How to Scale Successful AI Pilots in Government

DEPLOY AI AGENTS THE RIGHT WAY Over the past few years, I’ve watched teams and leaders race to deploy AI agents—chasing the latest LLM tools, spinning up proof-of-concepts, and hoping automation would “just work.” I made a lot of those mistakes myself. Looking back, I wish someone had handed me a blunt list of what actually matters when deploying AI agents in the real world. Here’s what I learned the hard way: If you start with technology instead of a real business problem, you’re setting yourself up for wasted effort. Everyone gets excited by the shiny stuff, but you only get real impact (and real wins) by picking a painful, high-value business problem and focusing relentlessly on solving that. Don’t trust your data “as-is.” No matter how confident you are, your data will need more cleaning, validation, and governance than you expect. It’s boring work, but skipping it will cost you months in rework and lost credibility. Involve stakeholders early—don’t treat AI agent deployment as a tech project only. If the business, end users, or compliance teams aren’t bought in, even the best agents will fail to gain traction. Automate what you can (retraining, monitoring, feedback), but never abdicate responsibility. “Set and forget” is a myth. Humans need to stay in the loop, especially when things go sideways or when continuous learning is needed. Version everything—models, data, code. It sounds trivial until something breaks and you can’t roll back or audit what changed. Align every metric to a business outcome. Technical wins are nice, but nobody outside the data team cares about incremental accuracy unless it moves the business needle—customer satisfaction, cost savings, regulatory wins. Document as you go. New teams will join, people will move on, and “tribal knowledge” fades fast. Documentation is how you scale and sustain real progress. Normalize sharing failures. It’s uncomfortable, but it’s how teams learn and avoid repeating mistakes. The fastest learning happens when people are open about what didn’t work. Watch out for risk and ethics. Bias, compliance, and privacy issues will creep in if you don’t proactively manage them. The cost of ignoring this is much higher down the road. Final point: Deploying AI agents isn’t “one and done.” Business needs and data drift, so build feedback and improvement into the process from day one. If you’re about to launch your first (or tenth) AI agent, keep it simple: Solve a real business pain. Get your data in shape. Keep the people loop tight. Share both your wins and your scars. #AILeadership #AIAgents #DigitalTransformation #EnterpriseAI #BusinessStrategy

“We spent millions on AI and have nothing to show for it.” That’s what the CEO told me. And they weren’t wrong… The results were underwhelming. Deadlines kept slipping. The board was asking tough questions. But instead of agreeing to pull the plug, I said something that surprised them: "Before you give up, let's take three steps back." I emphasized that AI can deliver exceptional outcomes, but only when you're rooted in what's actually achievable. Here's what I mean: STEP ONE: Know exactly what you're dealing with - The current state of your data quality - How prepared your infrastructure really is - What capabilities your team actually possesses STEP TWO: Balance your aspirational AI goals (what could be possible) with the reality of what you can deliver today (what is practical). Success in AI comes from marrying honest evaluation with executable strategy. So that’s exactly what we did: we stepped back, rethought the goal, and simplified the approach. We kept their ambitious vision but completely changed the execution: → Redefined success metrics to be measurable and achievable. → Broke their "moonshot" goal into 6 smaller milestones. → Started with one use case in a smaller capacity that could demonstrate clear ROI Six weeks later, they had their first AI success story. Not the revolutionary transformation they originally envisioned, but something better: proof that AI could work in their environment. - That early win gave the team confidence.  - The board renewed their commitment.  - And now they're scaling systematically. So the lesson here isn't about scaling back your vision. It's about finding the right path forward. Sometimes that means starting smaller to eventually go bigger. Big AI transformations don't happen overnight. They happen when you break them into manageable pieces and prove value incrementally. Start practical. Then scale ambitious. Have you ever had to shift from moonshot thinking to practical execution in AI? How did it go?