AI project in business: 8 questions to ask before you start

An AI project that starts with choosing a tool often starts on the wrong foot. Before comparing technical solutions, eight straightforward questions can help establish whether the problem is genuinely suited to AI, whether the necessary data exists, and whether the organisation is ready to test a solution under the right conditions.

What problem are you actually trying to solve?

An answer such as "save time" or "be more efficient" is not enough. You need to be able to name the exact task, who performs it today, and how much time it takes. You also need to understand why the task is a problem: high volume, repetitive work, difficulty finding information, risk of errors or another business constraint. Without that level of precision, it becomes difficult to determine whether the project has actually succeeded once it is complete.

Is the problem actually suited to AI?

Not every time-consuming task requires artificial intelligence. Some can be solved more simply through conventional automation, better data structuring or a change to the existing process. AI becomes particularly useful when the work involves processing documents, searching for information, classifying content or interpreting language. Before choosing a technology, you therefore need to check that AI is actually appropriate for the problem you have identified.

What data and documents already exist?

An AI system needs material to understand the organisation's rules, documents and specific vocabulary. If relevant documents, emails, files or decision histories already exist in an accessible form, the project starts from a solid base. If they are scattered, difficult to retrieve or stored in poorly usable formats, that work of gathering and structuring them needs to be planned first, before AI even enters the discussion.

Who will validate the results?

An AI project without someone assigned to check the results starts without a real safeguard. Identify who will review the system's proposals, in which situations validation is required, and how much time that validation will take. You also need to define what makes a result correct. Human validation is particularly important when the output can have a concrete consequence for a customer, an employee or a business decision.

What level of error is acceptable?

An AI system will almost never cover one hundred percent of cases from the outset. Some cases will be handled correctly, others will require verification, and some will remain entirely manual. The acceptable level of error depends mainly on the task. An error in document search does not have the same consequences as an error in a calculation, a commercial decision or a document sent to a customer. That threshold should therefore be defined before the project starts.

How will you measure the benefit?

The success of the project should be measurable through a concrete criterion: reduced processing time, a lower error rate, a shorter response time or additional capacity for the team. Ideally, the starting point should be measured before the test so that the results can be compared afterwards. This avoids judging the solution based on a general impression or on enthusiasm about the technology.

Where does the solution fit into the existing work?

A solution that works technically but forces users to completely change the way they work is unlikely to be adopted. Think from the outset about where AI fits into the process: which tool does the team use today, what information does it need to provide, and in what form should the result come back? The closer the integration stays to the real work, the easier it is to test the solution and measure its usefulness.

What happens if the system gets it wrong?

Planning for errors does not mean the project is bound to fail. It simply means knowing what to do when an error occurs. The output can be sent to a person for review, the case can be handled manually, or certain situations can be excluded from the AI scope. What matters is that this mechanism is defined before go-live, and that users clearly know when they can rely on the system and when they need to take over.

The takeaway

A well-prepared AI project starts with clear answers to these questions, not with choosing a technology. Organisations that take the time to answer them can often move faster afterwards because the technical project itself becomes easier to scope.