Find out if your organization is actually ready for AI
Know exactly where you stand and what to fix first, before you start the build.
Most organizations start building AI on a foundation they haven’t examined. The data is messier than the architecture diagram suggests, governance doesn’t exist yet, and the team that will own the system hasn’t been identified. The project stalls six months in.
A readiness assessment surfaces those gaps before they become expensive. Over two to three weeks, I work through five areas: data infrastructure and quality, tooling and vendor coverage, security and compliance posture, talent and organizational capability, and governance readiness. What comes out is a ranked list of blockers and a sequenced plan for addressing them.
The output is a working document: specific gaps, specific recommendations, and the order in which to close them so your first AI initiative has a real shot at shipping. Not a score on a maturity matrix.
What you get
A full picture of your readiness
An honest assessment across five dimensions: data infrastructure, tooling coverage, security posture, organizational capability, and governance readiness. No glossing over the gaps.
A ranked action plan
The blockers that matter most, in the order you need to close them. Not a generic maturity matrix, a specific and workable plan for your organization.
A clear go/no-go on your first initiative
An honest read on whether your planned AI project is viable given your current foundation, and what to change if it isn't.
A governance starting point
A first-pass AI governance framework appropriate for your stage: enough structure to manage risk without slowing the work to a crawl.
How it works
Stakeholder mapping and data collection
Conversations with the people closest to your data, tooling, and security posture, plus a document and system review. Two to three weeks, mostly async.
Five-dimension analysis
I work through data infrastructure and quality, tooling and vendor coverage, security and compliance posture, talent and organizational capability, and governance readiness.
Findings review
A working session with your leadership team to walk through the gaps, test the priority ranking, and pressure-test the recommendations.
Action plan delivery
A sequenced plan for closing the prioritized gaps, with enough specificity that you can hand it to your team and they know what to do first.
Proof, not promises
All client stories →AI Employee Assistance Chatbot
Designed and built a chatbot for a 40,000-employee organization to address questions about policies, asset tracking, and other internal tasks. Integrated with an updated knowledge base with an admin panel and the ticketing interface to create and track support tickets.
Leading financial institution in India
AI Vision for Fraud Prevention
Built a semantic image search interface to detect and present potential fraudulent gold loan applications to auditors for real-time fraud prevention.
Leading gold loan provider
Auto-scaling ML Inference
Designed and deployed a dynamically auto-scaling application for low-cost inference of ML jobs on geospatial data using GCP Cloud Run.
Public markets investor in MENA
Questions
What does an AI readiness assessment cover? +
Five areas: data infrastructure and quality (is your data clean, accessible, and structured for AI use?), tooling and vendor coverage (what you have, what's missing, what to avoid), security and compliance posture (data handling, access controls, regulatory exposure), talent and organizational capability (who owns AI and whether that structure will hold), and governance readiness (policies, decision rights, and accountability).
How long does it take? +
The core assessment runs two to three weeks: stakeholder interviews, system review, and analysis. The findings review and action plan delivery bring the total to about four weeks end-to-end. I keep it tight because the goal is a decision, not a report.
What do I get at the end? +
A written assessment covering each of the five dimensions, a ranked list of gaps and blockers, a sequenced action plan with enough specificity to act on, and a go/no-go read on your planned first initiative. Not a slide deck, a working document.
Who should be in the room? +
For the intake and findings review: your CTO or head of engineering, your CISO or whoever owns security, and at least one C-suite member who owns the AI decision. For the data-gathering: the people closest to your data pipelines and tooling stack.
Is this useful if we've already started with AI? +
Yes, often more useful. Most organizations six to twelve months into AI have already hit the gaps the assessment surfaces. The value shifts from prevention to diagnosis: understanding why the current initiative is slower than expected, and what to change.
Let's talk
30 minutes, no slides. We'll work the specific decision you're facing.