AI for real work · 5 of 5 · Integrate
From experiment to everyday work: integrating an AI solution
When a test produces valuable results, the improvement begins to feel close. Something already works under certain conditions, and there is a reason to keep investing. We now need to understand how to give it a stable place in the work.
The question broadens. Alongside technical capability, we must consider how it will be used, the connections it needs and the people who will sustain it. Development must bring those conditions together so that the value observed can be repeated.
That is the purpose of integration: turning what we have learned into a capability available to the business.
Develop from what we have learned
The proof of concept and pilot leave us with more than an initial solution. They reveal requirements, difficulties and limits that should guide development.
Before expanding, we should revisit what was validated and within which scope. That evidence helps define an initial version for everyday use. We can specify the work it will cover, the information it will need and the result it should deliver.
Decisions about the experience of using it then emerge. People need to understand what they can ask, what is happening and how to recognise when a result is ready for review or use. They must also be able to identify an outstanding requirement and know what to do about it.
Development gives these decisions a practical form. Part of the effort goes into the AI capability; another part goes into the interface, rules and connections that allow people to use it with judgement. The quality of the solution depends on how these parts work together.
Find its place in the process
An improvement enters work that already involves people, information and tools. To make it useful, we must understand where it belongs in that sequence and what changes because of its participation.
We need agreement on what starts the intervention, which information it receives and what should happen to its result. If it interacts with other systems, the meaning of the data, access permissions and conditions of use must be preserved.
These decisions directly affect everyday effort. A capability that requires people to repeat tasks or transfer information unnecessarily may lose some of the value it demonstrated during testing. Integration must consider the complete process.
The process also needs to accommodate interruptions. We need to know how work is preserved, who handles a problem and how people can continue if assistance is unavailable. Designing that continuity helps them use the solution with confidence.
Make its use a shared practice
Introducing a tool creates a need to learn among the people who will use it. They need to understand its contribution, its limits and how it relates to their own responsibility for the work.
That understanding develops through practice. Training should allow people to work through the process, review results and resolve questions that arise during use. It should also include practising when to consult someone else or return to the usual way of working.
Listening to users at this stage reveals friction that can still be addressed. Their participation provides information about the solution’s actual usefulness and the effort needed to adopt it.
Responsibilities must be assigned at the same time. The business needs someone who makes decisions about the process, someone who maintains information and someone who handles technical operation. In small teams, these roles may belong to the same person. What matters is knowing who can act when needed.
Adoption begins to take hold when those responsibilities and practices are clear to the team. Each person understands what they can do and how to help the whole process work.
Maintain the conditions that produce the result
Work changes. Information is updated, needs emerge and tools are modified. An integrated solution must keep pace with that development.
It is useful to retain a record of the version in use and the conditions under which it was evaluated. When something important changes, we can check whether quality remains acceptable and whether people can continue using the result in the same way.
Review should also cover sources and permissions. Information may become outdated, and responsibilities may change hands. Maintaining a solution includes attending to these relationships.
The NIST AI Risk Management Framework includes evaluation throughout the life cycle of a system. In business practice, that means reserving capacity to review changes, address problems and maintain the conditions of use.
Understanding that effort allows us to estimate the cost of sustaining the improvement. Technical services are part of the calculation, alongside people’s time and the work of keeping things current.
Recognise whether the value holds
After launch, we need to return to the question that started the project: what did we want to improve, and how would we know we had achieved it?
We can now observe everyday use. We need to understand whether the solution becomes part of the work, the quality it produces, the effort it requires and its influence on the intended result. These dimensions complement one another. Recorded activity helps explain use, while measuring the process allows us to judge its value.
When results differ from expectations, we should investigate the causes. We may find a technical problem, a changed condition or a need for learning. Monitoring should help us recognise those differences and decide what to adjust.
It may also show that the improvement holds and that expanding its scope is worthwhile. Any expansion needs its own assessment. Previous learning gives us a better basis for deciding which conditions carry over and which need to be checked again.
A capability the business can make its own
The process began with a purpose and an opportunity. We then chose a priority, prepared the conditions and sought evidence. Integration brings that work together so that an improvement can become part of everyday activity.
Along the way, more than technical capability grows. The business also learns about its own processes and how to improve them. That knowledge remains available for future decisions.
AI gains value when people can use it to achieve their purposes. Starting with a specific task gives that potential a direction and makes it possible to begin building.