Almost every company has run an AI pilot by now. Far fewer have AI running reliably in production, touching real customers and real revenue. The gap between a slick demo and a dependable system is where most initiatives quietly stall.
The good news: the reasons pilots fail are predictable, and so are the practices that get you past them. Here's the playbook we use with clients.
1. Start with a business outcome, not a model
The most common mistake is starting with the technology — “let's use GenAI” — instead of a problem worth solving. Anchor every AI project to a specific, measurable outcome: fewer support tickets, faster document processing, higher conversion. If you can't name the metric it will move, it's not ready to build.
2. Get your data foundation right
AI is only as good as the data it stands on. Before building, make sure the data your use case depends on is accessible, reasonably clean, and governed. You rarely need perfect data — but you do need to know where it lives, who owns it, and whether you're allowed to use it the way you intend.
3. Design for security and governance from day one
The fastest way to kill an AI project is a security or privacy surprise late in the process. Decide early how sensitive data is handled, where models run, what's logged, and who can access what. Building these guardrails in from the start is far cheaper than retrofitting them — and it's what earns trust from leadership and customers.
4. Build feedback loops and real evaluation
A demo that works on ten examples tells you almost nothing about production. You need a way to measure quality on real inputs, catch regressions, and improve over time. That means evaluation datasets, human review where it matters, and monitoring once you're live.
5. Plan for operations and change management
Shipping the model is the halfway point. Someone has to run it, watch it, and update it — and, just as importantly, your people have to adopt it. Budget for MLOps and for the change management that helps teams trust and actually use what you've built.
Where to start
Pick one high-value, low-risk use case. Prove it end to end — outcome, data, security, evaluation, operations — and let that success fund the next. That's how AI goes from an interesting experiment to a durable advantage.