manufacturing, focusing on the concept of the digital thread. They explore the challenges manufacturers face in connecting disparate data systems, the importance of customisation, and how a Configuration Lifecycle Management (CLM) approach can provide a reliable foundation for digital threads. Understanding the Digital ThreadThe digital thread represents the traceability of all decisions and information regarding a product from its inception and throughout its lifecycle. According to Laura Beckwith, the digital thread allows manufacturers to trace decisions made during the requirements stage through to engineering and ultimately to manufacturing and service. This traceability is not just about having data; it's also about ensuring that various teams and systems can access the right information to facilitate informed decision-making.Challenges in Implementing the Digital ThreadDespite the promise that digital threads hold, manufacturers face significant challenges in connecting data from multiple systems. Beckwith highlights the example of a smartphone, which undergoes various phases from design to manufacturing. Each phase involves distinct software systems—like CAD for design and ERP for manufacturing—many of which do not communicate well with one another. This lack of integration often leads to inefficiencies, such as manual data entry and miscommunication between teams.The Impact of Customisation on ComplexityAs customisation becomes the norm, the complexity of managing product data increases exponentially. Beckwith notes that while smartphones may have limited customisations, products like cars offer vast configurability. For instance, when configuring a car, consumers can choose from an extensive array of options. Behind the scenes, however, manufacturers must manage numerous engineering constraints and compliance regulations. This is where the digital thread becomes essential, enabling manufacturers to track and manage these complex configurations effectively.The Role of Configuration Lifecycle Management (CLM)The upcoming CLM Summit 2026 will focus on mastering customisation complexity and building a reliable data foundation for configurable products. Beckwith explains that a scalable CLM approach is crucial for establishing a reliable digital thread. It ensures that all product configurations, such as the combination of seat heating and memory seats in a car, are tracked accurately. This not only aids in the manufacturing process but also enhances customer service by allowing manufacturers to address issues based on specific configurations.More broadly, the digital thread provides manufacturers with a framework for managing the growing complexity of modern product development. By enabling seamless communication between data systems and implementing effective CLM practices, organisations can better align engineering, manufacturing, and service functions. For more information visit: https://configit.com/TakeawaysThe digital thread provides traceability of product...

As AI systems move rapidly from experimentation into production, organizations are discovering that adoption alone is not the hard part, understanding, governing, and trusting AI in live environments is. In this episode of the Tech Transformed, Shubhangi Dua speaks with Camden Swita, Head of AI, New Relic, about why AI observability has become a critical requirement for modern enterprises, particularly as agentic AI and AI-driven operations take on increasingly autonomous roles.The discussion explores how traditional observability models fall short when applied to probabilistic systems, why many AI ops initiatives stall at proof-of-concept, and what security and IT leaders must prioritize to safely scale AI in production.Be the first to see how intelligent observability takes you beyond dashboards to agentic AI with business impact at New Relic Advance, February 24, 2026.Why AI Adoption Is Outpacing Operational ReadinessWhile AI adoption is accelerating rapidly, most organizations still lack visibility into what their AI systems are actually doing once deployed. Generative AI is already widely used for natural language querying, coding assistants, customer support bots, and increasingly within IT operations and SRE workflows. As these systems move into production, new challenges emerge around cost control, governance, performance quality, and trust. Leaders recognize AI's potential value, but without deep observability, they struggle to determine whether AI-enabled systems are delivering consistent outcomes or introducing hidden operational and security risks.How Observability Must Evolve for Agentic AI and AI OpsThe episode then examines how observability itself must evolve to support agentic and autonomous AI systems. While core observability principles still apply, AI introduces a new layer of complexity that requires visibility into model behavior, agent decision-making, and multi-step workflows. Modern AI observability extends traditional application performance monitoring by capturing telemetry from LLM interactions, agent orchestration layers, and automated evaluations of output quality against intended use cases. Without this visibility, teams are effectively operating blind, unable to diagnose failures, validate compliance, or confidently deploy AI at scale. At the same time, AI is increasingly being embedded into observability platforms to reduce noise, accelerate root cause analysis, and improve incident response.Making Agentic AI Work in PracticeSuccessful adoption starts with low-risk, high-friction tasks such as incident triage, dashboard interpretation, and runbook summarization, rather than fully autonomous remediation. These use cases deliver immediate productivity gains while preserving human oversight. Over time, stronger feedback loops, better context management, and human-in-the-loop learning allow agents to become more reliable and useful. Looking ahead, Camden predicts that 2026 will be a turning point for agentic AI in production, driven by maturing AI observability platforms, richer semantic data, and knowledge graphs that connect technical telemetry to real business outcomes.Listen to Are “Vibe-Coded” Systems the Next Big Risk to Enterprise Stability?When Vibe Code Breaks OpsAI-generated code is pushing prototypes into production faster than ops can cope. How observability becomes the...

Did you know that on average, 35 per cent of calls to automotive dealerships go unanswered? In today's competitive market, missed calls mean missed sales and dealerships are turning to AI and analytics to fix this. In this episode of Tech Transformed, host Jon Arnold and Ben Chodor, Chief Executive Officer of CallRevu, about how AI is reshaping the way dealerships handle calls, manage repair orders, and engage with customers throughout their journey. They explore the role of real-time analytics in improving interactions, the importance of answering every incoming call, and why AI has become essential in modern dealership operations.Customer Experience Has ChangedThe customer journey is no longer a simple transaction. Today, it spans pre-purchase research, purchasing, and post-purchase support. Chodor highlights that every interaction matters; customers now expect engagement and guidance at every stage, not just information. Competition in automotive sales is fierce, and customers expect fast responses. Chodor notes that dealerships leveraging AI can provide updates on service times, answer inquiries promptly, and ensure no customer engagement is lost. Real-time insights also empower managers to make better operational decisions and improve the overall customer experience.AI in Automotive DealershipsAI technology is changing the way dealerships operate. Chodor discusses how CallRevu's technology listens to every sales and service call, providing real-time analytics to dealerships. This capability allows managers to intervene in calls, ensuring that customer concerns are addressed promptly. For instance, if a call goes unanswered, the system can alert management, enabling them to engage with the customer immediately, thus reducing missed opportunities.The integration of AI and analytics in automotive dealerships is not just about improving sales; it's about transforming the entire customer experience. From ensuring every call is answered to providing real-time insights for better decision-making, technology is reshaping how dealerships engage with customers. As the automotive industry continues to evolve, those who prioritise customer experience through innovative solutions will undoubtedly lead the way.If you would like to find out more information, go to https://www.callrevu.com/TakeawaysAI enhances customer engagement in automotive dealerships.Real-time analytics can significantly improve communication.Every call to a dealership is crucial for sales.AI helps reduce the number of calls...

Podcast: Tech Transformed PodcastGuest: Manesh Tailor, EMEA Field CTO, New Relic Host: Shubhangi Dua, B2B Tech Journalist, EM360TechAI-driven development has become obsessive recently, with vibe-coding becoming more common and accelerating innovation at an unprecedented rate. This, however, is also leading to a substantial increase in costly outages. Many organisations do not fully grasp the repercussions until their customers are affected.In this episode of the Tech Transformed Podcast, EM360Tech's Podcast Producer and B2B Tech Journalist, Shubhangi Dua, spoke with Manesh Tailor, EMEA Field CTO at New Relic, about why AI-generated code, also called vibe-coding, rapid prototyping, and a focus on speed create dangerous gaps. They also talked about why full-stack observability is now crucial for operational resilience in 2026 and beyond.AI Vibe Code Prioritising Speed over StabilityAI has changed how software is built. Problems are solved faster, prototypes are created in hours, and proofs-of-concept (POC) swiftly reach production. But this speed comes with drawbacks.“These prototypes, these POCs, make it to production very readily,” Tailor explained. “Because they work—and they work very quickly.”In the past, the time needed to design and implement a solution served as a natural filter. However, the barrier has now disappeared.Tailor tells Dua: “The problem occurs, the solution is quick, and these things get out into production super, super fast. Now you've got something that wasn't necessarily designed well.”The outcome is that the new systems work but do not scale. They lack operational resilience and greatly increase the cognitive load on engineering teams.New Relic's research indicates that in EMEA alone:The annual median cost of high-impact IT outages for EMEA businesses is $102 million per yearDowntime costs EMEA businesses an average of $2 million per hourMore than a third (37%) of EMEA businesses experience high-impact outages weekly or more often.Essentially, AI-driven development heightens risks and increases blind spots. “There are unrealised problems that take longer to solve—and they occur more often,” Tailor noted. This is because many AI-generated solutions overlook operability, scaling, or long-term maintenance.Modern architectures were already complex before AI came along. Microservices, SaaS dependencies, and distributed systems scatter visibility across the stack.“We've got more solutions, more technology, more unknowns, all moving faster,” he tells Dua. “That's generated more data, more noise—and more blind spots.”Traditional...

In a world where climate change is reshaping the way we grow, transport, and consume the things we rely on, understanding the first mile of supply chains has never been more critical. That's the stage where over 60 per cent of risks arise, yet it remains the hardest to measure and manage. In a recent episode of Tech Transform, Trisha Pillay sits down with Jonathan Horn, co-founder and CEO of Treefera, to explore how artificial intelligence is providing clarity, actionable insights, and sustainable solutions for this complex ecosystem.The First Mile and Climate PressuresHorn's perspective comes from a mix of experience: growing up on a farm, studying physics, and working in investment banking. That combination gives him a lens on both the natural systems that underpin agriculture and the data-driven tools that help manage risk.Extreme weather patterns like droughts, heavy rainfall, and hurricanes are putting pressure on crops such as cocoa, coffee, wheat, and soy. The consequences ripple outward: production costs rise, commodity prices fluctuate, and supply chains become less predictable. A simple example illustrates this clearly: certain chocolate biscuits in the UK have moved from being chocolate-filled to chocolate-flavoured, reflecting disruptions in cocoa production in West Africa caused by extreme weather and disease. These changes are not isolated; they affect global markets and everyday products.Turning Data into Actionable InsightsAI can help make sense of the complexity. Treefera, for instance, combines satellite imagery, sensor data, and other datasets to provide insights on crop yields, supply risks, and climate impacts. Horn describes it like a car dashboard: “You don't need to know every technical detail to understand what's happening and act accordingly.”The value of AI lies not in flashy algorithms but in its ability to translate raw data into practical decision-making tools. By analysing multiple signals from weather events to agricultural output, AI can highlight trends, flag potential disruptions, and support planning for traders, insurers, or supply chain managers. The goal is clarity and action, not simply more information.Data, Regulation, and Responsible UseAlongside operational complexity, organisations face questions about data governance. Emerging regulations such as the EU AI Act aim to ensure AI is used responsibly, and companies need to maintain control over proprietary information while leveraging technology effectively. Horn stresses the importance of frugal, transparent AI applications that produce meaningful insights without unnecessary complexity.In practice, this means balancing innovation with compliance: using AI to understand risks, improve planning, and support sustainability without overstating its capabilities or creating new vulnerabilities. The conversation underlines a key point: the impact of AI is most tangible when it's applied thoughtfully, in service of real-world decisions.In short, AI is helping organisations navigate the increasingly unpredictable intersection of climate, risk, and supply chain complexity. The first mile, long a blind spot, is becoming visible not through hype or marketing claims, but through practical, data-driven insight that helps people respond to the world as it is, not as we wish it to be.TakeawaysAI can significantly improve the management of supply chains.Climate change is causing more extreme weather patterns, affecting agriculture.Data sovereignty is crucial for companies to maintain...

Mass customisation has long been the holy grail for industrial manufacturers, offering the ability to provide highly tailored products while maintaining efficiency, scalability, and profitability. However, as products become increasingly complex, traditional methods of managing configurations are starting to reveal their limitations.In a recent episode of Tech Transformed, host Christina Stathopoulos, Founder of Dare to Data, spoke with Stella d'Ambrumenil, Product Manager at Configit, about the operational realities and future potential of generative AI technology in manufacturing.The Challenge of ComplexityModern manufacturers often operate somewhere between make-to-order and assemble-to-order models. While these approaches allow flexibility, they also expose companies to a major problem, such as fragmented configuration processes. Sales teams, engineers, and manufacturing units may all handle different aspects of customisation separately, relying on spreadsheets or outdated product documentation. The result is inefficiency, errors, and an inability to scale effectively.“The problem isn't just that you have lots of options,” Stella explains. “It's that the knowledge about those options is scattered. If configuration is handled differently across departments, you inevitably get mistakes and lost time.”Configit Ace® Prompt: Bridging the GapEnter Configit Ace® Prompt, the latest tool designed to tackle this very problem. At its core, Configit Ace® Prompt converts unstructured data into structured configuration logic that can be used across all departments. Formalising configuration knowledge ensures that customisation is accurate, repeatable, and manageable.This approach not only reduces errors but also democratizes access to critical product information. Engineers, product managers, and sales teams no longer need to interpret fragmented data manually — they can work from a single source of truth. Early adopters report significant time savings, fewer mistakes, and smoother collaboration.Why Configuration Lifecycle Management MattersConfigit Ace® Prompt is a key enabler of Configuration Lifecycle Management (CLM). CLM is an approach to maintaining consistent data and processes across the entire product lifecycle — from design and engineering to manufacturing and service. This is crucial for companies seeking to scale customisation without creating chaos in operations.By adding generative AI technology, manufacturers can implement a CLM approach faster to automate logic creation, catch configuration errors early, and ensure that complex products are delivered efficiently.Looking Ahead: CLM Summit 2026For professionals interested in deepening their understanding of configuration management, Configit's CLM Summit 2026 — an online event scheduled for May 6 & 7 - will provide insights into best practices, advanced strategies, and tools like Configit Ace® Prompt. It's an opportunity to see how companies can leverage configuration management to stay competitive in a world of growing product complexity.For more insights, visit: configit.comTakeawaysManufacturers face increasing challenges with product complexity and customisation demands.Configit Ace® Prompt helps convert unstructured product knowledge into usable configuration logic.Configuration Lifecycle Management (CLM) is crucial for establishing and maintaining a shared source of truth.Product data...

As organisations navigate the rapid rise of AI, the challenge is no longer simply acquiring technology; it's preparing people to use it effectively. Many companies are realising that access to AI tools alone doesn't translate into business impact. Employees need meaningful opportunities to develop skills that can be applied immediately, helping teams work smarter and make better decisions.In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Gary Eimerman, Chief Learning Officer at Multiverse, about the pressing challenge of closing the AI and data skills gap in the workforce. They explore how organisations can build an AI-ready workforce, focusing on non-technical employees and the importance of a skills-first approach to learning.The Skills-First ApproachMultiverse champions a skills-first approach to upskilling employees in AI and data, asserting that this targeted training drives measurable business impact, including increased productivity, revenue growth, and time savings. This strategy moves beyond general AI literacy to focus on practical, applied learning. By diagnosing both organisational needs and individual skill levels, the approach identifies gaps and prescribes tailored, project-based learning experiences. Employees don't just complete modules in isolation; they work on real-world projects that apply the skills they are learning from day one, reinforcing retention and ensuring that training contributes to tangible outcomes.Learning in the AI EraGary explains that learning in the AI era is not simply about providing tools or access to content; it's about driving behaviour change, aligning learning with business outcomes, and embedding a culture of continuous skill development. As AI reshapes both the work we do and the way we learn, organisations that invest in people-first strategies position themselves to thrive rather than merely adapt. This conversation demonstrates that the future of work is always on learning, and that meaningful investment in AI and data skills is no longer optional; it's a critical driver of business success.Unlocking Workforce PotentialBy combining practical, applied training with ongoing support and measurable outcomes, companies can not only close the AI skills gap but also unlock the full potential of their workforce in an era defined by rapid technological change.TakeawaysTechnology alone is never enough; people must be invested in.Reskilling is a necessity due to technological disruption.Organisations must focus on human behaviour change, not just software deployment.A skills-first approach is critical for effective learning.Learning should be project-based and applied immediately.Non-technical roles are increasingly adopting AI tools.Creating time and space for learning is essential.Highlighting success stories builds confidence in using AI.Measuring impact through metrics like revenue per employee is vital.The future of work requires a cultural shift towards continuous learning.Chapters00:00 Closing the AI and Data Skills Gap02:02 Challenges in Building an AI-Ready Workforce06:06 The Skills First Approach to Learning10:04 Supporting Non-Technical Employees in AI13:46 Measuring the Impact of AI Skills...

In the automotive industry, trust and transparency are no longer optional; they have become key components. Dealerships that communicate clearly and responsibly with their customers strengthen relationships and improve overall experiences. In this episode of Tech Transformed, host Trisha Pillay speaks with Sean Barrett, Chief Information Officer at CallRevu, about how dealerships can navigate the changing landscape of communication while maintaining accountability, compliance and operational resilience.The Evolution of Dealership CommunicationCommunication has always been at the heart of dealership operations. The phone system was once the primary lifeline between customers and dealerships, giving managers the visibility needed to ensure interactions were handled correctly. Today, communication extends far beyond the phone. SMS, MMS, instant messaging, and other channels allow customers to engage in multiple ways.Sean explains how integrating these channels into a single technology platform provides managers with a clear view of all interactions, ensuring employees follow policies and customers receive the attention they deserve. This approach strengthens trust and improves the overall customer experience.Compliance and Data Privacy in Automotive CommunicationAlongside multi-channel communication, compliance and data privacy are critical. Regulations like GDPR and UN R155 require dealerships to protect customer data while maintaining seamless communication. Transparent practices, combined with adherence to regional rules, help build trust and protect both customers and the dealership's reputation. Observing patterns in customer interactions also allows dealerships to make informed decisions, improve processes, and enhance service quality. Using these data insights, dealerships can make communication more effective and meaningful for every customer.Infrastructure That Keeps Dealerships OperationalReliable infrastructure underpins all communication efforts. Sean shares how dealerships can prepare for unexpected disruptions with geo-redundant systems, cloud-based platforms, and layered internet backups, including options like Starlink or fibre connections. These measures ensure dealerships stay operational, customers can reach them without interruption, and business continuity is maintained.Preparing for Emerging Communication ChannelsAs new channels emerge, proactive preparation is key. Dealerships that view communication as an investment, rather than a cost, position themselves for long-term success. Monitoring trends, adapting quickly, and fostering transparency help maintain strong customer relationships even as expectations evolve.Training and Staff DevelopmentStaff development is a critical component of a communication strategy. By using insights from technology platforms, dealerships can guide employee training, build accountability, and create a culture of learning. Confident, well-trained teams contribute to consistent, high-quality interactions that enhance customer trust.Success in automotive communication isn't just about adopting the latest tools—it's about building systems and practices that protect customers, support employees, and foster trust at every touchpoint. Sean Barrett's insights provide a roadmap for dealerships aiming to elevate communication strategies, improve customer satisfaction, and

In a world where customer expectations evolve faster than ever, organisations are rethinking how they manage and leverage data. Legacy, monolithic Customer Data Platforms (CDPs) are increasingly challenged by rigidity, slow adaptability, and regulatory pressures. In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Joe Pulickal, Director of Product Management at Uniphore, about the shift to composable CDPs and what it means for modern marketing technology.Moving Away from Monolithic CDPsOrganisations are moving away from rigid, all-in-one CDPs as regulations around data privacy, consent, and cross-border data flows intensify. Joe explains that companies can no longer rely on systems that lock them into a single architecture or make compliance retrofitting difficult. Data governance, consent management, and data sovereignty have become critical considerations in every technology decision, forcing leaders to rethink the underlying structure of their CDPs.Challenges in Composable SystemsWhile composable CDPs offer flexibility, they introduce new challenges. Organisations must define ownership and accountability within modular systems to prevent fragmentation and ensure consistent data quality. Leadership must consider how compute, storage, and access are distributed across modules while maintaining compliance and security standards. Joe notes that without clarity on ownership, organisations risk operational inefficiency and weakened governance.Flexibility and Modularity in Data ManagementThe core advantage of composable architectures lies in modularity. By decoupling components from data ingestion to activation, organisations gain the freedom to innovate without being constrained by a monolithic platform. Joe emphasises: “You need flexibility in where data lives, how compute happens, ultimately doubling down on sovereignty, security, and that composable idea that initially started with data.” This approach allows teams to adopt new tools, scale selectively, and respond to changing business or regulatory requirements with agility.Embracing First-Party Data StrategiesThe shift to first-party data strategies is essential in today's marketing landscape. With third-party cookies being phased out and privacy regulations tightening, companies must rely on direct, trusted data from their customers. Composable CDPs provide the framework to centralise first-party data while giving teams the ability to personalise experiences, maintain compliance, and safeguard trust. Joe highlights that organisations need to view data not just as an asset, but as a responsibility, balancing customer value with ethical management.Here are what leaders can do:Rethink data architecture: Move from monolithic to composable systems to gain flexibility, scalability, and regulatory alignment.Prioritise governance: Define ownership, consent management, and security practices across modular components.Focus on first-party data: Build direct customer relationships and leverage trusted data responsibly.Embrace modularity: Enable innovation, adaptability, and resilience in data management through composable design.This episode offers practical insights for leaders navigating the transition from traditional CDPs to composable architectures. It highlights how thoughtful design, governance, and first-party data strategies empower organisations to act with agility, comply with regulations, and...

As companies rethink how they provide customer experiences (CX), a new form of AI capability, agentic AI, is quickly changing how work is accomplished in contact centres. In the recent episode of the Tech Transformed podcast, Dialpad Lead Product Manager Calvin Hohener sits down with host Jon Arnold, Principal at J Arnold & Associates. They discuss the transition from legacy chatbots to more autonomous agents capable of completing tasks and improving customer interactions.The conversation highlights the importance of understanding the technology's impact on enterprise architecture, the need for clean data, and the strategic implications for C-level executives. Hohener emphasises the importance of starting with clear use cases and working closely with vendors to maximise the potential of AI in business operations.From Legacy Chatbots to Agentic AIMost people have used chatbots and found them lacking. Hohener explains why: earlier conversational AI was based on retrieval-augmented generation (RAG). These systems could take user input, search a knowledge base or the internet, and provide an answer. This was helpful for customer service queries, but limited.“Previous AI models could retrieve and return information, but now we're moving into a new phase with agentic AI.” Agentic AI can take action rather than just providing information. For AI agents to succeed, organisations must first organise their data. “How your internal knowledge is structured is crucial. Even if the data is unorganised, you need to know its location and ensure it's clean,” stated Hohener.Agentic systems depend on internal knowledge, including knowledge base articles, CRM notes, and process documentation. If this foundation is disordered, the agent's output will not be reliable. This isn't about achieving ideal data cleanliness from the start; it's about knowing what information exists, where it is, and whether it can be trusted. If an AI agent bases its decisions on outdated, conflicting, or incomplete content, it will struggle to perform tasks aptly, regardless of how sophisticated the model is. Enterprises need at least basic clarity about which systems hold which knowledge, who is responsible for them, and whether there is consistency across sources.Hohener noted that organisations often overlook how quickly conflicting information can undermine an otherwise well-designed agent. A single outdated procedure or mismatched policy in a knowledge repository can lead an AI to produce incorrect results or halt during workflow execution. Keeping internal content clean, deduplicated, and consistent gives the agent a reliable, valid source. This reliability becomes crucial when AI starts taking meaningful actions, not just providing answers.By focusing on data readiness early, enterprises not only reduce deployment obstacles but also set the stage for scaling agentic AI across more complex processes. In many ways, preparing data isn't just a technical task; it's an organisational one. How Human Agents Work with AI Agents?The Dialpad Lead Product Manager noted that human roles, too, will evolve with agentic AI entering the contact centre. For instance, human agents will take on more of an advisory role—reviewing conversation traces and helping adjust the models.”Instead of...

Client service teams are at a breaking point. Margins are shrinking, the demand keeps rising, and much of the day is consumed by work that doesn't move the needle. As a result, skilled people often spend hours reconciling spreadsheets, re-entering the same data across multiple systems, and chasing updates, time that should be spent on the work clients actually pay for. Every hour lost to manual admin is an hour of revenue slipping away. In this day and age, that's a hit no business can afford.AI isn't just a buzzword here; it's a practical lever. It can cut through the repetitive tasks that slow teams down, surface the information they need instantly, and free them to focus on high-value work. The companies winning aren't replacing staff; they're removing the obstacles that keep people from doing their best. In a world where speed and accuracy matter more than ever, ignoring that shift isn't optional.In the latest episode of Tech Transformed, hosted by Christina Stathopolus, founder of Dare to Data, Daniel Mackey, CEO of Teamwork.com, discussed how AI is reshaping the daily operations of client service teams. From automating repetitive admin tasks to surfacing critical information faster, AI is giving teams the bandwidth to focus on the work that truly drives value for clients. AI and Business Transformation in PracticeDuring the conversation, Mackey highlighted how AI is reshaping business operations, emphasising efficiency and productivity rather than job displacement. “AI has transformed our company,” he noted, pointing to tangible improvements across workflow and project management. Teams are now able to focus on strategic initiatives, leaving repetitive tasks to intelligent systems.The Teamwork.com CEO also shared a recent example from a government agency that integrated AI into its processes. By automating routine administrative work, the agency experienced better resource allocation and improved project outcomes. “They're more efficient, higher quality,” Mackey said. “AI allows them to focus on the bigger parts of the business.”Rethinking Productivity and Client DeliveryOne of the challenges in the industry is that most AI features are added onto existing tools that weren't designed for client services. Mackey discussed how TeamworkAI addresses this gap. Built into a platform designed specifically for managing client services end-to-end, TeamworkAI connects projects, people, and profits in one system.By integrating AI directly into client delivery workflows, organisations can streamline project management, reduce manual reporting, and ensure that technology enhances rather than disrupts service delivery. This approach allows businesses to use technology strategically, rather than simply automating isolated tasks.Technology and the Future of WorkThe discussion also touched on the broader impact of AI on traditional business models. Organisations that adopt AI thoughtfully can improve their internal processes, freeing employees from repetitive tasks and enabling them to contribute to higher-value projects. Mackey emphasised that the goal isn't just automation, it's profitable client delivery. AI can unlock both time and insight, allowing businesses to prioritise the most impactful work.AI is redefining how businesses allocate resources, manage projects, and deliver value to clients. By eliminating repetitive work and connecting projects,...

With the rapid evolution of Generative AI, customer experience (CX) is evolving rapidly, too. In a recent episode of the Tech Transformed podcast, Mike Gozzo, Chief Product and Technology Officer at Ada, sat down with host Christina Stathopoulos, Founder of Dare to Data. They talked about how generative AI is changing business-to-customer interactions.“I view it not just as a business opportunity, but we are here to solve a problem that has existed as long as commerce has,” Gozzo said. He emphasised that AI's goal isn't just efficiency. It is about building trust and clearly understanding customer needs to allow productive interactions.Artificial intelligence, he noted, “has really enabled what used to be much more costly to happen at scale.” The Ada Chief Product and Technology Officer pointed out that the best customer experiences are highly personalised. Comparing it to arriving at a luxury hotel where the staff already knows your name, even on your first visit. He noted that modern AI aims to make such experiences, which were once only for a select few, common for everyone.Looking to the future, Gozzo tells Stathopoulos he believes generative AI will foster more engagement between customers and brands. “If I consider the trend, I think we will have much more natural, personalised, and effortless interactions than ever before because of this technology.”Gen AI's impact on Customer Data When discussing operational challenges, especially regarding customer data management, the guest speaker stressed quality over quantity. Gozzo explained that in most AI set-ups, “the real value lies not in the data you've collected, but in the understanding of how your business runs, operates, and the people doing the tasks you want to automate.”Governance, Human Orchestration & the Future of AIBeyond personalisation, AI should be implemented responsibly and monitored closely. “The first thing with any AI deployment is to avoid thinking of it as software you buy, deploy, and forget. They need ongoing monitoring, engagement, and maintenance,” Gozzo tells Stathopoulos. He suggested thorough testing processes and collaboration with specialised companies like AIUC, which verify AI systems against common risks. “These tests need to happen quarterly or yearly because the underlying models change so rapidly,” he added.In addition to regularly conducting AI checks, the human element is also critical. AI might automate up to 80% of routine tasks, but humans will still play a vital role. Gozzo described the human role as that of an orchestrator, managing teams that include both humans and AI systems and effectively delegating tasks between them.Finally, Gozzo talked about AI's immediate impact on customer experience. “Our leading customers' AI agents are outperforming humans. They deliver higher-quality customer service experiences, and customers prefer interacting with their AI.” The key measure, he said, is the positive effect on business growth and customer lifetime value.The chief technology officer's parting advice to IT decision makers is: “The people on your team know how to make AI work. Capture their insights. Don't treat this as a technology project. The technologist will not dominate the next decade. This is about business leaders and experts doing the heavy lifting.”At the core of generative and agentic AI, Gozzo...

The era of 3G is ending. For many industrial businesses, smart infrastructure systems, remote device management, and IoT connectivity rely on networks that are now being phased out globally. The question isn't if—but when your operations could be disrupted.In this episode of Tech Transformed, Trisha Pillay speaks with Jana Vidis, Business Development Manager at IFB, about the worldwide 3G sunset, what it means for enterprises, and how proactive planning can prevent costly disruptions. They explore the reasons behind the transition to 4G and 5G, the impact on various industries, and the strategies organisations can implement to assess their reliance on legacy devices. Why the 3G Sunset Matters3G networks have powered connectivity for decades, offering wide coverage and reliability. But as global operators move to 4G and 5G, maintaining 3G is no longer sustainable. Carriers are discontinuing services, and support is dwindling, leaving legacy devices vulnerable to:Operational downtimeInconsistent performanceIncreased security risksJana emphasises:“Have a good understanding of what devices you have. Work with IT partners to prepare for future changes. Plan your transition and act before disruption hits.”Jana also stressed the importance of understanding current technology deployments, planning for transitions, and future-proofing investments to avoid disruptions. The conversation highlights the need for proactive measures in adapting to technological advancements and ensuring operational continuity.A Global TimelineThe transition is already well underway across multiple regions:North America: AT&T, Verizon, and T-Mobile 3G networks discontinued in February 2022; Canada's shutdown begins in early 2025.Europe: Most countries, including the UK, Germany, Hungary, and Greece, will complete shutdowns by the end of 2025.Asia: Japan phased out 3G in 2022, Singapore in July 2024, and India plans completion by the end of 2025.Africa: South Africa started in July 2025; other countries are slowing the transition.South America: Providers like Telefonica, Entel, and Claro completed shutdowns in 2022–2023.Middle East: Oman started shutting down in July 2024; Zain Bahrain in Q4 2022; Kuwait, Iran, and Jordan are following.Industrial devices still using 3G must transition now to avoid operational disruption. From smart infrastructure to remote IoT systems, legacy devices left unaddressed can cause downtime, inconsistent performance, and increased security risks.Takeaways3G networks are being phased out to enable 4G and 5G development.Businesses must assess their reliance on 3G devices before shutdowns.Legacy devices can

Tech leaders are often led to believe that they have “full-stack observability.” The MELT framework—metrics, events, logs, and traces—became the industry standard for visibility. However, Robert Cowart, CEO and Co-Founder of ElastiFlow, believes that this MELT framework leaves a critical gap. In the latest episode of the Tech Transformed podcast, host Dana Gardner, President and Principal Analyst at Interabor Solutions, sits down with Cowart to discuss network observability and its vitality in achieving full-stack observability.The speakers discuss the limitations of legacy observability tools that focus on MELT and how this leaves a significant and dangerous blind spot. Cowart emphasises the need for teams to integrate network data enriched with application context to enhance troubleshooting and security measures. What's Beyond MELT?Cowart explains that when it comes to the MELT framework, meaning “metrics, events, logs, and traces, think about the things that are being monitored or observed with that information. This is alluded to servers and applications.“Organisations need to understand their compute infrastructure and the applications they are running on. All of those servers are connected to networks, and those applications communicate over the networks, and users consume those services again over the network,” he added.“What we see among our growing customer base is that there's a real gap in the full-stack story that has been told in the market for the last 10 years, and that is the network.”The lack of insights results in a constant blind spot that delays problem-solving, hides user-experience issues, and leaves organizations vulnerable to security threats. Cowart notes that while performance monitoring tools can identify when an application call to a database is slow, they often don't explain why.“Was the database slow, or was the network path between them rerouted and causing delays?” he questions. “If you don't see the network, you can't find the root cause.”The outcome is longer troubleshooting cycles, isolated operations teams, and an expensive “blame game” among DevOps, NetOps, and SecOps.Elastiflow's approaches it differently. They focus on observability to network connectivity—understanding who is communicating with whom and how that communication behaves. This data not only speeds up performance insights but also acts as a “motion detector” within the organization. Monitoring east-west, north-south, and cloud VPC flow logs helps organizations spot unusual patterns that indicate internal threats or compromised systems used for launching external attacks.“Security teams are often good at defending the perimeter,” Cowart says. “But once something gets inside, visibility fades. Connectivity data fills that gap.”Isolated Monitoring to Unified Experience Cowart believes that observability can't just be about green lights...

Enterprises are discovering that the first wave of cloud adoption didn't simplify operations. It created flexibility, but it also introduced fragmentation, rising costs, and skills gaps that now make AI adoption harder to manage. In this episode of Tech Transformed, analyst and host Dana Gardner speaks with two leaders from across the IBM portfolio: Maria Bracho, CTO for the Americas at Red Hat, and Tyler Lynch, Field CTO for the HashiCorp product suite. They discuss how organisations can move from scattered cloud operations to a unified, automated model that supports AI securely and at scale. The conversation covers the pressures leaders face today, the role of automation, and the skills and operating model changes required as AI becomes core to enterprise strategy. What you'll learn Why tool sprawl and shrinking teams are increasing operational risk How AI amplifies gaps in data, security, and processes What skills and operating model changes CIOs must prioritise Why hybrid cloud is essential for multi-model AI workloads The growing importance of automation in cloud and AI delivery How poor data hygiene can rapidly increase AI costs Practical steps for building secure, reliable AI operations Key insights from the discussion Cloud complexity is accelerating Most organisations now run “a sprawl of tool sets and environments,” Bracho notes, often without the people or standardized processes to manage them. While cloud created opportunities, the operational overhead has increased. AI raises the stakes Training, tuning, and inference often run in different environments, each with separate performance and security requirements. Bracho describes AI as “the killer workload,” reinforcing the need for robust hybrid architectures. Skills gaps slow progress Lynch highlights the disconnect between AI teams and production engineering teams. Without alignment, model deployment becomes slow and risky — echoing findings from the HashiCorp 2025 Cloud Complexity Report, where most organizations say platform and security teams are not working in sync. AI exposes underlying weaknesses “AI is not going to solve complexity; it will amplify what you already have,” Bracho says. But with structured processes and automation, AI can reduce operator workload and help teams adopt best practices faster. Automation is becoming essential The Cloud Complexity Report shows that more than half of enterprises see automation as key to unlocking cloud innovation. With the foundations already laid, AI can accelerate progress by improving consistency and reducing manual effort. Modernization is continuous Both guests emphasise that AI success depends on long-term investment in people, operating rhythms, and security. Consulting can help organizations start strong, but lasting results come from internal alignment and disciplined execution. Episode chapters 00:00 Navigating cloud complexity08:11 Skills and operating model challenges15:13 Automation for cloud and AI productivity21:48 How consulting accelerates AI readiness24:10 Final guidance for CIOs About...

The semiconductor industry is at an inflection point. As systems become more intelligent, connected, and software-defined, chip design is growing too complex for humans alone. Advances in electronic design automation are reshaping how silicon is built and verified, enabling faster, smarter, and more reliable innovation from data centers to edge devices.How AI Is Changing EDA and Chip DesignIn the latest episode of Tech Transformed, host John Santaferraro speaks with Dr. Thomas Andersen, Vice President of AI and Silicon Innovation at Synopsys, about the real-world impact of AI in chip design. Together, they explore how AI and automation are redefining EDA, how generative AI is accelerating design efficiency, and what the Synopsys acquisition of Ansys means for the future of simulation and system-level integration.As Dr. Andersen explains, “AI is transforming EDA. Synopsys leads in silicon design, and the Ansys acquisition expands our capabilities across multiphysics simulation and system optimization.”From Silicon to SystemsThe integration of complex hardware and software has become one of the greatest challenges in semiconductor and OEM innovation. Traditional sequential development, where software waits for hardware, often causes delays and missed targets. Advances in EDA tools and virtual prototyping now enable engineers to initiate software design months before silicon is finalised, thereby accelerating bring-up and enhancing collaboration across the supply chain.“Generative AI enables more efficient design,” says Andersen. “AI reshapes engineering workflows, but human expertise remains essential.”The result is faster time-to-market, enhanced design verification, and greater overall system reliability.Listen to the full conversation on the Tech Transformed podcast to discover how Synopsys is advancing electronic design automation, improving engineering workflows and chip design from silicon to systems.For more insights follow Synopsys:X: @SynopsysInstagram: @synopsyslifeFacebook: https://www.facebook.com/Synopsys/LinkedIn: https://www.linkedin.com/company/synopsys/TakeawaysAI is transforming EDA and chip design by automating complex processes.Synopsys is a leader in silicon-to-systems design, providing critical software for chipmakers.The acquisition of Ansys expands Synopsys' capabilities beyond EDA.Generative AI is enabling more efficient and adaptable chip design.AI-powered observability is reshaping engineering workflows.The complexity of chip design has increased, requiring advanced tools and automation.Human expertise remains essential in chip design, despite advances in automation.EDA tools simulate chip...

Driving Enterprise Innovation with AI and Strong CI/CD FoundationsAs enterprises push to deliver software faster and more efficiently, continuous integration and continuous delivery (CI/CD) pipelines have become central to modern engineering. With increasing complexity in builds, tools, and environments, the challenge is no longer just speed, but it's also about maintaining flow, consistency, and confidence in every release.In this episode of Tech Transformed, host Dana Gardner joins Arpad Kun, VP of Engineering and Infrastructure at Bitrise, to explore how solid CI/CD foundations can drive innovation and enable enterprises to harness AI in more practical, impactful ways. Drawing on findings from the Bitrise Mobile DevOps Insights Report, Kuhn shares how teams are optimising mobile delivery pipelines to accelerate development and support intelligent automation at scale.Complexity of Continuous Integration“Continuous integration pipelines are becoming more complex,” says Kuhn. “Build times are decreasing despite increasing complexity.” Faster compute and caching solutions are helping offset these pressures, but only when integrated into a cohesive CI/CD platform that can handle the rising demands of modern software delivery.A mature CI/CD environment creates stability and predictability. When developers trust their pipelines, they iterate faster and with less friction. As Kuhn notes, “A robust CI/CD platform reduces anxiety around releases.” Frequent, smaller iterations deliver faster feedback, shorten release cycles, and often improve app ratings—especially in the fast-paced world of mobile and cross-platform development.AI Ambitions with Engineering RealityIt's easy to become swept up in the potential of AI without considering whether existing foundations can support it. Many development environments are not yet equipped to handle the iterative, data-intensive nature of AI-powered software engineering. Without scalable CI/CD pipelines, teams risk encountering bottlenecks that can cancel out the potential benefits of AI.To truly drive innovation, enterprises must align their AI ambitions with robust automation, strong observability, and disciplined engineering practices. A well-designed CI/CD platform allows teams to integrate AI responsibly, accelerating testing, improving deployment accuracy, and maintaining agility even as complexity grows.TakeawaysContinuous integration pipelines are becoming more complex.Build times are decreasing despite increasing complexity.Faster computing and caching are key to improving delivery speed.Flaky tests have increased significantly, causing inefficiencies.Monitoring and isolating flaky tests can improve build success rates.Maintaining flow for engineers is crucial for productivity.A robust CI/CD platform reduces anxiety around releases.Frequent iterations lead to faster feedback and improved app ratings.Cross-platform development is on the rise, especially with React Native.The future of software development will be influenced by AI.For more insights, follow Bitrise:X: @bitriseInstagram: @bitrise.ioFacebook:

For years, observability sat quietly in the background of enterprise technology, an operational tool for engineers, something to keep the lights on and costs down. As systems became more intelligent and automated, observability has stepped into a far more strategic role. It now acts as the connective tissue between business intent and technical execution, helping organizations understand not only what is happening inside their systems, but why it's happening and what it means.This shift forms the core of a recent Tech Transformed podcast episode between host Dana Gardner and Pejman Tabassomi, Field CTO for EMEA at Datadog. Together, they explore how observability has changed into what Tabassomi calls the “nervous system of AI”, a framework that allows enterprises to translate complexity into clarity and automation into measurable outcomes.Building AI LiteracyAI models make decisions that can affect everything from customer experiences to financial forecasting. It's important to understand that without observability, those decisions remain obscure.“Visibility into how models behave is crucial,” Tabassomi notes. True observability allows teams to see beyond outputs and into the reasoning of their systems, even if a model is drifting, automation is adapting effectively, and results align with strategic goals. This transparency builds trust. It also ensures accountability, giving organizations the confidence to scale AI responsibly without losing sight of the outcomes that matter most.Observability Observability is not merely about monitoring; it is about decision-making. It provides the insight required to manage complex systems, optimize outcomes, and act with agility. For organizations relying on AI and automation, observability becomes the differentiator between being merely efficient and achieving a sustainable competitive edge. In short, observability is no longer optional; it is central to translating technology into strategy and strategy into advantage.For more insights follow Datadog:X: @datadoghq Instagram: @datadoghq Facebook: facebook.com/datadoghq facebook.comLinkedIn: linkedin.com/company/datadogTakeawaysObservability has evolved from cost efficiency to a strategic role in...

““Without healthy employees, you don't have healthy customers. And without healthy customers, you don't have a healthy bottom line.” — Kate Visconti, Founder and CEO, Five to Flow.While artificial intelligence (AI) has hastened development and made enterprises more efficient, it also comes with more deadlines. The deadlines often merge into after-hours messaging. Burnout has become a default result of productivity, especially in the tech industry. In this episode of the Tech Transformed podcast, Shubhangi Dua, Podcast Host, Producer and B2B Tech Journalist, speaks with Kate Visconti, Founder and CEO of Five to Flow, about the critical issues of burnout and disengagement in the workplace. They discuss the five core elements of change management, the financial implications of employee wellness, and strategies for enhancing productivity through flow optimisation. Also Watch: Fixing the Gender Gap in STEMThe Wellness Wave Diagnostic to Help Fix Profit LeaksVisconti stresses the importance of creating a supportive work environment and implementing effective change management practices to improve organisational performance. The conversation also highlights the role of technology in productivity and the need for leaders to prioritise employee well-being to drive business success.With an ambition to change the way organisations define true performance, VIsconti developed a system – a data-driven framework called The Wellness Wave. As per the official Five to Flow website, The Wellness Wave is “a proprietary diagnostic that measures sentiment and business performance across five core elements.”Visconti sheds light on the original framework of the company. She says, “The original was adopted when we first kicked off as part of our consulting, and it's called the Wellness Wave diagnostic. It's literally looking across the five core elements — people, culture, process, technology, and analytics.”This framework helps companies identify and fix their profit leaks, which are the hidden financial losses caused by employee burnout, disengagement, and distraction. In her conversation with Dua, host of the Tech Transformed podcast episode, Visconti shares how understanding human behaviour can lead to significant improvements in business performance.According to Five to Flow's global diagnostics, only 13 per cent of flow triggers work at their best. For tech leaders, that means most teams are functioning well below their potential.Kate's top tip is to create flow blocks. “It's about designing uninterrupted time for peak focus. This is when your brain isn't in a stress state. For me, it's mornings with my coffee. For others, it might be in the afternoon. Communicate those times to your team and protect them like meetings.”These flow blocks aren't just productivity tricks; they show that focus is more important than frantic multitasking. “Multitasking is a fallacy,” Kate says. “You're just rapidly switching tasks and burning through mental...

“Cyber resilience isn't just about protection, it's about preparation.”Every business in this day and age lives in the cloud. Our operations, data, and collaboration tools are powered by servers located invisibly around the world. But here's the question we often overlook: what happens when the cloud falters?In this episode of Tech Transformed, Trisha Pillay sits down with Jan Ursi, Vice President of Global Channels at Keepit, to uncover the real meaning of cyber resilience in a cloud-first world. Are you putting all your trust in hyperscale cloud providers? Think again. Trisha and Jan explore why relying solely on giants like Microsoft or Amazon can put your data at risk and how independent infrastructure gives organisations control, faster recovery, and true digital sovereignty.Takeaways:The importance of cyber resilience in a cloud-first worldHow independent cloud infrastructure protects your SaaS applicationsCommon shared responsibility misconceptions that can cost organisations dataStrategies for quick recovery from ransomware and cyberattacksWhy digital sovereignty ensures control and complianceChapters:00:00 – Introduction to Cyber Resilience and Cloud Strategy05:00 – The Importance of Independent Infrastructure10:00 – Shared Responsibility and Misconceptions15:00 – Digital Sovereignty and Compliance20:00 – Practical Tips for CISOs and CIOs22:00 – ConclusionAbout Jan Ursi:Jan Ursi leads Keepit's global partnerships, helping organisations embrace the AI-powered cyber resilience era. Keepit is the world's only independent cloud dedicated to SaaS data protection, security, and recovery. Jan has previously built and scaled businesses at Rubrik, UiPath, Nutanix, Infoblox, and Juniper, shaping the future of enterprise cloud, hyper-automation, and data protection.Follow EM360Tech for more insights:Website: www.em360tech.comX: @EM360TechLinkedIn: EM360TechYouTube: EM360Tech

"5G is becoming a great enabler for industries, enterprises, in-building connectivity and a variety of use cases, because now we can provide both the lowest latency and the highest bandwidth possible,” states Ganesh Shenbagaraman, Radisys Head of Standards, Regulatory Affairs & Ecosystems.In the recent episode of the Tech Transformed podcast, Shubhangi Dua, Podcast Host, Producer, and Tech Journalist at EM360Tech, speaks to Shenbagaraman about 5G and edge computing and how they power private networks for various industries, from manufacturing, national security to space.The Radisys' Head of Standards believes in the idea of combining 5G with edge computing for transformative enterprise connectivity. If you're a CEO, CIO, CTO, or CISO facing challenges of keeping up the pace with capacity, security and quality, this episode is for you. The speakers provide a guide on how to achieve next-gen private networks and prepare for the 6G future. Real-Time ControlThe growing need for real-time applications, such as high-quality live video streams and small industrial sensors with instant responses, demands data processing to occur closer to the source than ever before. Alluding to the technical solution that provides near-zero latency and ensures data security, Shenbagaraman says:"By placing the 5G User Plane Function (UPF) next to local radios, we achieve near-zero latency between wireless and application processing. This keeps sensitive data secure within the enterprise network."Such a strategy has now become imperative in handling both high-volume and mission-critical low-latency data all at the same time. Radisys addresses key compliance and confidentiality issues by storing the data within a private network. Essentially, they create a safe security framework that yields near-zero latency to guarantee utmost data security.Powering Edge Computing ApplicationsThe real-world benefit of this zero-latency setup is the power it gives to edge computing applications. As the user plane function is the network's final data exit point, positioning the processing application near it assures prompt perspicuity and action."The devices could be sending very domain-specific data,” said Shenbagaraman. “The user plane function immediately transfers it to the application, the edge application, where it can be processed in real time."It reduces errors and improves the efficiency of tasks through the Radisys platform, with the results meeting all essential requirements, including compliance needs.One such successful use case spotlighted in the podcast is the Radisys work with Lockheed Martin's defence applications. "We enabled sophisticated use cases for Lockheed Martin by leveraging the underlying flexibility of 5G,” the Radisys speaker exemplified. Radisys team customised 5G connectivity for the US defence sector. It incorporated temporary, ad-hoc networks in challenging terrains using Internet Access Backhaul. It also covered isolated, permanent private networks for locations such as maintenance...

Now that companies have begun leaping into AI applications and adopting agentic automation, new architectural challenges are bound to emerge. With every new technology comes high responsibility, consequences and challenges. To help face and overcome some of these challenges, Temporal introduced the concept of “durable execution.” This concept has quickly become an integral part of building AI systems that are not just scalable but also reliable, observable and manageable.In this episode of the Tech Transformed podcast, host Kevin Petrie, VP of Research at BARC, sits down with Samar Abbas, Co-founder and CEO of Temporal Technologies. They talk about durable execution and its critical role in driving AI innovation within enterprises. They discuss Abbas's extensive background in software resilience, the development of application architectures, and the importance of managing state and reliability in AI workflows. The conversation also touches on the collaboration between developers, data teams, and data scientists, emphasising how durable execution can enhance productivity and governance in AI initiatives.Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?Chatbots to Autonomous Agents“AI agents are going to get more and more mission critical, more and more longer lived, and more asynchronous," Abbas tells Petrie. “They'll require more human interaction, and you need a very stable foundation to build these kinds of application architectures.”AI not just fuels chatbots today. Enterprises are increasingly experimenting with agentic workflows—autonomous AI agents that carry out complex background tasks independently. For example, agents can assign, solve, and submit software issues using GitHub pull requests. Such a setup isn't just a distant vision; the Temporal co-founder pointed to OpenAI's Codex as a real-world case. With this approach, AI becomes a system that can handle hundreds of tasks at once, potentially achieving "100x orders of magnitude velocity," as Abbas described.However, there are some architectural difficulties to stay mindful of. The AI agents are non-deterministic by nature and often depend on large language models (LLMs) like OpenAI's GPT, Anthropic's Claude, or Google's Gemini. They reason based on probabilities, and they improvise. They often make decisions that are hard to trace or manage.AI workflows as simple codeThis is where Temporal comes in. It becomes the executioner that keeps the system cohesive and in alignment. “What we are trying to solve with Temporal and durable execution more generally is that we tackle challenging distributed systems problems," said Abbas.Rather than developers stressing over queues, retries, or building their own reliability layers, Temporal allows them to write their AI workflows as simple code. Temporal takes care of everything else—reliable state management, retrying failed tasks, orchestrating asynchronous services, and ensuring uptime regardless of what fails below the surface.As agent-based architectures become more common, the demand for this kind of system-level orchestration will only increase.Listen to the full conversation on the Tech...

For CISOs and technology leaders, AI is reshaping business process management and daily operations. It can automate routine tasks and analyse data, but the human element remains critical for workforce oversight, customer interactions, and strategic decision-making.In this episode of Tech Transformed, Trisha Pillay talks with Anshuman Singh, CEO of HGS UK, about AI in the workplace. They discuss how AI can support employees, improve customer service, and require careful oversight. Singh also shares insights on preparing organisations for AI integration and trends leaders should watch in the coming years.Questions or comments? Email info@em360tech.com or follow us on YouTube, Instagram, and Twitter @EM360Tech.TakeawaysAI is reshaping workforce needs, not just replacing jobs.Routine tasks are increasingly being automated by AI.AI can free up capacity for more meaningful work.The narrative around productivity is changing with AI.AI will create new job opportunities, often better-paying.Human oversight is crucial in AI decision-making.AI can assist in customer service, enhancing empathy.Organisations should not wait for perfect AI solutions.Training and hands-on experience with AI are essential.A psychological safety net is necessary for AI experimentation.Chapters00:00 Introduction to AI and Human Element03:03 AI's Impact on Workforce Dynamics08:29 The Role of Human Oversight in AI10:46 AI Innovations in Customer Service16:34 Positioning for Growth in Business Process Management20:01 Preparing the Workforce for AI Integration25:35 Emerging Trends in AI and Workforce29:19 Final Thoughts on AI and Ethics

AI-Powered Canvases: The Future of Visual Collaboration and InnovationAs hybrid and remote work become the standard, organizations are rethinking how teams brainstorm, align, and innovate. Traditional whiteboards and digital tools often fall short in keeping pace with today's complex business challenges. This is where AI-powered canvases are transforming visual collaboration.In this episode of Tech Transformed, Kevin Petrie, VP of Research at BARC, joins Elaina O'Mahoney, Chief Product Officer at Mural, to explore how AI collaboration tools are reshaping teamwork in off-site locations. From customer journey mapping to process design, AI-powered canvases give teams the ability to visualize ideas, surface insights faster, and make better decisions—while keeping human creativity at the centre.AI-Powered Canvases, Visuals, and CollaborationA central theme in the conversation is the distinction between automation and augmentation. While AI can recommend activities, map processes, and identify participation patterns, decision-making remains a human responsibility.As O'Mahoney explains:“In the Mural canvas experience, we're looking to draw out the ability of a skilled facilitator and give it to participants without them having to learn that skill over the years.”This balance ensures that while AI-powered canvases streamline collaboration, teams still rely on human judgment, creativity, and contextual knowledge. One of the most powerful contributions is in AI-driven visuals, which can translate raw data or unstructured input into clear diagrams, journey maps, or process flows. These visuals not only accelerate understanding but also help teams spot gaps and opportunities more effectively.For example:In customer journey mapping, AI can quickly generate visual flows that highlight pain points and opportunities that would take much longer to uncover manually.In manufacturing, AI-powered canvases can create dynamic visuals of workflows, showing how new technologies might disrupt established processes.The Role of Visual Tools in Hybrid WorkIn blended work environments, teams often lack the in-person cues that guide effective collaboration. Visual canvases bring those cues into the digital workspace, showing where ideas are concentrated, highlighting gaps in participation, and enabling alignment across dispersed teams. By combining intuitive design with AI-driven support, platforms like Mural help organisations adapt to the demands of hybrid work while keeping human creativity at the centre.TakeawaysAI is reshaping visual collaboration in distributed teams.Visual elements enhance understanding and decision-making.AI can augment workflows but requires human oversight.There is no universal playbook for AI integration in businesses.Hybrid work necessitates effective digital collaboration tools.AI can help visualize complex customer experiences.Human intuition and creativity remain essential in AI applications.Training and guidance are crucial for effective AI use.Collaboration tools must adapt to diverse work environments.AI should be seen as a partner in the creative process.Chapters00:00 The Evolution of Visual Collaboration05:15 Augmenting vs Automating: The Role of AI10:36...

The issue is data fragmentation, where untrustworthy data is siloed across different databases, SaaS applications, warehouses, and on-premise systems,” Vladimir Jandreski, Chief Product Officer at Ververica, tells Christina Stathopoulos, the Founder of Dare to Data. “Simply, there is no single view of the truth that exists. With governance and data quality checks, these are often inconsistent, AI systems end up consuming incomplete or conflicting signals,” he added, setting the stage for the podcast.In this episode of the Don't Panic, It's Just Data podcast, Stathopoulos speaks with Jandreski about the vital role of unified streaming data platforms in facilitating real-time AI. They discuss the difficulties businesses encounter when implementing AI, the significance of going beyond batch processing, and the skills necessary for a successful streaming data platform. Applications in the real world, especially in e-commerce and fraud detection, show how real-time data can revolutionise AI strategies.Your AI Could Be a Step Behind Jandreski says that most organisations continue to be engineered on batch-first data systems. That means, they still process information in chunks—often hours or even days later. “It's fine for reporting, but it means your AI is always going to be one step behind.”However, “the unified streaming platform flips that model from data at rest to data in motion.” A unified platform will “continuously capture the pulse” of the business and feed it directly to AI for automated real-time decision making. Challenges of Agentic AI Considering that the world is moving toward the era of agentic AI, there are some key challenges that still need to be addressed. Agentic AI means autonomous agents make real-time decisions, maintain memory, use tools and collaborate among themselves. Because they act on their own decisions, regulating them is necessary. Building agents is not the main challenge, but the real challenge is “actually giving them the right infrastructure.” Jandreski highlights. Alluding to an example of AI prototyping frameworks such as Longchain or Lama Index, he further explained that those frameworks work for demos. In reality, however, they can't support a long-running system trigger workflows that demand high availability, fault tolerance, and deep integration with the enterprise data. This is because enterprises have multiple systems, and many of them are not connected. This way, the data forms into silos. When data is in silos, a unified streaming data platform becomes the key solution. “It provides a real-time event-driven contextual runtime where AI agents need to move from the lab experiments to production reality.”TakeawaysUnified streaming data platforms are essential for real-time AI.Batch processing creates lag, hindering AI effectiveness.Data fragmentation leads to unreliable AI decisions.A unified platform ensures data is fresh and trustworthy.Real-time AI requires a robust data infrastructure.Organisations must move beyond legacy batch systems.Governance and data quality are critical for AI success.Real-world applications...

"The tools we make are observability tools today. But it can never be the goal of our business to provide observability. The goal of our business as a vendor and as a partner with our customers is to give them understandability,” stated Nic Benders, the Chief Technical Strategist at New Relic.In this episode of the Don't Panic It's Just Data podcast, host Christina Stathopoulos, the Founder of Dare to Data, speaks with Benders about where observability is headed in IT systems. They discuss how AI is transforming observability into a more comprehensive understanding of complex systems, moving beyond traditional monitoring to achieve true understandability. Benders explained the importance of merging various data types to provide a complete picture of system performance and user experience. He believes AI can bridge the gap between mere observation of systems and a deeper understanding of their functionality. This could ultimately lead to enhanced incident response and operational efficiency. With maturing technology, complexity is expected to grow, too. The straightforward act of “observing” those complexities is like watching a green light on a machine. This is not enough. The major challenge is to “understand” the inside operations of the machine. This is the difference between simply seeing the data and knowing the "why."Observability to UnderstandabilityAs per Benders, the term observability "leaves a lot to be desired." While it's the industry's common label, it only describes seeing a system. The real goal, he argues, is to understand it.Alluding to an analogy, the technical strategist asks Stathopoulos to imagine a nuclear power plant full of a million blinking lights and screens. “You can have all the observability available, but if you're not an expert, you won't grasp what's actually happening,” says Benders. Typically, software has been developed by a single person who knows every inch of it. However, today, technology has become more perplexing. AI, alongside teamwork and collaboration, provides the tools to solve this problem. An engineer might manage code they didn't write, making a dashboard full of charts unhelpful. Understandability means moving beyond raw data to give context and meaning.Ultimately, Benders advises IT leaders to embrace change. The tech industry is constantly changing and advancing. Instead of fearing new tools, organizations should focus on what they need to grasp the unknown. As he puts it, "a lot of unknown is coming over the next few decades."TakeawaysObservability is not enough; understanding is crucial.AI can enhance the understanding of complex systems.The shift from observing to understanding is essential for modern IT.AI presents both challenges and opportunities in software development.New interfaces powered by AI can improve user interaction with data.AI can help reduce incident response times significantly.Collaboration with AI is becoming the norm in software development.Real-world applications show measurable benefits of AI in observability.IT decision-makers must prepare for ongoing changes in technology.Understanding the unknown is key to navigating future challenges.Chapters00:00 Introduction to Observability and Understandability05:00...

The phrase “AI agent” still brings to mind chatbots handling customer queries. Fast forward to today - AI agents are far more versatile, representing a new generation of systems capable of perceiving, reasoning, and acting autonomously. These bots are beginning to reshape how enterprises operate, not just in customer service but across software development, data analytics, and operational workflows.In this episode of Tech Transformed, Dare To Data Founder Christina Stathopoulos explores the rapid rise of AI agents with Ben Gilman, CEO of Dualboot Partners. Together, they unpack how AI agents differ from traditional automation and what this shift means for software development, enterprise operations, and the future of productivity.AI Agents vs. Traditional AutomationUnlike traditional automation, which follows strict, deterministic rules, AI agents can adapt to changing inputs, analyze complex data sets, and make autonomous decisions within defined parameters. This allows them to tackle tasks that were previously too intricate or time-consuming for automated systems. Dualboot Partners helps organizations harness these AI agents, integrating them into workflows to deliver real business value through a combination of product, design, and engineering expertise.“The biggest difference with an AI agent, between a standard tool, is that the agent can perceive information and reason about it, providing context and insights you don't normally get in an algorithm.” — Ben Gilman, CEO, Dual Boot Partners.The Future of AI in EnterpriseOrganisations face several hurdles when integrating AI agents, including defining clear use cases, understanding the probabilistic nature of AI reasoning, and incorporating agents into existing processes and workflows. Despite the challenges, the potential payoff is substantial. AI agents can boost productivity, improve decision-making, and make enterprises more agile. As these systems mature, humans and AI are increasingly collaborating as true partners, reshaping what the workplace and work itself look like.Takeaways:AI Agents vs. Traditional Automation: AI agents can perceive and reason, offering more context and adaptability compared to deterministic systems.Real-World Applications: Examples include virtual vet agents and data analytics tools that enhance productivity and decision-making.Challenges in Adoption: Organizations face hurdles in defining specific use cases and integrating AI agents effectively.Future of AI in Tech: AI agents are expected to significantly boost productivity and innovation in software development and enterprise operations, with AI-first approaches like Dualboot's "DB90" driving structured adoption and accelerating modernization.Chapters0:00 - 3:00: Introduction to AI Agents3:01 - 6:00: Differences from Traditional Automation6:01 - 12:00: Real-World Applications and Examples12:01 - 18:00: Challenges in Adoption18:01 - 22:00: Future Impact on Tech and Operations22:01 - 24:00: Conclusion and Final ThoughtsAbout Dualboot Partners

In a time when the world is run by data and real-time actions, edge computing is quickly becoming a must-have in enterprise technology. In the recent episode of the Tech Transformed podcast, hosted by Shubhangi Dua, a Podcast Producer and B2B Tech Journalist, discusses the complexities of this distributed future with guest Dmitry Panenkov, Founder and CEO of emma.The conversation dives into how latency is the driving force behind edge adoption. Applications like autonomous vehicles and real-time analytics cannot afford to wait on a round trip to a centralised data centre. They need to compute where the data is generated.Rather than viewing edge as a rival to the cloud, the discussion highlights it as a natural extension. Edge environments bring speed, resilience and data control, all necessary capabilities for modern applications. Adopting Edge ComputingFor organisations looking to adopt edge computing, this episode lays out a practical step-by-step approach. The skills necessary in multi-cloud environments – automation, infrastructure as code, and observability – translate well to edge deployments. These capabilities are essential for managing the unique challenges of edge devices, which may be disconnected, have lower power, or be located in hard-to-reach areas. Without this level of operational maturity, Panenkov warns of a "zombie apocalypse" of unmanaged devices.Simplifying ComplexityManaging different APIs, SDKs, and vendor lock-ins across a distributed network can be a challenging task, and this is where platforms like emma become crucial.Alluding to emma's mission, Panenkov explains, "We're building a unified platform that simplifies the way people interact with different cloud and computer environments, whether these are in a public setting or private data centres or even at the edge."Overall, emma creates a unified API layer and user interface, which simplifies the complexity. It helps businesses manage, automate, and scale their workloads from a singular perspective and reduces the burden on IT teams. They also reduce the need for a large team of highly skilled professionals leads to substantial cost savings. emma's customers have experienced that their cloud bills went down significantly and updates could be rolled out much faster using the platform.TakeawaysEdge computing is becoming a reality for more organisations.Latency-sensitive applications drive the need for edge computing.Real-time analytics and industry automation benefit from edge computing.Edge computing enhances resilience, cost efficiency, and data sovereignty.Integrating edge into cloud strategies requires automation and observability.Maturity in operational practices, like automation and observability, is essential for...

"The real challenge that many manufacturers have dealt with for a long time and will keep facing is the shift from mass manufacturing to mass customisation," stated Daniel Joseph Barry, VP of Product Marketing at Configit. In a world that has moved from mass manufacturing to mass customisation, makers of complex products like cars and medical devices face a hidden problem. For more than a century, since the time of Henry Ford, manufacturers have worked in a separate, mass-production mindset. This method in the recent industrial scenario has caused a lot of friction and frustration.In this episode of the Tech Transformed podcast, Christina Stathopoulos, Dare To Data Founder, talks with Daniel Joseph Barry, VP of Product Marketing at Configit. They talk about Configuration Lifecycle Management (CLM) and its importance in tackling the challenges that manufacturers of complex products face recurrently.The speakers discuss the move from mass manufacturing to mass customisation, the various choices available to consumers, and the need to connect sales and engineering teams. Barry emphasises the value of working together to tackle these challenges. He points out that using CLM can make processes easier and enhance customer experiences (CX).What is Configuration Lifecycle Management (CLM)According to Barry, Configuration Lifecycle Management (CLM) is an approach that involves managing product configurations throughout their lifecycle. He describes it as an extension of Product Lifecycle Management (PLM) that focuses specifically on configurations. In today's highly bespoke world, customers are buying configurations of products instead of just the products themselves. The answer isn't to work harder within existing teams but to adopt a new, collaborative approach. This is where Configuration Lifecycle Management (CLM) comes in. CLM creates a single, shared source of truth for all product configuration information. It combines data from engineering, sales, and manufacturing. Configit's patented Virtual Tabulation® (VT™) technology pre-computes all the different options, so there's no longer a need for slow, real-time calculations. Barry says, "It's just a lookup, so it's lightning fast.” This represents a prominent shift that removes the delays and dead ends, frustrating customers and sales staff. Such a centralised system makes sure that every department uses the same, verified information, stopping errors from happening later on. One such company, and Configit's customer, Vestas, a wind power company, automated its configuration process for complex wind turbines that have 160,000 options. By adopting a CLM approach, they cut the time to configure a solution from 60 minutes to just five.Tune into the podcast for more information on the transformational impact of Configuration Lifecycle Management (CLM). TakeawaysManufacturers are transitioning from mass manufacturing to mass customisation.Customisation leads to complexity and challenges in manufacturing.Siloed systems create inefficiencies and reliance on experienced employees.Configuration Lifecycle Management (CLM) can automate and streamline processes.Aligning sales and...

As global industries face mounting pressure to operate more efficiently and sustainably, many are turning to the combined power of artificial intelligence (AI) and the Internet of Things (IoT). From optimising energy usage to enabling real-time decision-making, these technologies are reshaping how businesses think about infrastructure, impact, and innovation. But the road to adoption isn't without its challenges, from data literacy to greenwashing.In this episode of Tech Transformed, Em360Tech host Trisha Pillay talks with Akanksha Sharma, Senior Director at the GSMA Foundation, about how these emerging technologies are creating tangible value, especially for small and medium-sized enterprises (SMEs) and industries with legacy systems like utilities. IOT and AISharma highlights that the 2020s will be remembered as the decade when IoT experiences exponential growth, supported by data from GSMA Intelligence projecting over 37 billion IoT connections worldwide by 2030, more than doubling the number recorded in 2021. She notes that, unlike previous technological waves, AI adoption is accelerating rapidly, moving from niche awareness to mainstream use within just a few years.When discussing climate action and carbon markets, Sharma stresses the need for transparent, data-backed verification mechanisms. She warns against superficial greenwashing practices and advocates for AI systems that prioritise accuracy and ethical standards to ensure genuine environmental benefits.TakeawaysData-driven infrastructure can turn sustainability into reality.AI and IoT are set to scale in the 2020s.Small and medium enterprises face unique operational challenges.Digital solutions can enhance the accuracy of carbon credits.Greenwashing misleads consumers about environmental benefits.Digital literacy is a major barrier to technology adoption.Start with the 'why' when adopting new technologies.Ethics in AI must be prioritised to avoid negative consequences.The world is changing due to climate change and technology.Collaboration is key to effective climate action.Chapters:00:00 – Transforming Sustainability with Data-Driven Infrastructure03:05 – The Role of AI and IoT in Enterprises09:10 – Challenges in Operational Efficiency and Sustainability13:42 – Real-World Impact of AI and IoT16:57 – Carbon Markets and Digital Solutions21:08 – Understanding Greenwashing23:30 – Barriers to Technology Adoption26:17 – Key Takeaways and PredictionsAbout Akanksha SharmaAkanksha Sharma leads the ClimateTech and Digital Utilities programmes at GSMA, where she drives innovation at the...

Many companies spend a lot on data technology, but often forget about the importance of data and AI literacy. Without the right skills, even the best platforms can fail to deliver results. Teams need to understand how to work with data and AI to make any strategy successful.In this episode of Tech Transformed, EM360Tech's Trisha Pillay chats with Greg Freeman, the founder of Data Literacy Academy about why knowing data and AI matters for anyone building a digital strategy.Data and AI LiteracyFreeman points out that many data strategies end up as technical documents rather than actionable roadmaps. He explains that organisations often spend heavily on infrastructure, expecting better tools to solve their problems but without employees who understand how to work with data and why it matters, these investments rarely deliver results.Freeman explains that data strategies often fail because only a small portion of employees less than 20 per cent are truly enthusiastic about data. Most strategies are designed with this minority in mind, creating an echo chamber that leaves the majority behind. As a result, data stays siloed, and business decisions don't improve. The Data Literacy Academy founder stresses that unless organisations engage the 80 per cent of employees who aren't already invested, their strategies are unlikely to succeed. When the focus is on tools rather than people, adoption falls behind.TakeawaysData and AI literacy are key to turning strategy into value.Tools alone don't work; people need confidence and context.Focus on engaging the data-hesitant majority, not just the enthusiasts.Cultural change, not just technical change, is what drives ROIChapters00:00 – Introduction02:07 – Beyond the Tech Stack04:41 – Why Strategies Fail08:41 – Literacy Barriers12:08 – Success in the Real World17:17 – Building Lasting Literacy22:20 – AI Needs Literacy Too26:33 – Final TakeawaysAbout Greg FreemanGreg Freeman is the founder and CEO of Data Literacy Academy, where he works with CDOs, CIOs, and business leaders to drive real cultural change around data. His mission is to help organisations tackle data illiteracy by building confidence and capability from the ground up, especially for employees who feel disengaged or anxious about data.With a background in sales leadership and tech startups, Greg brings both strategic insight and real-world experience.

"As agentic AI spreads across industries,” states Rishi Rana, the Chief Executive Officer at Cyara. “Everybody is curious to understand how that is going to transform customer experience across all the channels?"In this episode of the Tech Transformed podcast, Shubhangi Dua, the Host and Podcast Producer at EM360Tech, talks with Rishi Rana, the CEO of Cyara, about how agentic AI is changing customer experience (CX). They look at how AI has developed from simple chatbots to advanced systems that can understand and predict customer needs. Rana spotlights the need for ongoing testing and monitoring to make sure AI solutions work well and follow the regulations. They also discuss the obstacles businesses encounter when implementing AI, the importance of good data, and the future of AI agents in improving customer interactions.Agentic AI Transforming Customer Experience (CX)Customer experience (CX) is changing quickly and significantly, thanks to the rise of agentic AI. These advanced systems go beyond the basic chatbots of the past. While such a change may offer a future equipped with a smart, proactive customer journey, it doesn't come without its challenges. These obstacles require organisations to thoughtfully plan and carefully execute strategies.For years, chatbots provided a basic type of automated customer support. However, Rana explains that the evolution of AI is pushing boundaries. "AI in customer experience (CX) is changing from a basic level of chatbots that have been present for the last five or 10 years. Now they are turning into fully agentic systems that operate across voice, digital and human-assisted channels," said Rana. Moving Beyond Basic ChatbotsChatbots' lucrative development lies in the strengths of Large Language Models (LLMs) like Google's Gemini, Meta's Llama, and OpenAI's ChatGPT. This is because the AI-backing models are facilitating "voice bots" and other AI agents to move beyond simple response automation to intelligent orchestration. Intelligent orchestration results in anticipating user needs, adjusting in real-time, and guiding customers to hybrid solutions where AI and human agents work together. Ultimately, the goal is to greatly improve the customer experience (CX). Studies suggest that 86 per cent of people are willing to pay more for the same service, no matter what it is, when the customer experience is better.Advancements don't come without a price. Rana believes the lack of proper guardrails is a cause for concern. "AI is great, but you need to have guardrails and ensure the intent behind the questions and the objective behind the customer interaction is getting answered." This requires ongoing testing and monitoring across all channels to ensure consistency and avoid problems like hallucinations, misuse, or bias. These issues can result in major financial losses and damage to reputation. For instance, Rishi Rana mentioned that over "$10 billion in violations and liabilities due to incorrect information given to customers" occurred in 2024 alone.To successfully execute agentic AI, enterprises must shift left with AI by...