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DATA & AI STRATEGY · SMB & MID-SIZE
Data & AI Diagnostic
A structured diagnostic to assess where your organization stands on data and AI and define a prioritized roadmap that matches your business reality, not a consultant's template.
WHO IT'S FOR
SMBs and mid-size companies who want clarity on their AI potential before committing to a transformation program.
FORMAT
4-week engagement interviews, data audit, benchmark, prioritized recommendations delivered to the executive committee.
OUTPUT
Maturity score across 5 axes, top 3–5 prioritized use cases, quick wins identified, 12–18 month roadmap.
TRACK RECORD
Retail tech group · Industrial group · Fashion & e-commerce platforms · Supply chain leaders.
Why a diagnostic first?
Most SMBs and mid-size companies that fail at AI don't have a technology problem. They have a clarity problem. They invest in tools before understanding their data. They launch pilots before aligning on priorities. They hire AI talent before building the foundation.
The Data & AI Diagnostic gives leadership teams a shared, honest picture of their current maturity and a concrete starting point that avoids the POC graveyard.
What you receive
Maturity score
A clear score across the 5 axes benchmarked against sector peers. No jargon, no ambiguity.
Prioritized use cases
Top 3–5 AI use cases ranked by business impact and implementation readiness. With a "why this, why now" rationale.
Quick wins
Immediate actions that can be taken in under 90 days to build momentum and prove value internally.
12–18 month roadmap
A sequenced, realistic plan sized for SMB constraints milestones, dependencies and resource requirements. No over-engineering.
Data foundation plan
The infrastructure and governance steps needed before scaling AI so you build on solid ground.
Executive presentation
A board-ready deck that translates technical findings into strategic decisions. Built to drive alignment, not sit in a drawer.
The 5 diagnostic axes
Data governance & quality
Is your data structured, accessible and trustworthy? We assess data sources, ownership, pipelines and the gaps that would block any AI initiative.
01
Infrastructure & tooling
Cloud maturity, data warehouse, API architecture, existing AI tools. What you have, what you need, what you can defer.
02
Use case landscape
Mapping of existing and potential AI use cases across business units. Scored by impact, feasibility and data readiness.
03
Skills & organization
Who owns data and AI in the organization? Where are the gaps? What needs to be built, hired or trained?
04
Strategic alignment
How does AI connect to business objectives? Is there executive sponsorship? Are there cultural blockers to address?
05
How it works
Approach
The engagement was structured in three phases: a thorough diagnostic of data and AI maturity, a sector benchmark comparing the approaches of leading players, followed by the construction of a prioritized roadmap integrating both regulatory constraints and competitive opportunities.
The analysis showed that while ambition and governance are often on par with peers, companies need to build a coherent supplier data foundation and a cross-functional AI strategy to meet Scope 3 commitments and anticipate the extension of the digital product passport to their industry by 2028–2030.
Kickoff & interviews
Structured interviews with key stakeholders across business units, IT and leadership. Mapping of current data landscape and AI initiatives.
w1
Audit & benchmark
Deep dive into data assets, infrastructure and existing tools. Sector benchmark to contextualize findings against industry peers.
w2
Use case scoring
Workshop with business teams to identify, score and prioritize AI use cases. Filter for impact, feasibility and strategic fit.
w3
Roadmap & presentation
Synthesis of findings into a clear roadmap. Presentation to the executive committee with recommendations and next steps.
w4
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