--- title: "How to Choose an AI Consultant: 7-Point Framework (2026)" description: "How to choose an AI consultant with a proven 7-point framework. 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A named reference you can call directly — who describes a real production deployment with measurable outcomes — outweighs certifications, team size, and brand recognition combined. Gartner's 2025 buyer criteria research confirms that verifiable delivery history is the top selection driver for generative AI services." } }, { "@type": "Question", "name": "How much does AI consulting cost in 2025–2026?", "acceptedAnswer": { "@type": "Answer", "text": "Senior AI consultant day rates in Northern Europe ranged from €1,200 to €2,800 in 2025, depending on domain specialization and seniority. Fixed-scope AI strategy engagements typically range from €15,000 to €80,000. The pricing model — time-and-materials, fixed-scope, or outcome-based — is often more important than the headline rate. See our AI consulting pricing guide for detailed 2026 benchmarks." } }, { "@type": "Question", "name": "What are the biggest red flags when evaluating an AI consultant?", "acceptedAnswer": { "@type": "Answer", "text": "The three clearest red flags are: (1) no named client references available for direct contact, (2) ROI promises without a measurement methodology or baseline requirement, and (3) inability to describe a governance or EU AI Act compliance process. A consultant who deflects on any of these three areas in a discovery call should be eliminated from consideration." } }, { "@type": "Question", "name": "Should I choose a large consultancy or a specialist AI firm?", "acceptedAnswer": { "@type": "Answer", "text": "Size is not a reliable proxy for delivery quality in AI consulting. Large consultancies often have strong brand recognition but inconsistent AI delivery capability across practice groups. Specialist firms may have deeper technical depth but narrower industry coverage. Evaluate both against the 7-point framework — the score matters more than the firm's headcount or brand." } }, { "@type": "Question", "name": "How long does it take to select and onboard an AI consultant?", "acceptedAnswer": { "@type": "Answer", "text": "A structured selection process — internal requirements definition, RFP issuance, proposal review, discovery calls, reference verification, and contract negotiation — typically takes 6–10 weeks when run with discipline. Organizations that skip the internal requirements phase or reference verification step frequently extend this timeline to 4–6 months due to restarts and renegotiations." } }, { "@type": "Question", "name": "What is the difference between an AI consultant and an AI vendor?", "acceptedAnswer": { "@type": "Answer", "text": "An AI vendor sells a product or platform — a software solution with defined capabilities. An AI consultant designs, builds, or advises on custom AI solutions tailored to your specific context. The distinction matters for procurement: vendor selection focuses on product fit, while consultant selection focuses on delivery capability, team quality, and strategic alignment. Many engagements require both." } }, { "@type": "Question", "name": "Do I need an AI consultant if I have an internal data science team?", "acceptedAnswer": { "@type": "Answer", "text": "An internal data science team and an AI consultant serve different functions. Internal teams typically own ongoing model maintenance, data pipelines, and institutional knowledge. Consultants bring external delivery velocity, specialist capabilities (e.g., governance frameworks, regulated-industry experience), and independence in strategic assessment. Organizations with mature internal teams often benefit most from a defined-scope consultant engagement rather than an open-ended retainer." } }, { "@type": "Question", "name": "What differentiates top AI consultants with the highest rates and credentials?", "acceptedAnswer": { "@type": "Answer", "text": "Top-tier AI consultants charging €2,000–€2,800 per day in Northern Europe differentiate on three verifiable dimensions: named production deployments (typically 20+ live systems, not pilots), regulatory depth (ISO/IEC 42001 or EU AI Act classification experience for real audits), and outcome accountability (willingness to structure at least 20% of fees against measurable KPIs). Credentials alone — cloud certifications, Big 4 badges — do not justify the rate premium without these three signals." } }, { "@type": "Question", "name": "What are the scoring criteria for GenAI strategy consultancy selection?", "acceptedAnswer": { "@type": "Answer", "text": "Score every GenAI strategy consultancy across seven weighted criteria on a 1–5 scale: domain experience (weight 15%), technical depth (15%), governance and EU AI Act capability (20%), delivery proof with named references (20%), team transparency (10%), pricing model fit (10%), and post-delivery support with defined SLAs (10%). Eliminate any consultancy scoring below 25 of 35 total. Gartner's 2025 buyer criteria research confirms governance, delivery proof, and domain depth as the three highest-weight drivers." } }, { "@type": "Question", "name": "How do I know if an AI consultant understands the EU AI Act?", "acceptedAnswer": { "@type": "Answer", "text": "Ask them to classify your specific use case under the EU AI Act risk tier system — minimal, limited, high, or prohibited — and explain their reasoning. A consultant with genuine governance capability will describe the classification criteria, identify the compliance obligations that follow, and reference their documentation process. A consultant without this capability will give a vague answer about 'following regulations' without specifics." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "How to Choose an AI Consultant: A 7-Point Selection Framework", "description": "How to choose an AI consultant with a proven 7-point framework. 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Definition, Services & Who Needs It" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/ai-consulting-pricing-2026", "name": "Ai Consulting Pricing 2026" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/why-ai-projects-fail", "name": "Why Ai Projects Fail" }, { "@type": "ListItem", "position": 4, "url": "https://alicelabs.ai/en/insights/ai-implementation-roadmap", "name": "AI Implementation Roadmap: From Pilot to Production" }, { "@type": "ListItem", "position": 5, "url": "https://alicelabs.ai/en/insights/enterprise-ai-strategy-framework", "name": "Enterprise AI Strategy: 6-Step Framework for 2026" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 8, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Why Choosing the Right AI Consultant Defines Project Outcomes", "url": "https://alicelabs.ai/en/insights/how-to-choose-ai-consultant#why-consultant-selection-matters" }, { "@type": "ListItem", "position": 2, "name": "Step 1 — Define Your AI Requirements Before You Talk to Any Vendor", "url": "https://alicelabs.ai/en/insights/how-to-choose-ai-consultant#define-your-requirements-first" }, { "@type": "ListItem", "position": 3, "name": "The 7-Point AI Consultant Selection Framework", "url": "https://alicelabs.ai/en/insights/how-to-choose-ai-consultant#seven-point-selection-framework" }, { "@type": "ListItem", "position": 4, "name": "Red Flags: When to Walk Away From an AI Consultant", "url": "https://alicelabs.ai/en/insights/how-to-choose-ai-consultant#red-flags-to-watch-for" }, { "@type": "ListItem", "position": 5, "name": "AI Consulting Pricing: What to Expect in 2025–2026", "url": "https://alicelabs.ai/en/insights/how-to-choose-ai-consultant#ai-consulting-pricing-guide" }, { "@type": "ListItem", "position": 6, "name": "How to Structure Your RFP and Shortlisting Process", "url": "https://alicelabs.ai/en/insights/how-to-choose-ai-consultant#rfp-and-shortlisting-process" }, { "@type": "ListItem", "position": 7, "name": "Best AI Strategy Firms in 2026: Comparison by Buyer Situation", "url": "https://alicelabs.ai/en/insights/how-to-choose-ai-consultant#best-ai-strategy-firms-2026" }, { "@type": "ListItem", "position": 8, "name": "Final Vendor Comparison Checklist Before You Sign", "url": "https://alicelabs.ai/en/insights/how-to-choose-ai-consultant#vendor-comparison-checklist" } ] } ] }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "AI Consulting", "item": "https://alicelabs.ai/en/insights/ai-consulting" }, { "@type": "ListItem", "position": 4, "name": "How to Choose an AI Consultant: A 7-Point Selection Framework" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Best AI Strategy Firms in 2026 by Buyer Situation", "description": "Recommended AI consulting and strategy firms segmented by buyer situation: regulated industry, Nordic enterprise, mid-market, board-level strategy, Microsoft stack, and rapid PoC. Based on IDC MarketScape 2025 and Gartner Magic Quadrant 2024–2025.", "itemListOrder": "https://schema.org/ItemListOrderAscending", "numberOfItems": 7, "itemListElement": [ { "@type": "ListItem", "position": 1, "item": { "@type": "Organization", "name": "Deloitte", "url": "https://www2.deloitte.com/global/en/services/consulting/services/ai-and-data.html", "description": "Best for regulated industry AI implementation. Named a Leader in IDC MarketScape Worldwide AI Services 2024–2025; deep ISO/IEC 42001 and EU AI Act practice.", "sameAs": [ "https://en.wikipedia.org/wiki/Deloitte", "https://www.linkedin.com/company/deloitte/" ] } }, { "@type": "ListItem", "position": 2, "item": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai/en/ai-consulting", "description": "Best for Nordic enterprise and rapid PoC engagements. 50+ AI implementations across Sweden and Europe since 2023; local delivery teams in Stockholm; GDPR + EU AI Act-aligned delivery.", "sameAs": [ "https://www.linkedin.com/company/alicelabsai/" ] } }, { "@type": "ListItem", "position": 3, "item": { "@type": "Organization", "name": "Knowit", "url": "https://www.knowit.eu/services/solutions/artificial-intelligence/", "description": "Best for Nordic mid-market AI engagements. Regional specialist across Sweden, Norway, Finland and Denmark with fixed-scope pricing.", "sameAs": [ "https://en.wikipedia.org/wiki/Knowit", "https://www.linkedin.com/company/knowit/" ] } }, { "@type": "ListItem", "position": 4, "item": { "@type": "Organization", "name": "McKinsey & Company (QuantumBlack)", "url": "https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients", "description": "Best for board-level AI strategy and enterprise transformation roadmaps. Leader in Gartner Magic Quadrant for Data & Analytics Service Providers.", "sameAs": [ "https://en.wikipedia.org/wiki/McKinsey_%26_Company", "https://www.linkedin.com/company/mckinsey/" ] } }, { "@type": "ListItem", "position": 5, "item": { "@type": "Organization", "name": "Boston Consulting Group (BCG X)", "url": "https://www.bcg.com/capabilities/artificial-intelligence", "description": "Best for board-level AI strategy alongside McKinsey. BCG X maintains a dedicated AI consulting practice; Leader in Gartner Magic Quadrant.", "sameAs": [ "https://en.wikipedia.org/wiki/Boston_Consulting_Group", "https://www.linkedin.com/company/boston-consulting-group/" ] } }, { "@type": "ListItem", "position": 6, "item": { "@type": "Organization", "name": "Capgemini", "url": "https://www.capgemini.com/services/data-and-ai/", "description": "Best for Microsoft Azure / Copilot stack AI implementation. Top-tier Microsoft AI Cloud Partner with public Azure OpenAI delivery references.", "sameAs": [ "https://en.wikipedia.org/wiki/Capgemini", "https://www.linkedin.com/company/capgemini/" ] } }, { "@type": "ListItem", "position": 7, "item": { "@type": "Organization", "name": "Accenture", "url": "https://www.accenture.com/us-en/services/applied-intelligence-index", "description": "Best for global Microsoft-stack delivery at scale. Named a Leader in IDC MarketScape Worldwide Microsoft Implementation Services 2024.", "sameAs": [ "https://en.wikipedia.org/wiki/Accenture", "https://www.linkedin.com/company/accenture/" ] } } ] }, { "@context": "https://schema.org", "@type": "Service", "serviceType": "AI Strategy Consulting", "provider": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "areaServed": [ { "@type": "Country", "name": "Sweden" }, { "@type": "Country", "name": "Norway" }, { "@type": "Country", "name": "Finland" }, { "@type": "Country", "name": "Denmark" }, { "@type": "Place", "name": "European Union" } ], "offers": [ { "@type": "Offer", "name": "Fixed-Scope AI Strategy Engagement", "priceSpecification": { "@type": "PriceSpecification", "priceCurrency": "EUR", "minPrice": 15000, "maxPrice": 80000, "description": "Typical 2026 fixed-scope AI strategy engagement range (4–8 weeks)" } }, { "@type": "Offer", "name": "Senior AI Consultant Day Rate (Northern Europe)", "priceSpecification": { "@type": "UnitPriceSpecification", "priceCurrency": "EUR", "minPrice": 1200, "maxPrice": 2800, "unitText": "DAY", "description": "2026 senior AI consultant day-rate benchmark, Northern Europe" } }, { "@type": "Offer", "name": "Board-Level AI Strategy Engagement (Big 4 / Top-Tier)", "priceSpecification": { "@type": "PriceSpecification", "priceCurrency": "EUR", "minPrice": 150000, "maxPrice": 500000, "description": "Typical 2026 board-level AI strategy engagement range, top-tier firms" } } ] } ] --- [Alice Labs](/en/) Services [ What we do ](/#welcome)[ About Alice ](/#who-we-are)[ Case ](/en/case)[ Insights ](/en/insights)[ Contact ](/#email-form) 1. [Home](/en) [Insights](/en/insights) [AI Consulting](/en/insights/ai-consulting) How to Choose an AI Consultant: A 7-Point Selection Framework AI Consulting How-To Fresh Last reviewed: 15 July 2026 · 41d ago # How to Choose an AI Consultant: A 7-Point Selection Framework ## TL;DR Quick Answer Cited by AI > Choose an AI consultant by auditing their domain experience, technical stack, governance approach, references, pricing model, and post-delivery support before signing. Most AI projects fail not because of technology — but because of the wrong consultant. Use this framework to evaluate, compare, and select an AI partner that delivers measurable results. An AI consultant is an external specialist or firm hired to assess, design, and implement artificial intelligence solutions for an organization. Selection criteria typically include technical depth, industry experience, governance capability, and a proven delivery track record. ![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 · Updated July 15, 2026 14 min read 85% of AI projects that underperform are attributed to poor vendor or partner fit, not technology gaps [Gartner, Buyer Selection Criteria for Generative AI Services, 2025](https://www.gartner.com/en/documents/6269583) 50+ enterprise AI implementations delivered by Alice Labs across Sweden and Europe since 2023 [Alice Labs, internal delivery data, 2025](https://alicelabs.ai/en/ai-consulting) 10 core criteria enterprises use most often when selecting an AI consulting firm [Boris Friedrich, ADVISORI, April 2026](https://www.advisori.de/blog/how-to-choose-ai-consulting-firm-10-criteria-enterprises) What you'll learn(6 points) - The 7 non-negotiable criteria for evaluating any AI consultant or firm - How to structure your RFP and shortlisting process to avoid costly mistakes - Which red flags signal a consultant is over-promising on AI capabilities - How AI consulting is typically priced and what fair benchmarks look like in 2025 - The exact questions to ask during a discovery call or proposal review - How to score and compare multiple vendors using a structured checklist ## Key Takeaways - Global AI services spending is forecast to reach $126 billion by 2028, growing at a 30.4% CAGR from 2023 — meaning buyer selection mistakes now compound over a much larger consulting market (IDC Worldwide AI and Generative AI Services Forecast, 2024). - Partner quality is the single biggest predictor of AI project success, according to Opsio's 2026 consulting guide. - Gartner's 2025 buyer criteria report identifies governance fluency, delivery proof, and domain depth as the top three selection drivers for generative AI services. - AI consulting projects typically fail at the implementation stage — not the strategy stage — making delivery track record a more reliable signal than credentials alone. - A structured 7-point scoring framework reduces vendor selection time by eliminating subjective comparison early in the process. - Red flags include vague ROI promises, no named references, and an inability to describe a governance or risk management process. - Pricing models vary significantly: time-and-materials, fixed-scope, and outcome-based contracts each suit different project maturity levels. ### Contents 14 min left - [01 Why Choosing the Right AI Consultant Defines Project Outcomes ](#why-consultant-selection-matters) - [02 Step 1 — Define Your AI Requirements Before You Talk to Any Vendor ](#define-your-requirements-first) - [03 The 7-Point AI Consultant Selection Framework ](#seven-point-selection-framework) - [04 Red Flags: When to Walk Away From an AI Consultant ](#red-flags-to-watch-for) - [05 AI Consulting Pricing: What to Expect in 2025–2026 ](#ai-consulting-pricing-guide) - [06 How to Structure Your RFP and Shortlisting Process ](#rfp-and-shortlisting-process) - [07 Best AI Strategy Firms in 2026: Comparison by Buyer Situation ](#best-ai-strategy-firms-2026) - [08 Final Vendor Comparison Checklist Before You Sign ](#vendor-comparison-checklist) 01 / 08 Chapter ## Why Choosing the Right AI Consultant Defines Project Outcomes The consultant you select shapes every downstream decision — from architecture to change management. Choosing the wrong partner is the leading cause of AI project failure, not the technology itself. Consultant selection is a higher-leverage decision than platform or tool selection. Before you shortlist, benchmark the field with our [best AI consulting firms 2026](/en/insights/best-ai-consulting-firms-2026) comparison and stress-test economics against our [AI consulting pricing 2026](/en/insights/ai-consulting-pricing-2026) breakdown. According to Gartner's 2025 buyer criteria report, **85% of underperforming AI projects** cite poor partner fit as the root cause — not technology limitations. See our [AI consulting services](/en/ai-consulting) page for how Alice Labs approaches partner-fit. Opsio's 2026 consulting guide reinforces this: partner quality is the single biggest predictor of whether an AI project reaches production or stalls in a pilot loop. Gartner's 2025 research identifies three top selection drivers for generative AI services: governance fluency, delivery proof, and domain depth. A consultant missing any one of these is a project risk. The two failure modes are distinct but equally damaging. Understanding them before you issue your first RFP saves months of wasted procurement time. **Credentials ≠ Capability** A consultant holding major cloud vendor certifications is not the same as a consultant who has delivered a production AI system in your industry. Always ask for named references — not case study PDFs. 85% of underperforming AI projects cite poor partner fit as root cause Gartner, 2025 ### The Two Consultant Failure Modes to Avoid **Strategy-only firms** deliver polished roadmaps and frameworks — then hand off execution to a team that wasn't part of the conversation. The result: documents that gather dust and projects that never reach go-live. **Tech-only vendors** build technically sound systems that no one adopts. They optimize for architecture quality but cannot connect deliverables to business KPIs or navigate organizational change. The ideal consultant bridges both: strategic clarity plus delivery accountability in a single engagement team. Boris Friedrich at [ADVISORI (2026)](https://www.advisori.de/blog/how-to-choose-ai-consulting-firm-10-criteria-enterprises) notes that enterprise buyers increasingly require proof of both capabilities at the RFP stage — not just one. A structured selection process eliminates both failure modes by requiring evidence, not claims. The 7-point framework below is designed exactly for this purpose. For context on why AI initiatives break down at execution, see our analysis of [why AI projects fail](/en/insights/why-ai-projects-fail) — the patterns align directly with the failure modes described here. 02 / 08 Chapter ## Step 1 — Define Your AI Requirements Before You Talk to Any Vendor In short Before evaluating consultants, document your use case, data availability, internal AI maturity, and success metrics. Without this baseline, you cannot assess whether a consultant's proposal is genuinely tailored or generic. Most organizations skip the pre-selection phase entirely. They book discovery calls before they have documented what they actually need — and end up evaluating consultants based on proposals shaped entirely by the consultant's preferred pitch. Hashmeta AI (2026) identifies this as the primary driver of scope creep in AI consulting engagements: undefined requirements at outreach stage lead to misaligned deliverables at contract stage. Define these four areas internally before any vendor conversation begins: - **Use case specificity:** What specific problem are you solving? What does measurable success look like in 12 months? - **Data readiness:** Do you have labeled data, clean infrastructure, or are you starting from scratch? - **Internal AI maturity:** Do you have data engineers, MLOps capability, or AI governance policies already in place? - **Budget and contract preference:** Are you open to fixed scope, time-and-materials, or outcome-based pricing? **The Brief Test** Send a detailed two-page brief to shortlisted vendors. A serious consultant will ask clarifying questions within 48 hours. A generic consultant will send a templated proposal deck. ### Run a Quick AI Maturity Self-Assessment SysArt Consulting (2026) recommends matching your consultant profile directly to your organization's maturity level — a mismatch here is as costly as choosing the wrong firm entirely. Three maturity levels define which consultant type fits best: - **Exploratory (Level 1):** No AI in production, evaluating use cases. Prioritize strategic generalists who can help you scope before you build. - **Developing (Level 2):** One or two AI pilots live, no enterprise-wide strategy. Look for consultants with both strategy and implementation capability. - **Scaling (Level 3):** Multiple deployments running, need governance and optimization. Require specialist implementation partners with enterprise MLOps experience. Our [AI maturity model](/en/insights/ai-maturity-model) guide provides a detailed self-assessment framework you can complete before your first vendor call. Readiness Area Key Question to Answer Why It Matters Use Case Definition What specific problem are we solving? Prevents consultants from steering scope toward their preferred solutions Data Availability Do we have clean, accessible training or inference data? Determines whether the project can start immediately or requires data preparation work first Internal Capability Who will own this solution post-delivery? Shapes whether you need a consultant who trains your team or one who manages ongoing operations Governance Needs Do we operate in a regulated industry? Filters out consultants without EU AI Act or sector-specific compliance experience Budget & Timeline What is our realistic investment range and go-live target? Eliminates proposals that are structurally misaligned before you spend time evaluating them 03 / 08 Chapter ## The 7-Point AI Consultant Selection Framework In short Evaluate every AI consultant across seven criteria: domain experience, technical depth, governance capability, delivery proof, team transparency, pricing model, and post-delivery support. Score each 1–5 to enable direct comparison. This framework operates as a scoring rubric: assign each criterion a score of 1–5, for a maximum total of 35. Any consultant scoring below 25 should be eliminated from consideration before the proposal stage. Gartner (2025), ADVISORI (2026), and Delta Labs AI (2026) each independently converge on the same core criteria — the seven below represent the intersection of all three frameworks. ### Criterion 1 — Domain Experience Have they worked in your industry vertical? Do they understand your regulatory environment, data constraints, and buyer behavior? **What to ask:** "Name two clients in our sector and describe the AI system you delivered for each. What were the measurable outcomes at 6 and 12 months post-launch?" - **Strong response:** Named clients, specific metrics (e.g., "reduced manual processing by 40% within 9 months"), and recognition of sector-specific constraints. - **Weak response:** Generic industry references, no named clients, outcome claims without measurement methodology. ### Criterion 2 — Technical Depth Which models, frameworks, and infrastructure do they work with? Are they genuinely vendor-neutral or locked to one cloud platform's preferred stack? **What to ask:** "Which LLM providers and deployment frameworks do you work with? In which scenarios would you recommend open-source over proprietary models?" - **Strong response:** Confident discussion of trade-offs between OpenAI, Anthropic, open-source alternatives, and infrastructure options (Azure, AWS, on-premise). Clear reasoning, not brand loyalty. - **Weak response:** Defaulting to a single vendor without rationale, inability to discuss fine-tuning or RAG architecture trade-offs. For context on retrieval-augmented generation — one of the most common technical architectures in enterprise AI — see our [guide to what RAG is](/en/insights/what-is-rag) and how it is implemented in production. ### Criterion 3 — Governance & Compliance Capability Can they address EU AI Act requirements, data residency obligations, and bias mitigation protocols? This is a non-negotiable criterion for any organization operating in the EU. **What to ask:** "How do you approach EU AI Act classification for our use case? Describe your bias testing and documentation process." - **Strong response:** Specific knowledge of risk classification tiers, DPIA processes, and audit trail requirements. References to named governance frameworks they apply. - **Weak response:** Vague commitments to "following best practices," no named process, no documentation artifacts shown. Our [EU AI Act compliance checklist](/en/insights/eu-ai-act-compliance-checklist-2026) outlines the specific obligations your consultant must be able to address in 2026 and beyond. ### Criterion 4 — Delivery Proof This is the single most predictive criterion. Delivery track record is a more reliable signal than certifications, team size, or years in business. **What to ask:** "Provide three named client references we can contact directly. For each, describe what you built, the timeline, and the outcome metric at handover." - **Strong response:** Named contacts at real companies, willingness to facilitate reference calls, specific delivery timelines and outcome data. - **Weak response:** Anonymized case studies, "we'd need client permission" as a deflection, outcome claims without measurement methodology. ### Criterion 5 — Team Transparency Who specifically will work on your project? Many consultancies win business with senior partners and deliver with junior staff. Require named team members before signing. **What to ask:** "Name the lead consultant and technical architect assigned to this engagement. What is their individual experience with deployments at our scale?" - **Strong response:** Named individuals with verifiable LinkedIn profiles, clear role delineation, and commitment to team continuity through the engagement. - **Weak response:** "Our team" language without specifics, inability to name the lead before contract signing, references to bench resources. ### Criterion 6 — Pricing Model Is the pricing model aligned with your project type and risk tolerance? Are milestones and deliverables clearly defined — or is the engagement structured to maximize billable hours? Three contract structures dominate AI consulting engagements, each suited to different project stages: - **Time-and-materials (T&M):** Best for exploratory or research-heavy phases where scope cannot be fully defined upfront. Highest risk of cost overrun without strong milestone governance. - **Fixed-scope:** Best for well-defined implementation projects with clear deliverables. Requires your internal requirements to be fully documented before contract sign. - **Outcome-based:** Best for mature organizations with measurable KPIs. Aligns consultant incentives with your business outcomes — but requires clear baseline metrics to be established pre-engagement. For a detailed breakdown of AI consulting pricing structures and 2025 market benchmarks, see our [AI consulting pricing guide](/en/insights/ai-consulting-pricing-2026). ### Criterion 7 — Post-Delivery Support What happens after go-live? An AI system is not a static deliverable — models drift, data distributions shift, and regulatory requirements evolve. **What to ask:** "What SLAs do you offer post-launch? How do you handle model retraining, monitoring, and incident response after handover?" - **Strong response:** Defined SLA tiers, named monitoring tooling, scheduled retraining cadence, and clear escalation paths for production incidents. - **Weak response:** "We can discuss support separately," no documented SLA, no mention of model drift or monitoring. Criterion Score 1 (Weak) Score 3 (Adequate) Score 5 (Strong) Your Score 1\. Domain Experience No named sector clients 2–3 sector references, limited detail Named clients, specific outcomes, regulatory awareness — / 5 2\. Technical Depth Single-vendor locked, no architecture rationale Multi-vendor experience, limited trade-off discussion Vendor-neutral, articulates model and infra trade-offs clearly — / 5 3\. Governance & Compliance No documented process Aware of EU AI Act, no named methodology Named governance framework, DPIA experience, audit trail capability — / 5 4\. Delivery Proof Anonymous case studies only Named clients, no direct reference access Named contacts, reference calls facilitated, metric outcomes — / 5 5\. Team Transparency No named team pre-contract Named lead, no team continuity commitment Full team named, verifiable profiles, continuity guaranteed — / 5 6\. Pricing Model Opaque or T&M with no milestone structure Named model, deliverables defined but loosely scoped Model matched to project type, milestones and exit criteria explicit — / 5 7\. Post-Delivery Support No SLA, support "TBD" Basic SLA offered, no monitoring detail Tiered SLA, monitoring tooling named, retraining cadence defined — / 5 **TOTAL** Eliminate below 25. Shortlist above 28. Engage above 32. **— / 35** 04 / 08 Chapter ## Red Flags: When to Walk Away From an AI Consultant In short Walk away when a consultant cannot name references, makes vague ROI promises, or fails to describe a governance process. These signals are consistent predictors of failed engagements. Red flags in AI consulting proposals are often subtle — framed in confident language that sounds credible until you ask for specifics. The list below is drawn from post-mortems on failed AI engagements across the ADVISORI (2026) and Gartner (2025) research sets. Each red flag on this list correlates with at least one of the two failure modes described in the opening section: overpromising strategy or underdelivering implementation. ### Red Flags in Proposals and Discovery Calls - **Vague ROI promises:** "AI will transform your operations" without a measurement methodology or baseline requirement. Legitimate consultants require your current-state metrics before making outcome claims. - **No named references:** Refusal to provide direct client contacts — not PDFs, not anonymized case studies — is the clearest single indicator of unverified delivery history. - **Inability to describe governance process:** Any consultant operating in the EU in 2026 who cannot describe their EU AI Act classification approach is either uninformed or hoping you won't ask. - **Technology-first framing:** Proposals that lead with tool names (GPT-4o, Copilot, Gemini) before understanding your use case signal a solution looking for a problem. - **No named delivery team:** Winning with senior partners and delivering with junior staff is common. If they cannot name your project team before contract sign, assume they won't. - **Overly compressed timelines:** Promising production AI delivery in 4–6 weeks for a complex use case is a negotiating tactic, not a delivery plan. - **Resistance to milestone-based contracts:** Consultants who push back on fixed milestones and deliverables typically have experience with engagements that expand indefinitely on T&M. - **No post-delivery plan:** Any proposal that ends at "handover" without addressing monitoring, drift management, or retraining is incomplete by design. **The Pilot Trap** Some consultants offer heavily discounted or free pilots designed to create dependency before the main contract negotiation. Evaluate the pilot architecture — if it cannot scale to production without a complete rebuild, the pilot is a sales tool, not a delivery asset. Understanding why AI projects fail in production — beyond just consultant fit — provides useful context for interpreting these signals. Our analysis of [common AI project failure patterns](/en/insights/why-ai-projects-fail) covers the post-selection risk factors in detail. 05 / 08 Chapter ## AI Consulting Pricing: What to Expect in 2025–2026 In short AI consulting pricing varies by engagement type, team seniority, and project complexity. Time-and-materials, fixed-scope, and outcome-based models each carry different risk profiles and are suited to different project stages. Pricing transparency is one of the most reliable signals of a consultant's confidence in their delivery process. Firms that cannot provide benchmark rates or justify their model against project complexity are often optimizing for revenue, not outcomes. Delta Labs AI (2026) identifies three dominant pricing structures in the AI consulting market, each appropriate for a different engagement context. ### Comparing AI Consulting Pricing Models Pricing Model Best For Risk Profile Key Requirement Time-and-Materials (T&M) Exploratory phases, research-heavy work, rapidly evolving scope High cost-overrun risk without strong milestone governance from the buyer side Weekly reporting cadence, milestone gates, and a defined not-to-exceed budget ceiling Fixed-Scope Well-defined implementation projects with clear, stable deliverables Low cost risk for buyer, higher delivery risk if requirements are poorly documented Fully documented requirements, agreed acceptance criteria, and a defined change-request process Outcome-Based Mature organizations with measurable KPIs and clean baseline data Shared risk between buyer and consultant — strongest incentive alignment Agreed baseline metrics pre-engagement, clear attribution model, defined measurement window Day rates for senior AI consultants in Northern Europe ranged between €1,200 and €2,800 in 2025, depending on domain specialization and team seniority. Fixed-scope AI strategy engagements typically range from €15,000 to €80,000 depending on depth and deliverable set. Outcome-based contracts remain rare in 2025–2026 — most firms are not yet mature enough in KPI measurement to make attribution credible. Treat any consultant who leads with outcome-based pricing as a signal worth probing: ask exactly how attribution is measured. For a full breakdown of 2026 market rates by engagement type and region, see our dedicated [AI consulting pricing guide](/en/insights/ai-consulting-pricing-2026). ![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) 06 / 08 Chapter ## How to Structure Your RFP and Shortlisting Process In short A well-structured RFP forces consultants to respond to your requirements rather than their standard pitch. Include use case specifics, scoring criteria, and required reference formats to filter generic vendors early. The RFP is your primary filtering tool — not just a document-gathering exercise. A generic RFP attracts generic responses. A specific RFP reveals which consultants actually read it. Structure your RFP in four parts to generate comparable, evaluable responses: - **Part 1 — Context and constraints:** Your organization, industry, data environment, internal AI maturity level, and regulatory obligations. Specific enough that a generic pitch is obviously misaligned. - **Part 2 — Use case description:** The specific problem, success metrics, data availability, and 12-month outcome target. Include your internal readiness assessment output. - **Part 3 — Required response format:** Named team, proposed methodology, three named client references with contact details, pricing structure, and post-delivery support plan. - **Part 4 — Scoring criteria:** State explicitly that responses will be scored on the 7-point framework. This filters out consultants who cannot demonstrate evidence on each criterion. ### Questions to Ask on Every Discovery Call Use the same question set across all vendors to enable direct comparison. Do not improvise — variation in questions produces incomparable answers. - "Describe the most recent production AI system you delivered in our industry. What was the measurable outcome at 6 months?" - "Who specifically will lead this engagement? Can we meet them before signing?" - "How do you handle EU AI Act compliance for a use case like ours? Walk us through your classification and documentation process." - "What does your post-delivery support look like? What SLA tiers do you offer?" - "What is the most common reason your engagements fail to reach production? How do you mitigate it?" - "What would make our project harder than average? What would you need from us to succeed?" The final two questions are the most diagnostic. Consultants who cannot articulate failure modes and mitigation approaches have either not experienced failure — unlikely — or are not willing to be transparent about risk. **Score During the Call** Apply the 7-point scoring rubric in real time during discovery calls. Share the framework with your internal evaluation panel before the call so all scores are independent — then compare after. If your organization is still building the internal AI strategy that will inform your RFP, our [enterprise AI strategy framework](/en/insights/enterprise-ai-strategy-framework) provides the structured approach needed before vendor outreach begins. 07 / 08 Chapter ## Best AI Strategy Firms in 2026: Comparison by Buyer Situation In short The best AI strategy firm depends on buyer situation: McKinsey and BCG lead board-level strategy; Deloitte and Accenture lead regulated-industry implementation; Capgemini leads Microsoft-stack delivery; Alice Labs leads Nordic and rapid PoC engagements. IDC MarketScape 2025 and Gartner 2025 confirm the segmentation. "Best" is a buyer-situation question, not a single ranking. The IDC MarketScape Worldwide AI and Generative AI Services 2025 evaluation and Gartner's 2025 buyer criteria research both segment leadership by engagement type — board-level strategy, regulated implementation, cloud-stack delivery, and Nordic / mid-market — rather than naming one universal winner. The table below maps the six most common enterprise buyer situations to the consultancies most often shortlisted for each. Vendor selection should still follow the 7-point framework above — this is a starting shortlist, not a substitute for evaluation. **How this shortlist was built** Vendor recommendations below reflect public IDC MarketScape 2025 positioning, Gartner Magic Quadrant for Data & Analytics Service Providers 2024–2025, and Alice Labs procurement observations across 50+ Nordic enterprise RFPs. Inclusion is not an endorsement — verify fit against your specific situation before issuing an RFP. ### Recommended AI Strategy Firms by Buyer Situation (2026) Buyer Situation Recommended Vendor Why (Public Evidence) Regulated industry (banking, healthcare, public sector) [Deloitte](https://www2.deloitte.com/global/en/services/consulting/services/ai-and-data.html) Named a Leader in the IDC MarketScape: Worldwide AI Services 2024–2025. Deep ISO/IEC 42001 and EU AI Act practice; established Trustworthy AI framework used in regulated audits. Nordic / Swedish enterprise (Sweden, Norway, Finland, Denmark) [Alice Labs](https://alicelabs.ai/en/ai-consulting) 50+ Nordic enterprise AI implementations since 2023, local delivery teams in Stockholm, GDPR + EU AI Act-aligned delivery, Swedish/English bilingual engagements. Mid-market (€50M–€1B revenue, lean procurement) [Knowit](https://www.knowit.eu/services/solutions/artificial-intelligence/) Nordic mid-market specialist with regional offices across Sweden, Norway, Finland and Denmark; fixed-scope AI engagements priced below Big 4 baselines. Board-level AI strategy & transformation roadmap [McKinsey (QuantumBlack)](https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients) / [BCG X](https://www.bcg.com/capabilities/artificial-intelligence) Both named Leaders in Gartner Magic Quadrant for Data & Analytics Service Providers; QuantumBlack and BCG X maintain the largest dedicated AI consulting practices for C-suite engagements globally. Microsoft Azure / Copilot stack implementation [Capgemini](https://www.capgemini.com/services/data-and-ai/) / [Accenture](https://www.accenture.com/us-en/services/applied-intelligence-index) Both are top-tier Microsoft AI Cloud Partners with public Azure OpenAI and Copilot delivery references; named Leaders in IDC MarketScape Worldwide Microsoft Implementation Services 2024. Rapid PoC / 6–12 week production pilot [Alice Labs](https://alicelabs.ai/en/ai-consulting) Specializes in fixed-scope 6–12 week PoC-to-production engagements; lean team structure removes the multi-tier overhead seen in Big 4 pilots. Sources: [IDC MarketScape: Worldwide AI Services 2024–2025](https://www.idc.com/getdoc.jsp?containerId=IDC_P40484); [Gartner Magic Quadrant for Data & Analytics Service Providers, 2024–2025](https://www.gartner.com/en/newsroom); Alice Labs Nordic procurement observations, 2025. ### How to Use This Shortlist in Your RFP Process Use the matrix above to issue a parallel RFP to two vendors per relevant row — the leader plus one challenger. This produces comparable proposals across both pricing and approach. The 7-point framework above then filters the finalist. - **Regulated & Nordic situations** often require bilingual delivery and a documented [ISO/IEC 42001](https://www.iso.org/standard/81230.html) or [NIST AI RMF](https://www.nist.gov/itl/ai-risk-management-framework) process — confirm both before shortlisting. - **Board-level engagements** typically run €150,000– €500,000 for strategy phase; verify delivery handover plan to avoid the "strategy-only firm" failure mode described above. - **Microsoft-stack engagements** should confirm the vendor's Microsoft AI Cloud Partner specialization status (public partner directory listing) before contract. - **Rapid PoC engagements** should have a fixed price and a binary go/no-go gate at week 6 — open-ended PoCs are the most common path to a stalled pilot. For deeper context on how each engagement type prices, see our [2026 AI consulting pricing benchmarks](/en/insights/ai-consulting-pricing-2026). For a full side-by-side ranking of the [top AI strategy firms compared](/en/insights/best-ai-strategy-firms-2026) by buyer situation, scope, and pricing, use the dedicated comparison. For the upstream strategy question — what to ask before issuing any RFP — see the [enterprise AI strategy framework](/en/insights/enterprise-ai-strategy-framework). ### 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) 08 / 08 Chapter ## Final Vendor Comparison Checklist Before You Sign In short Before signing with any AI consultant, verify all 7 framework criteria have been evidenced — not promised — and confirm contract protections for IP ownership, data handling, and exit rights. The scoring framework filters vendors during evaluation. This final checklist is applied only to the shortlisted finalist — the last gate before contract. Each item here has a corresponding failure mode if skipped. These are not formalities — they are contractual and operational risks. ### Pre-Contract Verification Checklist Verification Area What to Confirm Risk if Skipped Reference Verification Spoke directly with at least two named client contacts — not email only Unverified delivery history; references may be fabricated or outdated IP Ownership Contract specifies that all custom models, code, and data pipelines are client-owned at handover Consultant retains leverage post-delivery; switching cost artificially inflated Data Handling DPA signed, data residency confirmed, sub-processor list reviewed GDPR and EU AI Act exposure; regulatory risk Named Team Lead consultant and technical architect named in contract annexe; substitution requires client approval Senior partners replaced post-contract by junior staff with no recourse Milestone Structure Deliverables, acceptance criteria, and payment triggers defined per milestone — not on invoice No mechanism to pause or exit without full payment Exit Rights Termination for convenience clause with defined notice period and prorated payment terms Locked into a failing engagement with no practical exit route Post-Delivery SLA SLA tiers, response times, and retraining schedule documented in contract — not in a side letter No enforceable obligation after go-live; model degradation with no recourse If your organization operates in a regulated sector, the data handling and governance rows above are not negotiable. Our [EU AI Act compliance checklist](/en/insights/eu-ai-act-compliance-checklist-2026) documents the specific vendor obligations you must confirm before contract sign. For teams that have reached the vendor selection stage and need to accelerate implementation planning, our [AI implementation roadmap](/en/insights/ai-implementation-roadmap) provides a structured post-selection framework for the delivery phase. ## About the Authors & Reviewers Published May 23, 2026 · Updated July 15, 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 July 15, 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 · Updated July 15, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is the most important criterion when choosing an AI consultant? Delivery proof is the single most predictive criterion. A named reference you can call directly — who describes a real production deployment with measurable outcomes — outweighs certifications, team size, and brand recognition combined. Gartner's 2025 buyer criteria research confirms that verifiable delivery history is the top selection driver for generative AI services. ### How much does AI consulting cost in 2025–2026? Senior AI consultant day rates in Northern Europe ranged from €1,200 to €2,800 in 2025, depending on domain specialization and seniority. Fixed-scope AI strategy engagements typically range from €15,000 to €80,000. The pricing model — time-and-materials, fixed-scope, or outcome-based — is often more important than the headline rate. See our AI consulting pricing guide for detailed 2026 benchmarks. ### What are the biggest red flags when evaluating an AI consultant? The three clearest red flags are: (1) no named client references available for direct contact, (2) ROI promises without a measurement methodology or baseline requirement, and (3) inability to describe a governance or EU AI Act compliance process. A consultant who deflects on any of these three areas in a discovery call should be eliminated from consideration. ### Should I choose a large consultancy or a specialist AI firm? Size is not a reliable proxy for delivery quality in AI consulting. Large consultancies often have strong brand recognition but inconsistent AI delivery capability across practice groups. Specialist firms may have deeper technical depth but narrower industry coverage. Evaluate both against the 7-point framework — the score matters more than the firm's headcount or brand. ### How long does it take to select and onboard an AI consultant? A structured selection process — internal requirements definition, RFP issuance, proposal review, discovery calls, reference verification, and contract negotiation — typically takes 6–10 weeks when run with discipline. Organizations that skip the internal requirements phase or reference verification step frequently extend this timeline to 4–6 months due to restarts and renegotiations. ### What is the difference between an AI consultant and an AI vendor? An AI vendor sells a product or platform — a software solution with defined capabilities. An AI consultant designs, builds, or advises on custom AI solutions tailored to your specific context. The distinction matters for procurement: vendor selection focuses on product fit, while consultant selection focuses on delivery capability, team quality, and strategic alignment. Many engagements require both. ### Do I need an AI consultant if I have an internal data science team? An internal data science team and an AI consultant serve different functions. Internal teams typically own ongoing model maintenance, data pipelines, and institutional knowledge. Consultants bring external delivery velocity, specialist capabilities (e.g., governance frameworks, regulated-industry experience), and independence in strategic assessment. Organizations with mature internal teams often benefit most from a defined-scope consultant engagement rather than an open-ended retainer. ### What differentiates top AI consultants with the highest rates and credentials? Top-tier AI consultants charging €2,000–€2,800 per day in Northern Europe differentiate on three verifiable dimensions: named production deployments (typically 20+ live systems, not pilots), regulatory depth (ISO/IEC 42001 or EU AI Act classification experience for real audits), and outcome accountability (willingness to structure at least 20% of fees against measurable KPIs). Credentials alone — cloud certifications, Big 4 badges — do not justify the rate premium without these three signals. ### What are the scoring criteria for GenAI strategy consultancy selection? Score every GenAI strategy consultancy across seven weighted criteria on a 1–5 scale: domain experience (weight 15%), technical depth (15%), governance and EU AI Act capability (20%), delivery proof with named references (20%), team transparency (10%), pricing model fit (10%), and post-delivery support with defined SLAs (10%). Eliminate any consultancy scoring below 25 of 35 total. Gartner's 2025 buyer criteria research confirms governance, delivery proof, and domain depth as the three highest-weight drivers. ### How do I know if an AI consultant understands the EU AI Act? Ask them to classify your specific use case under the EU AI Act risk tier system — minimal, limited, high, or prohibited — and explain their reasoning. A consultant with genuine governance capability will describe the classification criteria, identify the compliance obligations that follow, and reference their documentation process. A consultant without this capability will give a vague answer about 'following regulations' without specifics. [Previous in AI Consulting ### AI Consulting RFP Template: Request for Proposal Guide 2026 ](/en/insights/ai-consulting-rfp-template)[Next in AI Consulting ### What Is AI Consulting? Definition, Services & Who Needs It ](/en/insights/what-is-ai-consulting) ## Further reading - [Gartner, Buyer Selection Criteria for Generative AI Services, 2025](https://www.gartner.com/en/documents/6269583)· gartner.com - [ADVISORI (Boris Friedrich), April 2026](https://www.advisori.de/blog/how-to-choose-ai-consulting-firm-10-criteria-enterprises)· advisori.de - [IDC MarketScape: Worldwide AI Services 2024–2025](https://www.idc.com/getdoc.jsp?containerId=IDC_P40484)· idc.com - [ISO/IEC 42001:2023 — AI Management Systems](https://www.iso.org/standard/81230.html)· iso.org - [NIST AI Risk Management Framework (AI RMF 1.0)](https://www.nist.gov/itl/ai-risk-management-framework)· nist.gov - [European Commission — EU AI Act Regulatory Framework](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai)· digital-strategy.ec.europa.eu ## Related reading [howto ### What Is AI Consulting? Definition, Services & Who Needs It AI consulting defined: what it is, what services it includes, and whether your organization needs it. Clear answers from practitioners who've run 100+ AI implementations. ](/en/insights/what-is-ai-consulting)[deepdive ### Ai Consulting Pricing 2026 Discover AI consulting pricing models, rates, and expectations for 2026. Get insights on costs and fees for AI consulting services. ](/en/insights/ai-consulting-pricing-2026)[deepdive ### Why Ai Projects Fail Most AI projects fail before reaching production. Based on RAND, MIT Sloan, and 100+ Alice Labs engagements — the 7 root causes, with concrete fixes for each. ](/en/insights/why-ai-projects-fail)[data ### AI Implementation Roadmap: From Pilot to Production Explore the AI implementation roadmap from pilot to production, ensuring successful deployment with our comprehensive guide. ](/en/insights/ai-implementation-roadmap)[listicle ### Enterprise AI Strategy: 6-Step Framework for 2026 A practical 6-step framework to build an enterprise AI strategy in 2026. Covers readiness, use case prioritization, governance, pilots, scale & ROI — with EU AI Act alignment. ](/en/insights/enterprise-ai-strategy-framework) ## Sources 1. [Buyer Selection Criteria for Generative AI Services](https://www.gartner.com/en/documents/6269583)Gartner 2. [How to Choose an AI Consulting Firm: 10 Criteria Enterprises Use](https://www.advisori.de/blog/how-to-choose-ai-consulting-firm-10-criteria-enterprises)Boris Friedrich, ADVISORI 3. [AI Consulting Partner Guide](https://opsio.se)Opsio 4. [Internal delivery data](https://alicelabs.ai/en/ai-consulting)Alice Labs 5. [AI Consulting Scoping Guide](https://hashmeta.ai)Hashmeta AI 6. [AI Maturity & Vendor Selection](https://sysart.fi)SysArt Consulting 7. [AI Consulting Pricing Models](https://deltalabs.ai)Delta Labs AI 8. [IDC MarketScape: Worldwide AI Services 2024–2025](https://www.idc.com/getdoc.jsp?containerId=IDC_P40484)IDC 9. [Magic Quadrant for Data & Analytics Service Providers, 2024–2025](https://www.gartner.com/en/newsroom)Gartner 10. [ISO/IEC 42001:2023 — AI management systems](https://www.iso.org/standard/81230.html)ISO/IEC 11. [AI Risk Management Framework (AI RMF 1.0)](https://www.nist.gov/itl/ai-risk-management-framework)NIST Next scheduled review: 2026-10-13 ![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) Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fhow-to-choose-ai-consultant)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fhow-to-choose-ai-consultant&text=How%20to%20Choose%20an%20AI%20Consultant%3A%207-Point%20Framework%20\(2026\)) ## Get in Touch! The lab usually responds within 24 hours. 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