Where AI creates value
Analysis of processes, data and constraints (budget, compliance, infrastructure). What is feasible, what is not, and why.
Expertise · Consulting
AI Transformation Consultant
Moving from "we should be doing AI" to systems that run, are used, and whose impact is measured.
The problem I solve
Most organisations do not have an AI access problem: models are available and demos are impressive. Their problem lies elsewhere. Proofs of concept pile up without ever reaching production. Priorities shift with every announcement. Nobody really knows which data is usable, what operations will cost, or who will maintain the system a year from now.
A successful AI transformation is not one big project. It is a well-ordered sequence of decisions: which use cases, in what order, with which architecture and which team. That is what I build with you.
What I bring
A view that covers business, architecture and delivery at once — because I have taken AI projects all the way to production, not just to the report.
In what order to move
Use cases prioritised by impact and risk, one roadmap per domain rather than a single "big AI project", measurable success criteria.
All the way to production
Functional scoping, architecture, choice of AI approaches, team coordination and production rollout — with a clean handover.
My approach
- Understandbusiness, usage, constraints
- Diagnosedata, processes, risks
- Prioritiseuse cases, value, effort
- Designarchitecture, AI choices
- Deliverteam, production, measurement
- Hand overdocumentation, upskilling
Deliverables
- Diagnostic report — opportunities, data maturity, risks and constraints.
- Prioritised use-case portfolio with value and effort estimates.
- AI transformation roadmap per domain and per quarter.
- Requirements for the first use case and target architecture.
- Upskilling plan for the internal team.
Examples
- Pharmaceutical AI strategy — roadmap covering R&D, quality, regulatory affairs and data, and scoping of a GxP document assistant. See the architecture
- EdTech feasibility audit — assessment, then design of an adaptive learning system integrated into an existing platform. See the architecture
- Sales intelligence — decision-support platform linking clients, products, suppliers and sales.
- About ten AI projects led from needs analysis to production, across pharma, industry, documentation and education.
Frequently asked questions
Where should an AI transformation start?
With the business, not the technology: map the processes where AI can reduce a cost, a delay or a risk, check the available data, then pick a first use case with high impact and low risk. A diagnostic of a few weeks is usually enough to produce a prioritised roadmap.
How long does an AI transformation engagement last?
A diagnostic with a roadmap usually takes a few weeks. Taking a first use case to production is more a matter of months, depending on complexity, data and team size.
Do we need an in-house data team to start?
No. Part of the work is precisely defining what the organisation should build internally and what it can delegate. I am used to upskilling junior teams during the project.
Do you work outside Algeria?
Yes: in Algeria, France, Europe, North Africa and remotely, in Arabic, French or English.
A transformation project, an architecture to design?
Companies, startups, institutions: describe your context in a few lines. I answer personally, with a first opinion on feasibility and approach.
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Engagements in Algeria, France, Europe, North Africa and remote · Arabic, French, English · contact@ouahabi-benhenni.com