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Choose enterprise AI tools by starting with a business workflow and its measurable outcome—not with a model brand or polished demo. Define the work, compare deployment options, verify the exact product’s data and contract terms, and pilot it on representative tasks before committing. The right choice is the one your organization can operate safely and reliably at a cost and level of risk it accepts.
1. Define the business problem before shopping
Begin with one specific workflow, such as drafting a first response to support requests or searching an approved internal knowledge base. Avoid goals like “use AI across the business”: they are too broad to evaluate and too easy to mistake for a business result.
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Write down the baseline and desired outcome
- Users: Who will use the tool, and who may be affected by its outputs?
- Workflow: What steps will change, and where will AI enter the process?
- Baseline: How is the work done now? Record relevant measures such as completion time, correction effort, error rate, or service quality where you can measure them.
- Target: What meaningful improvement would justify the expense and operational effort?
- Unacceptable outcomes: Which errors, disclosures, delays, or decisions would make the tool unsuitable?
These points become your evaluation brief. They also help determine whether a task is appropriate for AI, what human review it needs, and what evidence a vendor must provide.
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2. Choose the deployment model before comparing vendors
“Enterprise AI” covers different products and responsibilities. AWS’s vendor-published Generative AI Security Scoping Matrix distinguishes five broad scopes. Use them to frame procurement and security discussions, not as an independent endorsement or certification.
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| Deployment scope | What you are evaluating | What to plan for |
|---|---|---|
| Consumer application | A general-purpose application used directly by an individual. | Check whether its terms and controls are appropriate for organizational content and work. Do not assume a consumer service has enterprise controls. |
| Enterprise application with embedded generative AI | An existing or new business application that includes AI features. | Verify the AI feature’s data flow, settings, access controls, and terms for the specific application and plan. |
| Application using a pretrained third-party model | A solution your organization builds around a model supplied by another provider. | Assess both the model provider and the application or service layer; identify which parties receive data and own operational responsibilities. |
| Fine-tuned model | A pretrained model adapted using additional training data or methods. | Clarify data rights, model-change responsibilities, testing, hosting, and ongoing maintenance across the adaptation and deployment. |
| Self-trained model | A model developed using training work managed by the organization or its providers. | Plan for substantially greater responsibility for data, expertise, security, evaluation, infrastructure, and continued operation. |
More control or ownership can mean more work to secure, evaluate, maintain, and govern. It does not automatically make a solution safer or more suitable. A ready-made application may reduce build effort but limit control over components; an API-based build may allow a closer workflow fit while making your organization responsible for more of the application and integration. Decide which responsibilities your team can actually support.
3. Build a shortlist around evidence, not claims
Start with products already in your organization’s approved technology environment and other providers that can meet the workflow’s requirements. Request evidence for the exact product, service tier, region, and deployment you would buy: answers for a different SKU or hosting arrangement may not apply.
Ask the vendor how your data is handled
- What information leaves your environment, where is it processed and stored, and how long is it retained?
- Is customer input, output, or other content used to train or improve a model? Can that use be disabled, and under what terms?
- Which model providers and subprocessors can access or process the information? How will you be notified when those parties or arrangements change?
- What access, deletion, export, and administrative controls are available?
Read the rights and contract terms
Ask how the agreement addresses customer content ownership, permitted data use, output rights, intellectual-property claims, licensing, provenance, quality expectations, security obligations, audit or evaluation rights, incident notification, service levels, termination, and export. Confirm which terms apply to your intended use rather than relying on general product descriptions. NIST’s Generative AI Profile (AI 600-1), released July 26, 2024, recommends that acquisition and procurement due diligence address intellectual property, privacy, security, and other risks, and that contracts set expectations for content ownership, usage rights, quality, security, and provenance.
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Check the controls and service evidence
Request product-specific details on identity and role-based access, administrative permissions, network options, logging, security assurance, vulnerability handling, incident response, reliability, and service commitments. If a feature is important—such as restricting who can use a higher-risk model—verify that it exists in the product and configuration you would deploy. AWS describes examples of granular roles, private connectivity, guardrails, and invocation logging in its own environment; do not assume another vendor offers equivalent controls without evidence.
4. Compare candidates on the same work
Use a common evaluation brief and evidence set so that each candidate is judged against the same need. A comparison based on different prompts, data, user access, or assumptions about review effort can make an unsuitable product look better.
| Evaluation area | What to examine |
|---|---|
| Task quality | Correctness, completeness, usefulness, and recurring error patterns on representative work. |
| Data, rights, and provenance | Data flows and retention; training or improvement use; licensing, content and output rights; and the basis for claims about source material. |
| Security and supplier resilience | Access controls, security evidence, incident practices, dependencies, resilience, and relevant supplier ownership or control exposure. |
| Integration effort | Connections to identity systems, applications, databases, and workflow tools; compatibility, setup, troubleshooting, and security implications. |
| Reliability and recovery | Latency and availability in your evaluation, behavior during errors or outages, and available fallback or restoration paths. |
| Governance and usability | Administrative controls, monitoring, human review, policy fit, ease of use, and likelihood that intended users can adopt it appropriately. |
| Total cost and contract fit | Expected usage assumptions, implementation and operating effort, contract obligations, evaluation rights, and exit options. |
Do not collapse these dimensions into a single score until decision-makers have agreed which failures are disqualifying. A low-cost candidate is not a good fit if its data terms, error profile, integration burden, or recovery options fail a business requirement.
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5. Run a controlled pilot, not a staged demonstration
A demonstration shows what a product can do under selected conditions; it does not establish how it will perform in your workflow. A pilot should use representative tasks and data that your organization is authorized to use, with the intended users, controls, and review process.
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- Establish expected results: Have qualified reviewers define what a useful, correct, or safe result looks like before judging outputs.
- Record failures as well as successes: Track factual errors, omissions, inappropriate responses, data-handling concerns, and cases that require escalation or correction.
- Measure operational performance: Record latency and reliability in the pilot, human correction effort, integration work, and the cost assumptions used for the expected workload.
- Test the workflow end to end: Check identity, permissions, data connections, handoffs, logging, and what happens when a model, integration, or service is unavailable.
- Review with accountable owners: Decide whether the results meet the pre-agreed target and risk limits, need changes, or rule the candidate out.
Keep the pilot’s conditions and findings with the purchase decision. Results from one workflow or configuration do not establish performance for a different task, user group, region, or production setup.
6. Evaluate supplier and AI risks together
The provider is part of the risk surface, alongside the model, application, and data. NIST’s SP 1326 final, published in July 2026, organizes ICT supplier due diligence around Foreign Ownership, Control, or Influence (FOCI), Provenance, Resilience, Foundational Cyber Practices, and Supply Chain Tiers. It is due-diligence guidance, not a product certification.
Apply the questions proportionately to the tool’s intended use and exposure. Identify suppliers and other third parties with access to organizational content, understand important dependencies, and determine how you will monitor changes or incidents after purchase. NIST AI 600-1 also recommends assessing both open-source and proprietary vendors and tools, inventorying providers with access to organizational content, and preparing third-party monitoring and contingency plans.
7. Set operating controls before rollout
Buying the service does not decide how employees should use it or who is accountable when it affects a business process. Microsoft’s organizational AI governance guidance emphasizes risk, external dependencies, integrations, and policy; use those topics to assign responsibilities for the chosen workflow.
Agree on owners, access, and human oversight
- Name a business owner accountable for the workflow and an operational owner for the service and its integrations.
- Set acceptable-use rules, access levels, and approval requirements that fit the use case.
- Specify when a person must review, approve, correct, or escalate an output before it is used.
- Define what activity and output information is logged, who can access it, and how it will be monitored.
Plan for incidents, change, and exit
Agree on who responds to security incidents, unsafe or degraded outputs, service outages, and changes in the model or its dependencies. Define a fallback process that lets essential work continue, and establish how data and records will be exported or removed if the service is terminated. Set review triggers—for example, a material change to data handling, model behavior, supplier access, or the workflow itself.
8. Use risk frameworks as references, not badges
The NIST AI Risk Management Framework is voluntary and intended to improve how trustworthiness considerations are incorporated into AI design, development, use, and evaluation. NIST says AI RMF 1.0, released January 26, 2023, is being revised. It can structure internal discussion and risk management, but it is not a universal procurement certification and does not replace product-specific diligence. NIST AI 600-1, released July 26, 2024, adds generative-AI-specific risk guidance, while SP 1326 addresses ICT supplier due diligence. Use the relevant guidance for the questions it covers and verify that product evidence and organizational controls address your actual deployment.
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