AI consulting & strategy: from ideation to a roadmap you can own.
We help executives and IT leaders move from experimentation to an AI strategy aligned with business priorities: prioritised use cases, build/buy trade-offs, LLM choices, target architecture, governance and change management.
The problem
An AI strategy, or a collection of initiatives?
Under pressure from the AI agenda, many leadership teams pile up initiatives: an assistant here, a RAG pilot there, subscriptions to three different LLM providers. Every team moves forward on its own, with no common framework for data, security or return on investment. The executive committee signs off budgets without a consolidated view, and IT inherits an integration debt that grows with every orphaned POC.
The real question isn’t “which model should we use”, but: which use cases genuinely serve your strategy, what do we build versus what do we buy, and what operating model keeps AI running over the long term?
From ambition to trajectory, in 5 steps
Vision & alignment
Executive committee workshop to connect AI ambition with business objectives: where do we create value, which risks do we accept, what level of sovereignty are we aiming for.
Use-case portfolio
Identification and scoring of use cases by value, feasibility and effort, then sequencing into quick wins and structural initiatives.
Build / buy & LLM choices
Trade-offs between building, buying and integrating. Model selection (Claude, Gemini, ChatGPT, Mistral, hosted models) based on cost, performance, confidentiality and sovereignty.
Target architecture
AI architecture blueprint: data layer, orchestration, RAG, guardrails, observability and integration with the existing IT landscape, designed for production.
Governance & change
Operating model (CoE / AI Ops), committee structure, usage policies and a change management plan to embed AI within the business.
Deliverables
What you walk away with
- An AI vision & ambition statement, validated by the executive committee
- A scored use-case portfolio (value / feasibility / effort), sequenced
- Build/buy recommendations and a shortlist of LLMs per use case
- A target architecture blueprint, from the data foundation to the guardrails
- An AI governance model (CoE/Ops, committee structure, usage policies)
- A change management and upskilling plan
Industrialising AI, backed by the data & compliance DNA of Datanaos
Frequently asked questions
Do we need to have use cases in mind already?
No. Part of the engagement is precisely about surfacing and prioritising use cases. We start from your business priorities and strategy, not from a predefined list.
Are you tied to a particular LLM provider?
No, we remain agnostic. We compare Claude, Gemini, ChatGPT, Mistral and hosted models based on cost, performance, confidentiality and your sovereignty requirements, use case by use case.
How do you handle sovereignty and confidentiality?
Sovereignty is a decision criterion in its own right: hosting, data location, open or self-hosted models. We weigh the options with you according to your sector and your regulatory exposure.
How is this different from the diagnostic & scoping?
The diagnostic is a short entry offer that assesses your maturity. Consulting & strategy goes further: target architecture, governance and change management, all the way to an executable roadmap.
Move from experimentation to AI in production
Start with a short, fixed-price assessment: maturity, high-ROI use cases, and a prioritised roadmap. No commitment.