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AI guides
For your team

Practical questions for choosing on-site AI and reviewing document work.

AT A GLANCE

Compare the whole offer.
Not just the assistant.

What's included

Clarify what the supplier delivers and what your team must supply or build.

Fit for everyday work

Check whether the assistant fits everyday tasks and who adapts it.

Responsibility after setup

Ask who will operate, maintain and change the system after setup.

Acceptance criteria

Use the same representative task and agree on acceptance criteria before comparing options.

WHAT TO CHECK

Choose the question.
Read the practical guide.

Choose a practical question about your work, then examine delivery options, data handling and review responsibilities.

Buying guideAn AI assistant at your company. What should the supplier deliver?Start with a task your team needs to complete, not the assistant's opening screen. Compare supplier-delivered on-site AI, software-only tools and an in-house build by what each delivers, what remains your responsibility and how your team will decide whether the result is usable.Read the guide
Buying guideBuying on-site AI for a law firm Start with one contract comparisonPut an authorized agreement beside its proposed revision. Ask for a difference register that a lawyer can check against both files. That is a useful buying task for an AI assistant at your firm: defined inputs, a reviewable deliverable and an agreed response when something cannot be checked. It is not a handover of the matter.Read the guide
Workflow guidePharmaceutical document review Start with the evidenceAn approved protocol, its study report and authorized result exports serve different purposes. Bring them together in one source-linked review pack without blurring those roles. Before anyone uses a draft, reviewers should be able to see where each observation came from, which approved records conflict and what remains unknown.Read the guide
Buyer guideNot used for training Does not mean not retainedPut a proposed insurance document assistant on a procurement worksheet. One answer says that submitted documents are not used for model training. The next question remains unanswered: where can the extracted text, prompts and responses persist? A training restriction governs one use of information. Retention governs whether copies remain, for what purpose and under which conditions. For confidential finance and insurance work, the useful buying decision starts by separating those questions, not by treating either provider’s privacy headline as a complete data-flow description.Read the guide
SOURCES

Sources and limits
What to confirm

The Matrix product page describes one offer: local hardware, a private workspace and setup by engineers. The questions here help you compare delivery scopes, not assume every supplier includes the same work. Confirm the responsibilities in the offer you are evaluating.

INDUSTRY ANALYSIS

Industry changes.
Practical implications.

AI security, enterprise AI and the engineering work behind deployment. Original analysis of public research and product updates, with sources and limits.

Industry analysisNo matching finance records? Ask what the search coveredAn assistant reports no matching finance records. A reviewer then sees a successful indexing run. Those observations still leave a practical question unanswered: did the search cover the relevant material? Microsoft's Azure AI Search documentation shows why the answer needs a connected evidence record, from query conditions to source ingestion and document errors, rather than a reassuring status label.Read the analysis
Industry analysisThe change request lost its response. What would justify another attempt?An assistant submits an authorized supplier-record change, then stops waiting without a response. Before anyone authorizes another attempt, the useful artifact is an operation record: what was requested, which identifier protects against duplicates, what evidence remains available and how the original outcome can be reconciled. Microsoft's architecture guidance explains these distinctions. It does not establish that a particular supplier system implements them.Read the analysis
Industry analysisConflicting finance policies Show version status before decidingSuppose an AI assistant retrieves two versions of a finance policy with different instructions. One has a later modification date; the other is the version readers can see as approved. Choosing the newer file would conceal the question that matters: what evidence lets the responsible owner decide which policy governs this task? Search conditions and publication status can make that decision better informed. Neither makes it automatically.Read the analysis
Industry analysisAn assistant can read a supplier file. What else is it allowed to do?Consider a proposed manufacturing workflow: an assistant finds a supplier certificate, compares its contents with a requirement and prepares a change to an external record. Permission to find the certificate does not answer whether its contents may be sent to another service or whether the record may be changed. Current OpenAI documentation makes those distinctions useful, but it describes different product surfaces, not one verified manufacturing integration.Read the analysis
Industry analysisResearch AI access Who owns the permission?An approved lab and an activated member are not the same thing. OpenAI’s Rosalind Academic Plan makes that distinction useful for research administrators: institutional access, named membership and responsibility for permitted use need separate answers.Read the analysis
Industry analysisAI document extraction Evidence before approvalA date in a structured field is not yet a date your team should act on. Microsoft's September 29 engineering article puts extraction back in focus. The practical question is what evidence must travel with a value before another workflow accepts it.Read the analysis
Industry analysisAn assistant reads a document. Who can read its trace?Could text from an insurance customer document or a manufacturing supplier file also appear in an assistant's execution record? Microsoft's October 19 Browser Automation announcement raises a concrete review question: where sensitive trace content goes, who can read it and what happens to older copies. Routing, access protection and deletion need separate evidence.Read the analysis
Industry analysisKeep the test fixed. Then judge the control.A new agent control should face the evaluation that exposed the problem, not a more convenient replacement. Microsoft’s run-assert-eval example offers a useful discipline: preserve the test, review enforcement and inspect both prohibited behavior and legitimate work. Its changing applicability counts and remaining failures also show why a favorable aggregate cannot settle acceptance.Read the analysis
Industry analysisThe schedule starts the agent. Who owns the result?A report arrived in Teams during Microsoft's October 13 demonstration. That showed a delivery path, not a working calendar trigger: the author says the routine was dispatched manually. For a company considering scheduled AI assistants, the useful question is what connects the start, the accepted job, the finished report and its delivery—and who takes responsibility when that chain breaks.Read the analysis
Industry analysisAn AI-drafted CSF profile is a starting point, not assurance.A policy says a security review must happen. A record shows whether it happened. An AI assistant can help organize both, but putting them under the same framework heading does not make them equivalent evidence. NIST's initial public draft SP 1353 illustrates AI-assisted work with the Cybersecurity Framework 2.0 (CSF 2.0), while explicitly separating its examples from assessment or assurance methodologies. For buyers, the useful question is whether the assistant makes evidence easier to inspect, not whether it produces a convincing-looking profile.Read the analysis

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