EM360 Podcast

Takeaways#AI #analysts are crucial for integrating AI into business processes.Organisations need to rethink their data management strategies for AI.AI #data clearinghouse concepts help manage data access and security.Cross-functional collaboration is essential for successful AI integration.AI can enhance employee effectiveness rather than replace jobs.The future of work will see AI analysts in various business functions.Companies must adapt quickly to remain competitive in the AI landscape.SummaryThis episode of the #TechTransformedPodcast explores the role of AI Analysts. Host Keyari Page is joined by guest speaker Andy MacMillan, CEO of Alteryx.We learn that the term AI Analyst refers to an emerging role of #professionals who help organisations rethink their processes, workflows, and employee capabilities through the use of AI.This new role bridges the gap between AI systems and tangible #businessoutcomes. In the podcast, we also cover the importance of managing data for AI through an “AI data clearing house”. This helps business analysts prepare data for AI projects.Through this system, analysts and business owners are able to ensure that compliance and security measures are met.Tune in for insights on how AI is reshaping roles, boosting efficiency, and transforming customer experiences in the evolving business landscape.For more tech insights visit: em360tech.com

"The concept of Zero Ticket IT is that instead of reacting to the ticket and trying to solve the ticket, you go directly to the source of the issue." This statement by Sean Heuer, CEO of Resolve Systems, sets the stage for this episode of the Tech Transformed podcast.Shubhangi Dua, podcast host and producer at EM360Tech, , sits down with Heuer to unpack the ambitious yet achievable vision of Zero Ticket IT and how both agentic AI and intelligent automation are poised to change IT operations.Traditional IT ticketing systems, with their reactive nature and reliance on human intervention, are facing an overdue overhaul. Heuer shares a path towards a more efficient, proactive, and ultimately frictionless IT experience.What is Zero Ticket IT?Zero Ticket IT shifts the focus from reacting to individual tickets to directly addressing the source of the issue. As Heuer explains, a major portion (roughly 70 per cent) of IT tickets originate from employee requests and range from password resets to connectivity problems. Another substantial chunk comes from machine alerts, often leading to "alert storms" where a single underlying issue triggers a cascade of notifications.For instance, imagine an AI-powered conversational interface that can understand an employee's problem. Now this problem can be resolved using a vast knowledge base and service catalog. "There's no reason for a human to intervene if you locked your account. If you need to reset your password. There's no reason for a human to have to handle that. It should happen instantaneously," Heuer elaborates. This self-service approach immediately reduces ticket volume by a significant margin.AI Automation to Resolve Substantial IT RequestsAutomation can also solve challenges head-on by integrating AI operations (AIOps) solutions to analyse countless machine alerts, thereby identifying correlations and spotting the root cause. "Instead of getting a thousand incidents you have to manage, you get one incident," Heuer states, allowing for precise and rapid resolution.Heuer highlights that by implementing these two layers — direct interfaces for employees and intelligent automation for machine alerts — organisations can achieve a 60 per cent to 70 per cent reduction in total ticket volume.. Some Resolve Systems customers have even seen up to an 80 per cent reduction, with Heuer noting: "We have a telco customer that's gotten to 80 per cent reduction of incidents in their network and infrastructure. We have a retail company that has gotten to 75 per cent reduction. Auto-remediation is the solution for all employee requests."Heuer envisions a future where the core role of an IT technician evolves from reactive ticket-solving to proactively managing and optimizing AI and automation systems. The focus will be on identifying patterns, improving knowledge articles, and developing new automations. The Resolve...

Are you a CIO, CTO, CISO, or IT decision maker in the restaurant or retail industry, grappling with rising costs and tariffs to keep up with the rapid pace of technological change? The pressure to create high-quality solutions with AI while managing existing infrastructure can be challenging.In this episode of the Tech Transformed Podcast, Shubhangi Dua, podcast host and producer at EM360Tech sits down with Keith Szot, SVP Chief Evangelist at Esper, to talk about how enterprises can extend the lifespan of their current edge devices. They also discuss how such enterprises can easily integrate Edge AI solutions without a complete hardware overhaul."Starting out with IT Ops is a tough endeavor. If you are running revenue producing key business operating systems that are out in the field, it's not an easy job,” stated Szot. “With everything that's going on these days, it's not getting any easier. Now, considering tariffs, the impact of AI in terms of how you look at your hardware refresh cycle, it's really tough.”‘Android is the key'Specifically alluding to edge devices in restaurant and retail, Szot reflecting on the limitations of traditional operating systems says Android is the key. "If you look at the ISV and the solution provider community, the best and latest solutions for these markets are built on Android.”“You look at enterprise developers, if you're developing in-house, arguably it's a lot easier to find an Android developer and build a team to focus on creating Android applications than it is in Windows,” he added. “Android is the biggest developer ecosystem in the world in the history of humankind.Unlike operating systems tied to rapid consumer hardware upgrades, Android offers the flexibility of an open-source project. It allows for greater control over updates and longevity. Szot believes that the Android UX is more intuitive. He says that people use phones all the time. “And even if you're an iOS user, the paradigm, if you go to Android, still has a familiarity where the bar to understand how to use the software in the device arguably is lower." This minimises training needs and improves operational efficiency.In a nutshell, the conversation touches on extending hardware lifecycles for edge devices in the restaurant and retail industry, primarily through the Android flip, to enable the integration of AI at the edge and prepare for future trends like robotics and 5G.Key Takeaways Extending hardware lifecycles is crucial for cost management.Android is becoming the preferred OS for enterprise solutions.AI can enhance customer experiences in retail environments.Robust hardware design is essential for longevity.Transitioning from Windows to Android can save costs.Edge AI allows for on-premise processing without latency issues.Physical AI will revolutionize the retail and restaurant sectors.Quantum computing poses both opportunities and challenges for security.Device management is key to maintaining operational efficiency.Innovative hardware designs will create new customer experiences.Chapters00:00 Introduction to Edge Devices and AI03:12 Extending Hardware Lifecycles in Retail06:06 Transitioning from Windows to Android08:56 Practical Applications of Edge AI11:47 AI Integration in Restaurant Kiosks14:55 Managing Existing Hardware with Software Solutions18:01 The

The ability to effectively manage and optimise data is key in an organisation today. But with the sheer volume and complexity of enterprise data, traditional methods are struggling to keep up with the change. This is where the agentic AI approach has swooped in to transform how organisations handle their most valuable resource. "The promise of AI and agentic AI is that we're now building very meaningful automation into the platform such that these teams of 10 are now able to basically actually capture all of the metadata about all of the data cataloged across their entire company," stated Corey Keyser, the head of artificial intelligence (AI) at Ataccama.In this episode of the Tech Transformed podcast, Shubhangi Dua, a B2B tech journalist and Podcast host at EM360Tech speaks with Keyser from Ataccama, about agentic AI, data quality, and data governance. They explore how intelligent automation is shaping enterprise data management, the role of AI in improving data quality, and the importance of trust in AI systems. Additionally, Keyser shares significant insights on Ataccama's unique approach to data governance, practical applications of their AI agent, and how they are keeping pace with the constantly changing AI regulations. While the speed and efficiency of AI are undeniable, the question of trust remains. Keyser addressed this directly: "The short answer is you can never fully trust these automations, right? “That's why it's really critical to always have data stewards that we will serve. We will always have data engineers that we will serve. We're just looking to improve their productivity. We always assume that there will be humans in the loop who are verifying the tasks orchestrated by AI agents."Ataccama's One AI Agent exemplifies the practical application of these principles. Keyser added that the AI agent can go and create data quality rules in bulk. “Go through the evaluation and testing of those quality rules in bulk, and then also assign the rules in bulk. Something that would take potentially weeks, can now actually kind of take hours depending on the person."TakeawaysAgentic AI is about dynamic planning and semi-autonomous task execution.Data governance involves cataloging and managing organisational data.Data quality assessment is crucial for ensuring high trust in data.AI can significantly speed up the creation of data quality rules.Human oversight is essential in AI-driven automation processes.Atacama's AI agent improves productivity for data management teams.Regulatory compliance is a growing concern for AI applications.User experience is key to successful AI integration in organisations.The relationship between data and AI is symbiotic and essential.Organisations must adapt to evolving AI regulations and standards.Chapters00:00 Introduction to Agentic AI and Data Governance02:41 Understanding Data Quality

“Before starting a new AI project, it is really worthwhile defining the business priority first,” asserts Joanna Hodgson, the UK and Ireland regional leader at Red Hat.“What specific problem are you trying to solve with AI? Do we need a general purpose AI application or would a more focused model be better? How will we manage security, compliance and governance of that model? This process can help to reveal where AI adoption makes sense and where it doesn't," she added. In this episode of the Tech Transformed podcast, host Shubhangi Dua, podcast producer at EM360Tech speaks with Hodgson, a seasoned business and technical leader with over 25 years of experience at IBM and Red Hat. They talk about the challenges of scaling AI projects, the importance of open source in compliance with GDPR, and the geopolitical aspects of AI innovation. They also discuss the role of small language models (SLMs) in enterprise applications and the collaboration between IBM and Red Hat in advancing AI technology. Joanna emphasises the need for a strategic approach to AI and the importance of data quality for sustainable business practices. While large language models (LLMs) dominate headlines, SLMs offer a cost-effective and efficient alternative for specific tasks.The podcast answers key questions, like ‘how do businesses balance ethical considerations, moral obligations, and even patriotism with the drive for AI advancement?' Hodgson shares her perspective on how open source can facilitate this balance, ensuring AI works for everyone, not just those with the deepest pockets.Hodgson also provides her vision on the future of AI. It comprises interconnected small AI models, agentic AI, and a world where AI frees up teams to create personal connections and exceptional customer experiences.TakeawaysCuriosity is a strength in technology.AI is becoming embedded in existing applications.Regulatory compliance is crucial for AI systems.Open source can enhance trust and transparency.Small language models are efficient for specific tasks.AI should free teams to create personal connections.A strategic AI platform is essential for businesses.Data quality is key for sustainable business success.Collaboration in open source accelerates innovation.AI can be used for both good and bad outcomes.Chapters00:00 Introduction to the Tech Transform Podcast01:35 Pivotal Moments in Joanna's Career05:12 Challenges in Scaling AI Projects09:15 Open Source and GDPR Compliance13:11 Regulatory Compliance and Data Security17:30 Geopolitical Aspects of AI Innovation22:31 Collaboration Between IBM and Red Hat23:58 Understanding Small Language Models29:54 Future Trends in AI and SustainabilityAbout Red HatRed Hat is a leading provider of enterprise open source solutions, using a community-powered approach to deliver high-performing Linux, hybrid cloud, edge, and Kubernetes technologies. The company is known for Enterprise Linux.They offer a wide range of hybrid cloud platforms and open source...

Takeaways#Satellitecommunications are essential for remote field teams.They provide safety and tracking for workers in isolated areas.Disaster preparedness involves proactive planning and communication.User-friendly #technology is crucial for effective utilisation.Reliable communication impacts day-to-day operations positively.Employers show care for employee safety through #satellitetechnology investment.SummaryIn this episode of #TechTransformed, Jonathan Care discusses the importance of satellite communications for field teams. He is joined by Mark O'Connell, EMEA & Asia Pacific General Manager at Globalstar, and Grace Finn, Senior Account Manager at Peoplesafe. Together, they explore how satellite technology enhances safety, disaster preparedness, and operational efficiency for remote, lone workers.We learn the differences between satellite and cell communication. O'Connell emphasises the importance of satellite communication, stating that due to field workers, there is a requirement to not be reliant on cellphone towers.O'Connell further clarifies that field teams who are working remotely, and are away from terrestrial communications, need access to constant communication.Finn also expresses the importance of field worker safety, sharing: “If the unforeseen does happen, they're protected.” She emphasises that organisations should invest in satellite communications to ensure their teams' wellbeing and security.Tune in to learn the user-friendly aspects of the technology, its features for challenging environments, and the critical role it plays in ensuring reliable communication.For the latest tech insights visit: EM360Tech.com

"Having the insight and being able to stitch together your technical resources and business decisions together, is the prime place where observability can add value to you,” stated Manesh Tailor, EMEA Field CTO at New Relic.In this episode of the Tech Transformed podcast, Kevin Petrie, Vice President of Research at BARC, speaks with Manesh Tailor about the intersection of artificial intelligence (AI) and observability, and how this is positively changing business operations.Tailor emphasises how intelligent observability has changed beyond simple monitoring to provide real-time insights into customer experience and the entire technology stack. This enables informed decisions across engineering, operations, and business domains, directly linking technical performance to strategic business outcomes.He also discusses the different stages observability has been through and where it's leading to now. The current wave, Observability 3.0, takes advantage of AI to predict issues and even enable self-healing systems. New Relic has embraced this two-way street, using AI within its platform. This was in an ambition to help users and "AI monitoring" to track the performance of language models alongside traditional metrics. Such a platform provides a holistic view of system health and the cost implications of AI deployments.Alluding to the management of AI-powered applications, Tailor says collaboration is key between application and data science teams. Not only does it provide real time data but as a result leads to efficient decision making.Futuristically, the speedy proliferation of AI agents has both pros and cons for observability. This is where New Relic comes in. It addresses the challenges by constructing a platform-centric "AI orchestrator" with a growing library of AI-native agents. In essence, as AI-powered applications become increasingly integral to business operations, intelligent observability is no longer optional. TakeawaysObservability is crucial for understanding unknowns in systems.AI enhances observability by providing predictive insights.The evolution of observability includes intelligent monitoring.Collaboration between technical and business teams is essential.Cost efficiency is a key focus in modern observability.Real-time data is vital for effective decision-making.Self-healing systems represent the future of observability.AI and observability must work in tandem for success.The complexity of systems is increasing, requiring better tools.Observability is applicable across all organizational levels.Chapters00:00 Introduction to AI and Observability03:10 Defining Observability and Its Evolution05:49 The Role of AI in Observability08:46 Navigating AI-Driven Applications11:52 Target Users and Community for Observability14:57 Collaboration Across Teams17:55 Challenges and Opportunities in Observability20:47 The Future of Observability and AI23:54 Key Takeaways for CIOs and IT LeadersAbout New RelicThe New Relic Intelligent Observability Platform empowers businesses to proactively eliminate disruptions in their digital experiences. As the only AI-enhanced platform that unifies and correlates telemetry data, New...

Takeaways#AIagents are #autonomous entities that can perceive and act.Human oversight is essential in the initial stages of AI implementation.Data quality and trust are critical for effective AI agents.Guardrails must be integrated into the design of AI agents.Modularity in design allows for flexibility and adaptability.#AI should be embedded in data management processes.Collaboration between data and application teams is vital.SummaryIn this episode of #TechTransformed, Kevin Petrie, VP of Research at BARC, and Ann Maya, EMEA CTO at Boomi, discuss the transformative potential of AI agents and intelligent automation in business. They explore the definition of agents, their role in automating processes, and the importance of human oversight. Maya introduces us into the world of AI agents stating that, at its core, it's an autonomous entity within #AIsystems that can perceive its environment. This creates a deep dive into how they evolved from traditional automation to “observe, think, and act” in novel and autonomous ways.Maya addresses AI skepticism by acknowledging its growing autonomy while underscoring the current necessity of human oversight. She also highlights data's crucial influence on an agent's perception and decisions, emphasising the need for quality, trustworthy data in effective AI. Moreover, Maya and Petrie explore AI's practical implications, pointing to Google's agent-to-agent protocol as vital for managing language model interactions and enabling effective communication across diverse agents within complex systems.For the latest tech insights visit: EM360Tech.com

"If AI has proven anything, it will change pretty rapidly. Understanding its limitations and not asking too much of it is significant. What's successful is prototyping tools," said Rob Whiteley, CEO of Coder. "Such tools where AI can create an application, while not the world's most graceful code but will get you to working prototype pretty quickly. That would probably take me days or weeks of research as a developer, but now I have a working prototype so I can socialise it."In this episode of the Tech Transformed podcast, Dana Gardner, a thought leader, speaks with Rob Whiteley, CEO of Coder, about the transformative impact of agentic AI on software development. They discuss how AI is changing the roles of developers, the cultural shifts required in development teams, and the integration of AI agents in cloud development environments.Agentic AI is seemingly set up for favourable outcomes. Or is it? Agentic AI is believed to shake-up enterprise IT, offering a productivity boost similar to the iPhone's impact. This isn't about replacing developers but amplifying their output tenfold. It aims to allow the implementation of rapidly created solutions and iteration that has been unimaginable in the past. This shift requires valuing "soft skills" like communication and collaboration over pure coding proficiency, as developers guide AI "pair programmers."The synergy of AI agents, human intellect, and Cloud Development Environments (CDEs) is key. CDEs provide secure, governed, and scalable platforms for this collaboration, allowing developers to focus on business logic and innovation while AI handles the coding groundwork. This requires a move from rigid "gates" in development processes to flexible "guardrails" within CDEs. Such a move fosters innovation with built-in control and security.Flexibility and choice are vital in this constantly advancing AI space. CDEs enable organisations to select the best AI agents for specific tasks, avoiding vendor lock-in by expressing the development environment as code. This leads to practical applications like faster prototyping, enhanced code development, and automated testing, significantly boosting code output. Furthermore, agentic AI democratises development, empowering non-engineers to build solutions.Preparing for this future requires proactive experimentation through AI labs, engaging early adopters, and viewing AI as an augmentation of human skills. Watch the podcast for more insights on CDEs and the impact of AI agents on enterprise cloud development. TakeawaysAgentic AI is a transformative technology for software development.The role of developers is shifting from hard skills to soft skills.AI agents can significantly increase productivity in coding tasks.Organizations need to rethink their development strategies to integrate AI.Cloud development environments are essential for safely using AI agents.Choosing the right AI agent is crucial for effective development.Security and governance are critical when integrating AI into development.AI can empower non-developers to create applications.Guardrails are more effective than gates in managing AI development.Organisations should experiment with AI to find the best fit for their needs.Chapters00:00 Introduction to Agentic AI and Developer Roles03:20 Transformative Impact of AI on Development06:50 Cultural Shifts in Development Teams10:30 Integrating AI Agents in Cloud Development Environments12:49 Choosing the Right AI Agents15:21 Security and Governance in AI...

Can customer success be your secret growth weapon? This 'TechTransformed' episode explores its evolution from a cost center to a revenue driver. Marilee Bear, Chief Revenue Officer at Gainsight, and Christina Stathopoulos, founder of Dare to Data, discuss AI's role and how strong customer relationships boost your bottom line.

Are Agentic AI systems the next big leap in business technology? Christina Stathopoulos (Dare to Data) and Jeff DeVerter discuss the real-world impact on 'Tech Transformed.' From data infrastructure to ethical dilemmas and workforce transformation, this episode answers critical questions for CIOs facing the AI revolution. What are the practical steps to prepare your organisation? Tune in to find out.

From the integrities of the human workforce embracing enhancing soft skills over hard skills in the enterprise tech space to the adoption of artificial intelligence (AI) agents in customer service, this conversation covers it all. In this episode of the Tech Transformed podcast, Shubhangi Dua speaks with Nikhil Nandagopal, co-founder and CPO of Appsmith, about the metamorphological impact of AI agents in the workplace. He particularly emphasises the need for organisations to hone in on the advancing capabilities of agentic AI while still maintaining a focus on human collaboration and security. TakeawaysAI agents are autonomous entities designed to achieve specific goals.The centralisation of data through AI agents simplifies workflows.Conversational interfaces are becoming the norm for accessing information.Humans remain integral to AI workflows, acting as moderators.Job roles will evolve, requiring new skills and adaptability.Critical thinking is essential when interacting with AI outputs.Cybersecurity is a major concern with centralised AI systems.Self-hosting AI solutions can mitigate cybersecurity risks.The future of work will reward soft skills over hard skillsChapters00:00 Introduction to AI Agents and Their Impact03:34 The Shift Towards Conversational Interfaces05:07 Assisted Workflows and Human-AI Collaboration10:05 Job Market Evolution in the Age of AI13:23 Critical Thinking in the Age of AI15:29 Cybersecurity Concerns with AI20:31 Preparing for Cyber Threats in AI Systems22:51 The Future of AI Agents in the Workplace

In this episode of the Tech Transformed Podcast, Jon Arnold, Principal of J Arnold Associates speaks with Nikola Mrksic, CEO of PolyAI, discussing all things AI, specifically in contact centres. From the benefits of automation to the emergence of the most trending subject of the year – Agentic AI.Mrksic particularly spotlights some underutilised capabilities of AI such as how it can manage up to 90% of repetitive duties, allowing human agents to concentrate on other complex tasks. The conversation also explores the transition from basic service to a broader, more holistic customer experience, necessitating the need for rapid adaptation and experimentation.AI in contact centers isn't just about cutting costs. This conversation shows how it can truly make a difference – giving agents the tools to shine, providing customers with better, more quality experiences, and even letting AI take care of tasks behind the scenes securely, so humans can focus on what truly matters.TakeawaysAI is a dominant force shaping technology today.Contact centers have a high volume of repetitive tasks suitable for AI.AI can automate up to 90% of tasks in contact centers.The role of AI is not just cost-cutting but improving service quality.Agentic AI can perform tasks on behalf of users asynchronously.Customer experience is now a key focus beyond just service.Companies must adapt quickly to avoid falling behind competitors.Failing fast and experimenting is crucial for success with AI.AI can provide insights that traditional methods miss.Investing in AI should be about solving problems, not just keeping up with trends.Chapters00:00 Introduction to AI in Contact Centers02:01 Benefits of AI in Contact Centers07:37 Transforming Customer Experience with AI15:42 Understanding Agentic AI21:27 The Shift from Customer Service to Customer Experience30:25 Advice for Business and CX Leaders

The world is changing faster than ever. Businesses are drowning in data, yet struggling to extract the insights they need to stay ahead. Artificial intelligence (AI) holds the key, but traditional AI models are too slow, too static, and too disconnected from the real world. This is where real-time AI comes in. Real-time AI empowers businesses to make decisions in milliseconds, reacting to changing conditions and seizing fleeting opportunities. It's about more than just analysing historical data; it's about understanding the present and predicting the future, all in the blink of an eye.Imagine a world where customer service agents have access to the most up-to-the-minute information, resolving issues before they escalate. Envision supply chains that dynamically adjust to disruptions, ensuring products are always available. Envision marketing campaigns that personalise experiences in real time, maximising engagement and driving conversions. But real-time AI isn't just about speed; it's also about accuracy. The time to embrace real-time AI is now. Businesses that fail to adapt risk falling behind in an increasingly competitive world. By harnessing the power of real-time data and intelligent agents, enterprises can tap into new levels of performance, innovation, and growth. In this episode, Shubhangi Dua, an editor and tech journalist at EM360Tech, speaks to Madhukar Kumar, the Chief Marketing Officer at SingleStore, about the transformative potential of real-time AI for enterprises.TakeawaysReal-time AI is essential for modern enterprises.The evolution from generative AI to real-time AI is significant.Data accuracy and freshness are critical for AI success.AI agents will collaborate to enhance business processes.Enterprises must manage data silos to improve efficiency.Smaller companies can leverage AI to create innovative solutions.Data governance is crucial for protecting sensitive information.Real-time AI can significantly improve user experience.AI will enable professionals to focus on higher-value tasks.Harnessing data effectively will be a key differentiator for businesses.Chapters00:00 Introduction to Real-Time AI and Its Importance03:03 The Evolution of AI: From Generative to Real-Time05:54 Real-Time AI in Enterprises: Advantages and Examples11:01 The Future of AI Agents and Their Collaboration16:47 Preparing Enterprises for AI: Data Management and Security20:47 Business Advantages of Real-Time AI and Future Opportunities

In this conversation, Ryan Worobel shares his extensive experience in the technology sector, discussing the evolution from traditional monitoring to observability. He highlights the cultural and technical challenges organizations face during this transition, emphasizing the importance of collaboration and data management. Ryan also explores the role of AI in enhancing IT operations, advocating for a balance between automation and human expertise. He provides insights on implementing AI in organizations, the risks and opportunities associated with it, and the necessity of understanding company culture for successful adoption.Key TakeawaysObservability requires a cultural shift towards collaboration.Data management is crucial to avoid overwhelming teams.AI is transforming IT from reactive to proactive approaches.Organizations must start small when implementing AI.Understanding company culture is key to AI adoption.Uptime is essential; downtime is no longer acceptable.AI should supplement human expertise, not replace it.Effective data sorting can reduce noise in decision-making.Innovation is necessary to maintain a competitive edge.Organizations need to establish governance around AI usage.Chapters00:00 Introduction to Ryan Worobel and His Journey07:37 Proactive vs Reactive Approaches in IT13:31 Implementing AI in Organizations19:31 Conclusion and How to Connect with Ryan

In this conversation, Sam Page explores the evolving landscape of digital experiences, emphasizing the shift from the information age to the experience age, driven by advancements in AI. Learn about importance of creating meaningful digital interactions, the challenges posed by biases in AI, and the need for transparency and education in navigating these technologies. Sam introduces a framework for brands to understand, connect, and serve their audiences effectively, highlighting the potential of AI to enhance consumer experiences while addressing concerns about job displacement and data biases.Key TakeawaysThe digital experience is rapidly changing with AI at the forefront.Brands must prioritize meaningful digital experiences to connect with consumers.The shift from the information age to the experience age is significant.AI can create unique experiences tailored to individual needs.Concerns about AI include job displacement and biases in data.Transparency and trust are crucial in AI adoption.Education about AI should start from a young age.Brands can leverage AI to enhance customer connections.Spotify's DJ feature exemplifies effective AI use in consumer engagement.Understanding, connecting, and serving are key components for brands in the AI era.Chapters00:00 The Evolution of Digital Experience04:01 Transitioning from Information Age to Experience Age08:03 Addressing AI Concerns and Biases14:11 Navigating AI: Understand, Connect, and Serve

In this conversation, Phillip Mortimer discusses the transformative impact of AI on private markets, emphasizing the unique challenges posed by non-standardized data and the importance of balancing quantitative and qualitative insights in investment decisions. He highlights the significance of data privacy in AI applications and the evolving role of generative AI in automating workflows. Mortimer also addresses concerns about the future of AI, arguing against the notion of reaching an inflection point in returns due to existing limitations.Key TakeawaysAI is essential for navigating the complexities of private markets.Data scarcity and non-standardization make AI a necessity in finance.Human intuition remains crucial in investment decision-making.AI can enhance productivity but should not replace human judgment.Generative AI poses new data privacy challenges that must be addressed.Last mile SaaS can still thrive despite the rise of generalized AI.The future of AI is promising, with ongoing advancements in technology.Type 2 thinking in AI is a key area for development.Investors must consider the return on intelligence, not just ROI.AI's role in finance is to augment human capabilities, not replace them.Chapters00:00 Introduction to AI in Private Markets02:56 The Role of AI in Data Analysis06:00 Balancing Quantitative and Qualitative Insights08:50 Data Privacy Concerns in AI11:54 Generative AI and Last Mile SaaS14:59 Future of AI and Investment Returns

In this conversation, Michel Spruijt discusses the integration of AI and robotics in various industries, emphasizing the importance of balancing automation with human oversight. He highlights the challenges of designing multifunctional AI systems and the critical role of data interpretation in ensuring ethical use. Michel also shares insights on how organizations can adapt to AI, the significance of curiosity in career development, and the evolving job landscape due to technological advancements.Key TakeawaysAI and robotics are transforming industries, including cleaning.The balance between automation and human oversight is crucial.Data interpretation is more important than just data collection.Organizations should start small with AI investments.Curiosity is key to identifying unique opportunities in careers.AI will create new jobs while automating others.Human oversight is essential for ethical AI use.Staying curious can lead to career growth and innovation.AI should complement human work, not replace it.Understanding AI's capabilities can enhance productivity. Chapters00:00 Introduction to AI and Robotics in Industry03:00 The Balance of Automation and Human Oversight05:56 Challenges in Designing Multifunctional AI Systems08:48 Data Interpretation and Ethical Considerations12:07 Adapting Organizations to AI16:58 Identifying Unique Opportunities in AI Roles

The rise of artificial intelligence (AI) is transforming industries, and customer success is no exception. Current trends show a rapid increase in AI adoption. This is driven by the potential to personalise interactions, automate routine tasks, and gain valuable insights from customer data among other solutions. However, the transition requires careful consideration of how AI can blend with existing customer success practices. The goal is to ultimately develop a combination of AI capabilities and human empathy, leading to more satisfying and effective customer experiences.This is where natural language processing (NLP) comes in. The power of NLP can be leveraged to understand customer queries and sentiment analysis to determine their emotional state. In this episode, Kevin Petrie, VP of Research at BARC, speaks to Kate Neal, Senior Director of Customer Success at Gainsight, about the evolving role of AI in customer success. TakeawaysAI adoption in customer success is accelerating despite some hesitations.Gainsight provides a comprehensive customer operating system, “powered by AI”.Natural language processing can significantly enhance customer sentiment analysis.Human oversight is crucial in AI applications to ensure accuracy.Data quality is essential for effective AI implementation.AI can help reduce the administrative burden on customer success teams.Collaboration between data and AI teams is necessary for success.Understanding AI's capabilities is key for customer success leaders.AI is not a replacement for human jobs but a tool to enhance them.Chapters00:00 Introduction to AI in Customer Success03:44 Gainsight's Role in Customer Success07:11 AI Adoption Trends in Customer Service10:51 Use Cases of AI in Customer Success15:12 Natural Language Processing and Customer Sentiment19:48 Human Oversight in AI Applications22:06 Collaboration Between Data and AI Teams23:59 Getting Started with AI in Customer Service

James Smith discusses his extensive background in business intelligence and analytics, emphasizing the critical importance of adopting generative AI in organizations. He highlights the risks of delaying adoption, the transformational potential of AI, and the need for alignment between AI and business strategies. James also addresses the importance of measuring the impact of AI, ensuring ethical use, and leveraging AI to anticipate future trends. He concludes by sharing insights on how businesses can effectively implement generative AI to gain a competitive edge.Key takeawaysGenerative AI adoption is crucial for maintaining competitive advantage.Organizations that delay AI adoption risk losing market share.AI can transform the role of data teams in organizations.Effective communication is essential during AI implementation.Aligning AI strategy with business goals is critical for success.Measuring ROI is more important than just tracking user adoption.Keeping humans in the decision-making loop is vital for ethical AI use.Generative AI can empower all employees, not just a few.Organizations should test AI solutions against complex use cases.Chapters00:00 Introduction to James Smith and His Background03:12 The Importance of Generative AI Adoption06:05 Transformational Potential of Generative AI in Organizations08:54 Measuring the Impact of Generative AI11:54 Aligning AI Strategy with Business Goals15:05 Addressing Bias and Ensuring Ethical AI Use18:09 Leveraging Generative AI for Future Trends23:51 Conclusion and How to Connect with James Smith

Azfar Aslam, VP & Chief Technology Officer, Europe at Nokia discusses the evolving landscape of telecommunications, focusing on the integration of AI and quantum computing. He highlights the challenges of implementing AI in network management, the importance of quantum safe cryptography, and the need for reliability in technology. The discussion also touches on the competitive nature of the industry and the ethical considerations of AI, particularly in ensuring fairness and avoiding biases in decision-making.Key TakeawaysAI is transforming telecommunications but comes with challenges.Focus on solving big problems rather than getting lost in technology.AI-powered maintenance can prevent outages and improve reliability.Quantum computing has the potential to revolutionize network security.Organizations must prepare for the transition to quantum-safe cryptography.Reliability and trust are critical in adopting new technologies.Competition in telecommunications is fierce, requiring constant innovation.AI systems must be designed to avoid biases and ensure fairness.Traceability in AI decision-making is essential for accountability.The balance between technology and economics will drive future innovations.Chapters00:00 Introduction to AI in Telecommunications02:48 Challenges of AI Implementation06:11 The Role of Quantum Computing12:01 Quantum Safe Cryptography15:10 The Future of Quantum in Telecommunications17:58 Competition and Reliability in Tech20:54 Ensuring Fairness in AI Systems

Hear Matt Yates explore the transformative role of AI in contact centers, discussing technologies like natural language processing and sentiment analysis. They delve into the balance between AI efficiency and the irreplaceable human touch in customer service, highlighting the importance of transparency, training, and continuous improvement in AI integration.Key TakeawaysAI is revolutionizing contact centers and customer interactions.Natural language processing is key to understanding customer sentiment.Human agents are essential for nuanced customer interactions.AI models are not 100% accurate and can introduce bias.Transparency in AI decision-making is crucial for customer trust.Organizations should balance AI efficiency with human emotional intelligence.Predictive analytics can enhance customer loyalty and service.Continuous training is necessary for both agents and AI systems.Implementing AI should be done gradually to avoid disruption.Data-driven decision-making is vital for successful AI integration.Chapters00:00 Introduction to AI in Contact Centers06:02 The Role of Human Agents in AI-Driven Environments11:49 Ensuring Transparency and Accountability in AI17:53 Using Predictive Analytics for Customer Loyalty

Hear Wilson Chen discuss the complexities of AI in data analysis, particularly focusing on the challenges of bias, misinformation, and the importance of human expertise in interpreting AI-driven insights. Wilson shares insights from his experience as the founder of Permutable AI , a startup that builds real-time LLM engines, and emphasizes the need for a balanced view in understanding geopolitical trends and market intelligence. The discussion also highlights the critical checks necessary to ensure the accuracy and reliability of AI-generated information.Key TakeawaysAI systems can amplify existing biases in data.A balanced view of information is crucial for accuracy.Human expertise is essential in interpreting AI outputs.Organizations must critically assess AI-driven insights.Real-time data analysis can enhance decision-making.Misinformation can spread if AI is not properly regulated.Ethical considerations are vital in AI usage.The integrity of sources impacts AI reliability.AI can simplify complex geopolitical dynamics.Permutable.ai aims to provide actionable insights for businesses.Chapters00:00 Introduction to AI and Data Analysis05:01 Addressing Bias in AI Systems09:55 The Role of Human Expertise in AI14:53 Trusting AI-Driven Market Intelligence20:01 Conclusion and Future Insights

AI is catalysing the evolution of low-code platforms and reshaping the landscape of low-code development tools. These new technologies can provide a strategic advantage in streamlining internal operations. By leveraging AI effectively organisations are able to deliver truly personalised, adaptive, and intuitive interactions.However, organizations face challenges adopting these new technologies and risks like AI hallucinations need to be mitigated to ensure reliable outcomes.In this episode, Paulina Rios-Maya, Head of Industry Relations at EM360Tech, speaks with Nikhil Nandagopal, Co-founder and CPO of Appsmith, about the transformative impact of AI on low-code platforms and application development. Key takeawaysLow-code platforms are revolutionizing application development.AI tools can generate code but require careful review.Routine tasks can be automated, but decision-making still needs human input.AI adoption comes with challenges like hallucinations and misinformation.Organizations must adapt their culture and processes for AI success.Developers need skills in data modeling and security for AI applications.AI can simplify user interfaces and enhance user experience.Interconnected applications will rely on AI to bridge data gaps.Most AI projects fail due to underestimating necessary changes.Enterprises face more challenges in AI adoption compared to SMBs.Chapters00:00 - Introduction to AI and Low-Code Platforms02:59 - The Role of AI in Automating Tasks05:51 - Challenges and Risks of AI Adoption09:08 - Essential Skills for Developers in AI12:01 - Future of Interconnected Applications14:50 - Realities vs. Hype of AI in Enterprises

When it comes to the IT enterprise, the integration of AI is proving to be a transformative force. Modern IT departments face mounting challenges, from managing complex infrastructures and resolving system inefficiencies to ensuring robust cybersecurity and meeting escalating user demands. AI offers a solution by automating routine tasks, streamlining processes, and enabling predictive analytics that anticipate issues before they arise. This not only enhances operational efficiency but also frees IT teams to focus on more strategic initiatives, driving innovation and value creation across the organisation. However, adopting AI in enterprise IT systems comes with its own challenges, including ensuring data privacy, overcoming resistance to change, and maintaining the right balance between automation and human oversight. Effective AI integration requires a thoughtful approach—one that leverages AI's capabilities while retaining human control to ensure accountability and ethical decision-making.In this episode, Kevin Petrie, VP of Research at BARC, speaks to David Campbell, VP of Product at GoTo, about the challenges faced by modern IT departments, the role of practical AI in addressing these challenges, and the importance of balancing automation with human oversight.Key Takeaways: AI can modernise IT systems without disrupting operations.AI helps identify inefficiencies and provides actionable insights.Human oversight is crucial in AI-driven IT processes.AI can automate tasks and flag critical issues proactively.Security is a top priority for IT departments.Data quality is essential for effective AI strategies.AI can streamline knowledge sharing across IT teams.AI can enhance help desk workflows and reduce resolution times.Chapters: 00:00 - Introduction to AI in Enterprise IT02:06 - Challenges in Modern IT Systems04:33 - The Role of AI in IT Modernization06:57 - Balancing AI Automation and Human Oversight10:13 - AI's Impact on IT Security12:34 - Ensuring Data Quality for AI16:23 - Top AI Use Cases in IT Management

Today, organisations that thrive are those that foster a culture of curiosity. Encouraging curiosity empowers teams to question the status quo, explore innovative solutions, and stay ahead of emerging trends. By promoting an environment where employees feel encouraged to ask "what's next?" and seek learning opportunities, companies can adapt more swiftly to technological advancements and market shifts. This mindset drives innovation and cultivates resilience, enabling organisations to turn challenges into opportunities. Preparing for future technological trends requires more than just adopting the latest tools—it demands a workforce that is curious and agile. A culture of curiosity fuels engagement, cross-disciplinary collaboration, and creative problem-solving, all of which are critical for navigating the complexities of technologies like AI, blockchain, and quantum computing. By investing in curiosity through training, open communication, and a safe space for experimentation, organisations can build a future-ready workforce that embraces change and drives sustainable growth.In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Andrew Grill, author of Digitally Curious: Your Guide to Navigating the Future of AI and All Things Tech, about the future of work and the integration of AI. Key Takeaways: Digital curiosity empowers individuals to understand technology better.Leaders play a crucial role in fostering a culture of curiosity.Ethics in AI involves data privacy, transparency, and fairness.Curiosity leads to digital fluency, which is essential for the future.Organisations should encourage experimentation with new technologies.Understanding technology is vital for effective leadership.The future workforce will require continuous upskilling and adaptability.Chapters: 00:00 - Introduction to Digital Curiosity03:00 - The Importance of Digital Curiosity in Technology05:50 - Overcoming Barriers to Technology Engagement09:13 - Ethics and Responsibility in AI12:05 - Cultivating a Culture of Curiosity in Organizations14:58 - Future Technological Shifts and AI Integration

The world of IT Finance is shifting, driven by the transition from traditional capital expenditure (CapEx) models to operational expenditure (OpEx) in cloud-based environments. This evolution not only enables businesses to scale their resources dynamically while optimizing costs, but also introduces new complexities in forecasting, budgeting, and resource allocation. As organizations adopt cloud technologies, the ability to manage financial resources with precision becomes a cornerstone of competitive advantage. Augmented FinOps, a groundbreaking approach that merges traditional financial operations with AI-driven insights, transforms how companies navigate these challenges, empowering decision-makers with unparalleled visibility and control. The applicationof AI in FinOps promises to amplify its potential by automating routine tasks, uncovering patterns in vast data sets, and providing predictive analytics to enhance strategic planning. In this episode, Jon Arnold, Principal of J Arnold Associates, speaks with Kyle Campos, CTPO at CloudBolt, about Augmented FinOps and the role of AI and ML in automating cost management processes. Key Takeaways: FinOps is a community-driven approach to managing cloud spend.The shift from CapEx to OpEx has transformed financial operations.Augmented FinOps combines AI with traditional FinOps practices.Organizations must measure the “insight to action” gap in their processes. Machine learning can automate and optimize cloud spending decisions.C-suite expectations often misalign with operational realities.Tracking metrics is vital for improving FinOps maturity.Investing in FinOps is about optimizing resources, not just cutting costs.Chapters: 00:00 - Introduction to FinOps and Cloud Management02:52 - The Shift from CapEx to OpEx in Cloud Spending06:13 - The Role of AI in Financial Operations08:56 - Understanding Augmented FinOps12:13 - Best Practices for FinOps in the Age of AI15:00 - Leveraging Machine Learning for Cost Optimization18:13 - Insight to Action: Measuring FinOps Effectiveness21:04 - Conclusion and Key TakeawaysCloudBolt is The Cloud ROI Company™. It is singularly focused on solving the most pressing problem with cloud today: increasing return on investment (ROI). With the introduction of Augmented FinOps capabilities, CloudBolt is leveraging AI/ML-informed insights and applying intelligent automation and orchestration proactively and retrospectively to make complete cloud lifecycle optimization a reality. CloudBolt enables organizations to realize the full potential of any cloud fabric by closing the “insight to action” gap. By streamlining, clarifying, and optimizing spend and control, CloudBolt helps organizations place value at the center of every cloud decision. For more information, visit www.cloudbolt.io.

As artificial intelligence continues to shape industries and society, the need for robust AI governance has never been more critical. At the forefront of this governance are privacy-enhancing technologies (PETs), which play a key role in ensuring that AI systems operate in a way that respects and protects individuals' data. The European Union's AI Act, one of the most ambitious regulatory frameworks for AI, sets clear standards for transparency, accountability, and risk management. Understanding the implications of this legislation is crucial for businesses looking to innovate responsibly while avoiding potential legal and ethical pitfalls. Countries around the world are taking varied approaches to AI governance, with some prioritising privacy and ethical considerations while others focus on fostering technological innovation. This diversity presents challenges and opportunities for organisations striving to implement AI responsibly. In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Dr Ellison Anne Williams CEO and founder of Enveil, about the need for model-centric security and the potential of PETs to mitigate risks associated with sensitive data in AI applications.Key Takeaways: Privacy-enhancing technologies are crucial for data protection.The EU AI Act sets a precedent for global AI regulation.Organisations must start with the problems they aim to solve.Data sensitivity must be considered in AI model training.Privacy-enhancing technologies can facilitate cross-border data sharing.AI is a neutral tool that requires responsible governance.The implementation of privacy technologies is still evolving.Global standards for AI governance are necessary for ethical use.Chapters: 00:00 - Introduction to AI Governance and Privacy01:07 - Understanding AI Governance03:51 - Privacy Enhancing Technologies Explained08:28 - The Role of the EU AI Act12:42 - Implementing Privacy Enhancing Technologies17:20 - Harmonizing AI Governance with Privacy Technologies

In this episode, Paulina Rios Maya, Head of Industry Relations at EM360, speaks with Caroline Hicks, Senior Event Director of the AI Summit Global Series, about the upcoming AI Summit New York, which will take place December 11-12, 2024. They discuss the event's focus on AI's practical applications across various sectors, the importance of ethical considerations in AI, and the collaborative efforts among different industries to harness AI technology responsibly. The conversation highlights key discussions and innovations that will be showcased at the summit, emphasizing the transformative impact of AI on businesses and societyRegister now and enjoy a 15% discount on Delegate Passes with code EM36015OFF via https://newyork.theaisummit.com/em360tech-reg.Key Takeaways: AI is showcasing its practical impact across diverse sectors.The AI Summit New York focuses on ethical and responsible AI.Collaboration is key in the AI community.AI is transforming industries like healthcare and retail.The summit will address AI's challenges and responsibilities.Innovative applications of AI will be highlighted at the event.AI can foster inclusivity and equal opportunities.Real-world implementation stories will be shared by companies.

As AI reshapes industries and drives global innovation, the UK must urgently address its AI skills gap to remain competitive. Nations investing in AI education and training are gaining a clear advantage, leaving others at risk of falling behind. By equipping the workforce with essential AI expertise, the UK can strengthen its position as a leader in innovation and secure its economic future.Developing AI skills isn't just about maintaining a competitive edge—it's about creating opportunities. This dual approach ensures that experts can drive technological advancements while a broad understanding of AI empowers diverse sectors to integrate its potential. Investing in education, upskilling, and industry partnerships will ensure the UK workforce is ready to meet the demands of an AI-driven world. In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to James Kuht, CEO and Founder of Inversity, about integrating AI into education and the collaborative effort required from government and society to achieve this goal.Key Takeaways: The UK has a strong AI talent pipeline concentrated in key areas.Specialist and generalist AI skills are both important.AI can significantly boost productivity in knowledge-based tasks.Teachers need training to effectively integrate AI into education.AI skills will be a baseline requirement for future jobs.Government and society must collaborate on AI education initiatives.AI has the potential to reduce economic inequality.Most jobs will evolve rather than disappear due to AI.Chapters: 00:00 - The Importance of AI Skills for the UK03:01 - James Kuht's Journey in AI05:57 - Building a Competitive AI Workforce08:45 - Integrating AI into Education12:07 - The Role of Government and Society in AI Education15:01 - Addressing Inequality in AI Access17:58 - Future-Proofing the Workforce with AI Skills21:10 - The Impact of AI on Global Industries

AI operates in two primary environments: on-device and cloud-based. On-device AI processes data locally, ensuring privacy and speed by eliminating the need for internet connectivity. Cloud-based AI, on the other hand, leverages powerful remote servers to handle complex computations and large-scale data analysis, enabling more robust capabilities but often at the cost of latency and potential privacy concerns. Apple Intelligence exemplifies the strengths of on-device AI, with innovations like Siri, Face ID, and real-time photo enhancements all designed to prioritise user privacy while delivering seamless, responsive experiences. Unlike cloud-based AI, which may send sensitive data to external servers for processing, Apple's approach ensures that personal information stays on the user's device and is protected by advanced encryption. This difference builds trust and empowers users with faster, more reliable interactions. In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Karel Callens, CEO at Luzmo, about best practices for developers integrating AI into their products. Key Takeaways:Apple Intelligence operates on-device but can access cloud resources.Developers must implement robust security measures for user data.Clear data policies enhance user trust in AI solutions.Opt-in and opt-out options empower users regarding their data.Education on AI usage is crucial for consumer confidence.Shared security standards can mitigate AI misuse.Regulation is necessary to keep pace with AI advancements.Big tech companies have a responsibility to ensure ethical AI use.Chapters:00:00 - Introduction to AI: On-Device vs Cloud-Based02:54 - Understanding Apple Intelligence and Its Benefits05:47 - Security Measures in AI Integration09:03 - Building Trust Through Transparency and Regulation11:50 - Best Practices for Developers in AI Implementation15:04 - The Role of Education in AI Trust and Security17:47 - The Future of AI: Regulation and Responsibility

AI Personas are the cornerstone of how these systems interact with users, delivering tailored and engaging experiences. These personas—crafted from user research, behavioural insights, and cultural contexts—help define an AI's tone, style, and decision-making approach. Whether it's a friendly virtual assistant or a professional customer service bot, personas ensure that AI systems resonate with their audiences while maintaining a consistent identity. However, developing personas for AI isn't without its challenges. Ensuring that AI responses remain appropriate, ethical, and unbiased while preserving a unique persona requires careful consideration. From avoiding stereotypes to addressing edge cases, the process demands robust testing and a clear understanding of how diverse user interactions can unfold. When personas fail to account for the complexity of real-world scenarios, the risk of inappropriate or harmful responses increases. By combining creative storytelling with ethical AI design principles, organisations can navigate these challenges and build AI systems that are engaging and responsible in their behaviour.In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Cobus Greyling, Chief Evangelist at Kore.ai, about the influence of cultural norms and value systems on AI and strategies for maintaining control over AI behaviour. Key Takeaways: Personas in AI shape user interactions and trust.Cultural norms influence AI decision-making processes.Balancing control and agency is crucial for effective AI.Adversarial attacks can undermine AI reliability.Transparency is essential for user confidence in AI.Organisations should not offload too much responsibility to AI.AI should enhance human creativity, not replace it.Proof of value is necessary for AI technology implementation.Chapters:00:00 - Introduction to AI Personas and Their Impact02:34 - The Role of Personas in AI Behavior05:51 - Challenges in Ensuring Appropriate AI Responses09:07 - Cultural Norms and Value Systems in AI10:30 - Balancing Control and Agency in AI14:14 - Strategies for Maintaining Control Over AI Behavior21:24 - The Importance of Responsibility in AI Usage

Low-code and no-code platforms are revolutionising application development by empowering technical and non-technical users to quickly and efficiently build powerful applications. These platforms provide intuitive visual interfaces and pre-built templates that enable users to create complex workflows, automate processes, and deploy applications without writing extensive lines of code. By simplifying development, low-code and no-code tools open up software creation to a wider range of contributors, from professional developers looking to accelerate delivery times to business users aiming to solve specific problems independently. This democratisation of development reduces the demand for IT resources and fosters a culture of innovation and agility within organisations.The impact of low-code and no-code technology extends beyond just speed and accessibility; it's transforming how businesses adapt to change and scale their digital solutions. These platforms allow companies to quickly respond to evolving customer needs, regulatory requirements, and competitive pressures without the lengthy timelines associated with traditional development cycles. In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Michael West, Analyst at Lionfish Tech Advisors about LCNC platforms and their benefits. Key Takeaways:Low-code and no-code platforms enable business solutions without coding.These platforms broaden the developer base to include non-technical users.Choosing the right platform involves considering functionality, standards, and vendor viability.Low-code platforms can handle enterprise-level applications effectively.AI integration is transforming how applications are developed.Democratisation of development addresses the shortage of professional developers.The market for low-code and no-code platforms is rapidly evolving.Future trends will focus on AI capabilities and user experience.Chapters: 00:00 Introduction to Low-Code and No-Code Platforms02:59 The Evolution of Development Roles05:49 Key Considerations for Adopting LCNC Tools09:04 Democratizing Development and Innovation11:59 Future Trends in Low-Code and No-Code Markets

The intersection of cryptography and GPU programming has changed the face of secure data processing, making methods for encryption and decryption much faster and more efficient than ever imagined. Cryptography is the science of encrypting data with intricate algorithms initially designed to operate on very intensive computational powers. GPU programming provides the ability to utilise parallel processing of graphics processing units in cryptographic processes so they perform with unmatched speed. While continuously evolving, GPUs are furnishing the computational muscle to execute ever-higher-level cryptographic algorithms without performance penalties. Developers now fully avail of the power of GPU parallelism to perform several thousand encryption tasks simultaneously, which is difficult for traditional CPUs to keep up with. This efficiency is critical in this growing data and rising cyber threat era, where organisations need rapid encryption and robust security. In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Agnes Leroy, Senior Software Engineer at Zama, about the significance of encryption in high-stakes industries, the role of women in tech and the importance of mentorship in overcoming barriers in the industry.Key Takeaways: GPUs have evolved from graphics rendering to critical roles in data security.Fully homomorphic encryption allows computations on encrypted data.Quantum-resistant methods are crucial for future-proofing encryption.High-stakes industries require robust encryption to protect sensitive data.Diverse environments in tech foster innovation and collaboration.The future of encryption technology is exciting and unpredictable.Chapters: 00:00 - Introduction to Cryptography and GPU Programming01:08 - The Evolution of GPUs in Data Security03:33 - Challenges in Traditional vs Modern Encryption05:50 - Quantum Resistance in Encryption Techniques07:40 - The Future of GPUs in Data Privacy08:38 - Importance of Encryption in High-Stakes Industries10:00 - Potential Applications of Fully Homomorphic Encryption11:42 - Women in Tech: Overcoming Barriers15:33 - Conclusion and Resources

LLMs and AI have increasingly become major contributors to transforming content creation today. Understanding and using prompt skills appropriately can help organisations optimise AI to generate high-quality content efficiently. While AI offers multiple benefits, it's important to acknowledge the potential risks associated with its implementation. Organisations are advised to carefully consider factors such as data privacy, bias, and the ethical implications of AI-generated content. In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Prof. Yash Shreshta, Assistant Professor at the University of Laussane, about prompt engineering and its benefits. Key TakeawaysPrompts are commands given to AI to perform tasks.Prompt engineering allows users to communicate effectively with AI.Iterative processes improve the quality of AI outputs.Understanding LLM limitations is crucial for effective use.Collaboration can enhance the creative process with AI.The role of prompt engineering is rapidly evolving with AI advancements.Data privacy is a significant risk when using LLMs.Over-reliance on AI can lead to skill degradation.Organisations should integrate human creativity with AI.Regular training on prompt engineering is essential for maximising LLM benefits.Chapters00:00 Introduction to Prompt Engineering and AI01:30 Understanding Prompt Engineering04:15 The Importance of Prompt Engineering Skills06:37 Best Practices for Effective Prompts08:31 The Evolving Role of Prompt Engineering11:20 Risks and Challenges of AI in Organizations13:15 The Future of Creativity with AI

The fraud division has witnessed a dramatic transformation in the age of artificial intelligence (AI). As technology advances, so do the methods employed by fraudsters. Modern criminals use sophisticated techniques, such as deep learning and natural language processing, to deceive individuals and organisations alike. Such techniques allow them to mimic human behaviour, manipulate data, and exploit vulnerabilities in security systems.That's why organisations are embracing AI's strengths to combat these evolving threats. AI-driven solutions can provide real-time detection of fraudulent activities, analyse vast amounts of data to identify patterns and anomalies, and automate response processes. In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Xavier Sheikrojan, Senior Risk Intelligence Manager at Signifyd, about AI fraud. Key Takeaways:Fraudsters have evolved from individuals to organised criminal enterprises.AI enables fraudsters to scale their attacks rapidly and strategically.Phishing attacks have become more sophisticated with AI-generated content.Synthetic identities can be created easily, complicating fraud prevention.Opportunistic fraud is impulsive, while proactive fraud is well-planned.Businesses often fail to act post-breach due to resource constraints.Inaction after a breach can lead to repeated attacks by fraudsters.AI must be used to combat AI-driven fraud effectively.Balancing fraud detection with customer experience is crucial for businesses. Chapters: 00:00 Introduction to AI and Fraud01:32 The Evolution of Cybercrime05:43 AI's Role in Modern Fraud Techniques09:55 Opportunistic vs. Proactive Fraud12:44 Business Inaction and Its Consequences15:59 Combating AI-Driven Fraud with AI

As AI technologies become more integrated into business operations, they bring opportunities and challenges. AI's ability to process vast amounts of data can enhance decision-making but also raise concerns about data privacy, security, and regulatory compliance. Ensuring that AI-driven systems adhere to data protection laws, such as GDPR and CCPA, is critical to avoid breaches and penalties. Balancing innovation with strict compliance and robust data security measures is essential as organisations explore AI's potential while protecting sensitive information.In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Erin Nicholson, Global Head of Data Protection and AI Compliance at Thoughtworks, about the importance of compliance frameworks, best practices for transparency and accountability, and the need for collaboration among various teams to build trust in AI systems.Key Takeaways:AI systems are powerful but require ethical and compliant design.Lack of standardisation in AI regulations poses significant challenges.AI models often need help with explainability and transparency.Compliance frameworks are essential for implementing AI in critical sectors.Documentation and audits are crucial for maintaining AI accountability.Baselining pre-AI processes helps build public trust in AI systems.Organisations should map regulations to the most stringent standards.Cross-functional collaboration is vital for effective AI compliance.Chapters: 00:00 - Introduction to AI, Data Protection, and Compliance02:08 - Challenges in AI Implementation and Compliance05:56 - The Role of Compliance Frameworks in Critical Sectors10:31 - Best Practices for Transparency and Accountability in AI14:48 - Navigating Regional Regulations for AI Compliance17:43 - Collaboration for Trustworthiness in AI Systems

As organisations increasingly migrate to cloud environments, they face a critical challenge: ensuring the security and privacy of their data. Cloud technologies offer many benefits, including scalability, cost savings, and flexibility. However, they also introduce new risks, such as potential data breaches, unauthorised access, and compliance issues. With sensitive data stored and processed off-premises, maintaining control and visibility over that data becomes more complex. As cyber threats continue to evolve, robust data protection strategies are essential to safeguarding information in the cloud.In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Sergei Serdyuk, VP of Product Management at NAKIVO, about the factors driving cloud adoption, the importance of having a robust disaster recovery plan, best practices for data protection, and the challenges of ensuring compliance with regulations.Key Takeaways: Cloud adoption is accelerated by low barriers to entry.Scalability in cloud environments is easier than on-premises.Data in the cloud is vulnerable and needs protection.The shared responsibility model places data protection on the user.A comprehensive disaster recovery plan is crucial for businesses.Regular testing of disaster recovery plans is essential.Data protection strategies must include regular reviews and updates.Compliance with data protection regulations is complex and varies by region.Balancing security and operational efficiency is a key challenge.Chapters: 00:00 - Introduction to Cloud Technologies and Data Protection01:26 - Factors Accelerating Cloud Adoption03:48 - The Importance of Data Protection in the Cloud06:39 - Developing a Comprehensive Disaster Recovery Plan10:05 - Best Practices for Data Protection13:31 - Ensuring Compliance in Cloud Environments15:56 - The Role of Continuous Monitoring in Data Protection18:19 - Balancing Security and Operational Efficiency

Managed Service Providers (MSPs) are evolving beyond traditional IT support, becoming strategic partners in driving business growth. By embracing AI technologies, MSPs are improving operational efficiency, streamlining service delivery, and offering smarter solutions to meet modern challenges.As businesses navigate digital transformation, MSPs are crucial in optimising IT infrastructure, enhancing security, and providing tailored solutions that fuel innovation. With AI-powered tools, MSPs meet today's demands and help businesses stay competitive. In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Jason Kemsley, Co-founder and CRO of Uptime, about the proactive strategies that MSPs can adopt using AI, the challenges they face in implementation, and the ethical considerations surrounding AI solutions.Key Takeaways:MSPs are transitioning from traditional IT support to strategic partners.AI is enhancing operational efficiency but not replacing human roles.Proactive support is often neglected due to resource constraints.AI can help MSPs predict and prevent IT issues.Challenges in adopting AI include unrealistic expectations and lack of accountability.There is potential for MSPs to develop their own AI tools.Chapters:00:00 - Introduction to Managed Service Providers (MSPs)02:03 - The Evolving Role of MSPs in Business Growth04:00 - AI's Impact on Service Delivery Models07:22 - Proactive Support Strategies with AI10:16 - Challenges in Adopting AI for MSPs12:40 - Ethics and Accountability in AI Solutions

Trusted AI ensures that people, data, and AI systems work together transparently to create real value. This requires a focus on performance, innovation, and cost-effectiveness, all while maintaining transparency. However, challenges such as misaligned business strategies and data readiness can undermine trust in AI systems. To build trusted AI, it's crucial to first trust the data. A robust data platform is essential for creating reliable and sustainable AI systems. Tools like Teradata's ClearScape Analytics help address concerns about AI, including issues like generative AI hallucinations, by providing a solid foundation of trusted data and an open, connected architecture.In this episode, Doug Laney, Analytics Strategy Innovation Fellow with West Monroe Partners, speaks to Vedat Akgun, VP of Data Science & AI and Steve Anderson, Senior Director of Data Science & AI at Teradata, about trusted AI. Key Takeaways:Value creation, performance, innovation, and cost-effectiveness are crucial for achieving trusted AI.Trusting data is essential before building AI capabilities to avoid biases, inaccuracies, and ethical violations.A robust data platform is a foundation for creating trusted and sustainable AI systems.Generative AI raises concerns about hallucinations and fictitious data, highlighting the need for transparency and accountability.Teradata offers features and capabilities, such as ClearScape Analytics and an open and connected architecture, to address trust issues in AI.Chapters:00:00 - Introduction and Defining Trusted AI01:33 - Value Creation and the Importance of Driving Business Value03:27 - Transparency as a Principle of Trusted AI09:00 - Trusting Data Before Building AI Capabilities14:51 - The Role of a Robust Data Platform in Trusted AI21:09 - Concerns about Trust in Generative AI23:03 - Addressing Trust Issues with Teradata's Features and Capabilities25:01 - Conclusion

Balancing transparency in AI systems with the need to protect sensitive data is crucial. Transparency helps build trust, ensures fairness, and meets regulatory requirements. However, it also poses challenges, such as the risk of exposing sensitive information, increasing security vulnerabilities, and navigating privacy concerns.In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Juan Jose Lopez Murphy, Head of Data Science and Artificial Intelligence at Globant, to discuss the ethical implications of AI and the necessity of building trust with users. Key Takeaways:Companies often prioritise speed over transparency, leading to ethical concerns.The balance between transparency and protecting competitive data is complex.AI misuse by malicious actors is a growing concern.Organisations must educate users on digital literacy to combat misinformation.Confidently wrong information is often more trusted than qualified uncertainty.Chapters00:00 - Introduction to AI Transparency03:03 - Balancing Transparency and Data Protection05:57 - Navigating AI Misuse and Security09:05 - Building Trust Through Transparency12:03 - Strategies for Effective AI Governance

As organisations adopt AI, data literacy has become more critical than ever. Understanding data—how it's collected, analysed, and used—is the foundation for leveraging AI effectively. Without strong data literacy, businesses risk making misguided decisions, misinterpreting AI outputs, and missing out on AI's transformative benefits. By fostering a data-driven culture, teams can confidently navigate AI tools, interpret results, and drive smarter, more informed strategies.Ready to boost your data literacy and embrace the future of AI?Key Takeaways: Companies are optimistic about ROI from AI investments.Data literacy is crucial for effective AI implementation.Technical debt poses challenges for AI infrastructure.Data quality and governance are essential for AI success.Trust in AI systems is a growing concern.Organizations must start with clear business priorities.The Data Festival will provide practical insights for AI adoption.DATA festival is where theory meets practice to create real, actionable knowledge. This event brings together #DATApeople eager to drive the realistic applications of AI in their fields.Leaving the hype behind, we look at the actual progress made in applying (Gen)AI to real-world problems and delve into the foundations to understand what it takes to make AI work for you. We'll discuss when, where and how AI is best applied, and explore how we can use data & AI to shape ourfuture.Sign up now to secure your spot

Data labelling is a critical step in developing AI models, providing the foundation for accurate predictions and smart decision-making. Labelled data helps machine learning algorithms understand input data by assigning meaningful tags to raw data—such as images, text, or audio—ensuring that AI models can recognise patterns and make informed decisions. AI models struggle to learn and perform tasks effectively without high-quality labelled data. Proper data labelling enhances model accuracy, reduces errors, and accelerates the time it takes to train AI systems. Whether you're working with natural language processing, image recognition, or predictive analytics, the success of your AI project hinges on the quality of your labelled data.In this episode, Henry Chen, Co-founder and COO of Sapien, speaks to Paulina Rios Maya about the importance of data labelling in training AI models. Key Takeaways:Data labelling converts raw data into structured data that machine learning models can recognise.Reducing bias and ensuring data quality are critical challenges in data labelling.Expert human feedback plays a crucial role in improving the accuracy of AI training data and refining AI models.Chapters: 00:00 - Introduction and Background01:07 - Data Labeling: Converting Raw Data into Useful Data03:02 - Challenges in Data Labeling: Bias and Data Quality07:46 - The Role of Expert Human Feedback09:41 - Ethical Considerations and Compliance11:09 - The Evolving Nature of AI Models and Continuous Improvement14:50 - Strategies for Updating and Improving Training Data17:12 - Conclusion

Traditional KYC processes are inadequate against modern fraud tactics. While KYC helps with initial identity checks, it doesn't cover evolving threats like AI-generated deepfakes or ongoing account takeovers. Curious about how to protect your business from the latest threats like fake IDs, account takeovers, and AI-generated deep fakes? Tune in to our latest episode, where we dive into the essentials of full-cycle verification and real-time transaction monitoring. Find out how AI and machine learning can revolutionise your fraud detection efforts and why staying updated with regulatory changes is crucial for maintaining top-notch security. In this episode of Tech Transformed, Alvaro Garcia, Transaction Monitoring Technical Manager at Sumsub, speaks to Paulina Rios Maya, Head of Industry, about the manifestations of identity fraud during the user journey stages and the need for comprehensive fraud prevention measures.Key Takeaways:Identity fraud manifests at different user journey stages, including onboarding and transaction monitoring.Businesses must implement full-cycle verification and transaction monitoring solutions to detect and prevent fraud in real time.AI and machine learning are crucial in analyzing suspicious user behaviour and spotting complex fraud patterns.SumSub offers platform solutions that include KYC, business verification, transaction monitoring, and payment fraud protection.Chapters: 00:00 - Introduction and Overview00:35 - Identity Fraud in the User Journey02:01 - Types of Fraud and Fraud Prevention04:20 - Real-Time Monitoring and Enhancing Systems05:46 - Common Types of Fraud Faced by Financial Institutions08:40 - The Challenge of AI-Generated Deepfakes10:04 - Beyond KYC: Additional Measures for Fraud Prevention12:29 - Prevention Measures and Synthetic Identity Fraud15:21 - Effective Fraud Prevention Solutions17:45 - Assessing the Effectiveness of Fraud Prevention Strategies19:08 - Staying Up to Date with Regulatory Requirements21:31 - Conclusion

AI is revolutionising contact centres by automating routine tasks, reducing response times, and enhancing customer experience. AI is built to handle simple inquiries efficiently and at scale. It helps contact centres close the gap between customer expectations and conventional customer service by enabling engagement through digital channels. AI-driven analytics improve decision-making by capturing and analysing data from customer interactions. Organisations can overcome challenges by starting small and gradually building trust in AI's capabilities. In this episode, Paulina Rios Maya, Head of Industry Relations at EM360 speaks to Jon Arnold, Principal at J Arnold & Associates about the use of AI in contact centres. Key Takeaways:AI is a transformative technology that rethinks customer engagement and addresses customer problems in contact centres.AI enables engagement through digital channels and helps contact centres close the gap between customer expectations and conventional customer service.AI-driven analytics improve decision-making by capturing and analysing data from customer interactions.Organisations can overcome challenges by starting small and gradually building trust in AI's capabilities.AI helps protect privacy and mitigate fraud in contact centres.Chapters:00:00 - Introduction and Overview01:06 - The Transformational Power of AI in Contact Centers03:00 - Automating Routine Tasks and Enhancing Customer Experience06:24 - Engaging Customers through Digital Channels11:09 - Improving Decision-Making with AI-Driven Analytics15:28 - Overcoming Challenges and Building Trust in AI17:23 - Protecting Privacy and Mitigating Fraud in Contact Centers

Join us in this exciting episode of Tech Transformed, where we talk to Kelly Vero, a pioneering game developer, digital leader, and visionary in the metaverse. With a career spanning 30 years and a resume that includes contributions to legendary franchises like Tomb Raider and Halo 3, Kelly brings a wealth of knowledge and experience to the table. Kelly's unique journey in the tech world is nothing short of extraordinary. From joining the military to learn about ballistics for Halo 3 to founding the award-winning startup NAK3D, she has always pushed the boundaries of what's possible. Kelly Vero speaks to Paulina Rios Maya about the hurdles of being a woman in the tech industry, the principles of gamification, and the overhyped trends in AI and NFTs. They discuss what's genuinely beneficial versus what's just noise.Key Takeaways: The gaming industry offers opportunities for problem-solving and creativity.Role models can come from everyday people who inspire and support others.Gamification is about creating an engaging user experience that encourages return and contribution.The tech industry has seen beneficial changes in globalized platforms and a focus on quantum solutions.Chapters: 00:00 - Introduction and Background02:28 - The Gaming Industry and Problem Solving07:36 - Challenges and Role Models in the Tech Industry11:54 - The Principles and Ethical Considerations of Gamification18:30 - Beneficial Changes and Overhype in the Tech Industry20:25 - Creating Digital Objects and the NFT Standard23:45 - Introducing NAK3D: Bringing Non-Designers into Design

Strategic choices with significant implications mark Latin America's approach to AI. Many countries in the region have adopted AI technologies and frameworks developed by leading tech nations, focusing on imitation rather than innovation. This strategy enables rapid deployment and utilisation of advanced AI solutions, bridging the technological gap and fostering economic growth. However, reliance on external innovations raises questions about the region's long-term competitiveness and ability to contribute original advancements to the global AI landscape.In this podcast, Alejandro Leal, Analyst at KuppingerCole, speaks to Paulina Rios Maya, Head of Industry Relations, about how socio-economic factors, including limited research funding, infrastructural challenges, and the need for quick technological catch-up drive this pattern of imitation. While this approach has led to swift AI adoption, it underscores a dependence on foreign technologies and expertise.Key Takeaways: Trust, ethics, and legal considerations are essential challenges in integrating AI into the security infrastructure.Public-private partnerships and regional cooperation are crucial for advancing AI technology in the region.Interoperability and alignment with international and regional standards are key areas of focus to ensure the ethical use of AI in Latin America.Chapters:00:00 - Introduction: AI in Latin America00:58 - Key Initiatives in the Security Sector in Mexico05:12 - Mexico's Evolution in National Security07:33 - Challenges in Integrating AI into Security Infrastructure12:40 - Comparison with Other Latin American Countries19:26 - The Role of Public-Private Partnerships22:51 - Focus on Interoperability and Alignment with Standards23:46 - Conclusion: Ethical Use of AI in Latin America

AI fraud is not just a concern, it's a pressing issue. As artificial intelligence technologies advance, fraudsters are developing increasingly sophisticated methods to exploit these systems. Typical forms of AI fraud include deepfakes, which use AI to create convincing fake images, audio, or videos for disinformation, blackmail, or identity theft, and advanced phishing schemes that leverage AI to craft highly personalized and deceptive messages. Addressing and understanding AI fraud is not just crucial, it's urgent for individuals, businesses, and governments to protect against these evolving threats. Join Alejandro Leal and Pavel Goldman-Kalaydin, Head of AI and Machine Learning at Sumsub, as they delve into the growing issue of AI fraud.Themes:AI fraudDeepfakesGlobal trendsRegulationChapters:00:00 - Introduction and Background01:13 - Understanding AI Fraud02:11 - Examples of AI-Driven Fraud08:02 - Global Trends in AI-Driven Fraud13:55 - Preventing AI-Driven Fraud18:11 - The Future Evolution of AI Fraud22:38 - Conclusion and Final Thoughts

While generative AI and large language models often receive inflated acclaim, their true value is found in harnessing intelligence and data-driven insights. Despite the hype, AI has its shortcomings, such as large language models sometimes being solutions looking for problems and challenges in understanding its impact on advertising and making savvy investment decisions.In this podcast, Dana Gardner, President & Principal Analyst of Interarbor Solutions, and Paulina Rios Maya, Head of Industry Relations at EM360Tech, discuss why AI should be seen as a transformational technology rather than just another automation tool. Key TakeawaysAI should be viewed as a transformational technology that can refactor our actions rather than just an automation tool.Large language models may be a solution in search of a problem, and their high costs and sustainability impacts should be considered.Making informed decisions about AI investments requires considering the potential benefits and costs.Chapters00:00 - The Current State of AI Development02:17 - Viewing AI as a Transformational Technology03:45 - The Limitations of Large Language Models05:40 - The Impact of AI on Advertising06:37 - Navigating the Complexities of AI Investments

Infosecurity Europe is a cornerstone event in the cybersecurity industry. It brings together a diverse array of cybersecurity services and professionals for three days of unparalleled learning, exploration, and networking. At its essence, the event is committed to delivering indispensable value to its attendees through meticulously crafted themes and discussions.This year's focus revolves around resilience, artificial intelligence, legislation and compliance, leadership and culture, and emerging threats. With a comprehensive program featuring keynote speakers, strategic talks, insightful case studies, state-of-the-art technology showcases, dynamic startup exhibitions, and immersive security workshops, Infosecurity Europe offers a comprehensive immersion into the dynamic landscape of cybersecurity.Pioneering new approaches, the event introduces QR-based tools for seamless content collection and unveils a digital meetings platform to foster enhanced connectivity and collaboration. By participating in Infosecurity Europe, cybersecurity professionals equip themselves with the tools, knowledge, and connections vital for continuous growth, networking, and industry advancements.In this episode of the EM360 Podcast, Paulina Rios Maya, Head of Industry Relations at EM360Tech, speaks to Victoria Aitken, Conference Manager and Nicole Mills, Senior Exhibition Director, to discuss: Cybersecurity Trends Security events Infosecurity Europe Register here to attend Infosecurity Europe | 4–6 June 2024Chapters00:00 - Introduction and Overview of Infosecurity Europe01:33 - Key Themes and Topics at Infosecurity Europe14:04 - New Approaches and Tools16:21 - Women in Cyber and Analyst Sessions

Ever wonder how search engines understand the difference between "apple," the fruit, and the tech company? It's all thanks to knowledge graphs! These robust and scalable databases map real-world entities and link them together based on their relationships. Imagine a giant web of information where everything is connected and easy to find. Knowledge graphs are revolutionizing how computers understand and process information, making it richer and more relevant to our needs. Ontotext is a leading provider of knowledge graph technology, offering a powerful platform to build, manage, and utilise knowledge graphs for your specific needs. Whether you're looking to enhance search capabilities, improve data analysis, or unlock new insights, Ontotext can help you leverage the power of connected information.In this episode of the EM360 Podcast, George Firican, Founder of LightsOnData, speaks to Sumit Pal, Strategic Technology Director at Ontotext, to discuss: Knowledge Graphs Use Cases Ontotext GraphDBIntegration of AI Industry best practices