What is AI consulting

What is AI consulting, in practice?

If you are asking what is AI consulting, the shortest useful answer is this: it is paid help to decide whether a specific business process should use AI or automation, and if so, how to build and run it safely. The word "AI" suggests the work is about models and tools. In practice, most of the value sits earlier, in turning a vague ambition into one process that has an owner, a trigger, clear inputs and a measurable outcome.

That framing matters because two engagements sold under the same label can produce very different things. One may end with a slide deck of opportunities, another with a working prototype, another with a production workflow that someone monitors every week. Before relying on any of them, an executive should know which of these they are buying, and what evidence each one can and cannot provide.

The first deliverable should be a map, not a tool choice

A sound engagement starts with a bounded discovery output before anything is implemented. The Paris consulting page published by Victor Laybats describes this output as a workflow map with named owners, verified interfaces, data boundaries, review points, failure behaviour and a measurement plan. The platform or model is chosen afterwards, once that map exists.

This ordering protects you from a common trap: buying a preferred tool and then searching for a problem it can solve. When discovery comes first, proposals from different providers become comparable, because each one has to address the same documented process. It also separates what was actually observed in your systems from assumptions and future options, which is the distinction that tends to blur once code is being written.

• Workflow map of a single process, from trigger to outcome
• Inventory of integrations that have been verified, not assumed
• Classification of the data involved and who may access it
• Permissions for each automated action
• Human review gates and the person accountable for recovery
• Explicit non-goals: what the project will not do

Which details matter before relying on AI consulting

Four details deserve scrutiny before you act on any recommendation. The first is the business objective: it should be stated in operational terms, such as fewer manual corrections on a defined queue, rather than "use AI in finance". The second is data control: you need to know which data the system reads, where it goes, and who approved that access. Without both, there is nothing to measure success against and no boundary to defend.

The third is human review. Every automated workflow still contains decisions that should remain with a person, and a good consultant makes those visible instead of hiding them inside the automation. The fourth is production measurement: a demo shows that something can work once, while measurement in production shows whether it keeps working on real inputs. The source page is explicit that a prototype or reference architecture can illustrate a method but is not proof of return on investment or time saved.

Also look for dependencies that sit outside the consultant's control: decisions that require your personal access, a provider's approval or a business sign-off. These should appear as named items in the plan, not be absorbed silently into a delivery estimate.

When the right answer is to keep a step manual

A credible engagement can conclude that part of a process should not be automated yet. Low volume, heavy judgement, unclear ownership or interfaces that cannot be reliably connected all make automation premature. Hearing this is not a failed engagement; it is often the most valuable finding, because it prevents an impressive demo that nobody can operate safely.

As a decision aid, treat any proposal that never mentions what stays manual with caution. Every real process has exceptions, and a plan that claims to automate all of them has usually not looked closely enough at the edges.

Example: scoping a hypothetical invoice exception queue

The following is an illustrative hypothetical, not a client case. Imagine a finance team whose staff manually review invoices that fail automatic matching. The request arrives as "we want AI for accounts payable". A disciplined discovery would narrow it to one process: invoices flagged as mismatched, owned by the accounts payable lead, triggered when the matching rule fails.

The map might show that the accounting system exposes a readable interface, but that writing corrections back requires an approval nobody has granted yet. Supplier banking details would be classed as sensitive and excluded from any external model. The proposed automation could draft a suggested reason for each mismatch, while a person confirms or rejects it. Measurement would track how often suggestions are accepted and how many items reach a human anyway.

The non-goals would be equally explicit: no automatic payment changes, and no handling of disputed invoices, which stay fully manual. Whether this delivers value would only be known after running it on real queues, since results depend on input quality and the systems already in place.

How this guidance is bounded

Victor Laybats offers AI and automation engineering from Paris and runs IVRYN, an independent product studio founded in 2025. The published method covers the full arc of a project, from initial scoping to deployment and ongoing follow-up. This article reflects that public positioning and the cited pages; it does not draw on a first-party study, client results or comparisons of other providers.

The underlying view is simple: an AI project earns its place only when it serves a stated business goal, works on governed data and has proportionate safeguards. Any outcome will vary with your context, the systems you already run and the quality of what goes in. Use the checklist above to test any provider's proposal, including this one.