Why your AI pilot still isn't in production
A successful AI pilot shows that an application can work, but getting it into everyday use takes more. This article explains where that transition stalls and what you need to make it part of the job.
Ownership matters long after the demo ends
The demo looks promising: the application summarises documents or drafts useful replies. Afterwards, everyday work carries on as before. Someone championed the pilot, but nobody is responsible for its continued use. The innovation team considers it finished, IT has no agreement to support it, and the intended users have other priorities. A technically successful trial can stall this way without anyone actually deciding to stop it. There is simply no one responsible for organising the next step and seeing it through.
Start with a work process and an owner who can make decisions about how it runs. That might be the person responsible for handling incoming support requests. Agree on the problem you want to solve, who will use the application and who decides whether its output is good enough. Involve whoever will maintain it, too. This gives the pilot a destination: a specific change to work that already has someone accountable for it.
Your data needs to work every day
It is easy to start a pilot with a carefully prepared export. Duplicate records have been removed, missing details filled in and awkward exceptions set aside. That helps you explore whether an idea works. Everyday use also brings outdated documents, incomplete requests and conflicting information. The source keeps changing, too. Unless someone has worked out how fresh information reaches the application and which version takes precedence, the result still depends on manual preparation. That effort is easy to overlook when watching a polished demo.
Connect to the sources you intend to use while the trial is still underway. Include routine cases and messy exceptions. Agree on who updates the information and how the application responds when data is missing or a connection fails. Sometimes the appropriate response is to ask a colleague to investigate. This helps you identify which problems the application can handle and which need to be fixed at the source.
Human review needs a place in the workflow
Generating a draft reply is only part of the task. Someone needs to find it, review it, edit it and send it. If that means opening a separate tool and copying information across, you may be adding work. Decide exactly where the application helps and what happens next. For a support request, the suggested reply could appear beside the original message, together with its sources. The person handling the request can then check whether it addresses the question and respects the agreements with that customer.
Make that review an explicit part of the process. Agree on who checks the output, what they look for and when a suggestion should be rejected or referred to someone else. Keep a way to work without AI if the application fails. Collect corrections and discuss recurring errors so you can make targeted improvements. Include review time when assessing the benefit: a quickly generated draft is useful when it improves the overall handling of the request.
- 01
An owner with a mandate
decides on the rollout
- 02
Connected to the source
messy cases included
- 03
A fixed review moment
who looks, and at what
- 04
Support and fallback
who steps in, and how back
Even a small release needs ongoing support
An application used every day needs maintenance. Sources change, connections fail, and a model update can produce different answers. Before putting it into use, agree on who handles incidents, who can make changes and how you check that it still works properly. Keep enough information to investigate errors, with appropriate access controls for those records. Be clear about who can temporarily switch the application off, too. These arrangements belong in the handover, even when only a small group of people will use the first version.
Limit the first release to one clearly defined task, such as drafting replies for a single type of support request. Take it through the complete process: retrieve current information, display a suggestion, record the review and allow manual handling when needed. Then assess quality and effort with the process owner. Expand once you understand what works and what needs attention. The application can grow from everyday use, with support arrangements that grow alongside it.
From a successful trial to everyday use
Moving into production starts with the work around the application. Who owns it, where does its information come from, who reviews the output and who steps in when something goes wrong? Address those questions during the pilot. A small application that supports a clearly defined task gives you a practical basis for further development. It also reveals what everyday use actually requires.
Schedule an introduction