The Tech Blog Writer Podcast

How should finance leaders measure AI ROI when adoption has slowed and the cost of models, tokens and disconnected tools remains difficult to predict? In this episode of Tech Talks Daily, I welcome Jeremy Ung, Chief Technology Officer at BlackLine, back to the podcast to discuss how businesses can move from finance AI experimentation to operational deployment. Figures supplied for the interview show AI adoption in finance rising from 37% in 2023 to 58% in 2024, before moving only slightly to 59% in 2025. Jeremy argues that this apparent plateau reflects several pressures, including uncertainty around cost, regulatory requirements, auditability and the continuing debate over whether companies should build their own AI capabilities or purchase them through established platforms. Token spending is part of the problem. Unlike traditional software costs, model usage can be difficult to predict and allocate. Finance leaders want to understand whether applying AI to a workflow will produce enough value to justify that uncertainty. Jeremy believes companies should avoid creating artificial AI ROI metrics. The business measurements already exist. Is AI helping the company close its books faster? Is transaction matching becoming more accurate? Are collections improving? Is the work being completed faster or with fewer manual steps? We discuss what operationalizing AI in finance looks like in practice. Many processes still require employees to contact vendors, collect information, reconcile data and coordinate with other departments. Traditional software struggled with the variation found in these workflows, while AI can adapt to different processes and communication requirements. Accuracy and oversight remain necessary. Jeremy explains why companies need visibility into the prompts, reasoning, models, data, tools and permissions used by every AI agent. That information creates an operating record that finance teams, auditors and regulators can examine later. His analogy with food labeling provides a useful way to understand AI auditability. Consumers can inspect ingredients, calories and sourcing information before buying food. Finance leaders should expect comparable information about the models and data involved when an agent performs financial work. We also discuss the problem of fragmented data. Jeremy acknowledges the familiar rule of garbage in, garbage out, but argues that AI can help connect legacy platforms and mainframe systems that businesses previously found difficult to integrate. The role of finance professionals will change as agents perform additional work. Employees may spend less time completing individual tasks and more time setting goals, reviewing results, approving actions and directing teams of agents. Should CFOs continue buying additional AI tools, or concentrate on embedding existing investments into the financial workflows that determine business performance? Please share your thoughts with me.

What happens when an autonomous AI agent can complete thousands of actions before a traditional access review has even identified that something has gone wrong? In this episode of Tech Talks Daily, I speak with Alex Bovee, CEO and co-founder of C1, about identity security, runtime governance, shadow AI, and the controls companies need as humans and agents begin working together. Alex has spent much of his career in identity and security. He and his co-founder previously worked at Okta on zero trust products before creating C1 as an access control platform capable of operating at machine speed. That requirement has become increasingly important as AI agents begin accessing company data, calling tools, using credentials, and taking actions across enterprise systems. Alex describes agents as non-deterministic systems that can "reward-max." An agent may pursue its assigned objective so aggressively that it finds an unexpected or dangerous way to complete the task. It does not possess a moral compass or an intuitive understanding of what the company considers acceptable. Traditional identity processes were created for people. A company might review access every 90 days or investigate a security issue after an event. That approach becomes inadequate when an agent can execute thousands of actions within minutes. We discuss why identity is becoming a control plane for AI agents. Networks, data systems, and security tools all play important roles, but identity determines which resources an agent can access, which actions it can perform, and whether it acts independently or on behalf of a person. Without a defined identity or delegated authorization model, an organization may struggle to connect an agent's behavior with a responsible owner, a limited mission, and enforceable permissions. Alex explains the four connected capabilities inside C1's Agentic Control Plane. The first concerns shadow AI discovery. Companies need visibility across cloud services, SaaS applications, endpoint agents, hosted agents, local MCP servers, and credentials stored throughout the environment. This is particularly relevant because employees are downloading locally developed or "vibe-coded" MCP servers and running agent tools on their devices. These components can introduce software supply chain risks and expose local credentials. The second capability covers credential security. C1 has introduced a post-quantum credential vault designed to protect secrets and inject them into authorized agent workflows without leaving credentials scattered across devices and applications. The third area is runtime governance. Instead of reviewing behavior after an incident, organizations can evaluate an agent's actions against its assigned mission as they occur. If an agent is authorized to complete one business task but begins exploiting an internal tool, contacting an unapproved service, or attempting to extract data, runtime controls can block the action or request human approval. The fourth capability concerns agentic security intelligence. This uses information collected across identities, agents, permissions, credentials, and behavior to identify risks and support automated remediation. We also discuss human accountability. Alex says emerging regulatory thinking recognizes the need for a responsible person behind an autonomous agent. That connection allows businesses to establish ownership, delegate authority, and determine who remains accountable for the agent's behavior. The conversation then turns to the effect of AI on employees. Alex rejects the assumption that organizations will simply remove people as agents become more capable. His preferred analogy is that people are moving from manually producing every artifact to building and supervising the factory. Employees provide the inputs, direct the agents, examine the outputs, and correct the process when necessary. C1 has experienced this internally. Alex says its engineering team increased from roughly 150 weekly software merges to around 1,500, while engineering headcount grew by approximately 10% to 15%. That productivity requires careful human review. Generating work faster does not remove the need to assess whether the output is accurate, secure, useful, and aligned with the original objective. For CISOs and CIOs, the goal is to provide a governed path for AI adoption. A blanket prohibition may encourage employees to work around policy. Secure self-service access can give teams approved tools, defined permissions, and runtime protection. If an AI agent can operate at machine speed, are your organization's identity controls capable of observing, authorizing, and stopping it at the same pace? Listen to the conversation and share your thoughts with me.

What evidence would convince you that an AI agent is ready to make decisions involving employment, money, healthcare, or legal rights? In this episode of Tech Talks Daily, I speak with Vin Sharma, founder and CEO of Vijil, about the trust gap preventing many enterprise AI agents from progressing beyond proof of concept. Vin has spent approximately 30 years building software across security, operating systems, open source, cloud computing, machine learning, and AI. His previous work includes leading engineering at Amazon SageMaker and helping develop 11 AWS AI services. He argues that AI agents differ from conventional software because they combine autonomy with agency. They can interpret an objective, make decisions under ambiguous conditions, and take action. This raises a deeper question than whether an agent can complete a demonstration successfully: will it remain loyal to the interests of the person or business delegating the task? Trust is also specific to the job. Vin uses a simple analogy. You may trust a gardener to care for your lawn, but that does not automatically make the same person suitable to babysit your child. An AI agent must therefore be evaluated within the context of its users, task, operating conditions, authority, and potential consequences. Vin proposes testing three areas. Reliability asks whether the agent can perform its assigned task. Security examines whether it maintains its integrity when facing hostile or noisy conditions. Safety considers what happens when the agent fails and whether the resulting damage remains contained. This evaluation cannot end when the agent enters production. Models, integrations, data, users, and external conditions change. An agent may drift away from its original purpose, which means businesses need continuous monitoring, testing, and updating across the full AI agent lifecycle. We discuss how established security practices can be applied to this problem. Trusted execution environments, containment, least privilege, limited-duration access, and bounded models can reduce exposure. Smaller language models may also be better suited to narrow, high-risk tasks than a general model with broad permissions. Vin offers a three-part framework for governance: personas, purpose, and policy. Personas describe the people and attackers who may interact with the agent. Purpose defines the legitimate task. Policy sets the boundaries between permitted and prohibited behavior. For high-risk systems, his recommended starting position is that any action not explicitly permitted should be prohibited. A natural-language policy can then be converted into deterministic rules and controls governing the agent's behavior. Vin's most direct advice concerns evidence. Vibes, demonstrations, and benchmark scores do not prove that an agent is safe for a particular business process. A CISO should expect a complete risk assessment, while a business owner should receive proof that the agent will serve the organization's interests. His bridge analogy captures the issue perfectly. Engineers do not claim a bridge is safe because it looks impressive during a demonstration. They calculate load, tolerance, failure conditions, and provide test evidence. AI agents acting in consequential workflows deserve a comparable engineering discipline. If an agent developer asked you to trust their system today, would they be able to provide evidence of reliability, security, safety, loyalty, and contained failure? Listen to the episode and share your thoughts with me.

What if employees could access sensitive business applications from personal phones without storing company data on those devices? In this episode of Tech Talks Daily, I speak with Jared Shepard, CEO of Hypori, about virtual mobile infrastructure, BYOD security, employee privacy, zero trust, and the growing mobile threat created by AI. Jared's personal story deserves attention in its own right. He describes himself as a former homeless high school dropout who joined the Army, discovered an aptitude for IT, and applied what he learned to difficult technology problems in Iraq and Afghanistan. That experience gave him a firsthand understanding of what people working at the edge need from secure communications. The requirement that led to Hypori was unusually demanding. Users needed to obtain a phone from a local market, connect through a network assumed to be compromised, and access a protected enterprise environment without exposing sensitive information. Hypori's answer is virtual mobile infrastructure. According to the company, applications and enterprise data remain inside a protected cloud environment while the user receives a streamed visual experience. Sensitive data is not stored on the physical phone, tablet, or laptop. Jared explains why this differs from mobile device management. MDM attempts to secure, monitor, and control the endpoint. Hypori begins from the assumption that the endpoint may already be compromised. This can also protect employee privacy because the organization does not need visibility into the worker's personal device. We discuss how this approach could help government, defense, healthcare, banking, and smaller businesses that cannot maintain the same mobile security resources as a large enterprise. However, virtual infrastructure does not remove every responsibility. Organizations still need strong identity controls, protected cloud environments, reliable connectivity, policy enforcement, and careful vendor assessment. Jared also argues that AI is reducing the time between vulnerability discovery and exploitation. Security programs built around monthly patching may struggle when attack windows are measured in minutes. The conversation closes with leadership, resilience, and mentorship. Jared explains why hard work alone does not guarantee success and why valuable lessons can come from investors, generals, colleagues, friends, or the janitor who has spent 20 years observing how an organization works. Could virtual mobile infrastructure give employees secure access and personal privacy without forcing companies to control every device? Listen to the episode and share your thoughts.

Could your company be paying suppliers earlier than its competitors and unintentionally financing their advantage? In this episode of Tech Talks Daily, I welcome back Oliver Belin, co-founder and CEO of Calculum. Our previous conversation took place around ten years ago when Oliver was working with the Marco Polo Network and blockchain was attracting attention across trade finance. His latest venture concentrates on working capital, payment terms, and the role of AI in supplier negotiations. Oliver explains why working capital has moved higher on the agenda for procurement, treasury, and finance leaders. Companies can generate cash through sales, borrowing, inventory efficiency, faster customer collections, or changes to supplier payment terms. With borrowing costs higher and sales growth difficult in many markets, businesses are examining the cash already tied up within their operations. The difficulty is that procurement teams usually know their own supplier data but lack reliable information about the terms those suppliers accept from other customers. Negotiating without market benchmarks can lead to blunt policies, such as extending every supplier to 90 days. Oliver warns that indiscriminate extensions can create serious consequences. Smaller suppliers may experience cash flow pressure, increase their prices, reduce service, or direct capacity toward customers offering better terms. The buyer may improve its balance sheet while weakening an important part of its supply chain. Calculum uses transactional benchmark data to compare existing payment terms with the wider market. According to Oliver, the platform can show how frequently a supplier appears in its dataset, which terms it accepts elsewhere, and the probability that it will agree to a proposed change. AI and predictive analytics can then help companies concentrate on the suppliers where an adjustment would create the greatest financial impact and carry a higher probability of acceptance. This is particularly useful when an enterprise has tens of thousands of suppliers and procurement teams can only negotiate directly with a small proportion of them. Oliver says Calculum typically identifies free cash flow opportunities equivalent to approximately 8% to 11% of the spend analyzed. The amount identified does not automatically become realized cash. Procurement teams need targets, internal ownership, supplier conversations, and financing options to turn recommendations into results. He shares the example of an unnamed Fortune 500 pharmaceutical company that generated $227 million in free cash flow over 16 months. The program combined market-aligned payment terms with Supply Chain Finance, allowing participating suppliers to receive early payment in exchange for a discount based on the buyer's financial strength. Another UK company with approximately 4,000 suppliers generated €3 million in free cash flow within two months. Oliver attributes the speed partly to knowing which suppliers to approach first rather than attempting a broad, manual campaign. We also discuss supplier protection. Calculum identifies whether a business is a small or medium-sized enterprise, examines ultimate ownership, and considers financial strength. A financially vulnerable supplier may need early payment support rather than longer terms. Oliver's wider point is that AI cannot create reliable benchmarks from nothing. Useful predictions require traceable transactional data, clear objectives, and people prepared to act. Could better payment term intelligence improve your cash position while creating fairer, better-informed supplier relationships? Listen to the episode and share your thoughts with me.

What if the chemistry created by neurological disease could help activate medicine precisely where it is needed? In this episode of Tech Talks Daily, I speak with Sara Isbell, neuroscientist and co-founder of Enabled Therapeutics, about a proposed approach to one of medicine's most stubborn problems: delivering effective drugs to diseased brain tissue without exposing healthy areas to the same activity. Sara explains how the blood-brain barrier prevents many promising compounds from reaching the brain. When drugs do enter, they may spread across healthy and diseased regions alike, creating a difficult balance between therapeutic benefit and unwanted effects. We hear how an unexpected laboratory result led Sara and her co-founder to investigate whether pathological oxidative stress could convert a precursor molecule into a biologically active compound near the affected tissue. Sara describes this as pathology-gated therapeutic activation, where disease-associated chemistry provides the trigger that turns the medicine on. This remains developing science. At the time of recording, Enabled Therapeutics was preparing its first peer-reviewed manuscript and seeking partners to support further studies. Sara explains why reproducible evidence, regulatory guidance, and careful laboratory validation must determine whether the hypothesis advances. We also discuss how AI helps small biotechnology teams review literature, organize regulatory materials, connect ideas across scientific disciplines, and identify possible hypotheses. However, Sara offers an important reminder: AI can propose possibilities, but nature and experimental evidence decide what is true. Could following one unexpected result eventually offer researchers another way to approach neurological disease? Listen to the conversation and share your thoughts with me.

Could the real reason enterprise AI projects remain stuck in pilot mode be hidden inside the company's unstructured data? In this episode of Tech Talks Daily, I welcome back Oded Nagel, CEO of CTERA. We discuss why enterprise AI success depends on the condition, location, permissions, and business value of the data sitting underneath models and agents. Oded defines AI-ready data as information that is searchable, classified, and permission-aware. Many enterprises have petabytes of files distributed across offices, edge locations, legacy network-attached storage, and cloud platforms. Before introducing AI, leaders need to know what information they possess, where it resides, who can access it, and whether it remains valuable. The cost implications are significant. Copying every available file into an AI ecosystem can create expensive ingestion and storage bills. It may also reduce answer quality when stale, duplicated, irrelevant, or personal files enter the model's source material. Oded describes a customer classification project where approximately 80% of the data examined was stale or archival. The company also discovered personal content, including MP3 files, stored alongside enterprise information. Feeding such material into an AI system would consume resources without improving business results. We discuss Oded's recommendation to bring AI to governed data rather than moving data outside existing controls. Keeping intelligence close to the file system can preserve access permissions, audit logs, snapshots, and recovery mechanisms. Those protections become increasingly important when autonomous agents can read, move, modify, or delete files. Oded argues that every agent should be identifiable and its activity monitored. Businesses need to know which agent accessed which information, what action it performed, and whether the result can be reversed. Without those controls, a misunderstood instruction or malicious input could cause serious damage. The conversation also covers CTERA InsightAI, an agentic intelligence layer built into the company's data platform. Oded says it analyzes security activity and file-system metadata, allowing users to ask questions about stale data, file types, access patterns, deleted files, and ransomware impact using natural language. Rather than working through traditional dashboards and filters, users can question the data and request conclusions or recommended actions. Oded says some customers are piloting InsightAI while others already use it in production. For leaders measuring enterprise AI ROI, Oded recommends concentrating on storage costs, time savings, and speed to production. AI tools should make complex information easier to understand and reduce the time required to act. A ten-page report generated instantly provides limited value if nobody knows what decision to make from it. Does your company have enough visibility and control over its unstructured data to support production AI, or would classification uncover years of stale information and unnecessary expense? Listen to the conversation and share your thoughts with me.

What happens to the value of human judgment when AI makes execution faster, cheaper, and available to almost everyone? In this episode of Tech Talks Daily, I speak with Eric Wang, Vice President of Product and AI at QuillBot. Eric has worked in artificial intelligence since 2006, with previous leadership roles at Turnitin and Chegg. He now works on AI products used by millions of people to develop ideas, improve their writing, conduct research, and create new forms of content. Eric argues that AI's workplace impact extends far beyond automation. These tools are changing how people develop an argument, consider alternatives, cross traditional job boundaries, and turn an idea into something other people can understand. As technical execution becomes cheaper, Eric believes judgment, taste, and problem understanding become increasingly valuable. Someone with strong knowledge of a customer problem may be able to prototype software, produce marketing material, or develop a business proposal without depending on several specialist teams. That creates opportunities, although it also brings risks. AI can influence the direction of an argument, encourage misplaced confidence, and produce large volumes of content that sounds polished while saying very little. Eric shares an intriguing observation from QuillBot's user research: people increasingly refer to AI systems as "he" or "she." That small change in language may indicate that users are beginning to trust machines in ways they do not fully recognize. We also discuss how orchestrated workflows can give AI agents defined routes and boundaries, why Eric sees judgment and taste as durable business advantages, and what manual transmission cars can teach us about creativity in an automated world. Where should your organization draw the line between AI assistance and human judgment? I would love to hear where you stand, so will you share your thoughts with me?

Has enterprise AI finally reached the point where impressive demonstrations are no longer enough? In this episode of Tech Talks Daily, I speak with Bruce McMahon, Chief Product Officer at CallMiner, about what he describes as the industrialization of AI: the move from experimentation and excitement toward repeatable processes, measurable ROI, better customer experiences, and technology that can operate reliably at enterprise scale. Bruce explains why business leaders are increasingly asking a much simpler question about AI: how is this going to create value? Drawing on CallMiner's experience analyzing hundreds of thousands of hours of customer interactions every day, Bruce discusses how AI can surface operational inefficiencies and customer insights that were previously difficult to identify. The opportunity is not simply generating more data. Organizations need processes that get the right insight to the right person so something actually changes as a result. We also discuss how AI is changing workforce expectations. Bruce sees curiosity and adaptability becoming increasingly valuable, particularly among technical teams. As AI takes on more routine work, employees who question outputs, experiment with new approaches, and apply human judgment can become more valuable than those who rely solely on established technical knowledge. The economics of enterprise AI present another challenge. Foundation models, capabilities, and pricing continue to change rapidly, creating questions around vendor dependency and long-term costs. Bruce explains why companies may increasingly use a mixture of commercial, open-source, fine-tuned, self-hosted, and proprietary models rather than relying on one provider for everything. Governance becomes even more important as AI agents begin interacting directly with customers. We discuss red teaming, bias testing, compliance, data protection, monitoring, and why organizations need to decide which actions can be fully automated and which decisions must remain accountable to a human. Bruce also examines how AI is changing customer experience and the BPO industry. Rather than choosing between humans and AI agents, he sees value in designing systems where both can work together, with people handling interactions requiring judgment while AI manages high-volume and repetitive work. For CIOs, CTOs, COOs, customer experience leaders, and anyone responsible for enterprise AI strategy, this conversation provides a practical look at moving beyond AI pilots and turning the technology into a dependable part of business operations.  

What happens when an AI agent is compromised, manipulated, or simply does something nobody expected, but already has permission to access your most sensitive systems? In this episode of Tech Talks Daily, I speak with Geoffrey Mattson, CEO of SecureAuth, about why securing enterprise AI requires businesses to think beyond protecting models and start paying much closer attention to identity, authorization, access control, and what AI agents are actually allowed to do. Geoffrey argues that AI agents present a different security challenge from traditional software. Conventional applications can be tested against relatively predictable behavior. AI models are far less deterministic, particularly when prompt injection, excessive permissions, unexpected behavior, and autonomous actions enter the equation. His advice is to assume an agent could behave unpredictably and control what happens when it attempts to access a database, execute a financial transaction, call an API, or interact with another business system. We discuss what this means as companies race to introduce agentic AI. Geoffrey shares examples of employees granting AI tools permissions without fully understanding what they have approved, along with agents gathering information that creates unexpected privacy and compliance problems. This creates a difficult challenge for CIOs and CISOs. Boards want AI adoption because of its potential competitive value, while employees increasingly depend on AI tools to do their jobs. Simply blocking agents is unlikely to work. Security teams instead need mechanisms that allow innovation while controlling what those agents can access. Geoffrey explains why Zero Trust becomes particularly relevant here. Rather than authenticating a user or agent once and assuming it remains trustworthy, enterprises need to continually evaluate whether an action should be permitted at that specific moment. This leads to the concept of continuous authorization. Geoffrey explains how identity security is moving from asking "Who are you?" toward understanding intent, behavior, context, and authority for individual actions. This becomes increasingly important when one AI agent can create sub-agents, which can then create additional agents and pass permissions down the chain. We also discuss why agentic AI is exposing years of accumulated security debt. Many of the underlying problems are familiar: excessive privileges, inconsistent access controls, incomplete Zero Trust implementations, and systems that trust identities for too long. AI agents amplify those weaknesses because they can operate at machine speed. Geoffrey describes this as combining the unpredictability of humans with the power of machines. For CIOs, CISOs, security architects, identity teams, and business leaders deploying agentic AI, this conversation offers practical questions to ask before connecting agents to enterprise resources. What can the agent access? What authority does it have? Can that authority be reduced as tasks are delegated? Is every important action evaluated independently? And can access be revoked immediately when behavior changes? The goal is not to prevent organizations from using AI agents. It is to create a security layer that gives developers and employees room to experiment while ensuring agents only have the authority they need at the moment they need it. As autonomous AI becomes part of the enterprise workforce, identity alone may no longer be enough. Businesses increasingly need to understand intent, control authority, and continuously decide whether the next action should be allowed.

Companies are spending billions on GPUs, data centers, foundation models, and AI infrastructure. But what happens when the network connecting all of it cannot keep up? In this episode of Tech Talks Daily, I welcome back Avi Freedman, co-founder and CEO of Kentik, five years after our previous conversation. Avi has been operating large-scale networks since the 1990s, including more than a decade at Akamai, and brings a rare combination of founder experience and hands-on knowledge of how the internet actually works. We discuss why network performance is becoming an important factor in determining the return companies receive from their AI investments. If organizations cannot move data efficiently to models or deliver inference reliably to users and applications, expensive compute infrastructure can sit waiting while performance suffers and costs increase. Avi explains what technology leaders should measure to determine whether their network is helping or hindering AI workloads. This includes establishing performance baselines, synthetic testing across cloud and AI providers, understanding dependencies across the digital supply chain, and using observability to identify what changed when performance deteriorates. The conversation also examines network intelligence and why collecting telemetry alone is not enough. Organizations need to connect network data with the applications and users affected, understand historical behavior, determine which problems matter, and give network teams enough context to act quickly. Agentic AI introduces another opportunity. Avi explains how AI agents can increasingly perform the work of experienced network engineers by monitoring baselines, investigating alerts, troubleshooting problems, and recommending actions. But fully autonomous networks remain some distance away. Most enterprises currently want humans deciding whether significant production changes should be made. That leads us into governance. As businesses give AI systems access to increasingly important infrastructure, credentials, permissions, guardrails, and oversight become major considerations. Avi warns about ungoverned AI systems gaining proxy access to corporate infrastructure and explains why companies need clear boundaries around what agents can see and do. We also revisit a lesson from decades of internet infrastructure: individual components will fail. Rather than attempting to create networks that never fail, businesses should design for resilience through redundancy, over-provisioning, monitoring, and architectures capable of continuing when something inevitably breaks. For founders, CIOs, CTOs, network engineers, and infrastructure leaders building around AI, Avi offers practical advice on observability, network resilience, autonomous operations, AI infrastructure, and knowing when networking expertise should be developed internally or brought in from elsewhere. And we finish somewhere unexpected: how CEOs can use AI to make better decisions by explicitly asking it to disagree with them. Avi explains why turning AI from a sycophantic assistant into an argumentative colleague can expose weaknesses in an idea, improve communication, and help leaders test their thinking. AI may be transforming software, compute, and business operations, but none of it works without connectivity. As AI becomes part of the operational backbone of the enterprise, understanding the network underneath it becomes increasingly difficult to ignore.

Is AI really causing widespread job losses, or are a small number of announcements creating a much larger narrative? In this episode of Tech Talks Daily, I speak with Marvin Pohl, chief data scientist and cofounder of Clarecast, about AI layoffs, quiet restructuring, predictive workforce intelligence, and the responsibility that comes with forecasting company growth. Marvin's career began in physics and physical chemistry. After completing his PhD in Germany, he worked at Berkeley Lab and UC Berkeley before moving into data science at BASF. He describes how his role changed as generative AI entered the workplace. Initially, he encouraged skeptical colleagues to understand what language models could do. Today, he often finds himself warning people against accepting confident AI answers without checking the evidence. Clarecast was founded by Marvin, Jonathan, and CEO Bradley Taylor. The company combines employment profiles, job postings, technology adoption, stock information, industry data, and other signals to forecast how businesses may develop. Marvin says Clarecast covers over four million US companies and produces company-level forecasts extending 18 months. We discuss Clarecast's report on "quiet restructuring." The report considers whether AI-related workforce contraction may appear through slower hiring, unfilled positions, internal reorganization, automation, and the creation of new AI-related roles rather than widespread mass layoffs. Marvin says fewer than 100 companies in Clarecast's database had publicly attributed layoff announcements to AI. He describes this as a small proportion of the companies being analyzed and says projected US workforce growth appeared broadly flat rather than approaching a sudden collapse. However, Marvin is careful about what those findings can prove. The report presents a hypothesis, its model outputs are estimates, and correlation does not establish causation. Companies can change their hiring for many reasons, while employment data often takes time to reflect what has happened. Many of the AI-related announcements included in Clarecast's early analysis were also less than six months old. Marvin says a reliable assessment of whether companies followed through will require additional time because job postings, employment profiles, and reported headcount do not update immediately. We also discuss how Clarecast plans to apply its company intelligence to sales prospecting. Marvin argues that poorly personalized AI outreach is reducing response rates. Clarecast wants to help businesses identify a smaller number of companies that are showing signals of genuine need, allowing sales teams to spend additional time on relevant and personalized communication. How should business leaders use predictive intelligence without turning a probability into a predetermined outcome? Listen to the episode and share your thoughts with me.

What happens when an AI agent is authorized to make a payment, but nobody can verify the wider agreement behind it? In this episode of Tech Talks Daily, I speak with Zor Gorelov of Blue Language Labs about the infrastructure businesses may need as AI agents move from answering questions to negotiating, approving, purchasing, coordinating, and settling commercial activity. Many current business processes depend on human coordination. People reconcile spreadsheets, chase signatures, confirm deliveries, review exceptions, and resolve disagreements between systems. This work often remains invisible because employees absorb the ambiguity through emails, calls, and follow-up. Agent driven business changes the speed and volume of those interactions. One agent making an isolated payment can be handled as a software transaction. Several agents coordinating dependent actions across companies, banks, suppliers, platforms, and customers creates a much larger infrastructure problem. Zor argues that authorization answers only part of the question. An agent may have permission to pay, but every participant also needs to understand what the payment covers, which conditions apply, who can approve changes, what evidence confirms delivery, and when funds should be captured, refunded, or settled. Blue Language Labs is developing an open source protocol designed to structure those commitments. Blue Documents represent machine executable agreements containing participants, permissions, obligations, conditions, and the current state of a business process. Blue Mandates provide agents with revocable authority. A business can define spending limits, permitted actions, and thresholds requiring human approval. The meeting notes include the example of a restaurant operator allowing an agent to accept smaller bookings automatically while requiring approval for catering orders involving over 20 people. Blue Timelines provide an append only, hash linked record of actions, approvals, and changes. The aim is to give participants an independent history they can use when resolving disputes, instead of relying on conflicting emails or records controlled by one company. Zor brings the concept to life through a travel package assembled by an AI agent. The agent identifies a boutique hotel with spare inventory, a restaurant with available tables, and a local guide with unused capacity. Each business defines its terms, the agent assembles the offer, and the participants approve their roles. The customer purchases one package. Payment can be authorized at the beginning and captured according to agreed conditions as the hotel, restaurant, and guide confirm fulfillment. If one participant declines or fails to deliver, predefined rules determine whether the agent finds a replacement, changes the package, or triggers a cancellation. We also consider how Blue differs from traditional workflow systems, agent orchestration tools, and blockchain smart contracts. Blue is designed for coordination across separate businesses without requiring every participant to join one company platform or use global blockchain consensus. The opportunity could be especially valuable for smaller companies. Agents may allow several independent businesses to combine inventory, services, and expertise into offers they could not create individually. Adoption will depend on whether businesses, banks, and customers trust the protocol, accept shared definitions, and retain meaningful control. What would need to be written into a machine executable agreement before your organization could rely on another company's AI agent? Listen to the conversation and share your thoughts with me.

What separates an embedded finance partnership that changes customer behavior from an integration nobody would miss? In this episode of Tech Talks Daily, I speak with Rory Herriman, Chief Technology Officer and Chief Operations Officer for Zip's US business. Rory works across product, technology, operations, and business strategy, giving him a broad view of what happens after the API connection is complete and real customers begin using the service. Rory challenges a common understanding of embedded finance as placing one financial product inside another company's experience. Customers rarely wake up wanting embedded finance. They want to complete a purchase, manage their money, or solve a practical problem without an unnecessary interruption. The real test is whether the two businesses create something together that neither could provide independently. Rory calls this derived product market fit. Both products may succeed separately, but the combined experience must generate additional value for the customer if the partnership is going to last. Technology integration is only one part of the work. As businesses add customers and partners, they create new customer journeys, compliance obligations, servicing models, governance requirements, and operational processes. Rory argues that this complexity grows exponentially rather than linearly. This changes how technology teams should approach architecture. Instead of creating another custom integration for every opportunity, each partnership should contribute reusable capabilities to a wider platform. APIs, shared services, configuration tools, support processes, and governance models can then serve the growing ecosystem. We also discuss what partnership conversations reveal. Rory sees customer journey discussions during the first meeting as a positive sign. A conversation dominated by revenue division or integration mechanics may indicate that the participants have not established why the customer needs the combined service. His internal test is refreshingly simple. If the company launched the capability and removed it several months later, would the customer notice? If the answer is no, the partnership may have created technical activity without meaningful customer value. AI also enters the discussion. Rory believes AI can move financial services toward adaptive experiences where the product responds to the customer's circumstances. This offers opportunities for personalization and automated servicing, but it also increases the importance of responsible design, governance, customer consent, and clear accountability. For leaders building one-to-many embedded finance models, Rory's advice is to begin with the customer journey, establish alignment on values and service expectations, and build platforms that become stronger with each partnership. Would your customers miss the financial services you are embedding, or are they simply another feature occupying space in the journey? Listen to the episode and share your thoughts with me.

What happens to creator loyalty when somebody delivers the work, attracts an audience, and then waits weeks to be paid? In this episode of Tech Talks Daily, I speak with Rob Israch, President at Tipalti, about the payment infrastructure supporting the creator economy. Platforms may be able to add thousands of creators quickly, but the systems behind onboarding, tax collection, approvals, global payouts, communication, and reconciliation often struggle to keep pace. Rob cites research suggesting 87 percent of creators have experienced late payments. For a creator, payment is a direct test of whether a platform values their contribution. Delays, incorrect amounts, limited payment methods, or receiving funds in the wrong currency can damage trust and encourage successful creators to take their audiences elsewhere. This makes the payout experience part of creator retention. A platform may offer excellent creative tools and attractive commercial opportunities, but those benefits are easily undermined when creators have to chase payment updates or submit the same information repeatedly. Global growth adds another layer of difficulty. Rob explains that payment teams may need to account for approximately 26,000 rules, varying tax identification requirements, local payment methods, currency preferences, fraud checks, and screening against over five sanctions databases. If the correct information is not collected during onboarding, payment errors can increase by two or three times. The resulting problem concerns the complete workflow. Creator information must be collected securely, tax details validated, payment recipients screened, approvals completed, funds delivered through the preferred method, and every transaction reconciled with accounting systems. Creators also need timely communication when a payment is attempted, completed, delayed, or rejected. We discuss how automation can connect those stages and reduce the manual work that causes errors. Rob also describes practical roles for AI, including more responsive onboarding, automated fraud detection, tax validation, and immediate answers to payment-status questions. These capabilities can reduce support requests while giving finance teams more time to examine performance, risk, and growth. They also provide creators with something remarkably valuable: confidence that they will be paid accurately and kept informed when a problem occurs. Should creator payments remain a finance process, or should platforms treat them as part of the creator experience and retention strategy? Listen to the conversation and share your thoughts with me.

What happens when an enterprise AI agent can retrieve thousands of data points but cannot understand the customer, decision, or business moment in front of it? In this episode of Tech Talks Daily, I welcome back Boris Bialek, Vice President of Industries and Global Field CTO at MongoDB. We examine why the enterprise AI conversation has become more professional as organizations move beyond demonstrations and begin putting agentic systems into production. Boris argues that many companies do not have a shortage of data. Their problem is turning scattered data into information and then into usable knowledge. A bank balance is data. A complete view of a customer's relationship with the bank is information. Recognizing that the customer is currently researching a mortgage and may need assistance within the next 20 seconds is knowledge. This distinction leads to Boris's concept of a knowledge garden. Structured records, unstructured content, live signals, conversations, and business context are organized around a customer or outcome. Different departments can access the parts relevant to their work while AI agents receive the context needed to respond quickly. We also discuss integration debt. Boris recalls one system that required 18 seconds to assemble a customer view and says many enterprises are working with approximately 40 primary data sources. An agent can spend so much time coordinating access across APIs, caches, and applications that the business problem becomes secondary. Trust becomes equally important once an AI agent can act. Boris introduces two measures: the agent confidence score and the business risk score. The first evaluates whether an agent's output appears reliable based on its data, behavior, and context. The second considers the consequences of allowing that decision to proceed automatically. Together, these scores can help organizations decide which actions should pass automatically, which need further machine validation, and which should reach a human reviewer. Boris also explains why data lineage and complete audit trails must be designed into production systems from the beginning. For teams beginning this work, his advice is practical. Choose one business outcome, connect two or three relevant data sources, create a working prototype, and involve business and technical leaders in the same conversation. The goal is to demonstrate how data, context, confidence, risk, and human review work together before expanding the system. Does your organization have an AI data problem, or does it have a knowledge and context problem? Listen to the conversation and share your thoughts with me.

What happens when a warehouse management system believes stock is present, but nobody can find it on the warehouse floor? In this episode of Tech Talks Daily, I speak with Oana Jinga, co-founder of Dexory, who oversees the company's commercial strategy and product roadmap. Dexory has developed autonomous mobile robots capable of scanning inventory at heights of up to 18 meters while creating a continuously updated digital view of warehouse operations. The company says its robots have scanned one billion locations across 12 countries. Its customers include Maersk, DHL, Samsung, GE Appliances, Stellantis, GXO Logistics, and C.H. Robinson. However, the real story goes beyond the size of the robot or the number of locations scanned. It concerns what businesses can do once they have accurate information about their physical operations. Oana explains why warehouses often become data blind spots. Businesses usually know what entered the facility and what eventually left, but stock movements, damaged items, misplaced pallets, and inefficient use of space can remain difficult to track between those events. Dexory's robots scan approximately 10,000 to 12,000 pallet locations per hour. Oana recalls one customer discovering around £1.5 million in stock it had considered lost or written off. Other scans have revealed repeated pallet movements and potential opportunities to recover around 10% of warehouse capacity through better organization. We also discuss why visibility alone does not create business value. Dexory initially gave users large volumes of information, only to discover that extensive lists of problems could overwhelm warehouse teams. Its platform now prioritizes the actions requiring attention, helping users concentrate on a manageable number of issues each day. Oana explains why physical AI faces different challenges from software operating entirely within digital systems. Warehouses change constantly as people, vehicles, stock, temporary obstacles, damaged areas, and local working practices alter the environment. Robots and AI systems therefore require current physical data rather than relying on an old floor plan or assumptions recorded in another system. For companies considering warehouse robotics, Oana recommends starting with the operational problem. Leaders should observe how work happens, speak with employees about bottlenecks, define the result they want, and appoint an internal owner responsible for adoption. A robot sent to collect stock from an empty or incorrect location cannot complete its task, regardless of how capable its software may be. We also consider the future of warehouse work. Oana argues that robots can remove repetitive inventory walks and manual counting, allowing employees to interpret data, investigate problems, and improve operations. She also shares her experience as one of the few women in robotics a decade ago and explains why visible female role models can make the sector feel accessible to a wider group of people. Could physical AI help your warehouse teams make better decisions, or would inaccurate data and unclear ownership prevent the technology from delivering value? Listen to the episode and share your thoughts with me.

What can Formula One and football teach businesses about building customer relationships that continue long after a single event? In this episode of Tech Talks Daily, I speak with Ben Lewis, Vice President of Marketing at Infobip, about the company's work with AI-powered sports companions and what those experiences can teach customer experience leaders in every industry. Ben explains how Infobip worked with TGR Haas F1 Team to create RaceMate, an AI companion available through WhatsApp and Apple Messages for Business. Fans can access team information, driver histories, race schedules, trivia, personalized content and interactive experiences without downloading another application. We also discuss PitchMate, Infobip's conversational companion for global football fans. It remembers a fan's preferred team, can deliver personalized schedules and match information, and supports quizzes and other interactive features across an extended tournament. For me, one of the most useful lessons is the decision to meet fans inside messaging channels they already use. We have all downloaded an application for a conference, flight or one-off event, used it for several days and then forgotten it exists. RaceMate and PitchMate allow the conversation to remain available in the same place someone would message a friend. Ben also explains why Infobip measures success through returning users, conversation duration and the number of interactions rather than relying solely on clicks. TGR Haas F1 Team is currently using RaceMate to grow its fan community and provide useful content rather than constantly pushing merchandise. The same thinking can apply far beyond sports. We discuss travel companies contacting customers during unresolved claims, healthcare providers sending poorly timed automated messages and brands promoting products without recognizing that a customer is already involved in a dispute. Connected data can help prevent these disjointed experiences. Our conversation closes with practical advice for businesses adopting agentic AI. Ben recommends connecting customer data with campaigns, testing carefully, establishing guardrails and defining when an AI agent should transfer a conversation to a person. Are businesses investing too much in new customer applications when the better experience could already live inside WhatsApp, RCS or Apple Messages for Business? Please share your thoughts with me.

Late payments have become so common that many businesses simply accept them as part of commercial life. But should they? In this episode of Tech Talks Daily, I speak with Pat Bermingham, founder and CEO of Adflex, about why late payments continue to cost the UK economy an estimated £11 billion every year, why thousands of businesses fail because of cash flow pressures, and how technology could help change payment behavior rather than simply respond to it. Pat argues that late payments are rarely an administrative accident. In many industries they have become an informal financing mechanism, allowing larger organizations to protect their own cash flow while placing increasing financial pressure on smaller suppliers. Construction is one example, but the challenge extends across many sectors where long supply chains and uneven bargaining power make delayed payments the norm rather than the exception. We discuss why new government proposals to strengthen payment regulations represent progress, while also examining why legislation alone cannot solve a structural problem that has developed over decades. Instead, Pat believes technology can play a much bigger role. He explains how virtual commercial cards and Straight Through Processing (STP) allow buyers to access extended finance while suppliers receive payment far more quickly, without introducing additional friction into the payment process. Rather than forcing suppliers to accept card payments directly, the technology automates the process behind the scenes while improving reconciliation, increasing visibility and supporting healthier cash flow across the supply chain. The conversation also explores why many organizations still rely on fragmented payment systems created through years of acquisitions and disconnected technologies. Modernizing payment infrastructure can reduce delays, improve operational efficiency and help businesses build stronger supplier relationships rather than treating late payment as a normal business practice. Pat also shares how an earlier career as a music producer shaped his thinking about technology. Watching digital innovation transform music production helped him recognize how technology can simplify complex processes while also creating new business models that challenge established industries. For finance leaders, procurement teams, CIOs and business owners, this episode provides practical insights into improving cash flow, strengthening supplier relationships, modernizing payment processes and preparing for a future where prompt payment becomes both a commercial advantage and an increasing regulatory expectation. Changing payment legislation is important. Changing payment behavior is what will ultimately strengthen businesses, protect suppliers and create more resilient supply chains.

Most AI conversations begin with productivity. Joanna Pachnik thinks that's the wrong place to start. In this episode of Tech Talks Daily, I speak with Joanna Pachnik, founder of Blueclip, about why AI is changing far more than the speed of work. It's changing how businesses create value, what customers are willing to pay for, and what competitive advantage will look like over the next decade. Drawing on her experience leading global supply chain transformation projects at Ernst & Young and Mars before founding Blueclip, Joanna argues that knowledge is becoming increasingly accessible through AI. Research, analysis and reports that once took weeks and cost hundreds of thousands of dollars can now be produced in hours. That doesn't eliminate the need for expertise. It changes what expertise is worth. Rather than paying for information alone, organizations increasingly want implementation, measurable outcomes and practical experience that AI cannot easily replicate. Joanna explains why unique industry knowledge, benchmarking, practical experience and genuine human relationships may become more valuable as AI becomes more capable. We also discuss why so many enterprise AI initiatives struggle to deliver meaningful results. Joanna believes the technology is rarely the biggest obstacle. The real problem is poor data, undocumented processes and organizations trying to automate before building the foundations AI depends upon. Her advice is simple: prepare your data, document your processes, create a company knowledge layer, then introduce AI one use case at a time. The conversation also explores why AI should be viewed as a business transformation initiative rather than an automation project. Instead of accelerating existing processes, companies should ask whether those processes should exist at all. AI creates an opportunity to redesign how organizations operate, continuously improve decision-making and move people toward higher-value work. We also examine the importance of human oversight. Joanna believes AI should begin with people reviewing and guiding its outputs before gradually taking on more responsibility in carefully selected scenarios. Human accountability remains essential, particularly when AI supports material business decisions. For business leaders navigating AI strategy, digital transformation and enterprise innovation, this conversation offers practical advice on creating long-term value instead of chasing short-term AI hype. It explains why the companies that succeed will not necessarily be those using the most AI, but those prepared to rethink how they create value, organize knowledge and redesign their businesses around new possibilities. The future belongs to organizations that see AI as more than another productivity tool. It belongs to those willing to transform how they work, how they serve customers and how they create lasting business value.

What happens behind the scenes when you dial 911, and is the infrastructure ready for AI, satellite messaging, video, and precise location data? In this episode of Tech Talks Daily, I'm joined by John Snapp, VP of Technology at Intrado. John has spent around 23 years working with cellular, location, and 911 technologies. He explains how a mobile emergency call is located, routed through a dedicated network, and directed to the appropriate Public Safety Answering Point. We discuss where AI can provide practical support inside emergency communications. Translation can help telecommunicators understand callers without waiting for an interpreter. Real-time transcription can capture details and suggest established procedures. AI voice agents can also handle suitable nonemergency inquiries, giving trained staff additional time for calls where lives may be at risk. John is clear that emotional emergency calls still demand human understanding and authority. AI can supply information, identify possible synthetic voices, and reduce administrative work, but trained telecommunicators remain responsible for interpreting the situation and directing the response. Our conversation also examines the infrastructure beneath these capabilities. Legacy 911 networks were designed largely for voice and limited amounts of data. Next Generation 911 introduces IP connectivity capable of carrying text, images, video, and richer location information. John explains how this foundation has made satellite texting possible and why similar capabilities were far slower to introduce using older networks. Moving to NG911 creates its own problems. Different vendors can comply with the same technical standard while implementing it differently. Calls may also need to move between modern and legacy call centers, making interoperability testing between jurisdictions a major part of deployment. We also consider cloud resilience, local survivability, connectivity diversity, telephony denial of service attacks, AI generated swatting calls, and the danger of adopting automation before establishing governance. John recommends starting with lower-risk areas such as quality assurance and nonemergency calls, communicating openly about AI use, and expanding only after teams understand the operational impact. As emergency communications become richer and increasingly connected, how should public safety agencies balance faster innovation with the reliability and human judgment every caller depends on? Listen to the episode and share your thoughts with me.

When organizations review their cybersecurity posture, printers are rarely the first systems that come to mind. Yet they often account for around 20% of network endpoints while receiving, storing, processing, and transmitting sensitive business information every day. In this episode of Tech Talks Daily, I welcome back Jim LaRoe, CEO of Symphion, to discuss why printers and other connected IoT devices have become one of the most overlooked areas of enterprise cybersecurity and why AI-powered attacks are raising the stakes for organizations that continue to ignore them. Jim explains how many businesses continue to treat printers as simple office equipment rather than Linux-based network devices with privileged access to email systems, file servers, identity services, and critical business workflows. Because responsibility for these devices often sits between procurement, managed print providers, IT operations, and security teams, they can easily fall outside normal cybersecurity processes. We discuss how the threat landscape has changed over the past year as AI enables attackers to automate reconnaissance, credential theft, lateral movement, and ransomware deployment. Jim explains why organizations adopting Zero Trust principles also need to rethink how they secure and manage connected endpoints that have traditionally been overlooked. The conversation also explores certificate lifecycle management, cyber hygiene, firmware management, endpoint visibility, and why unsupported devices can introduce unnecessary risk into modern enterprise environments. For organizations managing hundreds or even thousands of printers across multiple locations, Jim explains why protecting these endpoints does not need to create additional operational burden. Instead, security should become an ongoing operational program that continuously monitors devices, detects configuration drift, applies security controls, and helps organizations maintain compliance without disrupting critical business workflows. We also discuss the governance challenge many organizations face. Before companies can reduce risk, someone needs to own it. That means establishing accountability, assigning budget, understanding which devices exist across the business, and recognizing that printers and IoT devices deserve the same attention as servers, laptops, and other managed endpoints. If you're responsible for cybersecurity, IT infrastructure, risk management, or digital transformation, this episode offers practical advice on protecting forgotten endpoints, strengthening Zero Trust strategies, improving endpoint visibility, and reducing the hidden risks that AI-powered attackers are increasingly looking to exploit. Sometimes the biggest cybersecurity vulnerability isn't the system you forgot to patch. It's the one you forgot was connected in the first place.

What happens to digital asset ownership when the cryptography proving that ownership can no longer be trusted? In this episode of Tech Talks Daily, I speak with Yoon Auh, cofounder of BOLTS Technologies, about quantum computing, blockchain security, and the need for crypto agility. Yoon brings an unusual perspective to the subject. Before moving into applied cryptography, he spent years building and operating high performance trading systems at firms including Credit Suisse, Goldman Sachs, Geode Capital, and Magnetar Capital. Yoon explains that blockchain ownership ultimately depends on digital signatures and public keys. Most major blockchain systems use variants of elliptic curve cryptography because it has historically offered speed, compact signatures, and dependable protection. However, sufficiently powerful quantum computers could eventually challenge the mathematics supporting that protection. The risk does not begin when such a quantum computer arrives. Yoon describes how attackers can collect encrypted traffic today, store it, and attempt to decrypt it later. This creates an immediate concern for governments, financial institutions, and businesses holding information that must remain private for many years. We also discuss QFlex, the post quantum ready API developed by BOLTS Technologies. The company describes its approach as cryptographic logistics, allowing different cryptographic methods to be selected at the transaction level. Yoon argues that a small payment and a multimillion dollar asset transfer should not automatically receive identical protection, particularly when stronger cryptography may require additional processing, storage, and cost. Another concern is uncertainty around the available post quantum algorithms. Yoon explains that cryptographic methods can survive years of examination before a weakness is discovered. His argument is that organizations need the ability to change algorithms quickly if one becomes vulnerable, rather than making a permanent choice and hoping it survives every new attack. The conversation also examines digital asset sovereignty. Who decides how a transaction is protected: the platform, the protocol, or the asset holder? BOLTS Technologies believes that choice should return to the holder, while QFlex aims to provide that control without hard forks, network downtime, or protocol changes. The interview also covers the company's research background and its pilot work with the Canton Foundation. Yoon closes with a lesson from his trading career. Backup and failover exercises often failed because they were treated as occasional events. His advice is to make exceptional processes routine, ensuring that the organization has already practiced changing systems before the moment arrives when it has no other option. Should digital asset holders control the cryptography protecting every transaction, or should platforms continue making that decision for them? Listen to the episode and share your thoughts with me.

What prevents a successful AI experiment from becoming a dependable production system that delivers measurable business value? In this episode of Tech Talks Daily, I speak with Ed Macosky, Chief Product and Technology Officer at Boomi, about AI pilot purgatory, integration, governance, model selection, token costs, and the technical skills businesses may need as adoption grows. Ed leads Boomi's product and engineering teams while also using AI tools inside his own organization. That gives him a view from both sides: creating technology for enterprise customers and applying it within active product development workflows. He believes many AI pilots begin with the wrong question. Teams become interested in the latest model or feature before defining the business problem they want to solve. The experiment may work during a demonstration, then fail when it encounters real data, access controls, security policies, and production systems. Placing company information inside a data lake and adding a language model does not automatically create a business application. The system must access current data reliably, respect employee permissions, connect with existing applications, and operate within governance rules that security teams can approve. Ed recommends beginning with a defined business opportunity and establishing the access required to support it. Existing APIs can already provide authentication, permissions, and governance. MCP can offer another route into enterprise systems, but those connections still require security, monitoring, and management. Team alignment also matters. An AI center may be racing to test models while an integration center concentrates on a different set of priorities. When those groups fail to coordinate, the pilot lacks the connectivity and automation required to become part of a production workflow. The discussion then turns toward fragmentation. Every technology wave produces new vendors, frameworks, and specialist tools. Early experimentation benefits from variety, but mature companies can eventually find themselves maintaining a complicated collection of products held together with custom code and, occasionally, the digital equivalent of duct tape. Ed does not recommend placing every function with one provider. He does argue for enough consolidation and abstraction to prevent experimentation from creating years of technology debt. Governance and observability should also work horizontally across different environments, including platforms such as SAP, Salesforce, and several AI model providers. That becomes increasingly important as businesses introduce autonomous agents. Leaders need to know which agents exist, what systems they can access, what actions they can take, and how each decision is recorded. AI gateways and agent control towers can provide a wider view across otherwise separate technology environments. Ed also introduces the idea of the frontier engineer. A prompt engineer concentrates on communicating effectively with a model. A frontier engineer understands how the model works, including its logic, mathematics, algorithms, and suitability for different workloads. He does not believe every company needs a large team of these specialists. However, he argues that enterprises need at least one person capable of assessing vendor claims and deciding whether a frontier model, specialist model, or open weight model fits a particular workload. Cost creates another reason to examine model selection. Sending every employee request or agent task to the most capable frontier model can become expensive. Some repeatable workloads may run on open weight models inside the company's cloud or hardware environment, giving finance teams greater cost certainty. Boomi is developing Boomi Prompt to route requests according to their complexity and requirements. A simple factual request might go directly to an API. A forecasting task may use a smaller model. A difficult analytical request could be sent to a frontier model. Ed uses the weather as a helpful example. Retrieving next Tuesday's forecast does not require a language model when a public weather API can return the answer directly. Asking a model to perform every form of automation wastes tokens, computing power, energy, and money. The episode closes with practical advice for CIOs. Avoid starting with a broad objective such as agentifying the entire business. Choose a department, identify a small number of tasks, define the expected return, and work backward. Once the team proves value and understands the operating requirements, it can repeat the process elsewhere. Could intelligent routing, stronger integration, and clearer business outcomes finally move enterprise AI beyond pilot purgatory? Listen to the episode and share your thoughts with me.

What must happen before a business can trust AI agents to detect and resolve operational problems without waiting for human intervention? In this episode of Tech Talks Daily, I speak with Josh Clay, Regional Vice President of Solution Engineering for Dynatrace in the UK, about autonomous operations, AI observability, fragmented telemetry, business outcomes, and the growing pressure to control token and data costs. Josh has spent much of his 11 years at Dynatrace discussing the road toward autonomous operations. The earliest version involved reducing the time organizations spent inside IT war rooms. He remembers calls with 30 or 40 people attempting to establish which team was responsible for an incident. He jokingly calls this the "mean time to innocence." Modern observability reduced many of those investigations from several hours to between 30 and 60 minutes. Agentic AI creates the possibility of going further by identifying a problem, understanding its cause, and resolving it before the customer experience is affected. That ambition also introduces risk. Josh cites Dynatrace research showing that 52% of respondents view security, privacy, and compliance concerns as barriers to AI adoption. He believes many organizations still lack full observability across their existing technology environments, making autonomous agents harder to supervise. Josh shares a warning from Alex Hibbert of Storia Group: AI can amplify existing technology problems. If telemetry is fragmented, data quality is poor, or teams cannot see how services depend on one another, adding autonomous agents may increase the speed and scale of the resulting failure. Trust therefore depends on visibility. Josh describes observability as a control plane for agentic AI because it can show what an agent is doing, why it made a decision, and what happened afterward. Defined guardrails and real time information can give leaders confidence without asking them to surrender control blindly. The adoption figures show how early this work remains. Josh says 50% of businesses have AI operating in limited production use cases, often performing one isolated task. Only 23% describe their deployments as connected across the wider organization. We discuss how observability has progressed beyond technical monitoring. An airport can measure whether technology changes improve e-gate availability and passenger processing times. A bank can examine whether application performance affects mortgage completion rates. These connections allow leaders to measure AI through business results rather than relying entirely on response times and infrastructure metrics. Reliable agents also need suitable data. Dynatrace says AI agents operating with deterministic data can work 12 times more accurately and three times faster while using two and a half times fewer tokens. These remain company findings, but they demonstrate why context and causality can affect cost as well as reliability. Fragmented telemetry creates another barrier. Logs may sit in one platform, front end monitoring in another, and metrics or traces somewhere else. Attempting to reconstruct every relationship for an AI agent can become expensive and difficult. Josh recommends bringing observability data into a connected environment where relationships between services, cloud resources, traces, metrics, and logs are already understood. He also warns against collecting information simply because it exists. Data hoarding increases ingestion costs and can introduce personal information into systems without a clear business need. The conversation then moves toward AI FinOps. Leaders want to know what agents cost, how many tokens they consume, and whether those costs produce a measurable return. Josh describes a Dynatrace proof of concept that identified potential annual savings just below £250,000 within one small environment. That example reinforces a recurring concern. Organizations are racing to place AI into production, then moving to the next project without reviewing whether the previous environment is appropriately sized or financially efficient. Josh hopes companies will develop a more pragmatic view of AI as another enterprise tool. That means establishing agreed methods for deployment, monitoring, cost management, incident response, and measuring business results. Could observability provide the confidence businesses need to move from isolated AI experiments toward autonomous operations? Listen to the episode and share your thoughts with me.

How can businesses secure AI agents that read sensitive information, update systems and communicate with other agents on behalf of employees? In this episode of Tech Talks Daily, I speak with Sachin Nayyar, founder and CEO of Saviynt, about AI agent identity security and the controls businesses need before autonomous systems enter production. Saviynt manages over 100 million identities for over 700 customers. Sachin explains how enterprise identity has expanded beyond employees to include partners, applications, machines and autonomous AI agents. An AI agent creates a different access problem because it is both an identity and an application. It can receive permissions, access information and perform actions, but its behavior can also be governed through software while it is being developed and while it is operating. Sachin uses an HR copilot to demonstrate why context matters. Two employees can ask the same question about salaries but should receive different answers based on their roles, locations and applicable policies. Those decisions must be evaluated while the request is being processed without creating delays that make the system unusable. The risk grows when an agent crosses from one technology environment into another. An agent built within Microsoft may need to access Salesforce, ServiceNow, Jira or another external system. Sachin warns businesses never to solve this problem by giving an agent a permanent administrative account. We discuss Zuma, Saviynt's identity security platform for AI agents and non-human identities. Sachin describes a four-part framework beginning with agent discovery and a central registry. Every agent should then receive one accountable human owner, temporary access for its assigned task and policy enforcement while it acts. Ownership becomes especially important when an employee leaves. Saviynt's approach begins an automated reassignment process and blocks actions if an agent attempts to operate without a current owner. The relevant security team can then investigate before allowing further activity. Sachin also explains why identity controls should enter the development process rather than being added after deployment. Saviynt is working with LangChain and other agent development platforms to make identity policies available while AI agents are being built. The conversation also covers Saviynt's partnership with Zscaler. Zscaler provides inline enforcement, while Saviynt contributes identity information about the agent, its owner, existing permissions and expected behavior. Sachin closes with an optimistic argument. Because businesses can place security controls into the code and enforce them while agents act, AI workloads may eventually become better governed than traditional human access. Could every AI agent inside your business be traced to one accountable owner, one approved purpose and a limited set of temporary permissions? Please share your thoughts with me.

What becomes possible when enterprise computer vision no longer depends on expensive GPU infrastructure? In this episode of Tech Talks Daily, I speak with Glenn Jocher, founder and CEO of Ultralytics, about YOLO26, CPU inference, edge AI, open vocabulary vision, deployment economics, and the practical work required to move computer vision from a promising pilot into production. Glenn's route into AI began inside the U.S. intelligence community. He worked with the National Geospatial Intelligence Agency and Defense Intelligence Agency on particle physics applications, attempting to detect and track antineutrinos. Antineutrinos are extraordinarily difficult to detect because they pass through almost everything. Glenn describes them as the perfect spy. While searching for better detection methods, he discovered that computer vision researchers were solving similar problems with images. His original attempt to transfer those techniques into particle physics did not succeed. However, the work introduced him to a field where the technology could create a visible effect on everyday life. That led him toward open source development and eventually the YOLO models for object detection, classification, segmentation, and tracking. Glenn believes computer vision research has historically placed too much attention on small gains in accuracy while overlooking deployment economics. A model can perform impressively inside a laboratory and still remain unsuitable for a factory, warehouse, store, vehicle, drone, or medical environment. Price, latency, power consumption, data privacy, and deployment speed can determine whether the technology is commercially useful. This led Glenn and Ultralytics toward smaller models capable of running close to where images and video are generated. YOLO26 continues that approach with architectural changes designed specifically for CPU inference. Glenn says the model can process camera streams in real time at 30 frames per second and run across Intel CPUs, AMD CPUs, and lower power devices such as Raspberry Pi computers. This matters because specialist GPUs can increase the equipment cost and power requirements of a computer vision project. Running inference on existing CPUs or edge hardware can make deployment economically possible across larger numbers of cameras and locations. The scale already involved is difficult to comprehend. Glenn says Ultralytics models now process approximately three billion inference jobs each day, equivalent to around 30,000 every second. These jobs include images, videos, and collections of images being analyzed to detect, segment, or track objects. He attributes the platform's maturity to thousands of mistakes and bugs corrected through a rapid feedback cycle. New models are released, users report problems and request features, and the team incorporates that information into later versions. We also discuss the respective roles of cloud and edge infrastructure. Glenn sees cloud platforms continuing to provide the computing power required for training, while computer vision inference often belongs at the edge. Local processing can reduce latency, control operating costs, and keep sensitive video or medical information closer to where it was created. The smallest YOLO model is approximately three megabytes, according to Glenn. That allows it to reach mobile phones, vehicles, drones, battery powered devices, and other environments where a large language model would be impractical. Open vocabulary vision provides another development. Traditional object detection models are trained to recognize a fixed collection of objects. If a model learns to detect dogs and the user later wants it to detect cats, retraining can cause it to forget earlier knowledge unless both categories appear in the new training data. Glenn explains how promptable models can identify common everyday objects from text or visual instructions without additional training. A user could request a person wearing a blue shirt and white shoes, for example, and the system could search an image for that description. That flexibility could benefit businesses whose requirements change regularly. It reduces the need to create and label a new data set every time the company wants the model to recognize another common object. The range of current applications is already extensive. Glenn describes YOLO being used across robotics, parking, industrial safety, PPE detection, warehouses, aviation, security, traffic management, food quality, and manufacturing. Some of his favorite examples involve environmental problems. One company uses YOLO with underwater vehicles to identify and recover plastic from the ocean. Other applications detect smoke and fire early enough to support forest fire response. For leaders considering computer vision, Glenn recommends beginning with a defined problem and measurable outcome. A manufacturing company may want to reduce defects, but it still needs labeled examples showing the model what acceptable and defective products look like. He advises testing the idea through a limited pilot, measuring the return, and expanding only when the evidence supports further investment. Computer vision has become easier to deploy, but practical problems involving data, cameras, integration, reliability, and operating conditions still separate a demonstration from a production system. Could CPU inference and open vocabulary models make computer vision practical for processes your organization previously considered too expensive? Listen to the episode and share your thoughts with me. Useful Links   Ultralytics website Ultralytics Platform      

What can private companies learn from government caseworkers about adopting automation and using data more effectively? In this episode of Tech Talks Daily, I speak with David Turner, General Manager and Senior Vice President of Government Services at Equifax Workforce Solutions, about public sector automation, data modernization, and the people responsible for delivering social services. The conversation begins with a surprising finding from an Equifax Government Services study of over 500 U.S. government employees. Every respondent expected efficiency to improve during the following year, while 95% believed automation would free time for higher value, human centered work. David explains why government employees may be more receptive to modernization than many people assume. Caseworkers operate under rising demand, staffing pressures, changing policy, and limited budgets. When technology removes repetitive administration or supplies information faster, they can see an immediate connection between the tool and the person waiting for support. We discuss how the meaning of automation has changed inside social services. A few years ago, it might have meant entering information into an online portal. Today, integrated connections can search data sources behind the scenes and return verified information during the caseworker's existing process. David describes continuous evaluation, which can identify when circumstances within a caseload have changed. Instead of searching every case for a possible update, a worker can direct attention toward the people whose income, address, or employment data indicates that further review may be needed. Income verification provides another example. Equifax says it can return income information in under one second, helping prevent the delays created when an applicant must leave the process to find a document. Those pauses matter when an eligibility decision already involves several stages and numerous external systems. The gig economy makes this work harder. Applicants may receive income from employment, contract work, digital platforms, and several side projects. Agencies need access to a fuller income picture without sending people back toward paper forms and manual verification. David also shares what Equifax has learned through its Day in the Life program. The team spends time with caseworkers to understand their processes, policy restrictions, and information constraints. He believes public sector leadership can remain closely connected to employees in the field because many agency executives previously performed those roles themselves. AI introduces further possibilities. David discusses how data and AI could eventually identify signs that someone who has left an assistance program may be experiencing financial difficulty again. Community groups, food banks, or other services could potentially offer support before that person returns to crisis. Such a system would also require careful decisions around consent, privacy, accuracy, and responsibility. The central lesson is refreshingly human. Technology creates value when it removes administrative pauses and gives experienced caseworkers additional time to understand someone's circumstances. Could public sector automation teach private companies how to connect technology investment with human outcomes? Listen to the episode and share your thoughts with me.

What happens when an AI experiment becomes a production service that your employees, customers, and daily operations depend upon? In this episode of Tech Talks Daily, I speak with Brian Klingbeil, Chief Strategy Officer at Ensono, about AI infrastructure resilience, operational dependency, FinOps, legacy modernization, and the growing pressure to prove that enterprise AI investments are producing meaningful returns. Brian has been speaking with major enterprises through Ensono's Executive Advisory Council. Three years ago, many participants were experimenting with proofs of concept. Today, they are being asked to present AI projects that are already in production, approaching production, or demonstrating a clear return through productivity, lower risk, service quality, or financial results. That progression creates a new problem. When an AI model begins supporting product delivery, customer service, logistics, software development, or internal operations, it becomes part of the company's operating infrastructure. Leaders must then ask familiar IT questions about availability, monitoring, security, incident response, disaster recovery, ownership, and cost. Brian believes FinOps often provides the first warning. Token consumption can be difficult for CFOs and business leaders to interpret, particularly when hundreds of agents are operating across different models. Ensono's internal platform has produced around 1,000 agents, prompting questions about which are effective, which are expensive, and who should carry the cost. We discuss why chargeback and showback could change employee behavior. When AI spending is absorbed by a central corporate budget, teams may have little reason to question whether an expensive model is suitable for a routine task. When the cost reaches their departmental budget, the decision can look very different. Architecture also matters. Brian recommends systems that are loosely coupled and tightly integrated. Companies should be able to replace a model, provider, FinOps tool, or service as the market changes, while still connecting each component closely enough to deliver useful business outcomes. That creates a genuine tradeoff. Providers such as Microsoft, Amazon, Google, OpenAI, and Anthropic can offer specialist capabilities that businesses may want to use. Avoiding every provider specific feature can limit what the technology delivers, while becoming too dependent on one provider can make future change expensive and disruptive. The conversation then turns toward legacy technology. Brian argues that many systems described as outdated still process airline reservations, banking transactions, insurance claims, government services, and other high volume workloads. Turning them off without suitable replacements would create far bigger problems than the word "legacy" suggests. AI can change the modernization decision. Ensono worked with Markerstudy Group to analyze six million lines of RPG code running on an IBM i platform. The resulting plan identified applications that should move elsewhere while preserving workloads that still benefited from the platform's reliability and transaction processing capabilities. Brian treats migration as one possible part of modernization. AI tools can document old code, support modern development environments, and allow younger developers to work with established platforms without immediately beginning a lengthy and expensive replacement program. We also discuss Ensono's use of AI operations. Brian says the company reduced mean time to repair by 50% while processing approximately 50,000 tickets each month. The example shows how AI value can be measured through service quality and operational performance rather than relying entirely on direct revenue. The result is a balanced conversation about moving quickly while building enough control to keep AI dependable. Organizations need space for experimentation, but production services also require ownership, budgets, recovery planning, and people who know what to do when something fails. If one AI model or provider disappeared tomorrow, how much of your business would stop working? Listen to the episode and share your thoughts with me.

What happens when an autonomous AI agent makes a financial decision using inaccurate, outdated, or poorly synchronized data? In this episode of Tech Talks Daily, I speak with Marcin Kaźmierczak, cofounder of RedStone Oracles and Credora Ratings, about why verifiable data is becoming so important to financial AI agents. RedStone originally developed its oracle infrastructure to supply smart contracts with reliable information from hundreds of sources. The same principles are now being applied as AI agents begin analyzing markets, recommending allocations, processing payments, and executing trades. Marcin explains what a blockchain oracle does and why smart contracts cannot independently access real world information. RedStone aggregates, cleans, and distributes financial data, including asset prices, liquidity, volatility, and market capitalization. According to Marcin, its network currently secures over $10 billion in total value locked and has operated for six years without a mispricing or downtime event. The discussion then moves into agent driven finance. AI agents can process information and act at considerable speed, but that speed introduces problems when the underlying information is delayed or the model hallucinates. Marcin describes the synchronization challenge created when an oracle updates every five seconds while an agent makes decisions every second. One example captures the risk. An AI trading agent reportedly generated $200,000 over three months before losing $250,000 in two transactions. Marcin explains how Credora Ratings can add financial risk context by rating assets and strategies from D to A. Companies can then instruct an agent to operate only within an approved risk range. We also discuss tokenized assets, the growing interest from major financial institutions, and why blockchain networks offer an attractive operating environment for autonomous finance. Marcin shares practical advice for leaders, including speaking with experienced implementers, testing agents in closed environments, identifying likely failure scenarios, and creating response policies before introducing real money. What evidence would you require before trusting an AI agent with a financial decision? Listen to the episode and share your answer with me.

What if a restaurant could identify a margin problem while the ingredients were still being unloaded, rather than discovering it several weeks later? In this episode of Tech Talks Daily, I speak with David Cantu, CEO of Craftable, about AI restaurant back-office technology, margin intelligence, inventory management, purchasing, invoice automation, and the continuing importance of human hospitality. David has spent decades working in restaurants and technology. He describes an industry dealing with staffing difficulties, rising leases, food inflation, lower traffic, and relentless margin pressure. David cites a National Restaurant Association study indicating that 40% of restaurateurs were not profitable during the previous year. Craftable connects purchasing, recipe management, inventory, accounting, sales data, and analytics. The company says its platform is used by over 10,000 restaurants, hotels, and venues. David argues that useful hospitality AI should automate the work that keeps managers, chefs, and operators away from guests. This includes producing sales forecasts, suggesting orders, planning preparation, identifying invoice anomalies, and comparing projected labor with actual requirements. The conversation becomes particularly practical when David describes a restaurant receiving a ribeye that has increased in price by 20%. If the dish is a popular, high-margin menu item, that vendor increase can quickly reduce its profitability. Craftable's Invoice AI can detect the change when the invoice is received. The operator can then consider running a higher-priced chef's special, promoting another steak, reviewing the menu price, or adjusting future orders. Waiting until month-end reconciliation would explain the lost margin but leave no opportunity to recover it during service. We also discuss the difference between AI intelligence and operator knowledge. AI can examine large volumes of information, but an experienced restaurateur understands the atmosphere, the team, the guests, and what is happening at that particular moment. David believes AI recommendations need to show their work. Managers should be able to inspect how a sales forecast, suggested order, staffing plan, or trend was calculated. Transparency helps people assess the recommendation without forcing them to search through another large analytics report. Craftable is also testing how to measure whether a recommended action produced a result. If a manager coaches a server with unusually high complimentary items or promotes a menu category with falling attachment rates, the platform can examine whether that action affected sales or margins. David is skeptical of hospitality AI added as a promotional layer without being built into the daily workflow. At industry conferences, he has seen vendors attach language models to existing products while offering little operational value. His hope for restaurant AI is highly human. He wants technology reducing administrative pressure while employees welcome guests, serve meals, develop their teams, and create the warm experiences that define hospitality. Which restaurant decision could protect profitability if the operator received the right information before the next service began? Listen to the episode and share your thoughts with me.

Why do so many enterprise AI initiatives begin with impressive demonstrations but struggle to produce measurable business value? In this episode of Tech Talks Daily, I speak with Dom Selvon, CTO and value partner at Valiance, about enterprise AI ROI, outcome-based consulting, build versus buy decisions, proprietary data, ontologies, and governance. Valiance is an AI-native consultancy that charges against client outcomes rather than hours worked. Dom explains why his "value partner" title is deliberate. The company begins by identifying the financial or operational result a client wants and connects its own compensation with achieving that result. Dom argues that many AI initiatives begin without a clear definition of success. The pressure to adopt AI is real, but companies frequently select technology before agreeing on the business problem, desired outcome, or measurement. He identifies three recurring mistakes. The first is framing the project around AI rather than the business need. The second is failing to establish a metric and baseline before work begins. The third is using a consulting model that rewards billable time without connecting payment to the client's result. We also discuss how generative AI is changing traditional build versus buy decisions. Companies historically bought software because custom development was slow, expensive, and difficult to maintain. Coding agents can now reduce the time and cost required to create software for specific internal needs. Dom does not believe SaaS will simply disappear. However, vendors selling convenience, workflow wrappers, or integration glue face new competition from customers who can create similar capabilities themselves. He argues that stronger SaaS positions will depend on assets a model cannot easily regenerate, including proprietary data, networks, regulatory standing, and deep workflow adoption. This leads to a wider discussion about competitive advantage. When companies have access to similar models, generated code begins to converge. Dom believes lasting differentiation comes from company data, institutional knowledge, connected systems, employee experience, and the semantic context surrounding that information. Dom explains why ontologies matter to enterprise AI. Raw data tells an agent what is stored in a particular field. An ontology describes the customers, orders, contracts, payments, relationships, and business rules represented by that data. This context allows people and agents to reason about information in a way that reflects how the company actually works. Governance also needs to be designed from the beginning. Dom argues that security, permissions, accountability, and compliance allow successful pilots to expand without forcing the business to rebuild everything later. How can leaders tell when AI is genuinely being adopted? Dom offers a surprisingly simple signal: people stop talking about AI. The technology becomes part of ordinary Monday morning work, and employees focus on completing the task rather than explaining the tool. Has your company defined the business result, measurement, proprietary context, and governance required to turn AI enthusiasm into operational value? Listen to the episode and share your thoughts with me.

What does a modern contact center need to deliver when the person reaching out may already feel anxious, embarrassed, and unsure where to turn? In this episode of Tech Talks Daily, I speak with Chris Lovell, service delivery lead for StepChange Debt Charity's contact center and product owner for its Genesys Cloud platform. StepChange supports hundreds of thousands of people facing financial hardship each year. Chris explains that approximately 40% of its clients receive Universal Credit, over 60% rent their homes, and many are dealing with an additional vulnerability alongside debt. That context makes the first interaction especially important. People may have delayed asking for support while their financial position became harder to manage. A failed call, long queue, unnecessary transfer, or request to repeat their story can increase stress at the moment they need reassurance and practical help. Before adopting Genesys Cloud, StepChange relied on fragmented contact center technology that experienced regular technical problems and outages. The charity also lacked detailed insight into why clients were making contact at different stages of their journey. People could enter the wrong queue, wait to speak with an advisor, and then discover they needed another team. The technology also affected employees. Chris says frontline colleagues eventually stopped proposing improvements because they did not believe the existing platform could support them. StepChange migrated to Genesys Cloud in four weeks through a three-phase delivery. The team began with lower-risk services, increased the size and complexity during the second phase, and moved the core debt advice operation during the third. Chris says the migration was completed without downtime. The initial objective was to reproduce the existing service on a stable cloud platform before introducing further capabilities. This sequencing gave the team time to correct early issues and adjust training before the largest group of advisors moved across. We discuss how improved intent capture and routing helped one team reduce misrouted calls by 60%. Clients reached the right advisor sooner, avoided repeated explanations, and could move toward a suitable debt solution faster. Advisors also began conversations in the right place instead of apologizing for delays or correcting the journey. Chris argues that contact center success cannot be judged through efficiency alone. StepChange examines whether clients understand their options, complete the advice journey, activate a sustainable plan, and continue toward becoming debt free. The circumstances remain difficult for many clients. Chris says approximately 28% are still in a negative budget after receiving advice, with an average monthly shortfall of around £600. Some conversations require time, empathy, and experienced human support. Around 85% of StepChange advice journeys now happen online. Digital access can offer privacy and flexibility, while advisors remain available for the emotional and complicated moments where a person needs reassurance. We also consider future plans for WhatsApp, web messaging, connected journeys, and AI-powered advisor support. Chris advises leaders to begin with genuine customer behavior rather than selecting a technology and searching for somewhere to use it. How can your contact center remove unnecessary effort while preserving the conversations where people most need to feel heard? Listen to the episode and share your thoughts with me.

What does it really take to build an AI-ready enterprise when your data is fragmented, teams operate in silos, and years of technology decisions have created complexity that no large language model can magically fix? In this episode of Tech Talks Daily, I speak with Raymon Ohmori, Senior Principal Software Engineer at Valiantys, and Jiecheng Dong, Senior Software Engineer at Valiantys, about the work that goes into enterprise AI adoption and why successful AI transformation begins long before companies deploy agents, copilots, or autonomous workflows. Using Valiantys' work with Mercedes as a case study, Raymon and Jiecheng explain how modernizing software delivery and connecting data across teams can create the foundation required for AI systems to deliver meaningful business value. We discuss why fragmented data, organizational silos, poor governance, and unclear business problems continue to prevent many companies from moving beyond AI pilots. The conversation examines what it means to become AI-ready in practice. Jiecheng explains why enterprises need performant, structured, and queryable data rather than simply feeding huge volumes of information into large language models. Raymon shares why businesses must begin with real problems, stakeholder needs, and clearly defined outcomes to justify the cost of AI and successfully move projects into production. We also discuss the growing role of agentic AI and autonomous workflows in software engineering. How should engineering teams prepare AI agents to become active participants in software development? What tools, context, permissions, governance, and observability do these systems need to operate effectively? And why might treating an AI agent more like a new colleague than another software tool help teams think differently about deployment? Raymon and Jiecheng also share their perspectives on AI-assisted software development and developer productivity. As AI becomes increasingly capable of writing code, the role of the software engineer is shifting toward architecture, system design, requirements gathering, trade-off evaluation, and translating business needs into technical specifications. We also discuss the challenge facing junior developers and why companies still need to create pathways for new engineering talent. Finally, we examine the practical steps CIOs, CTOs, and engineering leaders can take today to build more connected, AI-enabled enterprises. From improving data ownership and governance to identifying costly problems that AI can realistically solve, this conversation offers a practical guide for companies trying to move from AI experimentation to production systems that deliver measurable value. Where is your company on its AI journey? Are fragmented data, organizational silos, and unclear business problems preventing your AI projects from reaching production, or have you found effective ways to turn experimentation into measurable results? Share your thoughts with me.   Useful Links   Valiantys Website: https://www.valiantys.com/  Valiantys LinkedIn: https://www.linkedin.com/company/valiantys/    

What happens when AI moves beyond writing emails and summarizing meetings and starts influencing who gets hired, promoted, and paid? In this episode of Tech Talks Daily, I speak with David Lloyd, Chief AI Officer at Dayforce, about why HR is becoming one of the highest-stakes environments for artificial intelligence and how companies can introduce AI while protecting employee data, maintaining human accountability, and preparing for growing regulatory scrutiny. HR systems contain some of the most sensitive information companies hold, from salaries and performance records to benefits and personal data. At the same time, AI is increasingly being introduced across recruitment, workforce management, compensation, performance, and employee experience. David explains why this combination creates enormous opportunities but also places greater responsibility on employers to understand how AI systems operate and how decisions are made. A major theme throughout our conversation is the role of AI governance. David challenges the assumption that governance slows innovation, arguing that the right processes can help companies evaluate AI ideas quickly while reducing the risk of introducing systems that lack appropriate data, transparency, or regulatory safeguards. Dayforce recently achieved ISO/IEC 42001 certification for AI management systems and NIST AI Risk Management Framework attestation. David explains what independent validation means in practice and why companies evaluating AI vendors should ask for evidence of how systems are governed, tested, monitored, and audited. We also discuss the principle of "AI by choice." David argues that CIOs and HR leaders should never discover that a new AI capability has suddenly been activated across hundreds of employees without their knowledge. Companies need visibility into where AI is being used, what data employees can provide to models, and whether customer information is being used to train external AI systems. The conversation examines AI literacy and why HR leaders need to become comfortable with the technology themselves before guiding employees through changes to jobs and working practices. Employees are already experimenting with AI, sometimes through personal tools outside approved company systems. Rather than ignoring this behavior, David explains why companies should provide safe environments where people can learn while establishing clear rules around sensitive data. Human accountability remains central as AI takes on more responsibility. David discusses why people using AI should remain accountable for its outputs and why human oversight matters when technology influences decisions involving recruitment, compensation, performance, and careers. For CEOs, CHROs, CIOs, HR technology leaders, and anyone responsible for enterprise AI, this conversation provides practical guidance on responsible AI adoption, employee data, AI bias, model monitoring, regulatory compliance, vendor selection, and building AI governance that can stand up to scrutiny. The lesson is that governance does not have to be a brake on AI adoption. Done well, it can give companies the structure and confidence to move faster, make better decisions about where AI belongs, and continue using the technology when regulators, employees, customers, and boards start asking harder questions. https://www.dayforce.com/ https://www.linkedin.com/company/dayforce/ https://www.linkedin.com/in/dtlloyd/

What separates an impressive agentic AI demonstration from a deployment that produces measurable business value across an entire company? In this episode, I speak with Frank Theisen, Vice President of IBM Technology across Europe, the Middle East and Africa, about how businesses can move AI agents beyond isolated pilots and into the processes where work actually happens. Frank believes the conversation has changed considerably. Most large companies are deploying some form of AI, yet many still struggle to demonstrate a significant commercial return. The difference comes from connecting AI with end-to-end business processes rather than creating another assistant that sits outside the systems employees use every day. IBM has attempted to prove this internally through its "client zero" approach, using its own technology across human resources, IT, procurement, sales and software development before taking those practices to customers. The company reports that AI, automation and hybrid cloud have contributed to $4.5 billion in productivity gains over three years. Frank explains how IBM's AskHR service handles common employee inquiries and helps managers complete administrative tasks without learning how to operate several separate enterprise applications. IBM reports that AI now resolves 94 percent of common HR requests automatically, while similar work is taking place across IT support and procurement. The discussion then turns to orchestration. As companies acquire agents from multiple software providers, the problem becomes far larger than creating individual assistants. Businesses need to understand how agents communicate, which systems they can access, what identities they use and who remains accountable for their actions. Frank expects the number of applications, agents and non-human identities to grow rapidly. Without orchestration and governance, companies risk recreating the same application sprawl they have spent years attempting to reduce, this time with software capable of making decisions and generating additional code. Data presents another barrier. Publicly trained models rarely contain the proprietary information that gives a company its commercial advantage. That information remains distributed across databases, applications, mainframes and cloud services. Frank argues that enterprises need a governed, federated way to bring AI to their data without repeatedly copying everything into another repository. We also discuss digital sovereignty across Europe and the Middle East. Frank describes sovereignty as a matter of control across data, operations and technology. Companies need to decide which workloads require isolation, which regulations apply and where dependence on one provider could limit their future choices. Wimbledon provides a timely example of these principles in practice. IBM Bob helped modernize the tournament's digital platform by mapping and migrating approximately 15,000 articles, videos, photographs and related metadata. IBM says work that would traditionally require four or five specialists over several months was completed by one engineer within four weeks, with the assets themselves extracted in 47 minutes. Frank closes with three practical priorities. Understand where AI could affect the business, determine how successful use cases can be automated across complete processes, then address security, governance and provider dependence before expanding them. If your company already has dozens of AI pilots, should the next investment create another agent or coordinate the ones you already have? Listen to the episode and share your thoughts with me.

How quickly should an AI investment begin producing measurable business results? In this episode, I speak with Monica Kumar, Executive Vice President and Chief Marketing Officer at Extreme Networks, about the growing pressure on technology leaders to prove that AI investments are producing financial and operational value. The conversation draws on Extreme Networks' State of AI for Networking 2026 report, based on a global survey of 200 C-level executives and vice presidents of IT. The findings suggest that enterprise AI has entered a far less forgiving phase. Experimentation continues, but executives increasingly want evidence that deployments are reducing costs, improving productivity or creating better user experiences. The most striking result is the speed now expected. Some 57 percent of respondents said they expect measurable AI impact within weeks or sooner, compared with 16 percent in the previous year. Projects that once might have received six or 12 months to demonstrate value may now face questions within 30 or 60 days. Monica explains why these demands are changing which AI projects receive attention. Leaders are looking for use cases connected with existing operational problems, where results can be measured and communicated clearly. This makes enterprise networking an interesting test case. AI workloads depend on network compute, bandwidth, availability and access to current data. According to the research, 92 percent of respondents said AI is increasing demands on network compute and bandwidth. A fragmented or outdated network may therefore limit the performance of the AI applications running across it. The network can also provide an early opportunity to show what AI produces in practice. Monica discusses performance monitoring, predictive analysis, troubleshooting, compliance checks, capacity planning and security. These are repetitive, data-heavy activities where improvements can be measured in time saved, fewer support tickets and better service availability. A case from Middlesbrough College brings those claims into focus. The college reports that firmware tracking fell from as much as five hours each week to approximately five minutes, while the time spent troubleshooting decreased by around 90 percent. For a small network team, the value comes from giving people additional capacity without asking them to monitor every device or event manually. We also discuss why AI capabilities work better when embedded within normal business systems rather than added as another standalone tool. If an AI service remains outside the daily workflow, employees must move between platforms, transfer information and interpret the result themselves. Integrated AI can monitor the network, identify anomalies, recommend action and automate routine work within the environment where the team already operates. Monica also warns that the quality of AI depends heavily on its data. Before investing in another model or application, businesses need accurate, current and well-managed information. They must also examine whether their network has the capacity to support additional workloads and whether employees understand how to use AI responsibly. If executives expect AI results within weeks, are businesses selecting the right use cases, or simply imposing unrealistic deadlines on complicated technology programs? Listen to the episode and share your thoughts with me.

Could the disaster recovery plan designed to protect your company make a ransomware incident even worse? In this episode, I speak with Darren Thomson, Vice President and Chief Technology Officer for EMEA at Commvault, about Resilience Operations, commonly known as ResOps, and why cyber recovery now requires security, infrastructure, identity and data teams to work from one coordinated plan. Darren argues that many companies are accepting a difficult reality. Even with considerable investment in prevention and detection, a breach may eventually succeed. That does not make cybersecurity controls any less necessary, but it means recovery can no longer be treated as a secondary activity managed by another department. The problem is that security operations and infrastructure teams have traditionally worked toward different objectives. Security specialists concentrate on identifying and stopping threats. Infrastructure teams protect data, maintain backups and restore systems after outages. During a cyberattack, a successful recovery requires both sets of expertise. A backup administrator may be able to restore data quickly, but a forensic specialist must establish whether that data is clean. Without that confirmation, the company risks restoring malware and restarting the incident. Darren explains why a conventional disaster recovery plan may be particularly dangerous during ransomware. These plans were commonly designed for physical failures such as a lost data center. Data would be copied from one location to another so operations could continue. If the source data is infected, however, fast replication can carry the malware into the recovery environment. This is where ResOps enters the discussion. Darren describes it as an operating model rather than a product. It combines established practices from security and infrastructure management into a continuous program for testing, learning and improving recovery. Individual technology projects may come from the program, but resilience itself never reaches a final completion date. AI adds pressure on both sides. Criminals can use it to create faster and more effective attacks, while defenders can use machine learning to inspect large volumes of information, detect patterns and identify the newest clean recovery point. Companies must also protect AI systems as they would any other business application, including the models, data repositories and identities connected with them. Darren offers one practical starting point for CIOs and CISOs: Mean Time to Clean Recovery, or MTCR. This measures how long it takes to restore an application and its data with evidence that both are free from compromise. Before measuring MTCR, leaders must define their minimum viable company. These are the systems and services the business cannot operate without. Once that list exists, teams can test how long a verified clean recovery would take and replace assumptions with evidence. The initial answer may be uncomfortable. Teams may know how to restore an application without knowing whether the backup is clean. Security may know how to inspect the system but lack an established workflow with the recovery team. Darren sees those gaps as the starting point for a useful ResOps program because they provide everyone with a shared problem and a measurable objective. If your most important systems disappeared today, how long would it take to bring the minimum viable company back using verified clean data? Listen to the episode and share your answer with me.

Why can we track a meal traveling across town almost minute by minute, yet an international parcel can seemingly disappear between checkout and delivery? In this episode, I speak with Dieter Van Putte from Crossborder Global, a division of Bnode that provides global commerce and logistics services. Dieter oversees technology and operations across Landmark Global and Apple Express, giving him a close view of what happens when retailers attempt to sell and deliver products across international markets. Dieter explains why cross border delivery remains so difficult to track despite years of investment in supply chain technology. A domestic shipment may involve one main carrier, but an international order can pass between warehouses, road carriers, airports, airlines, customs authorities and final-mile delivery companies. Each participant may record events differently, operate at a different level of technical maturity or provide information at a different speed. We discuss why the customer rarely sees this complexity. They simply expect to know when their order will arrive, what duties and taxes they must pay, plus what will happen if the product needs to be returned. When retailers cannot provide those answers, the problem quickly becomes one of trust rather than logistics alone. Dieter also describes the less visible cost of manual reconciliation. Teams can spend hours checking carrier websites, comparing rates, reviewing spreadsheets, correcting customs information and translating inconsistent tracking events. Mistakes create further work through delayed parcels, billing disputes, customer inquiries and complaints. The direct labor cost matters, but the effect on repeat purchases and customer reviews may prove even more expensive. Our conversation then turns to the role of automation and AI. Dieter explains how technology can support product classification, customs documentation, landed-cost calculations and exception handling. We also examine logistics control towers, which combine data from multiple parties to provide an end-to-end view of an order. With enough reliable information, these systems can detect patterns, predict delays and recommend or initiate corrective action before the customer is affected. There is plenty of promise here, but good software cannot remove every customs rule, carrier handoff or data-quality problem. Retailers still need the right partners, accurate product information and a clear understanding of the promise they are making at checkout. Could better use of data finally make international delivery feel as dependable as domestic shipping, and what would need to change first? Listen to the conversation and share your thoughts with me.

Do you need to understand code before you can build a technology company or lead a team of developers? Five years after our previous conversation, I welcome Sophia Matveeva back to the podcast. Sophia is the founder of Tech for Non-Techies, where she helps founders and business professionals understand how technology products are created, tested, managed and turned into commercial ventures. A great deal has changed since we last spoke. Generative AI tools can now turn a written description into a working prototype within hours. For someone who has spent years believing a lack of coding experience disqualified them from building a technology business, that removes a significant barrier. Sophia believes this is the best time yet to be a non-technical founder, although her reasoning goes beyond AI-assisted coding. Research into billion-dollar technology companies shows that non-technical founders now make up a much larger share of founding teams than they did a decade ago. Many of these businesses sell technology to other companies, where commercial knowledge, customer relationships and an understanding of industry problems matter enormously. We discuss where AI belongs in the founder journey. Sophia recommends using tools such as Lovable or Replit to create a simple test product, show it to potential customers and learn whether people would use or pay for the idea. This allows founders to test their assumptions before committing substantial money to development. The boundary appears when that prototype becomes a real product. Once software stores customer information, processes payments or supports a commercial service, security and technical architecture cannot be treated as optional details. Sophia argues that professional developers are still needed to inspect the code, prepare the product for production and address problems a non-technical founder may not know exist. Her point is simple. AI can help a founder reach the testing stage sooner and at a lower cost. It cannot tell someone with no engineering experience whether the generated code is safe, maintainable or ready to support paying customers. Sophia also shares what she learned from managing her first development team. After raising investment, she attempted to compensate for her technical insecurity by taking a coding course and becoming involved in work she did not fully understand. The result was micromanagement, constant interruptions and frustrated developers. A better approach begins with business priorities. Founders should explain what customers want, ask developers about effort and tradeoffs, agree on what will be delivered during the next work cycle, then give the team the space required to complete it. They should also allow time for technical debt, the less visible maintenance work that prevents hurried development from creating larger problems later. We finish with advice for any business leader who wants greater technology fluency. Sophia recommends joining product meetings, contributing customer knowledge and building relationships with technical colleagues who want to understand the commercial side of the company. Neither side needs to become the other. They need enough shared language to make better decisions together. If AI has removed the cost of testing many technology ideas, what is stopping you from finding out whether yours could work? Listen to the episode, try Sophia's exercise and share your experience with me.

What happens when an AI agent begins influencing business decisions without fully understanding the systems, processes and dependencies behind them? In this episode, I speak with Bert van der Zwan, CEO of Bizzdesign, about the gap between enterprise AI expectations and the results many companies are seeing in practice. Bert has spent more than 25 years in software and SaaS leadership, including executive roles at Webex, Bynder, Twinfield and Unit4. Bert offers a candid assessment of the current AI market. He believes AI will have a lasting effect on businesses and society, but argues that expectations for near-term financial returns have become inflated. Many companies are spending money on tools and experimentation without reducing costs, consolidating software or producing new revenue. That does not mean experimentation is a mistake. Bert sees it as a necessary stage. The harder question is how companies move from a growing collection of pilots to AI capabilities that can operate dependably inside the business. One barrier is fragmented organizational context. Large enterprises have often grown through a combination of internal expansion and acquisitions, leaving behind disconnected applications, inconsistent data definitions and processes that cross several departments. An AI system working with only part of that picture may make a fast decision, but that does not make it a good decision. Bert argues that AI needs an authoritative view of how the enterprise works. Systems, processes, ownership, dependencies, approval status and policy restrictions must be visible and consistently defined. Without that shared context, AI may reproduce existing silos or make them worse. We also discuss the risks boards and technology leaders should consider as AI agents become involved in operational decisions. These include unreliable data, unclear accountability, legal exposure, weak governance and an incomplete view of the process being changed. Human oversight remains necessary, particularly when an automated decision could affect customers, employees or major investments. Bert then introduces the idea of "bespoke from the cloud." Traditional SaaS products were built around largely standardized interfaces and workflows. AI-assisted development could make software far easier to personalize around individual customers and use cases. This may give users greater control, but it could also challenge long-term software contracts and the economics that have supported the SaaS market. For leaders trying to connect AI spending with business results, Bert recommends beginning with visibility and a clearly defined outcome. Every initiative should be judged by whether it reduces costs, increases revenue or shortens the time required to deliver value. If AI depends on understanding how a company actually works, have businesses invested enough in creating that shared understanding before adding agents to their operations? Listen to the episode and share your thoughts with me.

Can you still trust an incoming phone call when AI can imitate a familiar voice, personalize the conversation and target information specifically to you? In this episode, I speak with Alex Quilici, CEO of YouMail, about how artificial intelligence is changing phone fraud and why the personal devices carried by employees are becoming part of the corporate attack surface. Alex explains how YouMail uses data from its consumer call-protection service to identify scam behavior, understand the type of fraud taking place and connect those patterns with the phone numbers involved. Advances in large language models have improved this analysis, but the same technology is also helping criminals build far more convincing campaigns. Generic robocalls are being replaced by personalized conversations designed to extract information, impersonate trusted people and manipulate victims. Fraudsters can use AI throughout the attack chain, from identifying targets and analyzing stolen data to generating dialogue and adapting an approach during the call. Alex argues that attackers have adopted these capabilities faster than many defenders expected because successful fraud produces an immediate financial return. The conversation also examines why voice biometrics can no longer be treated as sufficient proof of identity. As voice-cloning tools improve, companies may need to combine multiple forms of authentication and move sensitive communications into trusted applications. A call received through a banking app, for example, could give the customer greater confidence that the caller really represents their bank. For businesses, the risk extends beyond company-managed technology. Attackers can identify where someone works, learn about their role and contact them through a personal phone that may sit outside corporate monitoring. An employee's private number can therefore provide another route into the business through impersonation and social engineering. Alex also makes a persuasive case for collecting less personal data. Personalization can improve a service, but every additional piece of information becomes something an attacker might obtain during a breach. His advice is to identify the minimum information needed to deliver the intended experience rather than gathering data simply because it may prove useful later. Despite the seriousness of the threat, Alex offers evidence that coordinated action can produce results. He has seen brand-impersonation campaigns reduced from tens of millions of calls each month to around 100,000 through monitoring, disruption and cooperation between businesses and telecommunications providers. If AI is making fraudulent calls harder to recognize, should businesses stop treating the telephone network as a trusted communication channel by default? Listen to the episode and share your thoughts with me.

Why do AI agents and applications look impressive in demos but struggle when companies try to deploy them in production? In this episode of Tech Talks Daily, I speak with Nikunj Bajaj, co-founder and CEO of TrueFoundry, about why enterprise AI has become a systems problem, what companies need to move AI from proof of concept to production, and how better infrastructure can improve reliability, governance, security, observability, and cost control. Before founding TrueFoundry, Nikunj worked at Meta on conversational AI systems serving more than a billion users and contributed to the company's internal machine learning platforms. He explains how developers at Meta could concentrate on solving business problems while infrastructure handled logging, monitoring, deployment, and governance by default. In many enterprises, the same journey from an AI idea to a production application can still take weeks or months. Nikunj argues that increasingly capable AI models are not necessarily the biggest barrier to enterprise adoption. The harder challenge is building reliable systems around them. Companies need to know what happens when a model becomes unavailable, how an agent is behaving, which data it can access, how much it is costing, when a human should intervene, and whether there is a kill switch when something goes wrong. We discuss why AI proofs of concept often fail when exposed to real users. Controlled demonstrations rarely reproduce production conditions such as unexpected prompts, malicious actors, heavy workloads, model outages, latency, and dependencies between multiple components. Even when individual parts of a system perform reliably, combining them can create failure rates that businesses cannot accept for mission-critical workflows. The conversation also examines the infrastructure required as companies introduce multiple AI models and agents. Nikunj explains the roles of model gateways, MCP gateways, and agent gateways, and how bringing these components together through an AI gateway can give enterprises a control plane for observing and governing AI traffic. Cost is another major challenge. Nikunj explains why sending every request to the most powerful model can waste significant amounts of money when smaller or cheaper models could produce comparable results for simpler tasks. Intelligent model routing can help companies balance quality, latency, availability, and price. He shares how organizations using this approach have reduced model costs by as much as 75 to 80 percent in some production environments. We also discuss what reliable multi-agent systems require in practice. Companies need clearly defined boundaries for what agents can do, escalation routes to other agents or people, safeguards against infinite agent loops, and complete audit trails of interactions and decisions. For CIOs, CTOs, AI engineering teams, platform leaders, and companies trying to move generative AI and agentic AI into production, this conversation provides a practical guide to the infrastructure decisions that determine whether AI applications remain impressive prototypes or become reliable business systems. The next stage of enterprise AI will not be defined by models alone. Companies that can connect, observe, govern, secure, and control their AI applications while managing costs will be better positioned to turn experimentation into dependable production systems.

What if the biggest barrier to better customer service isn't how quickly employees work, but how much time they lose coordinating with everyone else? In this episode of Tech Talks Daily, I speak with Kevin Yang, Head of AI at Front, about why customer conversations are becoming a valuable source of business intelligence, how AI can improve work across entire teams rather than simply making individuals faster, and the hidden coordination costs affecting customer operations. Kevin brings a unique perspective to the conversation. Before joining Front following its acquisition of his AI voice-of-customer company, Syllable, he spent 15 years as an entrepreneur. While building an office food delivery business, he experienced firsthand how customer conversations could reveal problems that traditional surveys and dashboards failed to identify. By analyzing customer feedback at scale, his team could connect specific issues directly to retention, account growth, and referrals. Today, AI makes it possible for companies to analyze enormous volumes of customer conversations and turn unstructured feedback into intelligence that can inform decisions across product development, sales, marketing, and customer success. Kevin shares how Front analyzes conversations to understand why deals are lost, why customers leave, and which topics are associated with higher sales conversion rates. The result is a feedback loop that helps companies direct product investment toward problems customers genuinely care about while giving sales and marketing teams a clearer understanding of the conversations that influence buying decisions. But the episode also challenges the assumption that giving every employee an AI assistant will transform productivity. Front's Coordination Tax research found that teams can spend almost three hours coordinating work for every hour spent solving customer problems. When a single customer request requires input from sales, finance, support, operations, or external systems, employees can lose time to emails, Slack messages, meetings, handoffs, and information searches. Kevin explains why making one person faster does little to solve this problem if the rest of the workflow remains fragmented. The bigger opportunity is to use AI across end-to-end processes, automatically handling research and analysis while allowing people to concentrate on work requiring judgment, empathy, relationships, and human decision-making. We also discuss the growing use of AI agents in customer operations and why governance becomes harder as companies move from experimenting with one agent to managing many. Kevin outlines the need to measure whether agents follow processes correctly, understand customer satisfaction, identify where failures occur, and continuously improve the knowledge and guidance available to AI systems. For business and technology leaders considering where to apply AI, Kevin offers a practical starting point. Map the work your teams perform into three categories: tasks AI can automate, tasks AI can support with human review, and tasks that should remain human. This helps companies focus investment where AI performs well rather than forcing automation into customer interactions that depend on empathy, context, and relationships. For anyone responsible for customer experience, AI strategy, operations, or digital transformation, this conversation provides practical ideas for turning customer conversations into business intelligence, reducing coordination friction, designing better workflows, and introducing AI agents with greater visibility and oversight. The opportunity is not simply to make individuals work faster. It is to redesign how work moves across the organization so employees spend less time coordinating and more time solving the problems that matter to customers.

What happens when your next customer is represented by an AI agent that can research products, compare prices, evaluate suppliers, negotiate terms, and make purchasing decisions? In this episode of Tech Talks Daily, I speak with Ian Kahn, Partner and Customer and Commercial Excellence Platform Leader at PwC, about the rise of the Intelligent Customer Edge and why companies need to rethink how they sell, market, price, serve customers, and compete as artificial intelligence changes the buying process. Much of the enterprise AI conversation has focused on helping employees become more productive. Ian argues that this overlooks a much bigger change already taking place. Customers are using AI to research products, compare alternatives, evaluate pricing, and make decisions. In some consumer and business markets, AI agents are already being given permission to make routine purchases. Companies are no longer selling only to people. They increasingly need to serve customers whose AI agents expect accurate product information, transparent pricing, availability, service history, and performance data that can be discovered, verified, and understood by machines. This creates a serious problem for companies operating with fragmented front offices. Marketing, sales, pricing, commerce, and customer service have traditionally operated as separate functions, each with its own technology, data, processes, incentives, and performance measures. Customers do not experience companies through those internal structures. They expect consistent information and relevant experiences across the entire relationship. Ian explains why adding AI to each department independently will not solve this problem. Companies risk making existing processes faster without improving the customer experience or business performance. Instead, he argues that leaders need to reconsider the operating model behind the entire customer journey. The Intelligent Customer Edge is PwC's approach to bringing these commercial functions together into a connected system centered on the customer. Powered by proprietary company data and AI, the system can continuously learn from customer interactions, support real-time decisions, and help companies respond to changing customer needs. We also discuss the idea of the commercial brain and why proprietary data could become one of the most valuable competitive advantages available to companies adopting AI. Most businesses already possess customer records, transaction histories, operational information, market signals, service interactions, and other data their competitors cannot access. Yet much of that information remains fragmented across systems and departments. Ian explains how connecting these sources can create an intelligence layer that informs pricing decisions, marketing activity, sales opportunities, service interactions, and the moments that matter throughout the customer relationship. For CEOs, chief customer officers, marketing leaders, sales executives, CIOs, and technology teams, the conversation offers an important lesson about AI transformation. The companies achieving meaningful results are not starting with the technology. They begin with customer outcomes and redesign the work, decisions, workflows, and operating models required to achieve them. Human judgment remains an important part of that model. AI can process large amounts of information, identify patterns, provide recommendations, and handle routine tasks consistently. People continue to bring judgment, creativity, empathy, relationship-building, and strategic decision-making to customer interactions where trust and context matter. Ian argues that the goal is not to choose between people and AI. Companies need to design customer systems that use the strengths of both, determining where automation can improve speed and consistency and where people can create greater customer and commercial value. Trust, governance, explainability, and accountability also become more important as AI agents are given greater authority. Rather than treating guardrails as barriers to adoption, Ian explains why companies should design controls into AI-enabled customer processes from the beginning. The conversation also examines the cost of waiting. Customers are already adopting AI, and businesses that continue relying on fragmented front-office operations risk falling behind competitors capable of responding faster, providing better information, and creating more relevant customer experiences. Ian offers practical advice for companies deciding where to begin. Start with the customer journey. Understand how customer behavior is changing, identify where friction exists, determine how AI could improve the experience, and establish clear measures for customer outcomes and business value before investing heavily in new technology. For business and technology leaders under pressure to deliver growth, improve margins, control costs, and demonstrate returns from AI investment, this conversation provides a practical framework for redesigning the front office, using proprietary data more effectively, preparing for AI agents as buyers, and creating better customer experiences. Your customers are already using AI. Some AI agents are already making purchasing decisions. The question for companies is whether their customer systems, data, commercial models, and operating structures are ready to compete for business when the buyer on the other side of the transaction is no longer always human.

Why are companies investing heavily in AI, analytics, and data platforms while business leaders still struggle to see what is happening across their operations quickly enough to make confident decisions? In this episode of Tech Talks Daily, I speak with Massimo Merlo, Vice President for UK, Iberia, and Italy at Elastic, about why the next stage of enterprise AI adoption will depend less on who deploys the most advanced models and more on which companies can give people and AI systems access to relevant, trusted, and secure information when decisions need to be made. Massimo describes the problem as a lack of decision-grade visibility. Most large companies are not short of data. They have spent decades building data platforms, analytics systems, dashboards, cloud infrastructure, and reporting tools. Yet information remains fragmented across departments and applications, insights arrive too late, and employees often struggle to find the small amount of information that matters among enormous volumes of data. The result is a growing gap between having information and being able to act on it. Massimo explains why simply adding an AI model to this environment does not solve the underlying problem. If an AI system is connected to fragmented, outdated, poorly governed, or irrelevant information, it can produce convincing answers without providing reliable business outcomes. The quality of an AI model matters, but the context available to that model increasingly determines whether AI becomes a useful business asset or an operational liability. This leads to one of the biggest technology conversations emerging around enterprise AI: context engineering. Massimo explains how context engineering provides AI systems with the relevant data, tools, permissions, organizational knowledge, and guardrails required to complete a task safely. Rather than sending ever-larger volumes of information to AI models, companies need infrastructure capable of retrieving the right information and making it available at the moment a person or software agent needs to act. Fraud detection provides a practical example. An AI agent evaluating a transaction needs more than access to a powerful model. It requires customer history, behavioral patterns, company risk thresholds, permissions, compliance requirements, and the ability to recognize activity that falls outside normal behavior. Without that context, the system could block legitimate customers or approve fraudulent transactions while presenting its decision with complete confidence. We also discuss why digitally mature companies can still struggle with real-time decision-making. Massimo shares lessons from Elastic's work with organizations including Reed, the Met Office, and Rightmove, explaining why having sophisticated technology systems does not automatically make a company context mature. Information can still remain trapped between applications, teams, and databases, preventing employees and AI agents from seeing the complete picture when it matters. The conversation challenges another long-standing enterprise technology habit: adding more dashboards. Massimo explains why dashboards often provide visibility into what has already happened without helping people decide what to do next. Companies can continue adding reporting layers while employees become overwhelmed by information and remain unable to identify the actions that will improve customer experience, productivity, security, or business performance. A healthcare example demonstrates what becomes possible when companies solve this problem. Massimo shares how CogStack at King's College Hospital brought together unstructured patient information during the COVID-19 pandemic and made it searchable using natural language processing. Clinicians could find relevant information without waiting for technical teams to build new queries or systems, helping medical professionals access information when patient decisions needed to be made. For CEOs, CIOs, CTOs, data leaders, and technology teams trying to improve AI ROI, Massimo offers practical advice on where to begin. Do not start with another model, tool, or dashboard. Start with a business decision or workflow that is currently too slow, unreliable, or difficult to execute. Identify what information that decision requires, where the data is stored, who or what system needs access to it, which permissions should apply, and where information currently becomes delayed or disconnected. That process can reveal the visibility gaps preventing companies from turning their existing data and AI investments into measurable results. We also examine why search and retrieval are becoming infrastructure concerns for companies introducing AI agents. As software agents begin making recommendations and taking actions across business systems, their performance will depend on whether they can securely retrieve relevant information at scale. For business and technology leaders facing pressure to demonstrate returns from AI investment, this conversation provides a practical framework for improving enterprise search, context engineering, AI agent reliability, real-time operational visibility, and decision-making. The companies that gain the greatest value from AI may not be those collecting the most data or deploying the most models. They will be the companies capable of finding what matters, understanding its context, and getting trusted information to people and AI systems quickly enough to act on it. That is where better visibility can become better decisions, stronger productivity, and business growth.

What if one of the biggest obstacles to digital transformation isn't your technology stack, but the agreements connecting it all together? Recorded live at Docusign Momentum in London, this episode continues my conversations from the show floor by looking at one of the most overlooked challenges facing modern organisations. Companies have spent years investing in CRM platforms, ERP systems, HR software and cloud infrastructure, yet many of the agreements linking those systems together still rely on manual processes, email chains and static documents. Joining me is Stéphane Barberet, President of EMEA at Docusign. Having spent more than three decades helping organisations across Europe use technology to improve the way they work, Stéphane shares why he believes agreements have become one of the biggest blind spots in enterprise transformation and how AI is beginning to change that. We discuss why organisations are starting to view agreements as business intelligence rather than administrative paperwork, where businesses unknowingly lose value after contracts have been signed, and why removing friction from everyday workflows often delivers greater returns than simply introducing another AI tool. Stéphane also explains why organisations across financial services, healthcare, manufacturing and many other industries are all asking the same questions about AI, how leaders should approach adoption without trying to automate everything at once, and why measurable business outcomes matter far more than launching ambitious AI programmes. Throughout our conversation, we also explore how executives should measure success, what separates organisations making genuine progress from those still experimenting, and why the future of AI may be one where the technology becomes almost invisible, quietly improving the way businesses operate every day. After spending the day speaking with customers, executives and attendees at Momentum, one message kept coming back to me. The organisations creating the greatest value from AI aren't chasing the latest trend. They're solving meaningful business problems, building trust and helping their people spend more time on work that truly matters. Where do you see the biggest opportunities to remove friction from the way your organisation works? I'd love to hear your thoughts after listening and continue the conversation.  

Why are companies spending heavily on AI tools while struggling to show meaningful improvements in productivity, revenue, or business performance? In this episode of Tech Talks Daily, I speak with Matt Cloke, Chief Technology Officer at Endava, about what it takes to become an AI-native business, why deploying thousands of AI licenses does not amount to an AI transformation, and how companies can move from experimentation to measurable business outcomes. Matt has played a central role in Endava's own adoption of artificial intelligence and the development of Dava.Flow, the company's methodology for applying AI throughout the technology delivery lifecycle. With more than 11,000 employees and clients operating across multiple industries, Endava has treated itself as "client zero," testing AI internally before advising other companies about how to introduce it across their operations. Matt shares the story of a CEO who proudly told him that his company had completed its AI transformation after purchasing 10,000 licenses for an AI tool. Twelve months later, the business had seen little return on its investment and returned for help understanding what becoming AI-native actually required. The story captures one of the biggest problems with enterprise AI adoption today: buying technology is easy, but changing how people think about problems, redesign workflows, and create business value is much harder. We discuss why Matt believes becoming AI-native is primarily a mindset. Rather than treating AI as another application added to the technology stack, employees should become curious about where AI can improve existing processes, remove unnecessary work, and create new ways of delivering value. Matt also explains his idea that AI works best when it becomes invisible. Instead of requiring employees to constantly interact with chatbots and standalone AI applications, software agents can operate inside existing workflows, monitor information, prepare responses, identify problems, and bring people into the process when human judgment is required. His own use of AI agents provides a practical example. While attending meetings that prevented him from monitoring email for several days, Matt used agents to review incoming messages, redirect requests, identify urgent communications, and prepare draft responses. Rather than handing complete control to automation, he determined which actions required approval and where AI could operate independently. This leads to a wider discussion about human oversight and accountability. Matt argues that managing AI agents may increasingly resemble managing teams. Leaders do not inspect every decision made by every employee, but they establish responsibilities, controls, escalation points, and circumstances where intervention is required. Companies introducing agentic AI need similar approaches to supervision. We also examine two mistakes Matt frequently sees companies make. The first is treating AI adoption as a software rollout, buying tools for employees and expecting productivity gains to appear automatically. The second is creating centralized AI centers of excellence and expecting a small group of specialists to determine how every department should use the technology. Matt argues that employees closest to business processes are often best placed to identify opportunities for improvement. At Endava, the legal team runs monthly AI hackathons to redesign its own workflows, supported by technology specialists but led by people who understand the work itself. For companies operating in payments, financial services, and other regulated industries, the conversation turns to reliability, auditability, traceability, and risk. Matt explains how Dava.Flow allows companies to translate regulatory requirements and operational controls into policies that AI systems must follow and demonstrate throughout the delivery process. Rather than searching for a single killer AI application, Matt recommends examining end-to-end business workflows. Companies can map how information moves between employees, departments, and systems, identify unnecessary handoffs and manual processes, and determine where AI agents can improve speed, cost, and performance without replacing entire technology platforms. Leadership is another major theme throughout the episode. Matt believes the companies that achieve meaningful results from AI will be led by executives who personally use the technology, understand its capabilities, and demonstrate the behaviors they expect from their workforce. He shares how Endava brought senior leaders from legal, technology, people, and other business functions together to build software agents themselves. The experience changed how executives thought about technology investments, including one leader realizing that an existing vendor contract might no longer be necessary because the company could build the required capability internally. For CIOs, CTOs, technology leaders, and business executives under pressure to demonstrate returns from AI investment, this conversation provides practical lessons on becoming AI-native, redesigning workflows, managing software agents, maintaining human accountability, operating AI in regulated industries, and moving beyond technology adoption toward measurable business value. The companies that succeed with AI may not be those buying the most tools or making the biggest announcements. They will be the ones whose leaders understand the technology, whose employees rethink how work gets done, and whose AI systems quietly become part of everyday business operations.

What happens when AI makes employees more productive today but gradually weakens the expertise companies will depend on tomorrow? In this episode of Tech Talks Daily, I speak with Dr. Margaret Cunningham, VP of Security and AI Strategy and Field CISO at Darktrace, about cognitive tech debt, the growing risk that companies are gaining short-term efficiency from AI while unintentionally weakening critical thinking, technical expertise, problem-solving ability, and human judgment. Margaret brings a rare combination of experience to this conversation. With a PhD in Applied Experimental Psychology and a career spanning behavioral science, cybersecurity, privacy, human-centered security, and AI strategy, she examines technology adoption through the lens of how people actually think, learn, develop expertise, and make decisions. She explains cognitive tech debt by comparing it with the technical debt familiar to software teams. Companies can introduce technology quickly and enjoy immediate improvements in speed and output, only to discover weaknesses underneath those gains later. With AI, the debt may accumulate in people. Employees can appear highly productive while outsourcing the difficult cognitive work required to build judgment, recognize patterns, understand failures, and develop genuine expertise. We discuss emerging evidence that over-reliance on AI is already affecting professional skills. Software engineers may become less capable of diagnosing problems in code they did not create themselves. Medical professionals can lose decision-making capabilities when they become dependent on automated systems. Across knowledge work, deep reading and sustained concentration are increasingly being replaced by summarization, generation, and superficial review. Margaret describes the current period as the "bridge years," when AI systems are becoming increasingly capable but people still need to maintain the expertise required to recognize mistakes, question recommendations, recover from failures, and understand when automation should not be trusted. Companies cannot safely abandon human skills before technology can reliably perform those responsibilities without supervision. The conversation also challenges one of the most repeated promises surrounding enterprise AI adoption: that automation will remove routine work and allow employees to concentrate on higher-value activities. Margaret argues that companies have done a poor job of defining which tasks people genuinely want to give up and which skills they need to preserve. Some of the repetitive, slow, and difficult work being automated may be exactly where people develop pattern recognition, creativity, and professional judgment. This creates a serious challenge for cybersecurity teams and other high-stakes professions. If employees become reviewers of AI-generated outputs rather than practitioners developing expertise through experience, where will the next generation of senior engineers, security analysts, doctors, researchers, and technical specialists come from? Margaret explains why leaders need to understand which AI techniques are being used for different business problems rather than treating every form of artificial intelligence as interchangeable. Large language models, machine learning systems, behavioral analytics, and other technologies have different strengths and limitations. Knowing what questions to ask requires domain expertise, creating a difficult paradox for companies that may be automating away the very experience needed to govern these systems responsibly. We also examine the human consequences of AI adoption. Technical specialists who enjoy solving difficult problems can lose motivation when meaningful work is replaced by reviewing machine-generated outputs. Companies may struggle to understand who owns decisions made through collaboration between humans and AI, while younger employees could lose access to the experiences that previously helped people progress from beginners to experts. Margaret offers practical advice for business and technology leaders deciding how quickly to introduce AI across their workforce. Companies can identify the skills they need to preserve, create opportunities for employees to practice difficult cognitive work, use simulations and training to maintain expertise, ask teams which aspects of their jobs give them purpose, and resist pressure to automate every task simply because the technology exists. The message is not anti-AI. Margaret sees enormous potential for artificial intelligence in scientific research, cybersecurity, productivity, and solving difficult problems. But realizing those benefits requires a more intentional relationship between people and machines. For business leaders, CISOs, technology teams, AI practitioners, and anyone concerned about the future of human expertise, this conversation provides a practical framework for recognizing cognitive tech debt, deciding what should and should not be automated, preserving critical thinking skills, and building healthier forms of human-AI collaboration. AI can make people faster. The bigger question is whether companies can capture those productivity gains without losing the human capabilities they will need when the technology gets something wrong.

What happens to blockchain networks, digital assets, and the wider internet when quantum computers become powerful enough to break the cryptography protecting them? In this episode of Tech Talks Daily, I speak with Bruno Martins, Chief Technology Officer of the Algorand Foundation, about what quantum computing means for blockchain security, why post-quantum cryptography is becoming a technology priority, and how enterprises should evaluate blockchain infrastructure for payments, digital assets, identity, and other business applications. Bruno brings experience from across several major blockchain ecosystems, including Consensys and IOHK, alongside a background in applied cryptography, key management systems, enterprise blockchain development, and software engineering. His perspective provides a useful view of how the blockchain industry has changed from experimental projects and speculative use cases toward platforms expected to support real financial transactions and business operations. We begin with the quantum threat itself. Bruno explains why the cryptographic systems protecting blockchains, financial infrastructure, communications, messaging platforms, and much of the internet could eventually become vulnerable to sufficiently powerful quantum computers.  While the exact timeline remains uncertain, he argues that waiting for a cryptographically relevant quantum computer to arrive before beginning migration would leave companies with too little time to update infrastructure, applications, wallets, accounts, and user behavior. The conversation examines why post-quantum security is not simply a future technology problem. Large digital ecosystems can take months or years to migrate, and businesses need time to understand their cryptographic dependencies, introduce new standards, educate users, and build systems capable of adopting new security methods without disrupting existing operations. Bruno shares how Algorand has been working on post-quantum security for several years, including the deployment of Falcon signatures for state proofs and plans to introduce quantum-resistant account types and additional protections across consensus and network communications. We discuss why cryptographic agility may be more important than simply replacing existing cryptography with newer algorithms that have not yet experienced decades of testing in real-world systems. This leads to one of the most valuable technical lessons in the episode. Moving directly from classical cryptography to post-quantum cryptography introduces its own risks because newer cryptographic methods may later reveal weaknesses. Bruno explains why hybrid approaches, where digital assets and accounts can be protected by both established and quantum-resistant cryptography, could provide a more responsible path for institutions managing long-lived systems and valuable assets. We also examine how enterprises should evaluate blockchain platforms. With thousands of networks competing for developers, users, and institutional adoption, Bruno argues that businesses need to look beyond market attention and transaction speed. Throughput, decentralization, security, programmability, finality, operational risk, and the ability to trust the state of a ledger all influence whether blockchain infrastructure is suitable for real business operations. Payments provide a practical example. Companies issuing payment products backed by stablecoins need confidence that transactions are final and cannot later be reorganized or reversed by the underlying network. Bruno explains why instant finality can reduce operational uncertainty and risk for companies building financial applications on public blockchain infrastructure. The conversation also turns to AI agents and agentic commerce. If autonomous software agents begin negotiating, purchasing services, exchanging value, and conducting transactions with other agents, they will need payment rails, identity systems, trusted counterparties, and ways to establish ownership and accountability. Bruno explains why stablecoins, digital identity, decentralized finance, and blockchain infrastructure could become increasingly relevant as AI systems begin participating directly in economic activity. Throughout the episode, Bruno offers a balanced assessment of the blockchain industry itself. He discusses the problems created by technical fragmentation, competing standards, thousands of networks, and ecosystem tribalism. Greater cooperation between blockchain communities, particularly around wallets, hardware, cryptographic standards, and post-quantum security, could make it easier for enterprises and developers to build applications that work across ecosystems. For technology leaders, security professionals, blockchain developers, and anyone responsible for long-lived digital infrastructure, this conversation provides a practical introduction to quantum threats, post-quantum cryptography, cryptographic agility, blockchain finality, stablecoins, and the technical questions companies should ask before choosing distributed infrastructure. The quantum threat may not arrive tomorrow, but migrating complex systems takes time. The companies and technology platforms preparing today will be in a much stronger position to protect digital assets, maintain trust, and continue operating when current cryptographic standards eventually need to change.

What if companies rushing to deploy AI agents are overlooking the basic problem that much of their business data is still trapped inside PDFs, emails, attachments, spreadsheets, and paper documents? In this episode of Tech Talks Daily, I speak with Sylvestre Dupont, co-founder and CEO of Parseur, about why successful AI adoption begins with making business data usable, why traditional automation can often outperform more sophisticated AI systems, and how he built a profitable global technology company with six employees across six countries without venture capital funding. Sylvestre introduces the concept of data liquidity, the ability to move information from the documents and systems where it is trapped into the applications, workflows, and AI systems that can put it to work. Companies may have years of valuable operational data, but if that information remains buried inside what Sylvestre calls "digital concrete," even the most advanced AI models will struggle to produce useful results. The conversation examines why structured data extraction has become increasingly important as companies invest in AI agents, copilots, and automated workflows. Sylvestre explains that better models alone cannot compensate for incomplete, inaccessible, or poorly structured information. Before businesses can expect AI to automate complex processes or support better decisions, they need reliable ways to collect, structure, and move data between systems. We also challenge the assumption that every business problem now requires an AI solution. Sylvestre explains why AI should be treated as one tool among many and why deterministic automation remains the better option for repetitive processes where accuracy, consistency, and explainability matter. Parseur itself combines AI-powered document processing with template-based extraction and traditional workflow automation, using each approach where it performs best. Drawing on Parseur's experience processing more than 100 million documents annually, Sylvestre describes the different stages companies move through as they mature their automation strategies. Some begin by manually uploading documents and downloading extracted data. Others automate document ingestion and connect information directly to accounting platforms, CRM systems, and other business applications. The most advanced companies add exception handling and human review processes for situations where automation cannot reliably complete the task. Data privacy and security are another major part of the discussion. Sylvestre shares the questions technology leaders should ask before sending sensitive company information to AI-powered platforms, including where data is stored and processed, whether customer information is used to train AI models, how deletion requests are handled, and whether vendors genuinely understand the regulations and security standards they claim to follow. For founders and bootstrapped entrepreneurs, Sylvestre also shares an alternative perspective on building technology companies. Parseur has remained profitable, globally distributed, and customer-funded rather than pursuing the venture capital model of rapid expansion. Sylvestre explains why he prefers customers to determine the company's priorities, how asynchronous communication supports a team operating across multiple time zones, and why building a sustainable business can offer founders greater control over product decisions and company culture. This conversation offers practical lessons for technology leaders deciding where AI belongs in their operations, operations teams trying to reduce repetitive manual work, and founders questioning whether venture capital is the only route to building a successful global software company. The message throughout the episode is simple: AI can be extremely useful, but companies still need reliable data, appropriate technology choices, strong privacy practices, and well-designed business processes. Sometimes the smartest technology strategy begins by solving the boring problems first.