Build AI with rigor
Proprietary framework to identify, qualify and deploy high-value AI integrations, step by step, with precision.
Why CRAFT?
Most AI projects fail before they even start. Not from lack of technology. From lack of method. Teams rush into pilots under pressure, driven by the fear of missing the AI wave. The result: prototypes that never reach production, budgets absorbed with no measurable ROI, and discouraged teams.
CRAFT is the answer to this disorder. An operational, sequential framework designed to impose rigorous methodological discipline from initial analysis to governed deployment. No skipping steps. No starting over mid-course. Each phase directly feeds the next.
The verb "to craft" defines the posture: building with care, precision, and intent. Against projects driven by urgency or FOMO: CRAFT imposes a prior question for every decision: why, and for what measurable result?
The CRAFT framework
5 sequential phases, from initial strategic analysis to governed deployment. Each phase delivers concrete outputs. Grounded in MIT Sloan certification.
Cartograph
Strategic analysis. Understand the organization, sector and constraints before acting.
Review
In-depth diagnostic of processes, data and AI maturity.
Architect
Design of the optimal human-AI system and complete functional specification.
Frame
Opportunity prioritization and 12-month strategic roadmap.
Transform
Action, monitoring, deployment, training and governance.
The 4 founding principles
Rooted in MIT Sloan's Applied AI Strategy, these principles are non-negotiable in every CRAFT engagement.
Strategy before technology
AI amplifies strategy, it doesn't replace it. An AI project without a clear strategic intent is not an AI project: it's an expense.
Human in the loop
The best AI systems augment human judgment, they don't bypass it. Designing an AI integration means deciding who decides, and when.
Proprietary data = real advantage
Public models are accessible to everyone. Your data, your processes, your sector expertise are not. That's where the real differentiator lies.
Measure before you build
A system can look impressive and produce mediocre results. The evaluation protocol always precedes development. Always.
Radiologists paired with an AI tool performed worse than when working alone.
Identified cause: the physician's uncertainty and the AI's uncertainty are correlated. When both are uncertain, the doctor defers to the machine incorrectly, precisely where the system should flag rather than reassure.
Explicitly training users on when to trust AI is not optional. It is the core of human-AI system design.
Five beliefs
Strategy before technology
Every AI project must start from a clear strategic intent: reduce costs, differentiate, or serve a segment better than anyone else. Without this compass, you integrate technology for technology's sake, and pay dearly for added complexity.
Human in the loop
The best AI systems augment human judgment rather than bypass it. Designing an AI integration also means deciding who decides, at what point, and with what information. This human-machine collaboration design is often harder than the model itself.
Proprietary data as real advantage
What the organization owns exclusively (data, documented processes, accumulated sector expertise) is its only real differentiator in a world where all players access the same foundation models. AI competition is not played on algorithms; it's played on data and processes.
Measure before you build
A system can look impressive in a demo and produce mediocre results in production. Defining success metrics before building is not a bureaucratic formality: it's the only way to know whether the investment is worthwhile.
The transformation endures, not the project
A successful pilot is not a transformation. What matters is what remains twelve months after the engagement ends: skills acquired by teams, data accumulated in systems, and processes restructured around new AI capabilities. Everything else is anecdotal.
Engagement formats
Three engagement formats, adapted to the organization's maturity level and challenges.
Training
Group sessions for executive and operational teams. Tailored to the sector and the organization's AI maturity level. Full support materials provided after each session.
Coaching
Individual or small group support for decision-makers. Work on real cases from your organization. Structured follow-up between sessions to maintain momentum.
Advisory mission
Full strategic engagement following the 5 CRAFT phases. Immersion in the organization, deliverables at each stage, skills transfer to internal teams at the end of the engagement.
Numbers, not promises
Five recent engagements. Audited results, not estimates. Scope and duration vary across organizations.
What I'm often asked
How long does a full CRAFT engagement take?
Between 8 and 14 weeks for all 5 phases, depending on organization size and defined scope. Analysis and diagnostic phases (C + R) take 2 to 4 weeks combined. System design (A) takes 2 to 3 weeks. Roadmap (F) takes 1 to 2 weeks. The deployment phase (T) is ongoing and typically extends over several months.
What type of organization is CRAFT suited for?
CRAFT is designed for mid-to-large B2B organizations (between 50 and 5,000 employees) in sectors with strong process components: manufacturing, SaaS, financial services, energy, logistics, telecom. It is not suited for early-stage startups that don't yet have established processes to optimize or proprietary data to leverage.
What do you get concretely at the end of each phase?
Each phase produces a written deliverable, presented and discussed in a review session: a strategic report (C), a data and process maturity matrix (R), a complete functional specification (A), a 12-month roadmap (F), and a piloted deployment plan (T). These deliverables remain the client's property.
Do you need structured data before starting?
No. Phase C (Cartograph) starts from whatever exists, regardless of maturity level. Some organizations begin CRAFT with little or no structured data: this is precisely one of the points that phase R (Review) assesses and that phase A (Architect) addresses by designing the target data architecture.
How does the first conversation go?
30 minutes, no sales pitch. The goal is to understand your context, challenges, and AI maturity, to assess together whether CRAFT is the right framework for your situation. If it isn't, I'll tell you directly.
30 minutes to know if CRAFT is right for you.
No deck. No commitment. A direct conversation about your situation and AI challenges.
Book 30 minutes ↗