Find the AI worth building. Skip the rest.
A fixed-scope AI Readiness Audit for healthcare and manufacturing teams that need clarity before committing budget. In one to three weeks you get a clear map of which AI opportunities are worth pursuing, what they would cost to build, what risks matter, and what to do first.
Computer science, medical imaging
Technical leadership
Medical-AI research
Systems, not demos
The problem the audit solves
Most teams know AI matters but cannot tell a genuine opportunity from an expensive distraction. Vendors push tools, internal stakeholders push pet projects, and the first idea to get funded is rarely the one with the best payback.
The result is months spent on fragile prototypes that never ship, or compliance dead ends discovered far too late. The audit replaces that guesswork with a ranked, evidence-based plan you can actually commit budget to.
What you receive
Everything you need to decide what to build first — and what to leave alone.
Current-state review
An honest read on your data, systems, workflows, and team readiness — where AI can plug in today and where the groundwork is missing.
Opportunity map
Use cases ranked by impact, feasibility, risk, and effort, so the highest-value, lowest-risk work rises to the top.
Build-vs-buy recommendation
For each major use case, a clear call on whether to build custom, buy a tool, or wait — with the reasoning behind it.
Compliance and risk notes
Where relevant, GDPR, EU AI Act, and MDR/IVDR touchpoints flagged early — before they become rework. Awareness and readiness, not legal advice.
Prioritized roadmap
A sequenced plan with a clearly identified first quick win — the project most worth proving next.
Live readout session
A working session to walk through the findings with your team, answer questions, and align on next steps.
Two audit tracks
Same method, scoped to your context. Choose the track that matches your data and regulatory exposure.
Standard AI Readiness Audit
Manufacturing & SME · 1–2 weeks
For operations, quality, and digital-transformation teams that want to identify the first AI use case with measurable ROI before buying tools or hiring.
- ✓ Opportunity map across quality, maintenance, and process automation
- ✓ ROI framing for the top candidates
- ✓ Build-vs-buy and pilot design for the first quick win
Regulated / Health-Data Audit
Healthcare & medical AI · 2–3 weeks
For healthcare, medical-AI, and health-data teams that need regulatory awareness from day one — validation and traceability built into the plan.
- ✓ EU AI Act, GDPR Article 9, and MDR/IVDR implications
- ✓ Validation and traceability considerations per use case
- ✓ Data-minimization and residency options, including on-premise
Fixed scope, no surprises
Every audit is fixed-scope and fixed-fee, agreed up front in a short scoping call. You know exactly what is included and when you get it. Pricing depends on track and complexity and is shared in the scoping call.
The audit fee is credited
If you move forward, the audit fee is credited in full against the first month of a build or Fractional AI Lead retainer started within 30 days. The audit is designed to fund itself.
Where the audit leads
The audit is the front door. Most engagements follow a conservative, evidence-led path.
AI Readiness Audit
Decide what is worth building and what to do first.
Pilot / Proof-of-Value
Build the highest-impact quick win and prove the path before scaling.
Audit questions, answered
Start with an AI Readiness Audit
Book a 30-minute scoping call to bound the work and get a fixed quote. No commitment, just a clear next step.
What is an AI Readiness Audit?
An AI Readiness Audit is a fixed-scope assessment that decides whether an AI use case is worth building, before anyone writes code. It examines five dimensions: whether the data exists and can be reached repeatedly, whether the problem is feasible with current methods, what the return actually is once lifetime running costs are counted, where the system sits under the EU AI Act and GDPR, and whether to build or buy.
The output is not a technology recommendation. It is a ranked list of use cases scored on impact, feasibility, risk and effort, with a defined first project and the evidence behind the ranking.
A common and legitimate result is that nothing should be built yet, because the binding constraint turns out to be an undefined process or data that does not exist. Reaching that conclusion in weeks costs a fraction of discovering it halfway through a build.