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From curious to building, discover Chris’ AI journey over the past year.
When did you join Access and what drew you to the role?
I’ve been at Access for over 20 years in several different roles.
Can you tell us a bit about your role as Director - Professional Services Transformation, People & Payroll at Access? What does a typical day look like for you?
It’s a mix of meetings with colleagues or clients and thinking about how we can move the business forward using new technology.
How did you start your AI journey?
A year ago, I could tell you what AI was. I could not tell you what it could do for me.
I was curious, though. That turned out to be the most important qualification I had.
I started experimenting. Small stuff, see what sticks. And I hit walls. Plenty of them. AI would give me something half-baked, or confidently wrong, or just not solve what I actually needed. The temptation was to write it off. “It can’t do this.” But I kept asking a different question: what’s stopping it?
That question changed everything.
When AI couldn’t remember context, I learned how to give it memory. When it hallucinated, I learned the difference between probabilistic reasoning and deterministic processing and how to use external tools to keep things reliable. When a task was too complex for a single prompt, I learned about multi-agent workflows: breaking problems into discrete steps, with different agents handling each one. When I needed something repeatable, I stopped asking AI to do the task and started asking AI to build the tool.
That last shift, from AI-does-the-task to AI-builds-the-tool, is probably the single biggest leap in how I think about this technology.
Along the way I picked up n8n, got deep into workflow automation, learned how to build with Access Evo, and started writing code with AI assistance for things I would never have attempted from scratch. Tools I’ve built are now running in production, saving hours every week across the team. ConfigBot. The PMO monitor. The Clari call analyser. An employee change request workflow. HTML tools that anyone can open in a browser with no install required.
None of that existed a year ago.
The part I’m most proud of isn’t the tools though. It’s the people. I run a weekly internal training series (we call it Fri-AI-Day, and yes, we’re past week 19). Watching colleagues go from sceptical to genuinely experimenting, from experimenting to building, that’s where the real leverage is. One person understanding AI well is interesting. A team of people who can each spot an opportunity, design a solution, and build it? That’s compounding.
Can you share with us some learnings about your experience with AI so far?
Here’s what I’ve learnt about learning in this space: the technical knowledge matters, but it’s secondary. The architecture, the tooling, the APIs, you can learn all of that as you go. What you can’t substitute for is the mindset. Staying curious when something doesn’t work. Asking why instead of assuming a ceiling. Being willing to look a bit silly while you figure something out.
A growth mindset is not a soft skill in AI. It’s a prerequisite.
I’m not done. If anything, the pace is accelerating. Agentic workflows, MCP, platform-native AI, there’s a lot still ahead. But the gap between “I don’t know how to do this” and “I can figure this out” has collapsed dramatically over the last twelve months.
What advice would you give to someone who wants to get started with AI?
If you’re at the start of this journey, the honest advice is simple: stay curious, keep your assumptions loose and don’t mistake a wall for a ceiling.