AI enablement and adoption · Melbourne
Pierre Legrand
Most AI use is one person helping themselves with one task, and it never leaves their desk. A few people cross into building something others rely on. The usual response is to applaud them. Deploying them is how a small function reaches an organisation it could never physically get to.
I build the AI experiences people actually use, and the standards that keep them working after launch. In practice that means sitting with a team, finding the friction in the job they really do, building the thing that fixes it, and staying until somebody else owns it.
Two rules govern everything I ship. An agent has to live inside the tool people already have open, because the moment somebody stops, opens a chatbot somewhere else and pastes the answer back, adoption quietly dies. And anything only I can run is a dependency rather than a solution, so nothing is finished until it belongs to somebody else.
A third rule covers the arithmetic. Models are unreliable with numbers, so the model never produces a number that has to be correct. A deterministic engine does that, with an audit trail behind it.
AI in most organisations is a twelve month conversation about which tools, which licences, which training. The one that matters sits three to five years out, and it is about shape.
Most structures were designed for a world where every step needed a person. Handoffs between teams. A layer that checks the layer below. Roles that exist to carry information between functions that could not talk to each other. That design was correct when it was drawn, and it gets less correct every quarter.
Put agents into that shape and you get a faster version of the same shape. Automation creates capacity, and capacity is only worth something if you point it somewhere.
The useful questions are structural and slow. What does a role look like when the first pass is already done, and done consistently? Where does review belong once you are checking exceptions? Which team boundaries exist only because a handoff used to need a person? Who owns an agent when it is running, and what happens when it is wrong?
You can buy a tool in a quarter. You cannot redraw an organisation in one, which is why the organisations still choosing tools are already behind.
A manual review replaced by an agentic workflow: a Copilot Studio agent orchestrated through Power Automate, scoring delivery items against 37 quality criteria, writing to SharePoint and surfacing through custom Power BI. The first production run assessed 921 items. It runs as an ongoing diagnostic now, giving a baseline and a trend, and it was used to find where the work breakdown taxonomy was failing so coaching could be aimed at the right places.
The first version was a full application I wrote against the Jira REST API. It worked, and Architecture ruled it out because nobody else could maintain it. I rebuilt it on tools the business already licenses. Two other people own it today.
A commercial platform I designed and built end to end with Claude's agentic coding tools, guiding Australian families from a terminal diagnosis through to settling an estate. Next.js on Vercel with Supabase, Clerk and Stripe. I own the commercial model, the pricing, the vendor relationships and the responsible-AI guardrails: hard checks for privacy, cultural safety and clinical accuracy that fire before anything publishes, on a platform holding wills, medication records and final wishes.
Independently assessed and certified to CyberCert Gold. Built on a context architecture I designed, where an orchestrator reads a task and routes it to specialist workers with defined trigger conditions. Have a look.
Built in two days on Copilot Studio, Power Automate, SharePoint and Zendesk so an advisor could give an accurate answer about future premiums while still on the call. The agent ran the conversation and a deterministic engine produced the number, with an audit trail, because a premium has to be right every time.
It was endorsed on demonstration and then stopped above me in favour of the manual process. The lesson I took from it: a working prototype is a persuasion tool and carries no mandate of its own. My first question now is who has to say yes, and what would make it easy for them.
Invited to speak on a Leading AI panel at a leadership summit, to a room of 450 leaders, on moving an organisation from individual experimentation to capability it can keep, and on where human judgement has to stay.
Two decades across insurance, superannuation and financial services, beginning as an analyst programmer, with six of those years consulting inside client environments. People leadership along the way, including a team of 13 analysts, an acting Head of Digital Delivery post, and five years running my own company.
I read organisations vertically, at enterprise, portfolio and team level, because the people signing the cheques are rarely the people doing the work. Production work runs on the enterprise stack: Microsoft 365, Copilot Studio, Power Platform, Atlassian Rovo and Jira. I build with Claude and Claude Code every day, in Node.js, React and Python.