AI consultancy and services

What ai consultancy and services should mean for your organisation

For an executive team, ai consultancy and services is useful only when it turns a business intention into a workflow someone can run, test and stop. The label covers very different offers, from strategy workshops to model integration to ongoing operation. Before comparing providers, decide which of those you are buying and what a finished first step looks like.

This guidance comes from Victor Laybats, a Paris-based engineer who works on AI and automation projects from initial scoping to deployment and follow-up. It reflects a documented method, not a study of the market or a record of client outcomes. Treat it as a way to ask better questions, not as evidence that any particular approach will pay off in your context.

Start with one accountable process, not with a tool

The most common mistake is opening with "we need AI" or with a preferred platform. A sounder starting point is a single process described in plain terms: what triggers it, what business result it should produce, who owns it and which systems it touches. If nobody can name the owner, the project is not ready for implementation, whatever the technology.

An explicit business objective also gives you permission to stop. A good first engagement may conclude that part of a process should stay manual because volume is low, judgement is high, ownership is unclear or the interfaces are not supported. That conclusion is a legitimate deliverable. It protects the organisation from an impressive demonstration that nobody can operate safely afterwards.

What a discovery phase should deliver before any code

Ask for a bounded discovery output before you approve a build. It makes proposals comparable, because each provider must describe the same process in the same concrete terms. It should also separate what was observed in your environment from assumptions and future options, so you know which parts of the plan are still guesses.

Dependencies deserve particular attention. Some decisions require personal access, approval from a software provider or internal business authorisation. These should appear as visible items with owners, not be folded silently into a delivery estimate. A practical checklist for the discovery output:

• A workflow map with every step and handoff
• A verified inventory of the integrations involved
• A classification of the data the process reads and writes
• The actions the automation is allowed to take, and those it is not
• Human review gates and who staffs them
• Known failure cases and a named recovery owner
• Explicit non-goals and a measurement plan

Controlled data, safeguards and human review

A useful AI project rests on data whose origin, sensitivity and access rules are known. If you cannot say which records the system may read, where its outputs are stored and who can change its permissions, the safeguards are not yet defined. Quality matters too: results depend heavily on the inputs, and a model cannot correct for missing or inconsistent source data.

Human review is not a sign of weak automation; it is how decisions that remain human stay visible. Place review where an error would be costly or hard to reverse, such as before a message reaches a customer or before a record is changed in a system of record. The design should make it obvious when the system is uncertain and how a person takes over.

Measuring in production and the limits that apply

A prototype or reference architecture can explain a method, but it does not prove return on investment, time saved or a client result. Agree before launch on what will be measured once the workflow runs on real traffic: error rates, the share of cases sent to review, handling time, or whatever ties back to the stated objective. Without a baseline taken before the change, any later claim of improvement is hard to check.

Be wary of any provider promising fixed gains. Outcomes depend on your existing systems, your processes and the quality of what goes in. Equally, being associated with a studio or listed in a portfolio does not imply an endorsement or a guaranteed result; current capabilities should be checked directly. Legal or regulatory questions about your data should go to qualified advisers.

Example: scoping a hypothetical invoice-triage request

Hypothetical example, not a client case: a finance team asks for "AI to handle supplier invoices." Discovery narrows this to one process: incoming invoices by email are classified and matched to purchase orders, with the accounts payable lead as owner. The map shows that the email inbox and the accounting tool both have usable interfaces, but approval rules live in a spreadsheet nobody maintains.

The resulting scope automates extraction and matching only. Any invoice above an agreed threshold, or any mismatch, goes to a person; payment approval stays manual and is declared a non-goal. The measurement plan compares match accuracy and review volume against a few weeks of manual baseline. If the spreadsheet cannot be cleaned up, the recommendation is to fix ownership first and revisit automation later.