Many organisations now have an AI pilot or two behind them. A smaller number have one running in daily operations. The gap between those two groups is rarely about model quality. The model in the pilot was usually good enough. What was missing was everything around it.
Here are the reasons we see most often, and what to do about each.
1. The pilot used clean data, production has real data
Pilots are often built on a tidy extract: a few hundred documents, a cleaned spreadsheet, a curated set of tickets. Production data is messier. Fields are missing, formats change, documents contradict each other and access rules apply.
What to do: test on real data as early as possible, including the awkward cases. If the data needs work before the use case can succeed, it is better to learn that in the first conversations than after the demo.
2. Nobody agreed what "good enough" means
A pilot that "looks impressive" has no clear bar to pass. When it is time to decide whether to launch, the discussion turns into opinions.
What to do: agree the measure of success before the build starts. That usually means two kinds of measure: a business metric (resolution time, cost per case, accuracy, stock-outs) and operational ones (task success rate, escalation rate, cost and latency). Then build an evaluation suite of real examples that the system must pass before go-live.
3. Security and compliance were left until the end
A pilot that ran outside the company's environment, on a personal account or a vendor's sandbox, has to be rebuilt to pass a security review. That rebuild is often where momentum dies.
What to do: involve your security team from the first design review. Deploy into your own cloud tenancy, in the region you need, with access rules and data retention agreed up front.
4. It lived in a separate tool
A chat window that sits outside the tools people already use becomes one more tab to remember. Usage drops soon after launch.
What to do: put the AI where the work happens: inside the CRM, the help desk, the portal or the internal app. That often means the software engineering around the AI matters as much as the AI itself.
5. Nobody owned it after the demo
Pilots are often run by an innovation team or a vendor. When it is time to operate the system, there is no owner for monitoring, incidents, updates or cost.
What to do: decide early who will run the system, and plan for it. A production AI system needs monitoring for quality, drift, cost and latency, a way to roll back, and someone accountable for it.
6. The use case was chosen because it was interesting
Some pilots are chosen because they make a good demo, not because they matter to the business. When budgets are reviewed, they are the first to go.
What to do: rank use cases on value, feasibility, risk and time to impact before choosing one. Pick something that matters enough to be worth the effort of getting it live.
A different way to run a pilot
The pattern that works is simple to describe: treat the pilot as the first phase of a production system rather than a separate experiment. Use real data, deploy in your environment, agree the bar for go-live, build the interface people will use, and decide who owns it. Then run it in shadow mode alongside your team before it acts on its own.
It takes a little more effort at the start. It saves the much larger cost of building the same thing twice.
If you have a pilot that stalled, or one you are about to start, we are happy to take a look. Book a discovery call and bring the details.