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The AI diagnostic: what to look for first

Raphaël Dionne
Raphaël Dionne
Avenor Innovations · April 2026
An employee checking parts in a warehouse rack

Before integrating any AI solution, you need a diagnostic. Not a technology demo, but an honest read of how the work happens today. This is the step that separates projects that create value from the ones that end up in a drawer.

What a good diagnostic is not

A good diagnostic does not hunt for the best AI tool. It does not start with technology at all. Too many projects start with the solution, an agent, a model, a platform, before anyone has understood the problem. The result is impressive technology that fixes nothing important.

A diagnostic starts from the other end. Concretely, where does your organization lose time and money today?

The four things to map

A useful diagnostic looks at four dimensions.

  • Where the time goes. Which repetitive tasks fill your team's week? How many hours, and at what hourly cost?
  • Where errors get expensive. Which processes generate rework, misses, or billable delays?
  • What data is available. Does the information already exist in a usable form, in files, emails or systems? That is what makes a project feasible or not.
  • Appetite for change. Are the teams ready? A solution adopted by 30 % of the people creates no value, however good it is technically.

Prioritize by impact and feasibility

Once the opportunities are listed, rank them on two axes: impact, meaning how much time or error you remove, and feasibility, meaning data quality, complexity and integration. The right first project is almost always in the high-impact, high-feasibility corner. Rarely the most spectacular one.

What you should walk away with

At the end of a diagnostic you should have three clear things: a map of your processes, a list of opportunities ranked by impact, and a recommended first project with an order of magnitude for the effort. Not a theoretical deck, an action plan.

How long it takes

For a company of this size, a serious diagnostic usually lands in one to three weeks. It is short because the goal is not to analyze everything, only to find the two or three levers that actually matter.

Want a starting point? Our free AI diagnostic gives you a first read of the highest-return opportunities in your context, in a few minutes.

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What this article argues, we put into production for a client. The case walks through what was built, how, and what changed.

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