Beyond AI Pilots: What Valiantys and Mercedes Can Teach Enterprise Leaders
Release Date: 07/24/2026
Tech Talks Daily
Why do so many enterprise AI initiatives begin with impressive demonstrations but struggle to produce measurable business value? In this episode of Tech Talks Daily, I speak with Dom Selvon, CTO and value partner at Valiance, about enterprise AI ROI, outcome-based consulting, build versus buy decisions, proprietary data, ontologies, and governance. Valiance is an AI-native consultancy that charges against client outcomes rather than hours worked. Dom explains why his “value partner” title is deliberate. The company begins by identifying the financial or operational result a client wants...
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What does a modern contact center need to deliver when the person reaching out may already feel anxious, embarrassed, and unsure where to turn? In this episode of Tech Talks Daily, I speak with Chris Lovell, service delivery lead for StepChange Debt Charity’s contact center and product owner for its Genesys Cloud platform. StepChange supports hundreds of thousands of people facing financial hardship each year. Chris explains that approximately 40% of its clients receive Universal Credit, over 60% rent their homes, and many are dealing with an additional vulnerability alongside debt. That...
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What does it really take to build an AI-ready enterprise when your data is fragmented, teams operate in silos, and years of technology decisions have created complexity that no large language model can magically fix? In this episode of Tech Talks Daily, I speak with Raymon Ohmori, Senior Principal Software Engineer at Valiantys, and Jiecheng Dong, Senior Software Engineer at Valiantys, about the work that goes into enterprise AI adoption and why successful AI transformation begins long before companies deploy agents, copilots, or autonomous workflows. Using Valiantys' work with Mercedes as a...
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What happens when AI moves beyond writing emails and summarizing meetings and starts influencing who gets hired, promoted, and paid? In this episode of Tech Talks Daily, I speak with David Lloyd, Chief AI Officer at Dayforce, about why HR is becoming one of the highest-stakes environments for artificial intelligence and how companies can introduce AI while protecting employee data, maintaining human accountability, and preparing for growing regulatory scrutiny. HR systems contain some of the most sensitive information companies hold, from salaries and performance records to benefits and...
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What separates an impressive agentic AI demonstration from a deployment that produces measurable business value across an entire company? In this episode, I speak with Frank Theisen, Vice President of IBM Technology across Europe, the Middle East and Africa, about how businesses can move AI agents beyond isolated pilots and into the processes where work actually happens. Frank believes the conversation has changed considerably. Most large companies are deploying some form of AI, yet many still struggle to demonstrate a significant commercial return. The difference comes from connecting AI with...
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How quickly should an AI investment begin producing measurable business results? In this episode, I speak with Monica Kumar, Executive Vice President and Chief Marketing Officer at Extreme Networks, about the growing pressure on technology leaders to prove that AI investments are producing financial and operational value. The conversation draws on Extreme Networks’ State of AI for Networking 2026 report, based on a global survey of 200 C-level executives and vice presidents of IT. The findings suggest that enterprise AI has entered a far less forgiving phase. Experimentation continues, but...
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Could the disaster recovery plan designed to protect your company make a ransomware incident even worse? In this episode, I speak with Darren Thomson, Vice President and Chief Technology Officer for EMEA at Commvault, about Resilience Operations, commonly known as ResOps, and why cyber recovery now requires security, infrastructure, identity and data teams to work from one coordinated plan. Darren argues that many companies are accepting a difficult reality. Even with considerable investment in prevention and detection, a breach may eventually succeed. That does not make cybersecurity controls...
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Why can we track a meal traveling across town almost minute by minute, yet an international parcel can seemingly disappear between checkout and delivery? In this episode, I speak with Dieter Van Putte from Crossborder Global, a division of Bnode that provides global commerce and logistics services. Dieter oversees technology and operations across Landmark Global and Apple Express, giving him a close view of what happens when retailers attempt to sell and deliver products across international markets. Dieter explains why cross border delivery remains so difficult to track despite years of...
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Do you need to understand code before you can build a technology company or lead a team of developers? Five years after our previous conversation, I welcome Sophia Matveeva back to the podcast. Sophia is the founder of Tech for Non-Techies, where she helps founders and business professionals understand how technology products are created, tested, managed and turned into commercial ventures. A great deal has changed since we last spoke. Generative AI tools can now turn a written description into a working prototype within hours. For someone who has spent years believing a lack of coding...
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What happens when an AI agent begins influencing business decisions without fully understanding the systems, processes and dependencies behind them? In this episode, I speak with Bert van der Zwan, CEO of Bizzdesign, about the gap between enterprise AI expectations and the results many companies are seeing in practice. Bert has spent more than 25 years in software and SaaS leadership, including executive roles at Webex, Bynder, Twinfield and Unit4. Bert offers a candid assessment of the current AI market. He believes AI will have a lasting effect on businesses and society, but argues that...
info_outlineWhat does it really take to build an AI-ready enterprise when your data is fragmented, teams operate in silos, and years of technology decisions have created complexity that no large language model can magically fix?
In this episode of Tech Talks Daily, I speak with Raymon Ohmori, Senior Principal Software Engineer at Valiantys, and Jiecheng Dong, Senior Software Engineer at Valiantys, about the work that goes into enterprise AI adoption and why successful AI transformation begins long before companies deploy agents, copilots, or autonomous workflows.
Using Valiantys' work with Mercedes as a case study, Raymon and Jiecheng explain how modernizing software delivery and connecting data across teams can create the foundation required for AI systems to deliver meaningful business value. We discuss why fragmented data, organizational silos, poor governance, and unclear business problems continue to prevent many companies from moving beyond AI pilots.
The conversation examines what it means to become AI-ready in practice. Jiecheng explains why enterprises need performant, structured, and queryable data rather than simply feeding huge volumes of information into large language models. Raymon shares why businesses must begin with real problems, stakeholder needs, and clearly defined outcomes to justify the cost of AI and successfully move projects into production.
We also discuss the growing role of agentic AI and autonomous workflows in software engineering. How should engineering teams prepare AI agents to become active participants in software development? What tools, context, permissions, governance, and observability do these systems need to operate effectively? And why might treating an AI agent more like a new colleague than another software tool help teams think differently about deployment?
Raymon and Jiecheng also share their perspectives on AI-assisted software development and developer productivity. As AI becomes increasingly capable of writing code, the role of the software engineer is shifting toward architecture, system design, requirements gathering, trade-off evaluation, and translating business needs into technical specifications. We also discuss the challenge facing junior developers and why companies still need to create pathways for new engineering talent.
Finally, we examine the practical steps CIOs, CTOs, and engineering leaders can take today to build more connected, AI-enabled enterprises. From improving data ownership and governance to identifying costly problems that AI can realistically solve, this conversation offers a practical guide for companies trying to move from AI experimentation to production systems that deliver measurable value.
Where is your company on its AI journey? Are fragmented data, organizational silos, and unclear business problems preventing your AI projects from reaching production, or have you found effective ways to turn experimentation into measurable results? Share your thoughts with me.