AI at work

Ready for the demo. Ready for the working day.

Putting AI into regular use raises practical questions about information, permissions, quality and ownership. Nemely helps you answer them early, then build the evidence needed for a sensible rollout decision.

Talk through your use case Find your starting point

Microsoft Copilot StudioPower Platform governanceAgent evaluation

A few organizations we've worked with

Build confidence from the first use case.

Readiness is a set of decisions you can make and test. Start with the task, examine the outputs and agree who will keep the solution useful after launch.

Task, information & access

Make the important dependencies visible.

Who will use the solution, and what should improve? Whether you are adopting Microsoft 365 Copilot or building an agent, review the information it can reach, the work it may perform and where a person must decide. Nemely turns those dependencies into a focused pilot plan with business and technology owners.

Identify the owner of the task and the information it depends on.
Review information access, connection identities and permissions for each proposed action.
Agree what requires human review and how an issue reaches an owner.

Explore Nemely’s AI readiness service

Copilot Studio evaluation

Look beyond whether the answer sounds convincing.

Copilot Studio evaluations let makers assess agent responses against test cases and chosen criteria. Microsoft’s test-case view shows the response, context and evaluation detail together. We use that evidence with business review to investigate weak answers and compare changes.

Microsoft’s example test-case detail shows an evaluation of a sample travel response. It illustrates how to inspect results; the scores are not a Nemely assessment or a compliance certification.

Source: Microsoft Learn

Include realistic, ambiguous and unsupported requests in the test set.
Choose criteria that match the task and investigate failed examples.
Review consequential answers and actions with people; an evaluation score is not a safety certification.

Build evaluation skills with your team

How Nemely helps

Leave the assessment with decisions you can act on.

Nemely turns technical findings into a practical investment decision. Agree which Copilot or agent use case is worth piloting, what needs work first, the likely operating costs and how the business will judge the result.

AI Readiness Training and Workshops Continuous Improvement

Map the proposed use

Meet the people responsible for the task, platform and information. Identify the dependencies that could prevent a useful pilot.

Define the evaluation approach

Create representative examples, acceptance criteria and review responsibilities. Check answers and resulting business actions, including failed connections and requests the solution should decline or escalate.

Set the operating responsibilities

Agree ownership for knowledge, access, costs, monitoring and changes. Turn remaining gaps into a prioritised plan rather than leaving them in an assessment document.

Planning notes

Practical governance without an abstract programme

What connector policies do—and what they do not do

Power Platform data policies help govern which connectors are available and which can be used together. They complement application permissions, environment configuration and review procedures. They do not establish that an AI answer is accurate or that the business has authorised a particular action.

A release decision should have an owner

Document the intended use, important limitations, test evidence and unresolved issues. Make it clear who accepts the residual risks for that use case and who can change or withdraw the release. The level of formality should fit the impact of the task and your organisation’s requirements.

FAQs

Your questions, answered.

Is this a compliance certification?

No. This work supports practical readiness and operating decisions for your chosen Microsoft AI use case. Where legal, regulatory or industry-specific requirements apply, your responsible specialists should determine the obligations and required assurance. We can help translate those requirements into the implementation.

Do we need perfect data before starting an AI pilot?

You need information suitable for the selected task and a clear understanding of its limitations. A narrow use case with maintained sources can be a sensible start. Broader quality issues should be documented and prioritised when they affect what the pilot is expected to do.

Should a preview feature be part of the first release?

A preview may be suitable for a clearly scoped evaluation. Before depending on it for live work, review its current terms, support, availability and limitations. We separate experimentation from the operational commitment and avoid basing a rollout date on an unconfirmed roadmap item.

What affects the cost and timing of an AI readiness review?

Scope depends on the use cases, information sources, connected systems and controls to assess. Access to the relevant owners and evidence also affects timing. We agree the review outputs and decision points first; building a pilot or implementing controls is scoped separately.

Who should be involved in the first review?

Include the business owner of the proposed task and people responsible for its data, platform and security. Bring privacy, legal or other specialists into the decisions that need their input. A concrete example of the task helps the group agree acceptable results and review responsibilities.

Explore the Microsoft capabilities

Build for today. Keep a view of what comes next.

Microsoft is moving Dynamics 365, Power Platform and Dataverse updates to the AI at Work roadmap from September 2026. We use the current feature status when planning your solution, and evaluate previews separately before proposing them for production.

Explore Microsoft’s roadmap approach

Microsoft capabilities reviewed 6 October 2026. Licensing, configuration, language and regional availability vary by feature.

What are you looking for?

Ready for the demo. Ready for the working day.

A few organizations we've worked with

Build confidence from the first use case.

Make the important dependencies visible.

Look beyond whether the answer sounds convincing.

Who keeps it useful when the business changes?