Our AI strategy consulting helps leadership teams pick the few uses that genuinely matter, instead of piling up trials that go nowhere. In 4–8 weeks you leave with assessed use cases, a prioritised roadmap and clear rules for your data and your people alike.
An AI strategy engagement is not about picking a tool. It starts from your objectives (cutting a cost, serving customers better, standing out), then looks hard at your processes, your data and the skills you have in-house. The outcome fits into a handful of written decisions: what to launch, in what order, with how much human oversight and which measure of success.
The mistakes we come across tend to look alike. A company-wide subscription bought without knowing which tasks it is meant for. A pilot rushed out, never measured, and therefore never rolled out. Staff pasting sensitive documents into consumer tools because nobody set any rules. Or a project handed to IT alone, when the real gains lie in the business teams.
This consulting is designed for leaders of SMEs and mid-sized companies, business unit heads and IT directors who have to make calls without being AI specialists. It also suits software vendors wondering what role AI should play in their product. No technical background is needed: we put every option into plain terms, covering what it brings in, what it costs and what it exposes you to.
AI strategy consulting serves the people who have to decide, not just the ones experimenting.
You keep hearing about generative AI, ChatGPT for business and AI agents, without knowing where any of it applies to you. The diagnostic isolates the two or three uses that genuinely lighten the load and rules out those that would cost more than they return.
Several departments are each testing their own tool. You need a common framework to arbitrate, readable AI governance and a roadmap that business unit heads are willing to own.
You have to secure data, choose suppliers and keep the architecture coherent, while fielding urgent requests from the business. The engagement gives you reasoned priorities and usage rules everyone shares.
Your customers expect AI in the product. We help you decide which feature to build first, how to judge its quality and what it changes about your competitive positioning.
Written deliverables, handed over and talked through in person, which remain your property.
Your goals, your sector and your constraints set down in black and white, so that every AI project serves a clear intent shared by the leadership team.
Processes, data quality, existing tools and in-house skills: a candid assessment of what is ready and what is still missing.
Each idea scored on time saved, implementation effort and the cost of getting it wrong, then ranked so decisions rest on facts you can check.
Launch order, success indicators, approved tools and the split of roles between AI and your staff, recorded in a single reference document.
Every phase of the CRAFT Method produces a written document, presented in a review session, which remains your property.
An AI strategy often fails for the reason that held back MSC’s self-service app: nobody had watched the actual work. Victor Michel, the studio’s founder, went to watch customers at work, then prioritised the critical use cases with a group of pilot customers. Our advisory work follows the same principle: understand the process before choosing a technology.
The phases of the CRAFT Method, in order, with none skipped.
Interviews with leadership and business teams, analysis of processes and data: we understand before we propose anything.
For each use case retained, we describe the human-AI system: who decides, at what point, with which information in front of them.
Opportunities are prioritised and organised into a roadmap, with a measurement protocol agreed before any development starts.
Deliverables presented to your decision-makers. Roll-out can be run in-house, or we stay alongside you for implementation.
The factors that make or break an artificial intelligence strategy, as we see them in the field.
A list of ideas is not a strategy. To prioritise AI use cases in a company, score each one on four criteria: value (time saved, quality, revenue), implementation effort, the severity of a mistake and data availability. The best candidates combine a frequent task, an output that is easy to check and data that is already accessible. Spectacular but risky uses come later, once governance has been proven on simpler cases.
Buying ChatGPT Enterprise, Microsoft Copilot or another assistant for the whole company is a tool decision, not an AI strategy. Without designated tasks or usage rules, adoption stays confined to a few enthusiasts and everyone else soon gives up. The right sequence runs the other way: identify the tasks, measure how long they take, then choose the tool that fits, sometimes software you already pay for without using it.
AI agents, able to chain several steps and act inside your software, are the big trend of the moment. Their value is real on well-bounded tasks: preparing a file, chasing a customer, matching accounting documents. But the more an agent acts alone, the more you must define what it is allowed to do, when a human signs off and how each action is logged. A serious strategy sets those limits before roll-out.
The legal framework is part of the strategy, not a final check. The GDPR in France, the Swiss FADP and the EU AI Act, being applied in stages, all bear on tool choice, data hosting and how uses are documented. Our role is to build these requirements into decisions: classifying uses by level of risk, keeping an inventory of the systems in use, planning transparency towards the people concerned. Detailed legal analysis remains a matter for your lawyers.
European or Swiss hosting, open models running on infrastructure you control, US providers on suitable contractual terms: sovereignty comes up in almost every engagement. There is no single answer. We compare the options on the sensitivity of your data, running costs and how easily you could switch provider later, so that a hurried choice does not lock you in for years.
Three ways to buy AI consulting. Each has its place; here is what actually sets them apart.
| Criterion | Brutux Studio | Consulting firm | Integrator or IT services firm |
|---|---|---|---|
| Starting point | Your objectives and tasks, before any tool | A broad organisational diagnostic | A technology or partner vendor |
| Who you deal with | The founder, from first call to deliverables | A consulting team led by a partner | A project manager and a technical team |
| Deliverables | Written, presented in review, yours to keep | Detailed reports and slide decks | Technical specifications and code |
| Interface design | Central to the method: the human-AI system | Rarely addressed | Depends on the team |
| Tool neutrality | No tools sold, compared on your own cases | Generally neutral | Often tied to partnerships |
| Moving to delivery | Possible with the same studio: product, UX, integration | Often handed to a third party | Built in, on its own tech stack |
| Format | 4–8 week engagement, on-site workshops | Suited to large transformation programmes | Fixed-price project or time and materials |
AI strategy consulting: answers to the questions we are asked.
An analysis of your context, a maturity diagnostic, a selection of assessed use cases and a roadmap with its governance rules. Every stage produces a written deliverable, presented to and discussed with your decision-makers.
Plan on 4–8 weeks; the range depends mostly on how many teams we interview and how easily we can access the data. Rolling out the first use cases comes afterwards, led by your teams or with our support.
Not a thick report, but a few clear-cut decisions. In an SME, two or three well-chosen uses (quotes, customer replies, document search) often transform day-to-day work. The diagnostic stops you paying for tools nobody will use.
We will tell you plainly. Sometimes data needs structuring or a process needs simplifying first. Better to know that before committing a budget than to find out at the end of an expensive pilot.
Yes. Leadership and business teams own the choices, but IT is involved as soon as data, security or existing tools are at stake. Our recommendations take account of your architecture and the contracts you have already signed.
Start by listing repetitive, time-consuming tasks, department by department. Then score each on expected value, implementation effort, the severity of a mistake and access to data. Frequent, easy-to-check, low-risk cases go to the top: they build the confidence needed for what follows. In the CRAFT Method, this prioritisation happens in the Frame phase, with a measurement protocol for every case retained.
Ask for a written method, sample deliverables and engagements comparable to yours. Check that the studio can build what it recommends, talks about measurement before tools and is willing to conclude that a use is not worth it. Brutux Studio relies on the CRAFT Method, rooted in MIT Sloan’s Applied AI Strategy, and on SaaS products designed and shipped to production for its clients.
If you do not yet know where AI creates value for you, start with consulting: it stops you integrating or training blind. If your priorities are clear and involve software, integration can begin straight away. If the issue is mainly day-to-day use of tools you have already bought, training comes first. The three often follow on from one another.
By fixing the measure before building: time spent on the task, error rate, response time, user satisfaction. You record the starting point, then compare after deployment. Without that baseline, no ROI figure will stand up in front of a finance director, and projects end up judged on impressions.
30 minutes to talk about your project, in Thonon or by video call. I’ll tell you honestly what will make the difference.
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