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A polished demo is only evidence that a tool can perform a prepared scenario. A durable decision also needs evidence about limitations, security, governance, integration, data and intellectual-property rights, support, monitoring and what happens if the service changes or ends.
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What vendor lock-in means for an AI purchase
Vendor lock-in is the practical difficulty of moving to another provider, bringing a workflow in-house or stopping a service without losing useful data, functionality, knowledge or negotiating leverage. AI systems can create several kinds of dependency at once:
- Technical dependency: prompts, agents, connectors, model-specific features, proprietary file formats, identity integrations or custom code may not work elsewhere.
- Data dependency: inputs, outputs, conversation history, evaluations, embeddings, metadata and derived datasets may be difficult to export or may be subject to retention limits.
- Rights dependency: a contract may limit reuse of outputs, adapted models, fine-tuning data, documentation or newly developed integrations after termination.
- Operational dependency: only the supplier may understand the workflow, making it hard for another team to maintain or replace it.
- Economic dependency: migration, retraining, re-integration and temporary dual running can cost more than the original subscription.
Lock-in is not automatically unacceptable. A highly managed service may be the right choice when it materially reduces risk or cost. The decision is whether the benefits justify the dependency and whether you have a credible route to change providers if quality, price, risk or strategy changes.
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Define the job before looking at vendors
Write a short use-case statement before scheduling demonstrations. Describe the problem and desired outcome rather than naming a product or prescribing a technical solution. This follows the approach recommended in the National Institute of Standards and Technology (NIST) procurement guidance.
Use-case statement
Record:
- Who will use the system and who may be affected by its outputs.
- What inputs it will receive, including sensitive, regulated or confidential information.
- What output or action is expected, and where a person must review it.
- Which systems it must connect to, such as identity, document, ticketing or data platforms.
- What failure would cost in money, time, safety, legal exposure, reputation or customer trust.
- What minimum performance, response time, availability and accessibility are required.
- Which conditions are deal-breakers, such as prohibited data processing locations, missing audit logs or inability to export records.
Set these criteria before a supplier controls the narrative. Otherwise, a compelling interface can cause the team to redefine the problem around whatever the first product happens to do.
Classify the risk and review depth
Match scrutiny to the use case. A tool drafting low-consequence internal notes does not require the same controls as one influencing eligibility, health, employment, financial or safety decisions. For every use case, identify privacy, security, bias, explainability, reliability and misuse concerns, then specify who owns each control. The UK Government’s current Guidelines for AI procurement and OECD guidance both emphasize multidisciplinary evaluation and continued review through the system lifecycle.
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Build a representative test set
Do not compare products using only a vendor’s sample prompts. Assemble a safely de-identified set of real work that includes routine cases, edge cases and likely failure conditions.
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What to include
- Typical inputs from each important user group.
- Messy, incomplete or ambiguous examples that expose assumptions.
- Rare but high-impact cases, with an explicit safe-handling procedure.
- Long documents, tables, images, multiple languages or other formats the workflow actually needs.
- Adversarial or misleading inputs relevant to security and misuse.
- Cases requiring refusal, escalation or human review rather than a confident answer.
Protect personal and confidential information. Use synthetic or de-identified examples where possible, and document what was changed so that a test does not accidentally reward a tool for recognizing a particular person or record.
Run the same evaluation for every candidate
- Freeze the task instructions, prompts, files, integrations and acceptance rules.
- Run each candidate on the same examples and record the model or service version, date and relevant configuration.
- Assess correctness, completeness, relevance, consistency and the amount of human correction required.
- Record failure types, not just a single score: fabricated facts, omissions, unsafe recommendations, formatting errors, privacy leaks, bias or workflow dead ends.
- Measure latency, rate limits, downtime, manual work and escalation effort when those affect the business process.
- Have subject-matter reviewers judge material outputs. Preserve the evidence and their reasons, not merely an overall rating.
This is an implementation of official advice to test proposed solutions and understand capabilities and limitations; it is not a universal benchmark prescribed by NIST, the UK Government or OECD. The useful result is a decision record showing what was tested, what was not tested and how uncertain the result remains.
Compare the dimensions that matter
Set context-specific weights before testing. There is no universally valid scorecard, and a high average quality score should not compensate for a non-negotiable security or exit failure.
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| Dimension | Questions to ask every candidate | Evidence to retain |
|---|---|---|
| Task performance | Does it meet the pre-set criteria on representative examples? Which errors recur, and how severe are they? | Test records, reviewer decisions, error taxonomy and version details |
| Reliability and limits | How stable are results across repeated runs, load levels, languages, file sizes and service updates? What limitations does the supplier disclose? | Repeat runs, service limits, change notices and known-issue documentation |
| Risk and governance | What privacy, security, bias, transparency and misuse risks arise? Who monitors, investigates and responds? | Risk assessment, audit information, incident process and named owners |
| Explainability and human control | Can users understand relevant reasons, sources or uncertainty? Can they review, correct, override and appeal an output? | Interface demonstrations, logs, review procedures and escalation tests |
| Interoperability | Which APIs, formats, identity systems, hosting arrangements, data stores and custom components are required? Can another provider use the same inputs and continue the workflow? | Architecture diagram, interface specifications, export samples and dependency list |
| Data and intellectual-property rights | Who may use, retain, export, delete or reuse inputs, outputs, derived products and newly developed or adapted intellectual property? What survives termination? | Contract language, data-processing terms, licence grants and deletion certificates |
| Lifecycle burden | What integration, hosting, training, support, maintenance, monitoring and migration work is required, and who pays for it? | Implementation plan, staffing estimate, service levels and five-year cost model |
| Exit readiness | Can you recover useful data and derived work, document the workflow, transfer knowledge and terminate cleanly? Can the steps be tested? | Exit plan, export test, runbook, training records and acceptance criteria |
Demand demonstrations and evidence, not theatre
Ask suppliers to demonstrate the stated task with your acceptance criteria. Require them to show ordinary, difficult and refusal cases, not just a successful scripted path. Ask what the system cannot do, which dependencies are mandatory, how updates are handled and what support is included.
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Separate three kinds of evidence:
- Supplier assertion: a claim in marketing or a proposal that still needs verification.
- Demonstration: a live or recorded example that may not represent your data or operating conditions.
- Acceptance evidence: a repeatable result on your agreed test set, with a remedy if the requirement is missed.
OECD’s 2025 account of US federal AI acquisition practice describes market research, detailed demonstrations and tests, performance-based requirements, contract oversight and testing before award. Adapt those practices to your organisation’s authority and risk; public-sector procurement rules do not automatically apply to private buyers.
Design for portability from the start
Technical portability
Map the workflow rather than evaluating the model in isolation. Identify every place the service touches your systems: authentication, storage, retrieval, orchestration, monitoring, human approval and downstream actions. Prefer documented interfaces and standard, machine-readable formats where they meet the use case. Keep prompts, evaluation data, configuration, schemas and business rules in systems your organisation controls rather than in an inaccessible vendor console.
Ask whether a replacement provider can receive the same inputs and produce an acceptable continuation of the process without rebuilding everything. A nominal API is not enough if exports omit metadata, audit history, embeddings, feedback or the relationships that make the data useful.
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Define each data category separately:
- Customer, employee or public records supplied as inputs.
- Prompts, instructions, retrieval indexes, labels and evaluation sets created by your team.
- Generated text, images, code, classifications and other outputs.
- Derived products such as summaries, embeddings, fine-tuning artefacts, analytics and workflow logs.
- Configuration, documentation, custom connectors and adaptations paid for by your organisation.
For each category, specify access, permitted use, retention, export format, deletion, backup handling and post-termination access. “You own your data” may not answer whether the supplier can reuse it for training, whether outputs can be exported in bulk or whether a derived index is included.
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Knowledge portability
Require documentation that another qualified team could use: architecture, data flows, prompts and policies, integration credentials process, monitoring definitions, incident history, test results, known limitations and operating procedures. Include knowledge-transfer sessions and a handover period in the plan, not as an informal promise at the end.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Negotiate flexibility into the agreement
NIST’s 2021 Guidelines for AI Procurement recommends interoperability and open licensing terms to help avoid lock-in. It discusses open standards, royalty-free licensing, intellectual-property arrangements and independent maintenance of systems that may become obsolete. These are negotiation options, not blanket legal advice; suitability depends on your jurisdiction, sector and bargaining position.
Have legal and technical reviewers examine:
- Export rights, formats, frequency, fees, assistance and access duration after termination.
- Ownership or licence rights for inputs, outputs, derived products, custom integrations and newly developed intellectual property.
- Restrictions on reverse engineering, independent maintenance or using another provider.
- Subcontractors, hosting locations, security obligations, breach notification and audit access.
- Service-level commitments, support response, change notice, version deprecation and material-change termination rights.
- Data deletion, backup expiry, certification and treatment of records required for legal or regulatory retention.
- Transition assistance, knowledge transfer, documentation and rates for migration work.
Ask for an export sample and a rehearsal of the termination process before signing. A clause that sounds flexible but has never been exercised may still conceal an operational barrier.
Calculate lifecycle value, not the introductory price
Build a total-cost model over the period you expect to use the system. Include:
- Discovery, testing and procurement effort.
- Integration, custom development, data preparation and security review.
- Usage, seats, storage, hosting, model calls and overage charges.
- Training, accessibility work, change management and user support.
- Monitoring, evaluation, audits, incident response and periodic revalidation.
- Maintenance caused by model, API or policy changes.
- Human correction, escalation and the cost of errors or unmet requirements.
- Migration, dual running, retraining and contract-exit assistance.
OECD’s 2025 report describes US guidance that uses performance-based requirements, contract oversight, periodic value evaluation and possible sunset criteria. A sunset or renewal gate is useful when a service’s benefits, risks or costs can change materially. Define the evidence required to continue, renegotiate or stop rather than allowing automatic renewal to make the decision by default.
Monitor after selection and test the exit
Selection is the beginning of evaluation, not the end. Establish review points and named accountability for quality, cost, emerging risks, security, stakeholder experience and supplier changes. Re-run a suitable portion of the test set after major model, prompt, data, integration or policy changes.
Minimum monitoring record
- What is measured, how often and against which baseline.
- Who can pause the system, approve a change and investigate an incident.
- How users report errors, bias, unsafe outputs or privacy concerns.
- What thresholds trigger remediation, additional human review or suspension.
- How costs, usage and service-level performance are reconciled with invoices.
- When the use case is re-approved, retired or moved to another provider.
Exit rehearsal
- Request a complete export of agreed data, metadata, logs, configurations and derived work.
- Verify that the files are readable without the supplier’s application and that relationships and timestamps remain meaningful.
- Reconstruct the workflow in a test environment using the documentation and transferred knowledge.
- Confirm which functions cannot be reproduced and decide whether to redesign, replace or accept each gap.
- Exercise access removal, backup deletion, retention exceptions and evidence of completion.
UK Government guidance asks buyers to define end-of-contract responsibilities and data processes, and highlights ongoing testing, training, support and knowledge transfer. It also notes that explainable outputs can make it easier for the organisation or another supplier to continue or build on an AI system, limiting lock-in.
Use frameworks as prompts, not as a substitute for fit
The NIST AI Risk Management Framework (AI RMF) is voluntary. NIST describes it as a way to improve the incorporation of trustworthiness considerations into the design, development, use and evaluation of AI products, services and systems. NIST’s current page says AI RMF 1.0 is being revised, so check the current version before using it operationally. A framework can organize questions about risk and controls; it cannot tell you whether a particular tool meets your task, contract or exit requirements.
Public-sector statistics illustrate why procurement capability matters but do not predict a product’s quality. OECD’s Digital Government Outlook 2026, analysing the 2025 Digital Government Index, reports that 10 of 36 OECD countries (28%) reported any financial or non-financial impact measurement of government AI use cases. The same analysis reports that 21 of 36 countries (58%) provided central support for procuring AI goods and services. These are country-level government findings, not measurements of private companies or proof that any tool succeeds.
A decision record you can defend
At the end of evaluation, keep a concise record that another decision-maker can audit:
- The use case, users, affected people, intended outcomes and risk classification.
- Pre-set requirements, deal-breakers, weights and reasons for each weight.
- The test set, test conditions, versions, reviewers, results and unresolved uncertainty.
- Security, privacy, bias, explainability, governance and integration findings.
- Five-year lifecycle costs, including human work and exit effort.
- Data, intellectual-property, export, deletion, support and transition terms.
- Monitoring owners, review dates, change triggers and sunset criteria.
- The exit design, export rehearsal result and assumptions that would invalidate it.
This record prevents a vendor’s sales narrative, a temporary discount or a single impressive output from becoming the de facto strategy. It also gives users a basis for revisiting the decision when requirements or the market change.
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