Two Days as Chair at the Tech Show: What I Learned, What Surprised Me, and What You Need to Take Back to Your Office

I’ve spoken at a lot of events. But chairing a two-day stage is a different experience entirely, and the AI and Data Innovation stage at the Tech Show reminded me exactly why I do this work.Over two days, I had the privilege of guiding conversations with leaders, practitioners, and thinkers who are doing the real, unglamorous, necessary work of making AI actually function inside organisations.

Here’s what stayed with me.

Day One: The Gap Between Excitement and Value

I opened the stage with something I say often in my consulting work, because it keeps being true: the companies succeeding with AI aren’t necessarily the ones using the most advanced tools. They’re the ones investing in clarity, strategy, and skills. Technology moves fast, but culture and people determine whether AI becomes a competitive advantage or a liability.
That framing set the tone for the day, and the speakers delivered on it.
Miguel Rodrigues shared his journey of building technical teams from a human-centric perspective, and several things he said gave me some food for thought. Diversity, he argued, isn’t just a metric, it directly impacts product bias. Different backgrounds provide different lenses. A homogenous team will miss what a diverse one catches. And the most important engineering skill today isn’t raw coding speed, it’s domain expertise deep enough to audit what the AI produces. If you don’t know what right looks like, you cannot catch when the AI is wrong.
Three broader themes emerged consistently across the Day One sessions:

• The first was governance, not as a bureaucratic checkbox, but as a genuine enabler. From AI agents to healthcare solutions to bias reduction in datasets, every conversation kept coming back to the same point: without governance, you don’t have AI adoption, you have AI risk.
• The second was the human-in-the-loop. We talk about this concept constantly in the industry, but what struck me on Day One was how the speakers grounded it in practice. It’s not enough to say humans should review AI outputs. You have to design systems, cultures, and workflows that actually make that happen, and that keep humans sharp enough to do it well.
• The third was clean data. Unglamorous, unsexy, and absolutely non-negotiable. If your data is a mess, your AI will be a mess. Full stop.

  My closing thought to the room that evening was this: when you go back to your offices on Monday, don’t just ask “what tool should we buy next?” Ask: “Do we have the culture to support it? Is our data ready? And are we being responsible enough to sustain it?”

The Tech Show

Day Two: From What AI Can Do, to What AI Actually Changes

If Day One was about foundations, Day Two was about alignment, the word I kept coming back to all afternoon.
AI is not about model sophistication. It’s about alignment. Between strategy and execution. Between governance and innovation and between technology and people.
The sessions brought this to life in ways I found genuinely energising. The NHS case study was particularly striking, featuring Sandra Nwobi, Data Insight Officer at South East London Integrated Care System, and Sonali Kinra, Deputy Medical Director at North Central London Integrated Care Board. The idea they brought to life, “AI Knowledge Intersections,” where staff from different departments share real-time learnings so that tribal knowledge becomes organisational intelligence, is not a technical solution. That’s a cultural one.
The panel on fraud, featuring Olawale Oladoja, Subject Matter Expert in Fraud Preventions at Barclays, brought a sharp reminder that the stakes of getting this wrong are very real. Fraudsters are already using AI, deepfake voices, synthetic identities, and the response has to be equally sophisticated, and equally human. As the panel put it, we need a united front. If a fraudster fails at one bank, the next bank should already be alerted. That only works if organisations share data, share intelligence, and trust each other enough to act collectively.

The Takeaway I’m Carrying With Me

If there is one thing I took from these two days, and I hope you did too, it’s this:

AI is not a technical project. It is a trust project.

Whether you’re building an engineering team, helping clinicians adopt a diagnostic tool, or protecting a customer’s bank account in milliseconds, the technology only works if people trust the data, trust the process, and trust the outcome.
Mary Avelin said it best during one of the panels, when asked how we raise awareness about bias and governance: hold onto your data and your facts, and share that knowledge with the people around you.
That’s it. That’s the whole thing.
So here’s my ask: don’t leave these conversations at the event. Take them back to your colleagues, your leadership teams, your clients. Talk about clean data. Talk about governance. Talk about keeping humans genuinely in the loop, not as a tagline, but as a practice.
These two days reminded me that the room always knows more than any one speaker. The real value of events like this isn’t the stage, it’s what happens when the right people are in the same space, asking the right questions together.
Thank you to every speaker who showed up with honesty and insight. And thank you to everyone in the audience who engaged, challenged, and made both days worth every minute.