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AI Readiness Assessment

Most AI projects stall before anyone picks a model: scattered data, no owner, no platform to serve it. Our AI readiness assessment scores your data, platform, governance and use cases in three weeks and tells you what to fix first.

AI Readiness Assessment

Data foundation

We map where your data lives, how clean it is, and whether your pipelines can feed a model in production rather than a demo. This is where most gaps show up first.

Architecture and platform

We review your platform, whether that is Azure and Databricks or something else, against what AI workloads need: compute, storage, orchestration, and the path from notebook to production.

Governance, security and compliance

We check whether you can run AI without a compliance problem: access control, lineage, audit trails, and fit with GDPR, HIPAA or CSRD. In regulated industries this gap kills projects late.

Use cases and ownership

We work with your teams to find where AI pays off, then check who would own each use case after launch. A ranked backlog with owners beats a list of ideas.

Data foundation

We map where your data lives, how clean it is, and whether your pipelines can feed a model in production rather than a demo. This is where most gaps show up first.

Architecture and platform

We review your platform, whether that is Azure and Databricks or something else, against what AI workloads need: compute, storage, orchestration, and the path from notebook to production.

Governance, security and compliance

We check whether you can run AI without a compliance problem: access control, lineage, audit trails, and fit with GDPR, HIPAA or CSRD. In regulated industries this gap kills projects late.

Use cases and ownership

We work with your teams to find where AI pays off, then check who would own each use case after launch. A ranked backlog with owners beats a list of ideas.

Data foundation

We map where your data lives, how clean it is, and whether your pipelines can feed a model in production rather than a demo. This is where most gaps show up first.

Architecture and platform

We review your platform, whether that is Azure and Databricks or something else, against what AI workloads need: compute, storage, orchestration, and the path from notebook to production.

Governance, security and compliance

We check whether you can run AI without a compliance problem: access control, lineage, audit trails, and fit with GDPR, HIPAA or CSRD. In regulated industries this gap kills projects late.

Use cases and ownership

We work with your teams to find where AI pays off, then check who would own each use case after launch. A ranked backlog with owners beats a list of ideas.

What the three weeks look like.

WEEK 21

Discovery (week 1)

We interview engineering, data, security and the business owners who would use AI daily. We also collect documentation on your systems, data and past AI attempts. Deliverable: a mapped current state, agreed scope and access for week 2.

Technical deep dive (week 2)

Our architect and data engineers run a hands-on AI readiness audit: data quality sampling, pipeline reliability, architecture fit and governance controls. We test claims, not diagrams. Deliverable: evidence-based findings on every dimension.

Scoring and roadmap (week 3)

We score each dimension on our AI readiness assessment framework, rank your use cases by value and feasibility, and sequence the gaps by what they unblock. Deliverable: a scorecard, findings report and 6 to 12 month roadmap with effort estimates.

Readout and decision

We walk your leadership and engineering teams through the results in one session and answer the hard questions. Everyone who decides hears the same findings. Deliverable: a decision-ready readout.

WEEK 21

Discovery (week 1)

Technical deep dive (week 2)

Scoring and roadmap (week 3)

Readout and decision

We walk your leadership and engineering teams through the results in one session and answer the hard questions. Everyone who decides hears the same findings. Deliverable: a decision-ready readout.

WEEK 21

Discovery (week 1)

Technical deep dive (week 2)

Scoring and roadmap (week 3)

Readout and decision

We walk your leadership and engineering teams through the results in one session and answer the hard questions. Everyone who decides hears the same findings. Deliverable: a decision-ready readout.

Why an AI readiness assessment rarely points at the model

AI projects rarely fail on the model. They fail on the data underneath: sources spread across a dozen tools, no owner for quality, no lineage, nothing an assistant can cite. You are probably not ready if:

Analysts pull data from more than three tools to answer one question
Nobody can say who owns quality for your most important dataset
Your last AI pilot never left the demo
Compliance reviews happen after the build
You could not rank your top three AI use cases by value

Three or more? Start with the data. To score yourself first, use our AI readiness checklist.

For a U.S. healthcare organization the questions came first: can you govern PHI at column level, audit every access, and scale to ML workloads? The answer was a HIPAA-aligned platform on Azure and Databricks, with Unity Catalog governing every data and AI asset. For a global chemical company with over 10 million files in its data lake, 30,000 more every day and 1,000 people who needed them, the gap was access and ownership, not compute.

Most gaps the assessment finds map to work we do as a Databricks Partner and Microsoft Solutions Partner for Cloud & AI Platforms: data engineering on Databricks, cloud platforms on Azure, and governance and infrastructure as code. The roadmap is written so any team can run it.

Frequently asked questions, answered.

What is an AI readiness assessment?

A structured review of whether your organization can build and run AI in production. Ours scores four dimensions (data, platform, governance, use cases) in three weeks and ends with a scorecard, a findings report and a prioritized roadmap.

How much of our team's time does it take?

Expect 6 to 10 stakeholder interviews of about an hour each, plus one technical contact who can grant read access to your systems and answer questions during week 2. We do the rest.

What do we get at the end?

A one-page scorecard, a findings report with the evidence behind each score, and a 6 to 12 month roadmap with effort estimates. All of it is yours to use with any team.

Do we need a data platform before starting with AI?

Not a complete one, but you need a governed path to the data your first use cases depend on. The assessment tells you whether that path exists and what it would take to build it.