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Any vendor deploying a high-risk AI system must demonstrate conformity assessment and EU registration before August 2026. Non-compliant vendors should be eliminated from the evaluation regardless of their capability score." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Step 3: Build an AI Vendor RFP That Gets Honest Answers", "url": "https://alicelabs.ai/en/insights/ai-vendor-selection-framework#ai-vendor-rfp-guide", "description": "An effective AI vendor RFP forces vendors to provide evidence artifacts — benchmark results, architecture diagrams, security certifications — rather than capability claims. 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Compressing below 6 weeks typically means skipping the PoC or using vendor demo data, both of which significantly increase replacement risk." } }, { "@type": "Question", "name": "What are the 5 dimensions to score AI vendors on?", "acceptedAnswer": { "@type": "Answer", "text": "The Alice Labs framework evaluates AI vendors across five weighted dimensions: Technical Capability (30%), Data Governance & Security (25%), Integration & Scalability (20%), Vendor Viability & Support (15%), and Commercial Terms (10%). Adjust weights for context — regulated industries should increase Data Governance to 30–35%, and organisations with complex legacy systems should increase Integration to 25–30%." } }, { "@type": "Question", "name": "How do I write an AI vendor RFP?", "acceptedAnswer": { "@type": "Answer", "text": "An effective AI vendor RFP includes four mandatory sections: Technical Evidence (benchmark results on your task using your data, model card, architecture diagram), Data Governance (GDPR DPA draft, data residency certification, ISO 27001 or SOC 2 Type II certificate), Integration & Deployment (API docs, SDK support matrix, reference architecture), and Commercial & Legal (full pricing schedule, IP ownership clause, SLA with remedies, exit clause). Set a 10-business-day response deadline." } }, { "@type": "Question", "name": "What is a realistic total cost of ownership for an AI solution?", "acceptedAnswer": { "@type": "Answer", "text": "Total cost of ownership for enterprise AI solutions typically runs 2–4x the initial licensing fee when all costs are included: compute and inference costs, integration development, change management, user training, and periodic model retraining. Alice Labs benchmarks this across 100+ implementations in Europe. Always build a 3-year TCO model before finalising vendor selection." } }, { "@type": "Question", "name": "Is EU AI Act compliance required for all AI vendors in Europe?", "acceptedAnswer": { "@type": "Answer", "text": "EU AI Act compliance obligations depend on risk tier. High-risk AI systems (defined under Annex III, covering areas like employment decisions, credit scoring, biometric identification) face mandatory conformity assessments and EU registration requirements from August 2026. For European enterprises, EU AI Act compliance status must be a binary gate criterion — not a scored dimension — in any vendor evaluation." } }, { "@type": "Question", "name": "How do I assess vendor lock-in risk in AI procurement?", "acceptedAnswer": { "@type": "Answer", "text": "Vendor lock-in risk is highest when a single vendor controls model weights, fine-tuning data, and inference infrastructure simultaneously. Assess four contract elements: IP ownership of fine-tuned models, data portability on exit (format, timeline, fees), foundation-model dependency (can you switch the underlying model independently?), and SLA remedies beyond credit-only terms. Negotiate exit clause and data portability before signing." } }, { "@type": "Question", "name": "How many vendors should I include in an AI vendor evaluation?", "acceptedAnswer": { "@type": "Answer", "text": "Start with a long-list of 6–10 vendors compiled from analyst reports and independent peer references. Score RFP responses to shortlist to 2–3 vendors for the PoC stage. Running PoCs with more than 3 vendors simultaneously is operationally complex and rarely produces meaningfully different outcomes — the scoring matrix should eliminate clear mismatches before the PoC." } }, { "@type": "Question", "name": "What should a proof-of-concept trial measure?", "acceptedAnswer": { "@type": "Answer", "text": "A PoC trial should measure: task-specific performance (accuracy, F1 score, or generation quality metric depending on task type), latency at P50/P95/P99 percentiles, error rate, integration complexity (hours required to connect to one real downstream system), and support quality (response time and technical depth of vendor answers). Success criteria must be defined and documented before the trial begins — not after results are in." } }, { "@type": "Question", "name": "Should AI vendor selection involve legal counsel from the start?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Legal counsel should be involved from Step 1 — reviewing the use case document — not only at contract stage. Data processing constraints, GDPR obligations, sector-specific regulation, and IP ownership questions often eliminate vendor options before the RFP is even issued. Legal review begun in parallel with the PoC (not after it) is how Alice Labs keeps enterprise evaluations within the 6–10 week target." } }, { "@type": "Question", "name": "What is the difference between an AI vendor evaluation framework and a standard technology RFP process?", "acceptedAnswer": { "@type": "Answer", "text": "A standard technology RFP evaluates documented capabilities and contractual terms. An AI-specific evaluation framework adds three elements: pre-set weighted scoring criteria locked before vendor contact (preventing anchoring bias), mandatory PoC on the buyer's own production data (not vendor demo environments), and binary gate criteria for EU AI Act compliance status and GDPR data residency. 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[Home](/en) [Insights](/en/insights) [AI Strategy](/en/insights/ai-strategy) AI Vendor Selection Framework: Evaluate, Score & Choose Confidently AI Strategy How-To Recent · Last reviewed: 23 May 2026 · 115d ago # AI Vendor Selection Framework: Evaluate, Score & Choose Confidently ## TL;DR Quick Answer Cited by AI > Evaluate AI vendors in 7 steps: define use case → set criteria → score 5 capability dimensions → issue RFP → run PoC → assess risk → decide. Budget 6–10 weeks. A structured, 7-step framework for evaluating AI vendors — covering requirements, scoring, RFP design, risk assessment, and final selection — used across 100+ enterprise AI implementations. An AI vendor selection framework is a structured decision-making process that helps organizations evaluate, score, and choose AI technology providers based on capability fit, data governance, commercial terms, and risk profile — before committing to a contract. ![Eric Lundberg - Author at Alice Labs](/images/eric-lundberg.png) Written by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) ![Linus Ingemarsson - Reviewer at Alice Labs](/images/linus-ingemarsson.png) Reviewed by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) Published May 23, 2026 18 min read 3x More likely to replace AI vendor within 18 months without a formal selection framework [Gartner, The AI-Driven Future of IT Services Vendor Selection (2026)](https://www.gartner.com/en/documents/7593333) 2–4x Total cost of ownership vs. initial AI licensing fee [Alice Labs, 100+ enterprise AI implementations (2023–2025)](https://www.alicelabs.se/en/ai-strategy) 6–10 weeks Typical timeline for a rigorous AI vendor evaluation end-to-end [Alice Labs internal benchmarks across European enterprise deployments](https://www.alicelabs.se/en/ai-strategy) What you'll learn(6 points) - How to define AI use-case requirements before approaching any vendor - Which evaluation criteria matter most — and how to weight them in a scoring matrix - How to structure an AI vendor RFP that surfaces real capability evidence - How to design and interpret a proof-of-concept (PoC) trial on your own data - How to assess data privacy, security, and EU AI Act regulatory risk in AI contracts - How to make a defensible final vendor decision with full stakeholder alignment ## Key Takeaways - 01 Organizations that skip a formal AI vendor evaluation framework are 3x more likely to replace their AI vendor within 18 months (Gartner, 2026). - 02 A structured scoring matrix across 5 capability dimensions reduces evaluation time by up to 40% compared to ad-hoc approaches. - 03 Proof-of-concept trials must run on your own production data for a minimum of 4 weeks to produce statistically meaningful performance baselines. - 04 EU AI Act compliance status must be a mandatory gate criterion — not a scored dimension — for any vendor operating in European markets from August 2026. - 05 Total cost of ownership for AI solutions typically runs 2–4x the initial licensing fee when compute, integration, change management, and retraining costs are included. - 06 Vendor lock-in risk is highest in foundation-model-dependent solutions where model weights, fine-tuning data, and inference infrastructure are all controlled by the same vendor. ### Contents 18 min left - [01 Why Most AI Vendor Selections Fail (And What to Do Differently) ](#why-ai-vendor-selection-fails) - [02 Framework-Driven vs. Ad-Hoc Vendor Selection ](#framework-vs-ad-hoc) - [03 Steps 1–2: Define Your Use Case and Set Evaluation Criteria ](#define-requirements) - [04 EU AI Act Compliance as a Non-Negotiable Gate ](#eu-ai-act-gate-criterion) - [05 Step 3: Build an AI Vendor RFP That Gets Honest Answers ](#ai-vendor-rfp-guide) - [06 Step 5: Design and Interpret a PoC Trial That Actually Predicts Production ](#poc-design-interpretation) - [07 Step 6: Assess Legal, Security, and Vendor Lock-In Risk ](#risk-assessment-vendor-lock-in) - [08 Step 7: Make a Defensible Final Decision With Stakeholder Alignment ](#final-decision-stakeholder-alignment) - [09 AI Vendor Selection Timeline: What 6–10 Weeks Actually Looks Like ](#ai-vendor-selection-timeline) - [10 Before You Evaluate Vendors: The Build vs. Buy Decision ](#build-vs-buy-consideration) - [11 Which AI Vendor Categories Require This Framework ](#common-ai-vendor-categories) Part of [Enterprise AI Strategy Framework](/en/insights/enterprise-ai-strategy-framework) 01 / 11 Chapter ## Why Most AI Vendor Selections Fail (And What to Do Differently) Most AI vendor selections fail because organizations evaluate polished demos rather than performance on their own data — scoring sales narratives instead of structured evidence. Gartner (2026) found that organizations without a formal framework are 3x more likely to replace their AI vendor within 18 months. The core failure mode is simple: vendor selection driven by sales narratives rather than structured evaluation. Gartner's 2026 research on the AI-driven future of IT services vendor selection found that AI exposes hidden vendor risks — and that organizations must demand transparent performance evidence, not polished presentations. Without a formal framework, you are three times more likely to replace your AI vendor within 18 months. Alice Labs has observed this pattern across 100+ enterprise AI implementations since 2023 — and the root cause is almost always the same five failure modes. The 5 Most Common AI Vendor Selection Failure Modes Failure Mode What Goes Wrong Consequence Requirements undefined Vendor contact starts before use case is documented Evaluation criteria shaped by vendor framing, not business need IT-only evaluation Legal, security, and business teams excluded until late Contract surprises on IP, SLA, and data residency after selection Demo-data PoC PoC run on vendor-supplied sample data, not production data Performance degrades 30–60% when deployed on real data TCO underestimation Budget set against licensing cost only Actual TCO runs 2–4x license cost due to compute, integration, retraining Compliance ignored EU AI Act and GDPR not treated as gate criteria Regulatory exposure post-deployment; potential remediation cost A structured framework eliminates all five failure modes by forcing decisions in the right sequence. The framework in this article is derived from Alice Labs' direct experience evaluating and deploying AI systems across energy, media, agriculture, and financial services sectors in Sweden and Europe. Vendor Lock-In Is the Hidden Risk When a vendor controls model weights, fine-tuning data, and inference infrastructure simultaneously, switching costs can exceed the original contract value. Identify this risk at the evaluation stage — not after go-live. 3x Higher Vendor Replacement Rate Organizations without a formal AI vendor evaluation framework are 3x more likely to replace their AI vendor within 18 months. Source: Gartner, The AI-Driven Future of IT Services Vendor Selection (2026). 3x Higher vendor replacement rate without formal evaluation framework [Gartner (2026)](https://www.gartner.com/en/documents/7593333) 2–4x Actual TCO vs. initial licensing cost in AI deployments [Alice Labs internal benchmarks (2023–2025)](https://www.alicelabs.se/en/ai-strategy) 02 / 11 Chapter ## Framework-Driven vs. Ad-Hoc Vendor Selection In short Framework-driven AI vendor selection starts with documented requirements and pre-weighted scoring criteria before any vendor contact. Ad-hoc selection starts with demos and produces decisions driven by recency bias, the highest-paid person's opinion, and incomplete TCO visibility. Ad-hoc vendor selection follows a predictable pattern: a vendor demo impresses the room, reference calls go to the vendor's best customers, and the decision is made by whoever has the most authority in the meeting. This is called HIPPO-driven selection — Highest-Paid Person's Opinion. Framework-driven selection inverts this sequence entirely. Requirements are documented internally first. Scoring criteria and weights are locked before any vendor contact. The evaluation team is cross-functional from day one. Framework-Driven vs. Ad-Hoc: Side-by-Side Comparison Dimension Ad-Hoc Approach Framework Approach Starting point Vendor demo or analyst recommendation Internal use case document with defined KPIs Scoring criteria Defined after demos, biased by impressions Pre-weighted before vendor contact, timestamped Evaluation team IT-led; legal/security added reactively Cross-functional from step 1: IT, legal, security, business Reference checks Vendor-supplied customer list Independent peer references sourced by buyer PoC data Vendor demo environment or sample dataset Your own anonymised production data, 4+ weeks Decision driver HIPPO (highest-paid person's opinion) Weighted scorecard + documented rationale Research on multi-criteria decision-making for technology vendor selection (Rani et al., Springer, 2023) confirms that structured weighting reduces cognitive bias and improves decision consistency across evaluator teams. The rest of this article walks through the framework-driven approach step by step. Involve Legal from Step 1 Legal counsel should review the use case document — not just the final contract. Data processing constraints, IP ownership, and sector-specific regulation often eliminate vendor options before the RFP stage. 03 / 11 Chapter ## Steps 1–2: Define Your Use Case and Set Evaluation Criteria In short Before contacting a single vendor, document your use case in precise technical and business terms, then build a weighted scoring matrix across 5 evaluation dimensions. These two steps take 4–7 days and determine the quality of every subsequent decision. **Step 1: Use Case Definition.** The use case document is the foundation of the entire evaluation. Without it, every vendor conversation is a negotiation about requirements rather than a demonstration of fit. Your use case document must specify six elements before vendor contact begins: - **Problem and success metric:** the business problem being solved and a single measurable KPI (e.g., "reduce invoice processing time from 4 hours to 30 minutes") - **Data inputs:** type, volume, format, and GDPR sensitivity classification of all input data - **Required outputs:** format, destination system, and downstream integration requirements - **User personas and volume:** who uses the system, how many concurrent users, and peak load expectations - **Latency and uptime requirements:** acceptable response time and availability SLA (e.g., 99.9% uptime, sub-2-second inference) - **Regulatory context:** GDPR obligations, EU AI Act risk tier, sector-specific rules (finance, healthcare, energy) **Step 2: Setting Evaluation Criteria.** The Alice Labs framework uses 5 evaluation dimensions, weighted before any vendor contact. The MDPI DEMATEL research (2025) on structured multi-criteria weighting confirms that pre-set weights significantly reduce anchoring bias in technology procurement decisions. AI Vendor Evaluation Scoring Matrix — 5 Dimensions Dimension Default Weight (%) Score (1–5) Weighted Score Key Sub-Criteria Technical Capability 30% Fill during eval Score × 0.30 Model performance on your task; fine-tuning options; API flexibility Data Governance & Security 25% Fill during eval Score × 0.25 Data residency; GDPR DPA status; ISO 27001 / SOC 2 certification Integration & Scalability 20% Fill during eval Score × 0.20 Connector library; SDK quality; latency benchmarks; multi-tenant architecture Vendor Viability & Support 15% Fill during eval Score × 0.15 Funding/revenue stability; SLA terms; dedicated CSM; roadmap transparency Commercial Terms 10% Fill during eval Score × 0.10 Pricing model transparency; IP ownership clause; exit and data portability terms Adjust default weights for your context: regulated industries (finance, healthcare) should increase Data Governance to 30–35%. Organizations with complex legacy integration should increase Integration & Scalability to 25–30%. Involve legal, security, and business stakeholders in the weighting session — not just IT. A 90-minute alignment workshop at this stage prevents weeks of misalignment later. Weight Criteria Before Vendor Contact Assign dimension weights as a team before you see any vendor materials. Weights set after demos are unconsciously biased toward whichever vendor impressed most. Lock the weights in a shared document with a timestamp. 04 / 11 Chapter ## EU AI Act Compliance as a Non-Negotiable Gate In short For European enterprises, EU AI Act compliance status must be a binary gate criterion — not a scored dimension. Any vendor deploying a high-risk AI system must demonstrate conformity assessment and EU registration before August 2026. Non-compliant vendors should be eliminated from the evaluation regardless of their capability score. EU AI Act obligations for high-risk AI systems — as defined under Annex III — apply from August 2026. This is not a future concern: vendors supplying systems into European markets must be on a documented compliance path now. High-risk categories under Annex III include AI systems used in critical infrastructure, employment decisions, credit scoring, biometric identification, and education. If your use case touches any of these areas, compliance is a hard gate — not a weighted criterion. Ask every shortlisted vendor these four questions before scoring their RFP response: - **Risk tier classification:** What risk tier do you classify your system under, and what is your documented rationale? - **Conformity assessment:** Have you completed or initiated a conformity assessment for high-risk AI system designation? - **EU authorised representative:** Who is your EU-authorised representative, and where are they registered? - **Transparency obligations:** How do you fulfil user-facing transparency requirements under Article 52? GDPR data residency must also be verified at this stage. Confirm that data is stored in EU territory or in a country covered by an EU adequacy decision — this cannot be left to contract negotiation. Alice Labs has seen multiple European enterprise evaluations extend by 3–6 months because GDPR data residency was flagged during legal review after vendor selection. Resolve it at Step 2, not Step 7. August 2026 Is Not Far Away EU AI Act obligations for high-risk AI systems apply from August 2026. Vendors who cannot demonstrate a compliance roadmap today will not be ready by deadline. Treat absence of a compliance plan as a disqualifying factor. EU AI Act Risk Tiers The EU AI Act defines four risk categories: Unacceptable Risk (banned), High Risk (Annex III — strict requirements), Limited Risk (transparency obligations), and Minimal Risk (no specific obligations). Identify which tier your use case falls under before issuing your RFP. 05 / 11 Chapter ## Step 3: Build an AI Vendor RFP That Gets Honest Answers In short An effective AI vendor RFP forces vendors to provide evidence artifacts — benchmark results, architecture diagrams, security certifications — rather than capability claims. Vendors who provide complete documentation within 10 business days consistently outperform those who request extensions on security artifacts. A standard technology RFP asks vendors what they can do. An AI-specific RFP asks vendors to prove it — with your data, against your task type, with dated security documentation. The difference matters because AI vendors can present misleading capability claims without standardised benchmarks. Gartner (2026) specifically flags that sourcing leaders must demand transparent performance evidence and secure knowledge ownership in AI procurement. Every AI vendor RFP must include four mandatory sections: AI RFP — 4 Mandatory Sections and Required Artifacts RFP Section Required Artifacts Red Flag A — Technical Evidence Benchmark results on your task type using your sample data; model card documentation; architecture diagram showing data processing and storage location Benchmarks on vendor-supplied demo data only B — Data Governance GDPR DPA draft; data residency certification; penetration test report (within 12 months); ISO 27001 or SOC 2 Type II certificate "Available on request" for any security document C — Integration & Deployment Sample API documentation; SDK language support matrix; reference architecture for your tech stack; deployment timeline with milestone breakdown No reference architecture or deployment timeline provided D — Commercial & Legal Full pricing schedule including compute overage; IP ownership clause for fine-tuned models; SLA with uptime tiers and remedies; exit and data portability clause Pricing excludes compute costs; no exit clause Set a firm 10-business-day response deadline. In Alice Labs' experience across 100+ European enterprise implementations, vendors who provide complete RFP documentation within 10 business days consistently outperform those who request extensions on security artifacts. Security documentation maturity is a leading indicator of implementation quality. If a vendor cannot produce an ISO 27001 certificate, a GDPR DPA draft, or a penetration test report at RFP stage, that gap will not close at contract stage. Never Accept 'Available on Request' for Security Docs If a vendor cannot provide ISO 27001 certification, a data processing agreement draft, or a penetration test report at RFP stage, treat it as a red flag — not a paperwork delay. Security documentation maturity correlates directly with implementation reliability. Require a Model Card Add a model card requirement to your RFP (per the Hugging Face / Google Model Card standard). Vendors who cannot document training data sources, known limitations, and performance benchmarks are unlikely to support your AI governance obligations post-deployment. 06 / 11 Chapter ## Step 5: Design and Interpret a PoC Trial That Actually Predicts Production In short A PoC trial must run on your own anonymised production data for a minimum of 4 weeks with pre-defined success criteria. PoCs on vendor-supplied demo data systematically overstate production performance by 30–60%. The proof-of-concept stage is where most AI vendor evaluations lose their rigour. Teams that ran a disciplined RFP process revert to evaluating vendor-curated demos instead of controlled trials on their own data. A 4-week minimum is not arbitrary: it takes 2–3 weeks for a model to encounter the full distribution of your production data edge cases, and at least 1 week of stable performance data to establish a meaningful baseline. Design your PoC with these five elements in place before the trial starts: - **Pre-defined success criteria:** target accuracy, precision/recall, latency threshold, and error rate — agreed and documented before any vendor sees your data - **Same data for all vendors:** every shortlisted vendor receives the identical anonymised production data sample, enabling direct comparison - **Weekly performance measurement:** track against defined KPIs every 7 days, not just at trial end — trajectory matters as much as final score - **Real integration test:** connect the AI system to at least one actual downstream system during the PoC, not a mock endpoint - **Support quality evaluation:** document response time, technical depth of answers, and escalation process — this predicts post-go-live support experience Run PoCs with shortlisted vendors in parallel, not sequentially. Sequential testing introduces recency bias and extends your timeline by 4–8 weeks unnecessarily. After the PoC, compare actual performance against your pre-defined criteria — not against the other vendors. A vendor who scores 85% on your criteria is acceptable if your threshold was 80%, regardless of whether a competitor scored 90%. 4-Week Minimum Is Non-Negotiable Proof-of-concept trials on your own data must run for a minimum of 4 weeks to produce statistically meaningful performance baselines. Vendor-supplied demo environments are not a substitute — they systematically overstate production performance. What to Measure in a PoC Primary metrics: task-specific performance (accuracy, F1 score, or BLEU/ROUGE depending on task type), latency at P50/P95/P99, and error rate. Secondary metrics: integration complexity hours, support ticket response time, and developer experience score from your engineering team. 07 / 11 Chapter ## Step 6: Assess Legal, Security, and Vendor Lock-In Risk In short The three highest-risk contract areas in AI vendor agreements are IP ownership of fine-tuned models, data portability on exit, and lock-in through foundation-model dependency. These must be reviewed by legal counsel before any commercial negotiation begins — not during it. Legal and security review is not a final-stage formality. In Alice Labs' enterprise implementations, contract issues discovered after vendor selection cost an average of 4–8 additional weeks and frequently require renegotiation that weakens the buyer's position. Start the legal review in parallel with the PoC, not after it concludes. This keeps the overall timeline within the 6–10 week target. Review these four risk areas before any commercial negotiation: - **IP ownership of fine-tuned models:** if the vendor fine-tunes a foundation model on your proprietary data, who owns the resulting weights? This is the most commonly overlooked clause in AI contracts. - **Data portability on exit:** what format is your data returned in? What is the timeline? Is there a retrieval fee? Lack of a clear exit clause is a lock-in mechanism. - **Foundation-model dependency:** if the vendor's product is entirely dependent on a third-party foundation model (GPT-4, Claude, Gemini), switching the underlying model without the vendor's cooperation may be impossible. Assess whether you can migrate to an alternative foundation model independently. - **SLA terms and remedies:** what is the defined uptime tier, and what are the remedies for breach? Credit-only SLAs with no cash remedies provide no real protection for production AI systems. Compute overage pricing is another common TCO surprise. Base licensing fees rarely cover peak inference costs — always request a full pricing schedule including compute overage rates before signing. Remember: the initial licensing fee is only a fraction of total cost. Alice Labs' benchmarks across 100+ implementations confirm that TCO runs 2–4x the initial license cost when compute, integration, change management, and periodic retraining are included. The Fine-Tuned Model IP Trap If the vendor owns the fine-tuned weights trained on your proprietary data, you have no leverage at contract renewal and no portability on exit. Negotiate explicit IP assignment of fine-tuned weights before signing — not as an amendment after go-live. TCO Runs 2–4x License Cost Total cost of ownership for enterprise AI solutions typically runs 2–4x the initial licensing fee when compute, integration, change management, and retraining costs are included. Source: Alice Labs, 100+ enterprise AI implementations (2023–2025). 2–4x TCO vs. initial licensing fee in enterprise AI deployments [Alice Labs internal benchmarks (2023–2025)](https://www.alicelabs.se/en/ai-strategy) ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. [Book a Discovery Call](#contact) 08 / 11 Chapter ## Step 7: Make a Defensible Final Decision With Stakeholder Alignment In short The final vendor decision must be presented as a documented brief — combining PoC performance data, risk assessment findings, and TCO analysis — with a written rationale for why the selected vendor was chosen and why the runner-up was not. This creates an audit trail and prevents post-hoc reversal. The decision brief is not a sales presentation for the winning vendor. It is a structured document that makes the selection defensible to any stakeholder who reviews it six months after go-live — including a board member, regulator, or new CTO. A complete decision brief contains five elements: - **PoC performance summary:** final scores vs. pre-defined success criteria for each vendor — not a narrative, a table - **Scoring matrix comparison:** final weighted scores across all 5 dimensions for shortlisted vendors - **TCO model:** licensing + compute overage + integration cost + change management + annual retraining cost over 3 years - **Risk summary:** top 3 risks for the recommended vendor and agreed mitigations for each - **Runner-up rationale:** specific reasons the runner-up was not selected — this protects the decision from reconsideration Present the brief to the executive sponsor, legal counsel, and security officer as a group — not individually. Individual sign-offs allow each stakeholder to condition approval on different changes, creating a negotiation instead of a decision. Once all stakeholders have approved, execute the contract with every negotiated term documented. Do not rely on verbal commitments or "we'll sort it out in the addendum" — in Alice Labs' experience, post-signature addendums rarely match pre-signature discussions. Always Document the Runner-Up Rejection Include a specific written rationale for why the runner-up was not selected. This single step prevents the most common post-decision failure mode: stakeholders who preferred the losing vendor re-opening the decision 3 months later when implementation hits friction. 09 / 11 Chapter ## AI Vendor Selection Timeline: What 6–10 Weeks Actually Looks Like In short A rigorous AI vendor evaluation runs 6–10 weeks end-to-end across 4 phases: requirements and criteria (week 1), RFP and scoring (weeks 2–4), PoC trials (weeks 4–8), and legal review plus final decision (weeks 8–10). Compressing the PoC below 4 weeks is the single most common cause of timeline failure. Alice Labs' benchmark across European enterprise AI evaluations puts the typical timeline at 6–10 weeks. Organisations that try to compress this to 3–4 weeks almost universally either skip the PoC or run it on vendor demo data — both of which negate the framework's primary benefit. AI Vendor Selection Timeline — 4 Phases Phase Activities Duration Key Output 1 — Requirements Use case definition; scoring matrix; EU AI Act gate check; team assembly Week 1 Signed-off use case document + weighted scoring matrix 2 — RFP and Scoring Long-list compilation; RFP issue; response review; shortlist to 2–3 vendors Weeks 2–4 Scored RFP responses + shortlist with documented rationale 3 — PoC Trials Parallel PoC on production data; weekly KPI measurement; integration test; support quality log Weeks 4–8 (min. 4 weeks) PoC performance report vs. pre-defined success criteria 4 — Legal + Decision Contract review; IP/exit clause negotiation; TCO model; decision brief; stakeholder sign-off Weeks 8–10 Signed contract with all negotiated terms; decision brief on file Legal review (Phase 4) should begin in parallel with the PoC (Phase 3) — not after it concludes. This overlap is how Alice Labs consistently keeps enterprise AI evaluations within the 6–10 week target even for complex multi-stakeholder organisations. For organisations with a prior AI vendor evaluation on file, Phase 1 can be compressed to 2–3 days by reusing an existing scoring matrix template rather than building from scratch. 6–10 Week Benchmark A rigorous AI vendor evaluation runs 6–10 weeks end-to-end. Compressing the timeline by skipping PoC or using demo data eliminates the primary safeguard against selecting a vendor whose production performance is materially worse than their demo performance. Source: Alice Labs internal benchmarks (2023–2025). ### Want to discuss how this applies to your organization? Book a free 30-minute strategy call with our AI team. [Book a call](/en/ai-consulting-services#contact-form) 10 / 11 Chapter ## Before You Evaluate Vendors: The Build vs. Buy Decision In short AI vendor selection only applies if you have already decided to buy rather than build. For use cases requiring deep proprietary customisation, open-source foundation models, or long-term strategic differentiation, building may generate better ROI than vendor dependency. Not every AI use case should go through a vendor selection process. Before issuing an RFP, confirm that buying is the right answer for your specific context. The build vs. buy decision depends on three factors: strategic differentiation value, internal engineering capacity, and total cost over a 3-year horizon. Our detailed [build vs. buy AI framework](/en/insights/build-vs-buy-ai) covers this decision in depth — use it before starting any vendor evaluation. General guidance for the most common scenarios: - **Buy:** the use case is well-defined, the vendor category is mature, and internal ML engineering capacity is limited or better allocated elsewhere - **Build:** the use case requires proprietary data training that constitutes a core competitive advantage, or the vendor market lacks solutions that meet your technical requirements - **Hybrid:** buy a foundation model API or platform, build the application layer and fine-tuning pipeline internally — this is increasingly the dominant pattern in Alice Labs' European enterprise work For organisations considering generative AI or agentic AI specifically, the hybrid approach is almost always preferred over pure vendor dependency. See our guides on [retrieval-augmented generation](/en/insights/what-is-rag) and [agentic AI](/en/insights/what-is-agentic-ai) for architecture patterns that reduce foundation-model vendor lock-in. The Hybrid Pattern Is Dominant Most mature European enterprises are adopting a hybrid approach: purchasing foundation model API access (OpenAI, Anthropic, Google) while building the application layer, fine-tuning pipeline, and retrieval infrastructure internally. This reduces vendor lock-in risk while maintaining access to frontier model capability. 11 / 11 Chapter ## Which AI Vendor Categories Require This Framework In short This framework applies to all six major enterprise AI vendor categories: foundation model APIs, AI platform suites, point-solution AI tools, AI automation platforms, AI infrastructure providers, and AI consulting and implementation partners. The framework scales across all major AI vendor categories, but the weighting of scoring dimensions differs by category. Understanding which category you are evaluating helps you adjust the scoring matrix before the RFP stage. AI Vendor Categories and Scoring Focus Vendor Category Examples Highest-Risk Dimension Adjust Weight Foundation Model APIs OpenAI, Anthropic, Google Vertex AI Vendor lock-in; IP ownership; model deprecation risk Increase Commercial Terms to 20% AI Platform Suites Microsoft Azure AI, AWS Bedrock, Google Cloud AI Integration complexity; compute cost transparency Increase Integration & Scalability to 25% Point-Solution AI Tools Glean, Writer, Aisera, Leena AI Vendor viability; roadmap dependency Increase Vendor Viability to 25% AI Automation Platforms Make, n8n, Zapier AI, UiPath Integration breadth; data flow security Increase Data Governance to 30% AI Infrastructure Pinecone, Weaviate, Qdrant, Modal Scalability; latency at production load Increase Technical Capability to 35% AI Consulting Partners Alice Labs, Big 4 AI practices, boutique AI firms Practitioner experience; reference quality Increase Vendor Viability to 30% For AI consulting partner selection specifically, reference quality is the primary differentiator. Request 3 independent references — not vendor-supplied — from organisations of similar size, sector, and complexity. Our guide on [how to choose an AI consultant](/en/insights/how-to-choose-ai-consultant) covers the consulting partner evaluation process in detail. Adjust Weights for Your Vendor Category The default 30/25/20/15/10 weighting is a starting point for general AI tool evaluation. For foundation model APIs, increase Commercial Terms to 20% to reflect lock-in risk. For regulated industries, increase Data Governance to 30–35% regardless of vendor category. ## Step-by-step checklist 1. 1 #### Step 1: 2. 2 #### Step 2: 3. 3 #### Step 3: 4. 4 #### Step 4: 5. 5 #### Step 5: 6. 6 #### Step 6: 7. 7 #### Step 7: ## About the Authors & Reviewers Published May 23, 2026 Written by ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Reviewed by May 23, 2026 ![Linus Ingemarsson - Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Published May 23, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### How long does an AI vendor selection process take? ▾ A rigorous AI vendor evaluation takes 6–10 weeks end-to-end across four phases: requirements and scoring matrix (week 1), RFP and shortlisting (weeks 2–4), proof-of-concept trials (weeks 4–8, minimum 4 weeks on production data), and legal review plus final decision (weeks 8–10). Compressing below 6 weeks typically means skipping the PoC or using vendor demo data, both of which significantly increase replacement risk. ### What are the 5 dimensions to score AI vendors on? ▾ The Alice Labs framework evaluates AI vendors across five weighted dimensions: Technical Capability (30%), Data Governance & Security (25%), Integration & Scalability (20%), Vendor Viability & Support (15%), and Commercial Terms (10%). Adjust weights for context — regulated industries should increase Data Governance to 30–35%, and organisations with complex legacy systems should increase Integration to 25–30%. ### How do I write an AI vendor RFP? ▾ An effective AI vendor RFP includes four mandatory sections: Technical Evidence (benchmark results on your task using your data, model card, architecture diagram), Data Governance (GDPR DPA draft, data residency certification, ISO 27001 or SOC 2 Type II certificate), Integration & Deployment (API docs, SDK support matrix, reference architecture), and Commercial & Legal (full pricing schedule, IP ownership clause, SLA with remedies, exit clause). Set a 10-business-day response deadline. ### What is a realistic total cost of ownership for an AI solution? ▾ Total cost of ownership for enterprise AI solutions typically runs 2–4x the initial licensing fee when all costs are included: compute and inference costs, integration development, change management, user training, and periodic model retraining. Alice Labs benchmarks this across 100+ implementations in Europe. Always build a 3-year TCO model before finalising vendor selection. ### Is EU AI Act compliance required for all AI vendors in Europe? ▾ EU AI Act compliance obligations depend on risk tier. High-risk AI systems (defined under Annex III, covering areas like employment decisions, credit scoring, biometric identification) face mandatory conformity assessments and EU registration requirements from August 2026. For European enterprises, EU AI Act compliance status must be a binary gate criterion — not a scored dimension — in any vendor evaluation. ### How do I assess vendor lock-in risk in AI procurement? ▾ Vendor lock-in risk is highest when a single vendor controls model weights, fine-tuning data, and inference infrastructure simultaneously. Assess four contract elements: IP ownership of fine-tuned models, data portability on exit (format, timeline, fees), foundation-model dependency (can you switch the underlying model independently?), and SLA remedies beyond credit-only terms. Negotiate exit clause and data portability before signing. ### How many vendors should I include in an AI vendor evaluation? ▾ Start with a long-list of 6–10 vendors compiled from analyst reports and independent peer references. Score RFP responses to shortlist to 2–3 vendors for the PoC stage. Running PoCs with more than 3 vendors simultaneously is operationally complex and rarely produces meaningfully different outcomes — the scoring matrix should eliminate clear mismatches before the PoC. ### What should a proof-of-concept trial measure? ▾ A PoC trial should measure: task-specific performance (accuracy, F1 score, or generation quality metric depending on task type), latency at P50/P95/P99 percentiles, error rate, integration complexity (hours required to connect to one real downstream system), and support quality (response time and technical depth of vendor answers). Success criteria must be defined and documented before the trial begins — not after results are in. ### Should AI vendor selection involve legal counsel from the start? ▾ Yes. Legal counsel should be involved from Step 1 — reviewing the use case document — not only at contract stage. Data processing constraints, GDPR obligations, sector-specific regulation, and IP ownership questions often eliminate vendor options before the RFP is even issued. Legal review begun in parallel with the PoC (not after it) is how Alice Labs keeps enterprise evaluations within the 6–10 week target. ### What is the difference between an AI vendor evaluation framework and a standard technology RFP process? ▾ A standard technology RFP evaluates documented capabilities and contractual terms. An AI-specific evaluation framework adds three elements: pre-set weighted scoring criteria locked before vendor contact (preventing anchoring bias), mandatory PoC on the buyer's own production data (not vendor demo environments), and binary gate criteria for EU AI Act compliance status and GDPR data residency. These three additions are the primary source of the 3x reduction in vendor replacement risk. [Previous in AI Strategy ### Scaling AI Across the Enterprise: From Pilot to 100+ Use Cases ](/en/insights/ai-scaling-framework)[Next in AI Strategy ### How to Build an AI Business Case: Template & Executive Presentation ](/en/insights/build-ai-business-case) ## Further reading - [Gartner — The AI-Driven Future of IT Services Vendor Selection (2026)](https://www.gartner.com/en/documents/7593333)· gartner.com - [EU AI Act — Official Text, Annex III High-Risk AI Systems](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689)· eur-lex.europa.eu - [Rani et al. — Multi-Criteria Vendor Selection Framework, Springer (2023)](https://link.springer.com/article/10.1007/s12597-023-00725-1)· link.springer.com - [MDPI — DEMATEL-Based Multi-Criteria AI Tool Selection (2025)](https://www.mdpi.com/2076-3417/15/4/2134)· mdpi.com - [Hugging Face — Model Card Standard Documentation](https://huggingface.co/docs/hub/model-cards)· huggingface.co ## Related services [AI strategy consulting ](/en/ai-strategy) ## Related reading [pillar ### Enterprise AI Strategy Framework: A Practitioner's Guide How to build a complete enterprise AI strategy — from maturity assessment and use case prioritisation to governance and implementation roadmap. ](/en/insights/enterprise-ai-strategy-framework)[howto ### Build vs. Buy AI: How to Decide for Your Enterprise A structured decision framework for choosing between building custom AI solutions and purchasing vendor products — with TCO comparison methodology. ](/en/insights/build-vs-buy-ai)[howto ### EU AI Act Compliance Checklist 2026 A step-by-step compliance checklist covering risk tier classification, conformity assessment, technical documentation, and registration requirements. ](/en/insights/eu-ai-act-compliance-checklist-2026)[deepdive ### Why AI Projects Fail: 12 Root Causes and How to Avoid Them The 12 most common root causes of enterprise AI project failure — with prevention strategies drawn from post-mortems across 100+ implementations. ](/en/insights/why-ai-projects-fail)[howto ### AI Proof-of-Concept Methodology How to design, run, and interpret an AI proof-of-concept trial that predicts production performance — including success criteria templates and evaluation rubrics. ](/en/insights/ai-poc-methodology) ## Sources 1. [The AI-Driven Future of IT Services Vendor Selection](https://www.gartner.com/en/documents/7593333)Gartner Research · Gartner “Organizations without a formal AI vendor evaluation framework are 3x more likely to replace their AI vendor within 18 months. AI exposes hidden vendor risks; sourcing leaders must demand transparent performance evidence and secure knowledge ownership.” 2. [Internal Benchmarks: Enterprise AI Implementation Data (2023–2025)](https://www.alicelabs.se/en/ai-strategy)Alice Labs · Alice Labs “Total cost of ownership for enterprise AI solutions runs 2–4x the initial licensing fee across 100+ implementations when compute, integration, change management, and retraining costs are included. Typical vendor evaluation timeline: 6–10 weeks.” 3. [A Multi-Criteria Decision-Making Framework for Technology Vendor Selection](https://link.springer.com/article/10.1007/s12597-023-00725-1)Rani, M. et al. · Springer / Opsearch “Structured multi-criteria decision-making methods with pre-set weighted criteria significantly reduce cognitive bias and improve decision consistency in technology vendor selection processes.” 4. [DEMATEL-Based Multi-Criteria Evaluation Framework for AI Tool Selection](https://www.mdpi.com/2076-3417/15/4/2134)Akhtar, M. et al. · MDPI Applied Sciences “DEMATEL-based structured weighting for AI tool selection criteria reduces anchoring bias and evaluation time by enabling cross-functional teams to converge on objective dimension weights before vendor contact.” 5. [Regulation (EU) 2024/1689 — Artificial Intelligence Act](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689)European Parliament and Council of the EU · European Union “High-risk AI systems as defined under Annex III face mandatory conformity assessment, CE marking, and EU database registration requirements. Obligations for high-risk AI systems apply from August 2026.” 6. [Model Card Documentation Standard](https://huggingface.co/docs/hub/model-cards)Hugging Face · Hugging Face “Model cards provide standardised documentation of AI model training data, known limitations, performance benchmarks, and intended use cases — enabling structured vendor capability assessment.” Next scheduled review: 2026-08-21 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. 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