In Part 1 we covered why to adopt AI now and what you can build. This part covers what happens after you decide to move.
A good AI agency changes what your company can do:
- What if your recruiting company could put great candidates in front of their clients in hours rather than weeks
- What if your insurance company could underwrite policies 10x faster than your competitors?
- What if your property management could schedule repairs in minutes and under budget, winning more buildings with superior service?
Each one is a speed advantage big enough to decide who wins the market.
So why do most AI projects stall? An MIT study in 2025 found that 95% of enterprise AI pilots fail to deliver measurable results. The split comes down to approach. Teams that go it alone rarely ship, while companies that partner with specialized vendors succeed 2–3x more often. Those are the companies pulling ahead of their competitors right now. Scoping and execution decide the outcome far more than the technology does, and that is the gap this article closes.
The failures are predictable, which means they are avoidable. Two things separate the winners: picking the right problem, and holding up your end as the customer.
How Do You Pick the Right Problem for an AI Agent?
Pick a small, well-defined problem where the agent's decisions are clear, the work happens often, and a mistake is cheap to catch. Your first project should ship a working agent, not a demo.
Score any task on three questions:
- How often does it happen? More often is better.
- How hard is the decision? Bounded and rule-based is better.
- What does a wrong answer cost? Cheap to catch is better.
The best first projects run at high volume, follow clear rules, and fail cheaply. For example:
- Good: an agent that recommends policy criteria for an insurance plan. Known inputs, defined rules, easy to review.
- Bad: an agent that writes your employees' emails. Anything can arrive, and the right answer depends on context the agent lacks.
One more piece of advice: aim higher than cost savings. The best projects change what your business can do. For a recruiter that is time-to-first-candidate. For an insurer that is quote speed. Target the workflow where being faster than rivals wins customers.
How Do You Define Success for an AI Project?
Define success as one measurable formula before work begins: a specific metric, improved by a specific amount, at less than a specific recurring cost. If you cannot write that sentence, the project is not ready to start.
- Vague: "improve efficiency." Not a target.
- Real: "cut quote time from two days to under ten minutes, at less than $2 per quote."
That formula tells the agency what to build, tells you when the project is done, and gives you an ROI you can defend to your board.
Protect the definition with two commercial terms:
- Milestone-based pricing. Pay for delivered, working results. This keeps overruns off your books.
- A fixed timeline and total cost up front. The best AI agencies quote a fixed price against milestones, ship working software instead of proofs of concept, and stay on the hook for the number.
What Do You Need to Bring to the Engagement?
The agency supplies the AI expertise. You supply three things no agency can substitute for:
- An accountable executive sponsor. One senior leader owns the outcome and can approve access and workflow changes.
- Clean access to systems and people. The agent needs your CRM, email, and documents, plus regular time with real users to test against. Gartner predicts organizations will abandon 60% of AI projects that lack AI-ready data through 2026.
- High expectations, held weekly. A good agency ships early and often and talks to you constantly. If your agency goes quiet, the project is drifting.
What if my team rebels against AI?
If your team fears being replaced, address it directly. Work with your agency on a clear message about what the agent will and will not do, so your people help build it instead of working around it.
What Should You Take Away?
Working with an AI agency comes down to a short list:
- Pick a small, bounded, high-volume problem where mistakes are cheap.
- Define success as one formula in dollars or hours before starting.
- Price on milestones so you pay for results, not effort.
- Bring one accountable executive, clean access, and high expectations.
Do these and you land in the small minority of AI projects that reach production and pay off. Skip them and no agency can save the engagement.
In Part 3 we turn to the other side: how to evaluate an AI agency, and how to tell whether the team in front of you actually knows how to build agents that survive production.