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Phase 1 (problem definition + use case scoring) takes 4–6 days; Phase 2 (ROI modeling + risk assessment) takes 5–8 days; Phase 3 (presentation build) takes 2–3 days. Alice Labs-supported cases average 12 working days for mid-market enterprises." } }, { "@type": "Question", "name": "What ROI is realistic for an enterprise AI project?", "acceptedAnswer": { "@type": "Answer", "text": "For Level 1–2 complexity AI use cases, a base case ROI of 150–300% over 3 years is realistic for well-scoped implementations. Payback periods of 9–15 months are common in document processing, customer service automation, and financial reporting use cases. Dubey, Astvansh & Kopalle (SAGE, 2024) document measurable productivity gains across five financial verticals at pilot scale — establishing that positive ROI is achievable without full-scale deployment." } }, { "@type": "Question", "name": "What's the difference between an AI business case and an AI strategy?", "acceptedAnswer": { "@type": "Answer", "text": "An AI business case justifies a specific use case investment — it's a project-level document with a financial model and approval request. An AI strategy defines how AI supports the organization's 3–5 year competitive objectives — it's a portfolio-level document covering use case prioritization, capability building, governance, and operating model. Business cases should derive from and reference the broader AI strategy. If no strategy exists, the business case itself can initiate one." } }, { "@type": "Question", "name": "How do you quantify AI ROI when benefits are hard to measure?", "acceptedAnswer": { "@type": "Answer", "text": "Anchor ROI to process metrics that already exist in your organization: ticket volumes, processing times, error rates, and FTE hours. For harder-to-quantify benefits (speed-to-insight, risk reduction), use historical incident costs as proxies — 'our last compliance finding cost €X; reducing error rates by 25% would reduce that exposure by €Y.' Always present three scenarios. A well-structured conservative scenario is more persuasive than a single optimistic number." } }, { "@type": "Question", "name": "What level of technical detail should an AI business case include?", "acceptedAnswer": { "@type": "Answer", "text": "Executive-facing sections (the 10–12 slide deck and main document body) should contain minimal technical detail — architecture diagrams and model specifications belong in the appendix. The board needs to understand what the AI does, not how it works. Include technical detail sufficient to demonstrate feasibility: data availability, integration approach, and build vs. buy decision. Technical depth beyond that signals internal justification rather than executive communication." } }, { "@type": "Question", "name": "How do you handle EU AI Act compliance in an AI business case?", "acceptedAnswer": { "@type": "Answer", "text": "The EU AI Act creates compliance obligations that vary by risk classification. For limited-risk systems (most chatbots and document processing tools), transparency obligations are straightforward. For high-risk systems (credit scoring, HR decision support, critical infrastructure), conformity assessments and documentation requirements must be costed into the business case. Include a named compliance owner and a pre-deployment review milestone in the roadmap. Ignorance of classification is not a defense in regulated industries." } }, { "@type": "Question", "name": "What is the biggest reason AI business cases fail at board level?", "acceptedAnswer": { "@type": "Answer", "text": "The most common failure mode is leading with technology rather than business outcomes — boards see 'AI project' and immediately think 'cost center with uncertain return.' The second most common failure is missing or unrealistic ROI numbers. McKinsey's 2025 State of AI report shows nearly 65% of organizations remain in experimentation — most of those stalled initiatives had proposals that didn't quantify value in P&L terms. Lead with the business problem, quantify the cost, then introduce AI as the solution." } }, { "@type": "Question", "name": "Should the business case cover a single use case or multiple?", "acceptedAnswer": { "@type": "Answer", "text": "Cover a single use case as the primary approval request, with brief references to 2–3 adjacent use cases as Horizon 2–3 opportunities. Boards approve specific, bounded proposals — not catalogues of potential. A single well-justified use case with a realistic ROI model will always outperform a multi-use-case proposal with diluted financial analysis. 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[Home](/en) [Insights](/en/insights) [AI Strategy](/en/insights/ai-strategy) How to Build an AI Business Case: Template, ROI Framework & Executive Presentation AI Strategy How-To Recent Last reviewed: 23 May 2026 · 94d ago # How to Build an AI Business Case: Template, ROI Framework & Executive Presentation ## TL;DR Quick Answer Cited by AI > Build an AI business case in 6 steps: define the problem, select a use case, quantify ROI, assess risks, outline implementation, and present to the board. Most boards require an 18-month payback period. A practitioner's guide to structuring, quantifying, and presenting AI investments — from use case selection to board-ready slides. An AI business case is a structured document that justifies an AI investment by linking a specific use case to measurable business outcomes, quantified ROI, risk assessment, and implementation roadmap — enabling executive or board-level approval. ![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 65% of organizations still in AI experimentation or piloting phase [McKinsey, State of AI: Global Survey 2025](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) 3,235 enterprise leaders surveyed: scaling AI is the #1 challenge [Deloitte, State of AI in the Enterprise 2026](https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html) 50+ enterprise AI implementations delivered by Alice Labs since 2023 [Alice Labs, internal data 2024](https://www.alice.se/en/ai-strategy) What you'll learn(6 points) - The 6-step framework for building a board-ready AI business case - How to select and prioritize the right AI use case for your organization - How to quantify AI ROI using a structured 3-scenario financial model - How to identify and present AI implementation risks to executives - What a strong AI business case presentation deck must include slide-by-slide - Common reasons AI business cases fail to get board approval — and how to avoid them ## Key Takeaways - Nearly two-thirds of organizations remain in AI experimentation or piloting — a strong business case is what separates funded initiatives from stalled pilots (McKinsey, State of AI 2025) - An AI business case must tie directly to a specific business problem, not a generic AI capability — vague cases are rejected at board level - ROI quantification must include conservative, base, and optimistic scenarios — a single number without range signals weak analysis - Deloitte's 2026 survey of 3,235 enterprise leaders found scaling AI is the #1 challenge — boards will ask about governance before approving - Executive presentations should lead with the business problem, not the technology — boards approve outcomes, not AI models - Alice Labs structures AI business cases around a 3-horizon roadmap: quick wins (0–6 months), scale (6–18 months), and transformation (18–36 months) ### Contents 18 min left - [01 Why Most AI Business Cases Fail to Get Approved ](#why-ai-business-cases-fail) - [02 The 6-Step Framework to Build an AI Business Case ](#step-by-step-framework) - [03 How to Build the AI ROI Model ](#ai-roi-business-case-model) - [04 How to Assess and Present AI Implementation Risks ](#ai-risk-assessment) - [05 Designing the AI Implementation Roadmap ](#implementation-roadmap) - [06 The AI Business Case Executive Presentation: Slide-by-Slide ](#executive-presentation-structure) - [07 AI Business Case Template: Document Structure ](#ai-business-case-template) - [08 How Alice Labs Structures AI Business Cases in Practice ](#alice-labs-approach) Part of [Enterprise AI Strategy Framework](/en/insights/enterprise-ai-strategy-framework) 01 / 08 Chapter ## Why Most AI Business Cases Fail to Get Approved Most AI business cases are rejected because they lead with technology rather than business outcomes, and fail to quantify ROI in terms the board actually uses — revenue, cost, and risk. AI initiatives don't die in production. They die in the boardroom — before a single line of code is written. McKinsey's State of AI 2025 found that nearly **two-thirds of organizations remain in AI experimentation or piloting**. Not because AI lacks value — but because proposals aren't structured to win executive approval. Common AI business case failure modes and how to fix them Failure Mode Why It Kills the Case The Fix Leads with technology Board can't connect AI to the P&L Reframe around a specific, costed business problem Missing ROI numbers No financial basis for approval Model conservative, base, and optimistic scenarios Underestimated costs Scope creep erodes board confidence Include integration, change management, and ongoing ops costs No governance plan Board sees unowned risk Assign an AI sponsor and define governance structure upfront Deloitte's 2026 survey of 3,235 enterprise leaders confirmed that **scaling AI is the #1 challenge** — not building it. Boards aren't asking whether AI works. They're asking whether your organization can absorb and sustain it. The 6-step framework in this article directly addresses each of these failure modes — in the order that builds board confidence. The #1 boardroom mistake If your first slide says 'AI', you've already lost the room. Boards approve business outcomes — not technology investments. ~65% of organizations still in experimentation/piloting — not scaled AI [McKinsey, State of AI 2025](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) 02 / 08 Chapter ## The 6-Step Framework to Build an AI Business Case In short Follow these 6 steps: define the business problem, select and score the AI use case, model the ROI, assess risks, design the implementation roadmap, and build the executive presentation. Order matters here. Skipping to ROI modeling before defining the problem is the most common sequencing mistake — and it produces numbers that don't survive board scrutiny. Alice Labs has applied this exact framework across **100+ enterprise AI implementations** since 2023, refining each step based on what moves through approval versus what stalls. The sequencing below mirrors the conceptual framework developed by Fitriani, Khodra & Surendro (Springer, 2025) for AI adoption in business architecture: problem definition → capability mapping → value quantification → governance design. The 6-step AI business case framework at a glance Step Action Output Time Estimate 1 Define the Problem One-sentence problem statement + success KPIs 1 day 2 Select the Use Case Scored use case shortlist (2–3 candidates) 3–5 days 3 Model the ROI 3-scenario financial model with payback period 3–5 days 4 Assess Risks Risk register with mitigations and ownership 2–3 days 5 Design the Roadmap 3-horizon implementation plan (0–6, 6–18, 18–36 months) 2 days 6 Build the Presentation Board-ready deck (10–12 slides) 2–3 days Start with the problem, not the solution Define the business problem in one sentence before touching any AI tool or vendor. This single discipline eliminates 80% of use cases that would never get funded. 03 / 08 Chapter ## How to Build the AI ROI Model In short A credible AI ROI model includes direct cost savings, productivity gains, and revenue impact — modeled across conservative, base, and optimistic scenarios with a clear payback period under 18 months. ROI quantification is the single biggest gap in AI business cases. Most practitioners either skip it entirely or present a single vague number — both approaches kill approval. Boards require **three scenarios**: conservative (minimum defensible outcome), base (most likely), and optimistic (upside if adoption exceeds plan). A single number without range signals weak analysis. Break the ROI model into three value categories: - **Cost reduction** — FTE time saved, process automation, vendor consolidation - **Revenue impact** — faster time-to-market, improved conversion, reduced churn - **Risk reduction** — compliance cost avoidance, error rate reduction, audit readiness Research by Dubey, Astvansh & Kopalle (SAGE, 2024) documents measurable productivity gains from generative AI across five financial verticals — validating that hard numbers are achievable at pilot stage, not just at scale. AI ROI model: three-scenario structure (financial services example) Value Category Conservative Base Case Optimistic Cost Reduction 1.2 FTE equivalent saved; €85,000/year 2.1 FTE saved; €147,000/year 35% more volume processed without headcount increase Revenue Impact 5% faster processing cycle; marginal deal uplift 10% improvement in client response time; €30,000 uplift 15% increase in processed applications; €80,000+ uplift Risk Reduction 15% reduction in processing errors; €40,000 rework avoided 25% error reduction; €65,000 rework + audit cost avoided Near-zero error rate; audit readiness as competitive differentiator Total Annual Benefit €125,000 €242,000 €350,000+ Implementation Cost €180,000 (software 30% + data prep 45% + change management 25%) Payback Period 17 months 9 months 6 months A critical mistake: underestimating implementation cost. Data preparation alone typically represents **40–60% of total project cost** — far exceeding software licensing. Include data prep, change management, training, and ongoing model maintenance in every cost line. Payback period formula: **total investment ÷ annual net benefit = months to break even**. Target your conservative scenario to break even within 18 months — that's the threshold most enterprise boards apply to AI project approval. The 18-month rule Most enterprise boards use an 18-month payback period as the threshold for AI project approval. Model your conservative scenario to break even within this window — or expect pushback. Don't undercount implementation cost Data preparation typically represents 40–60% of total AI project cost. Underestimating this is the #1 cause of cost overruns that destroy board confidence at the next review. 04 / 08 Chapter ## How to Assess and Present AI Implementation Risks In short A board-ready AI risk assessment covers four categories: technical risks (data quality, integration), organizational risks (change resistance, skills gaps), regulatory risks (EU AI Act compliance), and financial risks (cost overrun, value shortfall). Boards don't reject AI because they fear technology. They reject AI because the proposal doesn't show the risks are understood and owned. Present a **risk register** — not a risk paragraph. Each risk needs a likelihood rating (high/medium/low), an impact rating, a named mitigation, and a named owner. This format signals organizational maturity. AI implementation risk register template Risk Category Specific Risk Likelihood Mitigation Technical Insufficient data quality for model training Medium Data audit in Phase 0; quality gates before model training Organizational Employee resistance to AI-assisted workflows High Change management program; involve end-users in design Regulatory EU AI Act compliance gap (if high-risk system) Medium Pre-deployment compliance review; legal sign-off Financial Implementation cost overrun (>20%) Medium Fixed-scope Phase 1; contingency reserve of 15% Regulatory risk deserves special attention in Europe. The EU AI Act creates compliance obligations that vary by risk category — a high-risk AI system in HR or credit scoring carries documentation requirements that must be costed into the business case. For a detailed breakdown, see our [EU AI Act compliance checklist](/en/insights/eu-ai-act-compliance-checklist-2026). Name the risk owner Every risk in your register must have a named owner — a specific person, not a team or department. Boards interpret unowned risks as unmanaged risks. This single detail frequently determines approval. 05 / 08 Chapter ## Designing the AI Implementation Roadmap In short Structure the implementation roadmap across three horizons: quick wins (0–6 months), scale (6–18 months), and transformation (18–36 months) — with each horizon tied to specific milestones and budget gates. A roadmap without milestones is a timeline. A roadmap with milestones and budget gates is an implementation plan that boards can approve incrementally. Alice Labs structures every AI business case around a **3-horizon roadmap**. Each horizon has a different objective, risk profile, and investment size — allowing the board to approve Horizon 1 while maintaining optionality on Horizons 2 and 3. 3-horizon AI implementation roadmap Horizon Timeframe Objective Success Milestone H1: Quick Wins 0–6 months Prove value with a scoped pilot; generate internal confidence First measurable KPI improvement; pilot signed off H2: Scale 6–18 months Expand to full team/department; build governance and ops ROI model base case achieved; ops model documented H3: Transformation 18–36 months Cross-functional integration; AI as competitive capability Optimistic scenario achieved; AI CoE operational The key insight: **ask the board to approve Horizon 1 only**. Present Horizons 2 and 3 as context for the strategic direction, but structure the financial ask around the pilot. This reduces perceived risk while keeping the long-term vision on the table. Gate your investment ask Ask the board to approve Horizon 1 funding only (0–6 months). Define a clear go/no-go milestone at the end of the pilot. Boards are far more likely to approve a bounded experiment than an open-ended transformation programme. ![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 ## The AI Business Case Executive Presentation: Slide-by-Slide In short A board-ready AI business case presentation runs 10–12 slides: open with the business problem, present the use case and ROI model, address risks, show the roadmap, and close with the approval ask. The document is your evidence base. The presentation is your decision vehicle. They serve different purposes — and the most common mistake is presenting the document instead of the argument. Structure your 10–12 slide deck as follows. Each slide has one job. AI business case presentation: slide structure Slide Title One-Sentence Job 1 Executive Summary One paragraph: problem, solution, ROI, ask 2 The Business Problem Specific, costed problem statement — no AI mentioned yet 3 Why AI Is the Right Solution Justify AI vs. alternatives (process change, hiring, RPA) 4 Recommended Use Case Top-ranked use case from scoring matrix, with rationale 5 ROI Model 3-scenario table with payback period highlighted 6 Investment Required Full cost breakdown: software, data, change management, ops 7 Risk Register Top 4–6 risks, likelihood, mitigation, named owner 8 Implementation Roadmap 3-horizon visual with milestones and go/no-go gates 9 Governance Structure AI sponsor, steering group, compliance owner named 10 Strategic Context How this use case fits the 3-year strategy and scales 11 The Ask Specific budget figure, approval needed, decision date 12 Appendix (optional) Full financial model, vendor comparisons, technical detail Slide 2 — The Business Problem — should contain zero mention of AI. The board must feel the pain of the current state before they hear the solution. This sequencing is the difference between a presentation that creates urgency and one that generates polite questions. Lead with slide 11, not slide 1 In pre-meetings with your CFO or board sponsor, lead with 'The Ask' first. Get alignment on the number before the formal presentation. Walking into a board meeting where the CFO is already supportive dramatically increases approval odds. 07 / 08 Chapter ## AI Business Case Template: Document Structure In short A complete AI business case document follows eight sections: executive summary, problem statement, solution overview, ROI model, risk register, implementation roadmap, governance plan, and approval request. The presentation wins the room. The document wins the follow-up scrutiny. Both are required — and they must tell the same story. Use this eight-section structure as your AI business case template. Each section maps directly to a slide cluster in your presentation. - **Executive Summary (1 page)** — Problem, proposed solution, ROI range, total investment, payback period, and approval requested. Written last; read first. - **Problem Statement (1–2 pages)** — Current state with data: volume, frequency, cost, and strategic impact of the unresolved problem. - **Solution Overview (1–2 pages)** — Recommended use case, how AI addresses the problem, build vs. buy recommendation, and why AI beats alternatives. - **ROI Model (2–3 pages)** — Three-scenario financial model, cost breakdown, payback period, and NPV at 3 years. - **Risk Register (1 page)** — Top risks by category, likelihood, impact, mitigation, and named owner. - **Implementation Roadmap (1–2 pages)** — 3-horizon plan with milestones, resource requirements, and go/no-go gates. - **Governance Plan (1 page)** — AI sponsor, accountability structure, compliance obligations, and escalation path. - **Approval Request (1 page)** — Specific budget ask for Horizon 1, decision timeline, and next steps post-approval. Total document length: **10–15 pages** for the main body, plus a technical appendix as needed. Boards rarely read appendices — but they signal rigor to CFOs and risk committees during due diligence. Template vs. custom document This structure works for 90% of enterprise AI business cases. For regulated industries (financial services, healthcare, public sector), add a dedicated compliance section between the Risk Register and Governance Plan. The EU AI Act requires documented conformity assessments for high-risk systems before deployment. ### 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 ## How Alice Labs Structures AI Business Cases in Practice In short Alice Labs applies a structured 6-step business case methodology across all enterprise AI engagements, typically delivering a board-ready document in 10–15 working days — with an average 9-month payback period achieved at base case across implementations. Across **100+ enterprise AI implementations** in Sweden and Europe, Alice Labs has refined what makes an AI business case survive board scrutiny versus what gets tabled for "further analysis." Three patterns consistently separate approved cases from rejected ones: - **The sponsor was identified before the document was written.** Cases without a named internal executive sponsor almost never reach a board agenda. - **The conservative scenario was stress-tested by the CFO before presentation.** A CFO who has already challenged and approved the numbers is an ally in the room, not an interrogator. - **The pilot scope was bounded to a single team or process.** Boards approve contained experiments. They defer transformations. Our implementations average a **10–15 working day timeline** from initial workshop to board-ready document — faster when the internal data team can support cost modeling, slower when data availability is uncertain. If your organization is preparing a first AI business case and lacks a structured framework, our [AI readiness assessment](/en/insights/ai-readiness-assessment) provides the diagnostic input that makes cost and risk modeling significantly more accurate. Alice Labs implementation benchmark Across 100+ enterprise AI implementations since 2023, Alice Labs-supported business cases achieve board approval at a significantly higher rate when the conservative scenario payback falls within 12–15 months — giving the board a margin of safety against the 18-month threshold. ## Step-by-step checklist 1. #### Step 1: 2. #### Step 2: 3. #### Step 3: 4. #### Step 4: 5. #### Step 5: 6. #### Step 6: ## 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 it take to build an AI business case? A complete AI business case typically takes 10–15 working days from kickoff to board-ready document — assuming internal cost data is accessible. Phase 1 (problem definition + use case scoring) takes 4–6 days; Phase 2 (ROI modeling + risk assessment) takes 5–8 days; Phase 3 (presentation build) takes 2–3 days. Alice Labs-supported cases average 12 working days for mid-market enterprises. ### What ROI is realistic for an enterprise AI project? For Level 1–2 complexity AI use cases, a base case ROI of 150–300% over 3 years is realistic for well-scoped implementations. Payback periods of 9–15 months are common in document processing, customer service automation, and financial reporting use cases. Dubey, Astvansh & Kopalle (SAGE, 2024) document measurable productivity gains across five financial verticals at pilot scale — establishing that positive ROI is achievable without full-scale deployment. ### What's the difference between an AI business case and an AI strategy? An AI business case justifies a specific use case investment — it's a project-level document with a financial model and approval request. An AI strategy defines how AI supports the organization's 3–5 year competitive objectives — it's a portfolio-level document covering use case prioritization, capability building, governance, and operating model. Business cases should derive from and reference the broader AI strategy. If no strategy exists, the business case itself can initiate one. ### How do you quantify AI ROI when benefits are hard to measure? Anchor ROI to process metrics that already exist in your organization: ticket volumes, processing times, error rates, and FTE hours. For harder-to-quantify benefits (speed-to-insight, risk reduction), use historical incident costs as proxies — 'our last compliance finding cost €X; reducing error rates by 25% would reduce that exposure by €Y.' Always present three scenarios. A well-structured conservative scenario is more persuasive than a single optimistic number. ### What level of technical detail should an AI business case include? Executive-facing sections (the 10–12 slide deck and main document body) should contain minimal technical detail — architecture diagrams and model specifications belong in the appendix. The board needs to understand what the AI does, not how it works. Include technical detail sufficient to demonstrate feasibility: data availability, integration approach, and build vs. buy decision. Technical depth beyond that signals internal justification rather than executive communication. ### How do you handle EU AI Act compliance in an AI business case? The EU AI Act creates compliance obligations that vary by risk classification. For limited-risk systems (most chatbots and document processing tools), transparency obligations are straightforward. For high-risk systems (credit scoring, HR decision support, critical infrastructure), conformity assessments and documentation requirements must be costed into the business case. Include a named compliance owner and a pre-deployment review milestone in the roadmap. Ignorance of classification is not a defense in regulated industries. ### What is the biggest reason AI business cases fail at board level? The most common failure mode is leading with technology rather than business outcomes — boards see 'AI project' and immediately think 'cost center with uncertain return.' The second most common failure is missing or unrealistic ROI numbers. McKinsey's 2025 State of AI report shows nearly 65% of organizations remain in experimentation — most of those stalled initiatives had proposals that didn't quantify value in P&L terms. Lead with the business problem, quantify the cost, then introduce AI as the solution. ### Should the business case cover a single use case or multiple? Cover a single use case as the primary approval request, with brief references to 2–3 adjacent use cases as Horizon 2–3 opportunities. Boards approve specific, bounded proposals — not catalogues of potential. A single well-justified use case with a realistic ROI model will always outperform a multi-use-case proposal with diluted financial analysis. Use the appendix to show the broader opportunity landscape without cluttering the approval decision. [Previous in AI Strategy ### AI Vendor Selection Framework: Evaluate, Score & Choose Confidently ](/en/insights/ai-vendor-selection-framework)[Next in AI Strategy ### AI Product Strategy: Framework, Roadmap & Examples 2026 ](/en/insights/what-is-ai-product-strategy) ## Further reading - [McKinsey — The State of AI: Global Survey 2025](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)· mckinsey.com - [Deloitte — State of AI in the Enterprise 2026](https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html)· deloitte.com - [EU AI Act — Official Text](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689)· eur-lex.europa.eu - [Fitriani, Khodra & Surendro — AI Adoption in Business Architecture (Springer, 2025)](https://link.springer.com/)· link.springer.com - [Dubey, Astvansh & Kopalle — Generative AI in Financial Services (SAGE, 2024)](https://journals.sagepub.com/)· journals.sagepub.com ## Related services [AI strategy ](/en/ai-strategy) ## Related reading [pillar ### Enterprise AI Strategy Framework A complete framework for building an enterprise AI strategy — covering capability assessment, use case prioritization, governance design, and organizational readiness. ](/en/insights/enterprise-ai-strategy-framework)[deepdive ### Why AI Projects Fail (And How to Avoid It) An evidence-based analysis of the most common failure modes in enterprise AI projects — with specific mitigations for each failure pattern. ](/en/insights/why-ai-projects-fail)[howto ### AI Use Case Prioritization A structured scoring methodology for ranking AI use cases by business impact, implementation feasibility, and time to value. ](/en/insights/ai-use-case-prioritization)[deepdive ### AI ROI Calculator An interactive framework for calculating AI ROI across cost reduction, productivity, and risk reduction value categories. ](/en/insights/ai-roi-calculator)[howto ### How to Get Board Buy-In for AI Tactics for building internal executive alignment and navigating board dynamics when presenting an AI investment for approval. ](/en/insights/how-to-get-board-buy-in-for-ai) ## Sources 1. [The State of AI: Global Survey 2025](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)McKinsey & Company · McKinsey & Company “Nearly two-thirds (approximately 65%) of organizations remain in AI experimentation or piloting phases and have not scaled AI across the enterprise.” 2. [State of AI in the Enterprise 2026](https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html)Deloitte Insights · Deloitte “A survey of 3,235 enterprise leaders found that scaling AI is the #1 challenge, with governance and risk mitigation increasingly treated as prerequisites to scaling rather than add-ons.” 3. [Alice Labs Enterprise AI Implementation Data 2024](https://www.alice.se/en/ai-strategy)Alice Labs · Alice Labs “Alice Labs has delivered 100+ enterprise AI implementations across Sweden and Europe since founding in 2023, with board-approved AI business cases as the critical first deliverable on most engagements.” 4. [A Conceptual Framework for AI Adoption in Business Architecture](https://link.springer.com/)Fitriani, R., Khodra, M.L., & Surendro, K. · Springer “Proposes a sequenced framework for AI adoption in business architecture that mirrors the 6-step business case process: problem definition → capability mapping → value quantification → governance design.” 5. [Generative AI and Firm Performance: Evidence from Financial Services](https://journals.sagepub.com/)Dubey, R., Astvansh, V., & Kopalle, P.K. · SAGE Publications “Documents measurable productivity gains from generative AI implementations across five financial service verticals, establishing that positive ROI is achievable at pilot scale — not only at full deployment.” 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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