A diagnostic that ends with a ranked, costed roadmap. You leave knowing what to build first and what has to be true before you start.
This is the front door to your AI program. I score your readiness across data, infrastructure, talent, and governance, run an AI Design Sprint to surface and rank candidate use cases by impact, effort, and risk, and hand you a prioritized roadmap with cost attached to each move. The output is a decision your management and board can fund.
95%
of enterprises see no measurable return on GenAI (MIT NANDA)
60%
of AI projects abandoned without AI-ready data (Gartner)
90 days
from assessment to a prioritized, costed roadmap
The Challenge
What teams expect from AI, and what happens without readiness
What teams expect
Single source of truth on day 1
Clean data flowing seamlessly to every team
AI ready from the start
Clean data flowing seamlessly to every team
Decisions backed by data
Clean data flowing seamlessly to every team
What actually happens
Pilots become production
Teams expect a working demo to graduate into a system the business runs on. Without readiness, 95% of enterprises report no measurable P&L impact from GenAI, because the gap is integration, data, and governance, not model quality.
The data is ready to power AI
Teams expect their data to be good enough to build on. Without readiness, Gartner expects 60% of AI projects to be abandoned through 2026 on data that was never AI-ready, and the gap surfaces halfway through a build, after the budget is committed.
Investment lands where the value is
Teams expect a clear first move everyone agrees on. Without readiness, leadership holds a long list of ideas with no shared way to compare them, and budget scatters across low-value experiments with no decision basis for shareholders.
What you receive from the assessment
Six concrete deliverables. Each one is built to stand on its own and to feed the next, so the page you finish reading is the engagement you actually get.
AI Potential Map
A structured map of where AI can create value across your organization, processes, and your products and services, produced through AI Potential Mapping in the AI Design Sprint. This is the canvas every other deliverable draws from.
Ranked use-case shortlist
Every candidate scored and sorted by impact, effort, and risk into a shortlist worth funding. You move from a long list of ideas to a small set of initiatives that can actually reach production.
Data and infrastructure scorecard
A scored audit of your data layer and AI infrastructure: single source of truth, structured attributes, modeled variants and classifications, assigned ownership, and integration posture for AI workloads.
Governance gap read
A clear read on your governance posture and EU AI Act exposure: where enrichment standards, review gates, traceability, and human accountability hold, and where they exist only on paper.
Costed 90-day roadmap
A sequenced roadmap of the highest-return initiatives, each with effort, risk, expected return, and a clear next step. Every prioritized move carries a build-versus-buy framing so you own the decision.
Executive decision memo
A memo written for management and shareholders that frames the roadmap as a fundable decision, with KPIs and a measurement baseline defined before any build begins.
Where your organization sits on the AI readiness curve
The assessment places you on a clear curve and names the move to the next tier. Most organizations begin closer to the left than they expect.
Exploring
Real ambition, scattered execution. Pilots run in pockets, data lives across ERP, PLM, PIM, spreadsheets, and regional databases, and no single owner is accountable for end-to-end readiness.
- •Pilots that demo well and never reach production
- •No single source of truth for the data AI would use
- •Use cases held as a list, not ranked by impact, effort, and risk
- •Governance written down but not enforced at the edges
Scaling
First wins are in production and the question shifts to repeatability. Data ownership is assigned, governance gates exist, and the work is sequencing the next initiatives and proving outcomes one at a time.
- •A governed data layer with structured, owned attributes
- •Review gates and traceability on critical changes
- •A prioritized roadmap with cost and a defined first move
- •One measured outcome per use case before scaling the next
AI-native
AI is designed into the operating model, not bolted on. Accountability runs beyond IT, regulation is a design input, and new initiatives ship on top of governed data with measured return.
- •Ownership spans data, commerce, IT, and leadership
- •EU AI Act and audit-readiness built into workflows
- •Build-versus-buy decided per initiative on evidence
- •A measurement baseline that turns AI spend into a P&L line
The Approach
The four readiness dimensions I score
Every credible readiness assessment scores the same four dimensions. I audit each one so you know the blockers and catalysts before you commit budget.
Data
The single biggest predictor of whether an AI initiative reaches production. I score your data layer against the AI-ready checklist that decides what is feasible.
- ✓One authoritative source of truth for the data AI would use
- ✓Core attributes structured, not embedded in free text
- ✓Variants, compatibility, and classifications explicitly modeled
- ✓Data ownership assigned and enforced end to end
Key Insight
60% of AI projects abandoned without AI-ready data
Gartner
Infrastructure
Whether your platform can run AI workloads and connect them to real processes. I assess integration posture and the path from prototype to production.
- ✓Integration posture across ERP, PLM, PIM, and source systems
- ✓Deployment options: cloud models via API versus self-hosted
- ✓Path from no-code prototype to a production-grade system
- ✓Scaling and monitoring readiness for live AI solutions
Talent
Whether your teams can build, govern, and adopt AI. I score capability across functions so the roadmap matches the people who will run it.
- ✓Capability scoring across technical and business teams
- ✓Adoption beyond a small core team
- ✓Change-management readiness and resistance signals
- ✓Where a delivery partner closes a gap versus internal build
Governance
Whether AI accelerates trust or creates risk. I read your governance posture and EU AI Act exposure before any automation touches a critical workflow.
- ✓Category-level enrichment and review standards
- ✓Traceable, auditable changes with human approval where it matters
- ✓EU AI Act exposure and ISO/IEC 42001 alignment
- ✓Explainability and data-residency controls as design inputs
Engagement Model
The AI Design Sprint, from a blank map to a funded decision
A fixed-duration sprint that moves you from scattered ambition to a prioritized, costed plan in about 90 days. Five stages, each with a named deliverable.
Map
I align on the business outcomes that matter, interview leadership and technical teams, and map AI potential across your organization, processes, and products and services.
- ✓Business objectives and success definitions
- ✓AI potential map across the three sprint lenses
- ✓Current AI inventory and stakeholder map
Prioritize
I score every candidate use case by impact, effort, and risk, and score your readiness across data, infrastructure, talent, and governance. The two scores together decide what is worth funding.
- ✓Use-case shortlist scored by impact, effort, and risk
- ✓Readiness scorecard across the four dimensions
- ✓Data and governance gap read with EU AI Act exposure
Prototype
For the top candidates I run a feasibility check, often a no-code prototype, to test the assumptions that carry the most risk before any budget is committed to a full build.
- ✓Feasibility check on the highest-priority candidates
- ✓No-code prototype validating the riskiest assumptions
- ✓Cost-benefit evaluation per shortlisted initiative
Roadmap
I sequence the prioritized initiatives into a ranked, costed 90-day roadmap, each move carrying effort, risk, expected return, a build-versus-buy framing, and a clear next step.
- ✓Ranked, costed 90-day AI roadmap
- ✓Build-versus-buy framing per initiative
- ✓KPI and measurement baseline for the first build
Decide
I package the findings into an executive decision memo for management and shareholders, so the roadmap arrives as a fundable decision with a defined first move.
- ✓Executive decision memo for management and shareholders
- ✓Defined first move with owner and timeline
- ✓Roadmap you own, whether or not I deliver it
Phase 01
Map
I align on the business outcomes that matter, interview leadership and technical teams, and map AI potential across your organization, processes, and products and services.
- ✓Business objectives and success definitions
- ✓AI potential map across the three sprint lenses
- ✓Current AI inventory and stakeholder map
Phase 02
Prioritize
I score every candidate use case by impact, effort, and risk, and score your readiness across data, infrastructure, talent, and governance. The two scores together decide what is worth funding.
- ✓Use-case shortlist scored by impact, effort, and risk
- ✓Readiness scorecard across the four dimensions
- ✓Data and governance gap read with EU AI Act exposure
Phase 03
Prototype
For the top candidates I run a feasibility check, often a no-code prototype, to test the assumptions that carry the most risk before any budget is committed to a full build.
- ✓Feasibility check on the highest-priority candidates
- ✓No-code prototype validating the riskiest assumptions
- ✓Cost-benefit evaluation per shortlisted initiative
Phase 04
Roadmap
I sequence the prioritized initiatives into a ranked, costed 90-day roadmap, each move carrying effort, risk, expected return, a build-versus-buy framing, and a clear next step.
- ✓Ranked, costed 90-day AI roadmap
- ✓Build-versus-buy framing per initiative
- ✓KPI and measurement baseline for the first build
Phase 05
Decide
I package the findings into an executive decision memo for management and shareholders, so the roadmap arrives as a fundable decision with a defined first move.
- ✓Executive decision memo for management and shareholders
- ✓Defined first move with owner and timeline
- ✓Roadmap you own, whether or not I deliver it
Technologies we work with
Battle-tested tools across the modern cloud-native stack
Discovery and Sprint Methods
Readiness and Governance Frameworks
Measurement and Decision Output
FAQ
Explore More
More of the AI catalog
AI services that work together across Engineering, In-Product, and Business Operations. Pick what fits your next move.
Let's Talk
See where your AI value really is
Begin with the diagnostic that scores your readiness, ranks your use cases, and hands you a costed 90-day roadmap your board can act on.
Based in Düsseldorf, Germany, working with clients across Europe