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Optim-AI

Opportunities

What AI can really bring to your company

Artificial intelligence creates value where work is repetitive, text-based, high-volume and involves little decision-making. It creates little where you have to be accurate every time or take decisions you are accountable for. Opportunity analysis means sorting your activities by these two criteria, then costing the three or four projects that fit within your constraints.

By function

Where the gain lies, function by function

The orders of magnitude depend on your business volume. What does not depend on you is the nature of the tasks at which AI is good.

In a production workshop, an operator and a technician examine a machine's control screen together.
Sales and marketing

Writing proposals, qualifying incoming enquiries, preparing meetings from the customer history.

A net gain as soon as the volume of proposals exceeds a few dozen a month.

Administration and support

Extracting information from poorly structured documents, sorting and routing requests, first-level responses.

This is almost always the first profitable project, because the work there is repetitive and low-stakes.

Production and engineering

Searching technical documentation, help with writing code and tests, analysing incident reports.

A real but demanding gain: it requires teams capable of judging the output.

Management

Summarising lengthy documents, preparing decisions, formatting analyses from your own data.

Low cost, immediate effect on available time, no IT project needed.

Organisation

What changes in the organisation, beyond the tool

This is the part that AI projects most often get wrong, and the one that decides whether they are adopted.

Roles shift towards checking

Producing a first draft becomes cheap. Judging whether that draft is good becomes the scarce skill, and therefore the core of several existing roles.

Traceability becomes a daily concern

When some outgoing documents are machine-assisted, you need to know who approved what. It is a question of process, not technology.

Gaps between teams widen

Without a common framework, some teams get two years ahead and others do not touch it. The gap becomes a management problem before it becomes a tool problem.

Recruitment criteria change

The ability to frame a problem and check a result counts for more than speed of execution on tasks that can now be automated.

Opportunity analysis

Growing your company with your constraints, not against them

Short answer

What is an opportunity analysis, in concrete terms?

A list of possible projects, each costed in terms of money, time and expected gain, filtered by your real constraints: available budget, people who can be assigned, regulatory obligations, state of your information system. We keep three or four, set the rest aside in writing, and stand by this choice before your committee.

The four constraints we always look at

  • The money available now, not next year's theoretical budget. A project that does not fit within this quarter's cash flow is not a project, it is an intention.
  • Your people's time. An AI project takes up time from operational staff for scoping and checking. If nobody is available, the project will fail, whatever its technical quality.
  • The state of your data. Data that is scattered, duplicated or has never been cleaned inevitably limits what can be built. It is often the first real project.
  • Your obligations. Regulated sector, health data, public procurement, customer requirements: they narrow the scope and must be known before promising anything.

The deliverable is short: a table of the projects selected, a table of the projects set aside with the reason, and the cost of doing nothing. That last figure is the one that triggers decisions.

Frequently asked questions

The opportunity questions that keep coming up

Where should you start when the budget is limited?
With a high-volume, low-stakes task that is easy to measure, usually in administration or support. It produces a visible result within a few months, without a heavy IT project, and lets the company learn in an area where a mistake is not costly.
How do you measure the return on investment of an AI project?
By choosing the indicator before launching, and taking it from those you already track: processing time, number of cases handled per person, rework rate, cost per request. The calculation must include the time spent on human checking, which is the cost most often forgotten.
Should it be a company-wide project or local initiatives?
Both, in this order: local initiatives to learn quickly and at low risk, then a common framework to prevent each department from building its own isolated solution. Starting with a large cross-functional programme is expensive and rarely produces a result within twelve months.
My sector is highly regulated. Is AI an option?
Yes, provided the uses are sorted. In a regulated sector, uses that touch on the regulated decision are ruled out from the start, and the focus is on preparation, document search and summarising, where the human remains the decision-maker. The constraint narrows the scope; it does not eliminate it.
The 3S gridThe three filters a use case has to pass.The auditThe starting point: what you already do, measured.Risks and controlThe other half of the picture, the one that protects the gain.AI audit servicesDiagnostic, audit and projects: how these projects are costed, then delivered.Questions and answers on AI in businessThe real cost of a project, return on investment: the short answers.

Next step

Let's cost your first three projects

A thirty-minute conversation is enough to find out whether an audit is warranted in your case.

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