Succeeding at your first agentic AI deployment: 5 pitfalls to avoid
Lessons from the field: the mistakes that sink enterprise AI agent projects, and how to sidestep them from the framing stage.
Agentic AI projects that fail rarely do so for technical reasons. Here are the five pitfalls we see most often.
1. Aiming too wide
"An agent that handles all of customer service" is an eighteen-month project. "An agent that processes standard refund requests" is a six-week project. Start narrow.
2. Neglecting input data
An agent is only as good as what it reads. If your procedures only exist in your teams' heads, the first step is not the agent: it is formalization. It takes two weeks and benefits everyone, AI or not.
3. Binary autonomy
Neither "a human validates everything" (no gain), nor "the agent decides everything" (no trust). Define thresholds: the agent acts alone below an amount, proposes above it, escalates when in doubt.
4. Forgetting to measure
Without a baseline before deployment (processing time, error rate, satisfaction), you cannot prove the gain — and the project dies at the first shift in budget priorities.
5. Leaving the project to IT alone
Agentic AI is as much an organizational project as a technical one. The winning pair: a business owner of the process + a technical lead. External support acts as a catalyst, not a substitute.
Preparing a first deployment? Let's talk: a 45-minute assessment is usually enough to identify the right pilot process.