SERVICE SCOPE
What’s in scope.
01
Use case and data ownership
Identify the decision, service or workflow that should improve. Map the required sources, where they sit, who owns them and the access needed to use them.
02
Governance and security
Review data quality, lineage, privacy, retention and security. Record the controls and responsible-use requirements that must be evidenced before the use case proceeds.
03
Platform and experiment design
Compare the workload with Microsoft Fabric, Microsoft Foundry and related service options as relevant. Check regional availability, architecture and team capability, then define a bounded experiment with useful success measures.
A readiness package tied to a business owner.
The output should make the gaps and next decision visible without treating a platform purchase as the answer.
✓
Prioritised use-case register and data-source map.
✓
Readiness, governance and access findings.
✓
Regional and service-fit check.
✓
Security and responsible-use requirements.
✓
Experiment brief, success measures and a target-state decision blueprint.
✓
A sequenced roadmap and operating responsibilities.
When this review fits.
Use it when there is pressure to adopt AI or improve analytics but data ownership, access or platform requirements remain uncertain. Begin with one defined use case so the assessment can be tested against a concrete outcome.
01
Check whether the information can support the proposed decision
A priority AI or analytics idea can fail before model selection if the relevant data is inconsistent, inaccessible or missing an accountable owner. Start with one business question and trace the evidence needed to answer it. Distinguish an unavailable source from a source whose meaning, freshness or quality is uncertain.
Fabric and Azure AI services address different parts of the data and application landscape. Their availability does not establish readiness. The review should connect business value, source access, governance and an evaluation approach, then identify which gaps block a controlled experiment and which can be handled within it.
02
Bring a use case and representative evidence
Prepare sample source records, current reports or manual outputs, data owners and relevant access constraints. Include examples of an acceptable result and mistakes that would make the outcome unusable. Map the source systems and intended users before deciding where data should be stored or processed.
The customer’s business owner should define usefulness and the information owner should approve access and intended use. The output should include a source and readiness map, unresolved governance decisions and an experiment brief with evaluation measures. Data engineering, model configuration and production deployment should be distinguished from the assessment. Regional availability and processing arrangements must be checked for the actual selected services.
03
Decisions the assessment should make visible
Decision
Evidence to examine
What to agree
Use-case fit
Business decision, users, expected result and cost of failure.
The narrow outcome that a first experiment can meaningfully test.
Data readiness
Quality, access, ownership, freshness and source limitations.
Which gaps require remediation before the experiment starts.
Platform and governance
Integration, processing requirements and operating responsibility.
The services to evaluate and the controls their use requires.
BEFORE YOU DECIDE
Practical questions.
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Is the result an AI-readiness certificate?
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Is one region suitable for every service?
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Is model performance assured?
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Do we need to select a model before the review?
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Can the review use incomplete data?
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What should the business owner contribute?
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Does a readiness review include building the solution?
BUILD YOUR BUSINESS CASE
Find the data constraints before funding the AI build.
The problem to solve.
The desired AI experience can hide unresolved questions about data quality, access, lineage and the ability to operate the system safely.
What success should look like.
Produce a practical readiness view around the chosen use case, with dependencies and remediation work visible before delivery starts.
Bring these into the discussion.
A useful starting point for your team and the people approving the investment.
Useful reading for your proposal.
Evidence, examples and questions to take into the decision.
Insight
13 April 2026
Data foundations
Put data quality and ownership first
Prepare a concrete inventory of the issues an AI system could inherit.
Read the insight
Insight
20 July 2026
AI governance
Define what AI may see and do
Use the database example to discuss access and human review.
Read the insight
Customer story
27 November 2025
Related customer experience
Bioforum: trusted data and reviewed AI outputs
Related delivery experience that shows why validation belongs in the design.
Read the customer story
LET’S GROW.
Work through the next step with Braintree.
Bring the use case and representative data. We can help identify what is ready and what needs work first.
