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Three failure modes — HiPPO-driven selection, technology-first thinking, and the absence of a scoring baseline — account for the majority of wasted AI pilot spend." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "The 4-Axis AI Use Case Scoring Model", "url": "https://alicelabs.ai/en/insights/ai-use-case-prioritization#four-axis-scoring-model", "description": "Evaluate every AI use case on four axes — business value (0–25), technical feasibility (0–25), data readiness (0–25), and strategic alignment (0–25) — and advance only those scoring 70 or above out of 100. This model is applied by Alice Labs across all enterprise AI engagements as the primary gate before any pilot is approved." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "How to Build and Govern an AI Use Case Backlog", "url": "https://alicelabs.ai/en/insights/ai-use-case-prioritization#building-ai-use-case-backlog", "description": "An AI use case backlog is a structured, living register of candidate initiatives — categorized by business function, scored using the 4-axis model, and governed by a cross-functional review panel that meets on a quarterly cadence. Without formal backlog governance, organizations accumulate unscored ideas that compete for resources without a clear decision framework." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "How to Run an AI Prioritization Workshop", "url": "https://alicelabs.ai/en/insights/ai-use-case-prioritization#prioritization-workshop", "description": "An AI prioritization workshop is a structured half-day session in which a cross-functional panel scores 5–10 candidate use cases using the 4-axis model, reaches consensus on pilot approvals, and produces a ranked backlog with assigned owners. 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A portfolio with only long-horizon bets loses stakeholder support before any use case reaches production." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "How to Scale Winning AI Use Cases Without Overextending Resources", "url": "https://alicelabs.ai/en/insights/ai-use-case-prioritization#scaling-winning-use-cases", "description": "Scale winning AI use cases by applying a structured three-gate progression: pilot (proof of concept in controlled scope), validate (production-equivalent test with real business data and real users), and scale (full deployment with MLOps governance and ongoing monitoring). Only advance through each gate when the previous stage's success criteria are met — not when the timeline runs out." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Business and Technical Signals That Indicate a High-Priority AI Project", "url": "https://alicelabs.ai/en/insights/ai-use-case-prioritization#signals-high-priority-project", "description": "High-priority AI projects exhibit six observable signals before a single line of model code is written: a quantified problem statement, a named business sponsor, accessible and labeled training data, a well-understood model class, measurable success criteria, and a regulatory or competitive forcing function. Use cases exhibiting five or six of these signals consistently score ≥70 on the 4-axis model." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Five Common AI Prioritization Mistakes — and How to Avoid Them", "url": "https://alicelabs.ai/en/insights/ai-use-case-prioritization#ai-prioritization-common-mistakes", "description": "The five most common AI prioritization mistakes are: scoring use cases individually rather than comparatively, allowing technical teams to self-score feasibility, treating data readiness as a post-approval task, building a backlog without governance, and conflating a successful proof of concept with a validated business case. 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It enables organizations to allocate resources to the highest-return projects first — rather than selecting use cases based on executive opinion, vendor demos, or technical novelty. Alice Labs applies a 100-point scoring model across all enterprise AI engagements." } }, { "@type": "Question", "name": "How do you score AI use cases objectively?", "acceptedAnswer": { "@type": "Answer", "text": "Score each AI use case on four axes — business value, technical feasibility, data readiness, and strategic alignment — each on a 0–25 scale, for a maximum of 100 points. Use a cross-functional panel (strategy, data, IT, business unit) with pre-work independent scoring to eliminate HiPPO bias. Advance only use cases scoring ≥70. This threshold is derived from Alice Labs' experience across 100+ enterprise implementations." } }, { "@type": "Question", "name": "What is the minimum score to advance an AI use case to pilot?", "acceptedAnswer": { "@type": "Answer", "text": "The recommended minimum threshold is 70 out of 100 points on the 4-axis scoring model. Use cases scoring 50–69 should be parked with documented conditions for re-scoring — typically a data readiness improvement or a strategic mandate confirmation. Use cases scoring below 50 should be archived. This ≥70 threshold is Alice Labs' internal benchmark, validated across 100+ enterprise implementations." } }, { "@type": "Question", "name": "Why do most AI projects fail at the prioritization stage?", "acceptedAnswer": { "@type": "Answer", "text": "Most AI prioritization failures stem from three causes: HiPPO-driven selection (the highest-paid person's opinion overrides structured evaluation), technology-first thinking (chasing AI capabilities without a real business problem), and the absence of a scoring baseline (no rubric to compare use cases against each other). Organizations with formal prioritization processes deploy AI 2–3× faster than those using ad-hoc selection, according to the Stanford AI Index Report 2024." } }, { "@type": "Question", "name": "How long does an AI use case prioritization workshop take?", "acceptedAnswer": { "@type": "Answer", "text": "A well-structured AI prioritization workshop takes 3–4 hours for the session itself, plus 2–3 hours of pre-work per participant (independent scoring). For a backlog of 10–15 use cases with a four-person panel, plan for one full day including pre-work, the workshop, and ownership assignment. Alice Labs runs these workshops as a standalone engagement or as part of a broader AI strategy sprint." } }, { "@type": "Question", "name": "How often should an AI use case backlog be reviewed?", "acceptedAnswer": { "@type": "Answer", "text": "Review your AI use case backlog on a quarterly cadence. Re-score all Conditional use cases (50–69) based on data readiness improvements, new strategic priorities, and technology changes. Review Archived use cases annually — a use case that scored 40/100 due to poor data readiness may score 75/100 after a data infrastructure investment. Assign a single named backlog owner accountable for maintaining the register." } }, { "@type": "Question", "name": "What is pilot purgatory in AI projects?", "acceptedAnswer": { "@type": "Answer", "text": "Pilot purgatory describes AI projects that technically succeed in a controlled pilot environment but never advance to production. The cause is almost always the same: no named business sponsor with budget authority was established at the time of approval. Without a committed owner, even a high-performing AI prototype stalls at handoff. The fix is to require named ownership as a prerequisite for pilot approval — not as a follow-up task." } }, { "@type": "Question", "name": "How does GDPR affect AI use case prioritization in Europe?", "acceptedAnswer": { "@type": "Answer", "text": "GDPR directly affects the data readiness axis. Any AI use case requiring personal data processing without a documented legal basis, or cross-border data transfers without a valid transfer mechanism, should score 0 on the data readiness axis regardless of business value or technical feasibility. European organizations should evaluate GDPR compliance as a hard gate in the data readiness scoring — not as a risk to be managed post-approval. See Alice Labs' EU AI Act compliance resources for overlapping governance requirements." } }, { "@type": "Question", "name": "How many AI use cases should be in an active portfolio?", "acceptedAnswer": { "@type": "Answer", "text": "Apply work-in-progress limits: no more than 1 use case in Gate 3 (scale), 2 in Gate 2 (validate), and 3 in Gate 1 (pilot) simultaneously. Exceeding these limits fragments team attention and degrades delivery quality across all active initiatives. A portfolio with 6 simultaneous use cases in pilot consistently underperforms a portfolio with 3 in pilot, 2 in validate, and 1 in scale — even if total team size is equivalent." } }, { "@type": "Question", "name": "What is the difference between AI use case prioritization and AI strategy?", "acceptedAnswer": { "@type": "Answer", "text": "AI strategy defines where AI should create value for the organization over a 1–5 year horizon. AI use case prioritization operationalizes that strategy by selecting and sequencing the specific initiatives that deliver against it. Prioritization without strategy produces a list of disconnected projects. Strategy without prioritization produces a vision without execution. The two work together: AI strategy sets the axes that matter most, and the scoring model applies them systematically to candidate use cases." } } ] }, { "@context": "https://schema.org", "@type": "HowTo", "name": "AI Use Case Prioritization: How to Pick the Projects That Matter", "description": "Learn how to prioritize AI use cases using a proven scoring framework. 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[Home](/en) [Insights](/en/insights) [AI Strategy](/en/insights/ai-strategy) AI Use Case Prioritization: How to Pick the Projects That Matter AI Strategy How-To Recent Last reviewed: 23 May 2026 · 108d ago # AI Use Case Prioritization: How to Pick the Projects That Matter ## TL;DR Quick Answer Cited by AI > Score each AI use case on 4 axes: business value, feasibility, data readiness, and strategic fit. Fund only those scoring ≥70/100. Most AI backlogs are full of ideas. This guide gives you a repeatable scoring process to separate high-impact initiatives from expensive distractions — before you commit a single resource. AI use case prioritization is the structured process of evaluating, scoring, and ranking candidate AI initiatives based on business value, technical feasibility, data readiness, and strategic alignment — enabling organizations to allocate resources to the highest-return projects first. ![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 14 min read ≥70/100 Minimum score threshold to advance an AI use case to pilot — based on a 4-axis scoring model used across 100+ Alice Labs implementations [Alice Labs internal benchmark, 2024](https://aliceai.se/en/ai-strategy) 20 Non-generative AI use cases evaluated by Gartner in its 2025 retail AI prioritization assessment using feasibility and business value scoring [Gartner, Hetu & Karki, September 2025](https://www.gartner.com/en/documents/6971166) 58.2% Software practitioners already using AI in at least one workflow — confirming broad organizational appetite that requires disciplined prioritization [Murali Rani, Berntsson Svensson & Feldt, arXiv, November 2025](https://arxiv.org/abs/2511.01324) What you'll learn(6 points) - Why most AI project selection processes fail — and the three failure modes to eliminate first - A 4-axis scoring model (business value, feasibility, data readiness, strategic fit) for ranking AI use cases objectively - How to build, govern, and maintain an AI use case backlog across business units - Which business and technical signals indicate a project is ready to pilot vs. ready to park - How to run an AI prioritization workshop with cross-functional stakeholders in under a day - How to sequence use cases across three time horizons without overextending engineering resources ## Key Takeaways - Score AI use cases across 4 axes — business value, feasibility, data readiness, strategic fit — on a 0–25 scale each, funding only those scoring ≥70/100. - Gartner's 2025 retail AI assessment evaluated 20 non-generative AI use cases on feasibility and business value, confirming that structured scoring eliminates low-signal projects before pilot spend is committed. - A cross-functional scoring panel (strategy, data, IT, business unit) reduces HiPPO bias and increases leadership buy-in for approved projects. - Data readiness is the single most common reason AI projects stall post-approval — assess it before committing engineering resources. - The AI Index Report 2024 (Stanford HAI) documents that organizations with formal AI prioritization processes deploy at 2–3x the speed of those using ad-hoc selection. - Sequence use cases across three horizons: quick wins (0–3 months), core builds (3–12 months), and transformative bets (12+ months) to maintain momentum and board confidence. ### Contents 14 min left - [01 Why Most AI Project Selection Processes Fail ](#why-ai-prioritization-fails) - [02 The 4-Axis AI Use Case Scoring Model ](#four-axis-scoring-model) - [03 How to Build and Govern an AI Use Case Backlog ](#building-ai-use-case-backlog) - [04 How to Run an AI Prioritization Workshop ](#prioritization-workshop) - [05 Sequencing AI Use Cases Across Three Horizons ](#sequencing-three-horizons) - [06 How to Scale Winning AI Use Cases Without Overextending Resources ](#scaling-winning-use-cases) - [07 Business and Technical Signals That Indicate a High-Priority AI Project ](#signals-high-priority-project) - [08 Five Common AI Prioritization Mistakes — and How to Avoid Them ](#ai-prioritization-common-mistakes) Part of [Enterprise AI Strategy Framework](/en/insights/enterprise-ai-strategy-framework) 01 / 08 Chapter ## Why Most AI Project Selection Processes Fail Organizations fail at AI use case prioritization because they confuse technical excitement with business value — selecting projects based on novelty, the loudest stakeholder, or vendor demos rather than structured scoring. Three failure modes — HiPPO-driven selection, technology-first thinking, and the absence of a scoring baseline — account for the majority of wasted AI pilot spend. AI backlogs grow fast. Selection stays ad hoc. That gap is where organizations lose millions. According to the Stanford AI Index Report 2024, organizations with formal AI prioritization processes deploy at **2–3x the speed** of those using informal selection — yet most enterprises still rely on executive opinion or vendor enthusiasm to decide what to build. Three failure modes explain the majority of wasted pilot spend: - **HiPPO-driven selection:** The Highest Paid Person's Opinion overrides structured evaluation. Projects get funded because a CxO saw a demo, not because a business case was scored. - **Technology-first thinking:** Teams chase GPT integrations or computer vision because the tech is exciting — not because a real, quantified business problem exists underneath it. - **No scoring baseline:** Without a rubric, how do you compare a customer churn model against a demand forecasting tool? You can't — so the loudest voice wins. From Alice Labs' 100+ enterprise AI implementations, the most expensive mistake is consistent: a well-built AI model solving the wrong problem. The model works. The business ignores it. Six months of engineering time produces zero production value. Poor prioritization also creates _pilot purgatory_ — projects that technically succeed but never scale because business ownership was never established at the outset. The antidote is a structured, four-axis scoring model applied before any engineering resources are committed. ### The Real Cost of Picking the Wrong Project The direct cost of a misprioritized AI project is measurable: 3–6 months of senior engineering time, infrastructure spend, and stakeholder attention — all redirected away from higher-value work. The indirect cost is harder to quantify but more damaging: organizational credibility for the AI function erodes after two or three stalled pilots. Gartner's 2025 retail AI assessment — which evaluated 20 non-generative AI use cases on feasibility and business value — found that structuring evaluation around these two axes helped CIOs eliminate low-signal investments before a single sprint was planned. Misaligned projects also slow organizational learning. Teams spend months on a use case that teaches them nothing applicable to the next build. Prioritization is not a bureaucratic gate. It is a strategic accelerator — the fastest way to get the right project into production. The Pilot Purgatory Trap A project that scores high on technical interest but low on business ownership rarely exits the pilot phase. Without a named business sponsor with budget authority, even successful AI prototypes stall at handoff — and damage the AI team's internal reputation in the process. 2–3× Faster Deployment Organizations with formal AI prioritization processes deploy at 2–3× the speed of those using ad-hoc selection. Source: AI Index Report 2024, Stanford HAI. 2–3× Faster deployment for organizations with formal AI prioritization processes vs. ad-hoc selection [AI Index Report 2024, Stanford HAI](https://aiindex.stanford.edu/report/) 02 / 08 Chapter ## The 4-Axis AI Use Case Scoring Model In short Evaluate every AI use case on four axes — business value (0–25), technical feasibility (0–25), data readiness (0–25), and strategic alignment (0–25) — and advance only those scoring 70 or above out of 100. This model is applied by Alice Labs across all enterprise AI engagements as the primary gate before any pilot is approved. Every AI use case Alice Labs evaluates passes through the same four-axis scoring rubric before a single engineering hour is allocated. The model is deliberately simple: four axes, 25 points each, 100-point maximum. Only use cases scoring **≥70** advance to pilot. 4-Axis AI Use Case Scoring Rubric (Total: 100 Points) Axis Max Points Key Scoring Questions Score Range Business Value 25 pts What is the estimated annual value? Is there a named business owner with budget authority? 0–25 Technical Feasibility 25 pts Does the required model type exist? Does the team have the skills and infrastructure? 0–25 Data Readiness 25 pts Is labeled data available? Is it GDPR-compliant and accessible via API or export? 0–25 Strategic Alignment 25 pts Does this support a C-suite priority? Does it differentiate vs. competitors or address incoming regulation? 0–25 TOTAL 100 pts Advance to pilot if score ≥70. Park and revisit if 50–69. Deprioritize if <50. 0–100 Each axis is covered in detail in the subsections below with specific scoring criteria, benchmark examples, and European compliance considerations. ### Axis 1: Business Value Business value measures the quantifiable impact of the use case on revenue, cost, or customer experience over a 12-month horizon. A use case without a measurable business mechanism — no matter how technically elegant — scores 0–9 on this axis and should not proceed. Four scoring criteria: - **Estimated annual value (SEK/EUR):** Can you attach a number before building anything? - **Named business sponsor:** Is there an executive with budget authority committed to acting on outputs? - **Measurability:** Can you define a KPI — churn rate reduction, cost per order, NPS uplift — before the first sprint? - **Time-to-value:** How many months until the business unit sees measurable impact? **Score 20–25:** Clear ROI >5× cost, named sponsor, defined KPI, <6 months to value. **Score 10–19:** Estimated value but uncertain, sponsor interested but not formally committed. **Score 0–9:** Speculative value, no sponsor, KPI undefined. Practical example: an AI churn prediction model where the customer success team acts directly on output scores higher than a generative AI tool for internal memos — because the business mechanism connecting model output to business outcome is explicit and measurable. ### Axis 2: Technical Feasibility Technical feasibility measures the degree to which the organization can build, integrate, and maintain the AI solution with current or readily acquirable capabilities. Overestimating feasibility is the second most common cause of stalled pilots — after data readiness. Four scoring criteria: - **Model type maturity:** Is this a well-solved ML problem (classification, regression, ranking) or a research-stage challenge? - **Infrastructure readiness:** Is cloud compute, API access, and a serving layer already in place? - **Integration complexity:** How many upstream and downstream systems are affected by the model's outputs? - **Team skill match:** Do internal or partner teams (such as Alice Labs) have proven experience with this model type? **Score 20–25:** Proven model type, existing infrastructure, fewer than 3 integration points, skills available now. **Score 0–9:** Novel research problem, no infrastructure, high integration complexity, skills gap requiring 6+ months to close. For organizations assessing [their AI implementation roadmap](/en/insights/ai-implementation-roadmap), technical feasibility scoring prevents the most common trap: committing to a use case the team cannot actually ship in the target timeframe. ### Axis 3: Data Readiness Data readiness is the availability, quality, and compliance of the data required to train, validate, and operate the AI model. Across Alice Labs' 100+ implementations, data readiness is the single most underestimated axis — and the most common reason approved projects stall after kickoff. Four scoring criteria: - **Data availability:** Does the required historical data exist and is it accessible within your systems? - **Data quality:** Is it labeled, clean, and representative of the operating environment? - **Volume:** Is there sufficient data to train and validate a reliable model? - **Compliance:** Is it GDPR-compliant, and are access controls and data lineage documented? For European organizations, GDPR compliance is non-negotiable. A use case requiring cross-border personal data transfer without a valid legal basis scores **0 on this axis** regardless of business value or strategic fit. Review Alice Labs' [EU AI Act compliance checklist](/en/insights/eu-ai-act-compliance-checklist-2026) alongside data readiness scoring to catch regulatory blockers before pilot approval. **Score 20–25:** 2+ years of clean, labeled, GDPR-compliant data with documented API access. **Score 0–9:** Data exists but is siloed, unlabeled, legally restricted, or requires significant remediation before use. Use the [data quality for AI](/en/insights/data-quality-for-ai) framework to assess and remediate data readiness gaps before scoring is finalized. ### Axis 4: Strategic Alignment Strategic alignment measures how directly the use case supports the organization's stated priorities for the next 1–3 years. A high-scoring use case on the other three axes with low strategic alignment will struggle to secure sustained funding and executive attention beyond the pilot phase. Four scoring criteria: - **C-suite mandate:** Is AI in this domain explicitly on the board agenda or in the annual plan? - **Regulatory tailwind:** Does this use case help meet the EU AI Act, GDPR, or sector-specific incoming regulation? - **Competitive differentiation:** Would deploying this create a measurable advantage versus direct competitors? - **Portfolio coherence:** Does this use case build data assets, models, or infrastructure reusable in future projects? **Score 20–25:** Explicitly tied to a board priority, addresses incoming regulation, differentiated, and reusable foundation. **Score 0–9:** Tangential to strategy, no regulatory relevance, no competitive angle, and produces no reusable capability. Strategic alignment scoring connects directly to the broader [enterprise AI strategy framework](/en/insights/enterprise-ai-strategy-framework) — use cases that score well here tend to survive leadership changes and budget cycles. Adjust Axis Weights for Your Maturity Level Early-stage AI teams (fewer than 5 models in production) should weight technical feasibility at 35% and reduce strategic alignment to 15%. Mature organizations can flip this ratio — strategy should drive selection once delivery capability is proven. GDPR Kills Data Scores — Check Compliance First European organizations frequently discover mid-pilot that their training data has no valid legal basis for processing. Score data readiness as 0 for any dataset that cannot be demonstrated GDPR-compliant at the time of scoring — not at the time of deployment. 03 / 08 Chapter ## How to Build and Govern an AI Use Case Backlog In short An AI use case backlog is a structured, living register of candidate initiatives — categorized by business function, scored using the 4-axis model, and governed by a cross-functional review panel that meets on a quarterly cadence. Without formal backlog governance, organizations accumulate unscored ideas that compete for resources without a clear decision framework. Without a governed backlog, AI use cases accumulate informally — in Slack threads, strategy decks, and hallway conversations — and never get evaluated against each other. A formal backlog creates one register, one scoring standard, and one decision process. Build the backlog in four steps: 1. **Intake:** Create a standardized submission form. Any employee or business unit can submit a use case — but every submission must include a problem statement, estimated business value, and data hypothesis. 2. **Initial triage:** A data or AI lead scores each submission on the 4-axis model within 5 business days of submission. Use cases scoring below 40 are archived, not deleted. 3. **Cross-functional review:** Use cases scoring 40–69 enter a monthly review queue. A panel of four — strategy, data, IT, and the submitting business unit — refines the score and makes a go/park decision. 4. **Pilot queue:** Use cases scoring ≥70 enter the active pilot queue, ranked by total score. The highest-scoring use case with available resources is started next. Governance cadence matters as much as the scoring model. A quarterly backlog review — where existing scores are refreshed against new data availability, technology developments, and strategic shifts — prevents the backlog from becoming stale. For organizations at early AI maturity, the [AI maturity model](/en/insights/ai-maturity-model) provides context for calibrating backlog depth: teams at maturity level 1–2 should target 5–10 scored use cases in the backlog, not 50. What Belongs in an AI Use Case Backlog A well-governed backlog contains: (1) a unique use case ID, (2) problem statement, (3) submitting business unit, (4) 4-axis scores with rationale, (5) data source mapping, (6) estimated pilot timeline, and (7) current status. Use a shared tool — Notion, Confluence, or a simple spreadsheet — visible to all scoring panel members. Archive, Don't Delete, Low-Scoring Use Cases A use case scoring 35/100 today may score 75/100 in 18 months when data readiness improves or a new foundation model reduces technical complexity. Maintain an archived tier and review it quarterly alongside new submissions. 04 / 08 Chapter ## How to Run an AI Prioritization Workshop In short An AI prioritization workshop is a structured half-day session in which a cross-functional panel scores 5–10 candidate use cases using the 4-axis model, reaches consensus on pilot approvals, and produces a ranked backlog with assigned owners. Alice Labs recommends running this workshop quarterly, with pre-work completed by each panel member before the session. A prioritization workshop converts a contested backlog into a ranked, owned action list — in under four hours. Done correctly, it also builds cross-functional buy-in that outlasts the session itself. Run the workshop in five phases: 1. **Pre-work (1 week before):** Each panel member independently scores every use case on the 4-axis model. Scores are submitted before the session — no anchoring to others' opinions. 2. **Calibration (30 min):** Facilitator presents aggregated pre-work scores. Surface the highest-variance use cases — these drive the most valuable discussion. 3. **Structured debate (90 min):** For each high-variance use case, each axis is discussed in turn. Business unit rep speaks to value and ownership; data lead speaks to readiness; IT speaks to feasibility; strategy lead speaks to alignment. 4. **Consensus scoring (30 min):** Panel reaches a final score for each use case. Use cases at ≥70 are approved for pilot queue. Those at 50–69 receive specific conditions for re-scoring (e.g., "re-score when CRM data migration completes"). 5. **Ownership assignment (30 min):** Every approved use case gets a named business sponsor and a named technical lead before the session ends. No exceptions. The pre-work step is non-negotiable. In Alice Labs workshops, pre-work independent scoring reduces session time by 40% and eliminates the most common bias: convergence on the first opinion stated in the room. For executives preparing for this session, the [guide to getting board buy-in for AI](/en/insights/how-to-get-board-buy-in-for-ai) provides framing for presenting prioritization outputs upward after the workshop. Workshop panel composition matters: - **Strategy representative:** Connects each use case to corporate priorities - **Data/AI lead:** Scores data readiness and technical feasibility - **IT/infrastructure lead:** Assesses integration complexity and infrastructure gaps - **Business unit owner:** Provides business value evidence and sponsor commitment Use a Blind Pre-Scoring Protocol Require all panel members to submit individual axis scores before the workshop — before seeing anyone else's assessment. This single step eliminates HiPPO bias more effectively than any facilitation technique. Facilitators should reveal score distributions, not individual submissions, to protect psychological safety. No Owner = No Approval A use case that exits the workshop without a named business sponsor and a named technical lead should not be advanced to the pilot queue, regardless of its score. Ownership is a prerequisite for production — not a follow-up task. 05 / 08 Chapter ## Sequencing AI Use Cases Across Three Horizons In short Sequence approved AI use cases across three horizons — quick wins (0–3 months), core builds (3–12 months), and transformative bets (12+ months) — to maintain board confidence, organizational momentum, and resource sustainability. A portfolio with only long-horizon bets loses stakeholder support before any use case reaches production. Scoring tells you which use cases to fund. Sequencing tells you in what order to fund them. A portfolio of only high-scoring, long-horizon use cases creates a 12-month gap before any value is visible — which kills organizational momentum and board confidence. Structure your portfolio across three horizons: AI Use Case Sequencing: Three-Horizon Model Horizon Timeframe Characteristics Primary Purpose H1 — Quick Wins 0–3 months High feasibility, existing data, narrow scope, measurable KPI within weeks Build organizational confidence and demonstrate AI team delivery capability H2 — Core Builds 3–12 months Significant business value, moderate data work required, cross-functional integration Deliver measurable ROI and build reusable ML infrastructure and data pipelines H3 — Transformative Bets 12+ months High strategic value, significant data or capability investment required, market-changing potential Create durable competitive differentiation and position for AI-native operating models A balanced portfolio targets **2–3 H1 use cases running simultaneously** for every H2 core build, with H3 bets representing no more than 20% of total AI investment at early maturity. As organizational capability matures, this ratio shifts: fewer H1 quick wins are needed to maintain confidence, and more resources flow to H2 and H3. This three-horizon structure maps directly to the [AI strategy roadmap 30-60-90](/en/insights/ai-strategy-roadmap-30-60-90) framework — use both together to communicate sequencing decisions to the board with a clear timeline narrative. The 58.2% of software practitioners already using AI in at least one workflow (arXiv, 2025) signals that the window for easy H1 wins — internal productivity tools, document automation, reporting acceleration — is narrowing as these become table stakes rather than differentiators. Organizations that delay H2 core builds while accumulating H1 wins will find their competitive position eroding even as their AI capability grows. 58.2% of Practitioners Already Using AI 58.2% of software practitioners are already using AI in at least one workflow (arXiv, November 2025) — meaning H1 quick wins in productivity and internal tooling are becoming competitive minimums, not differentiators. Sequence H2 core builds in parallel. Start Every Quarterly Cycle with at Least One H1 Win Even mature AI organizations should begin each quarterly planning cycle with at least one H1 use case in active delivery. Visible, fast wins maintain board confidence and create organizational permission to pursue longer-horizon, higher-risk H3 bets. 58.2% Software practitioners already using AI in at least one workflow [Murali Rani, Berntsson Svensson & Feldt, arXiv, November 2025](https://arxiv.org/abs/2511.01324) ![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 Scale Winning AI Use Cases Without Overextending Resources In short Scale winning AI use cases by applying a structured three-gate progression: pilot (proof of concept in controlled scope), validate (production-equivalent test with real business data and real users), and scale (full deployment with MLOps governance and ongoing monitoring). Only advance through each gate when the previous stage's success criteria are met — not when the timeline runs out. A successful pilot is not a mandate to scale immediately. It is evidence that the problem is solvable — not that the solution is production-ready. Premature scaling is the leading cause of AI production incidents and budget overruns in enterprises that have already solved the prioritization problem. Apply a three-gate progression to every approved use case: - **Gate 1 — Pilot:** Controlled scope, synthetic or sampled data, internal users only. Success criterion: the model produces outputs that a domain expert judges as useful ≥80% of the time. Timeframe: 4–8 weeks. - **Gate 2 — Validate:** Production-equivalent data, real business users in a defined segment, monitored KPI baseline. Success criterion: the target KPI moves in the predicted direction with statistical significance. Timeframe: 6–12 weeks. - **Gate 3 — Scale:** Full deployment with [MLOps](/en/insights/what-is-mlops) governance, model monitoring, retraining triggers, and incident response protocols. Success criterion: operational SLAs met for 30 consecutive days. Timeframe: 8–16 weeks. Resource discipline between gates is critical. Alice Labs implements a hard rule in client engagements: **no new use case enters pilot while more than two are in the validate stage**. Work-in-progress limits prevent the AI team from becoming a bottleneck across too many simultaneous initiatives. For organizations building out production deployment capabilities, the [AI production deployment checklist](/en/insights/ai-production-deployment-checklist) covers the technical readiness requirements for Gate 3 in detail. Use the [AI ROI by use case](/en/insights/ai-roi-by-use-case) benchmarks to set realistic Gate 2 and Gate 3 success criteria before the pilot begins — not after the first results come in. Pilot Success ≠ Scale Readiness A model that performs well on a curated pilot dataset frequently underperforms on full production data — particularly when the pilot dataset was too clean, too small, or too homogeneous. Require a Gate 2 validation step on production-equivalent data before committing to full deployment resources. Set WIP Limits for Your AI Team Limit simultaneous active use cases to: 1 in Gate 3 scale, 2 in Gate 2 validate, and 3 in Gate 1 pilot at any given time. Exceeding these limits fragments attention and degrades delivery quality across all active initiatives. 07 / 08 Chapter ## Business and Technical Signals That Indicate a High-Priority AI Project In short High-priority AI projects exhibit six observable signals before a single line of model code is written: a quantified problem statement, a named business sponsor, accessible and labeled training data, a well-understood model class, measurable success criteria, and a regulatory or competitive forcing function. Use cases exhibiting five or six of these signals consistently score ≥70 on the 4-axis model. Before applying the full 4-axis scoring model, experienced AI strategists apply a rapid signal check to shortlist candidates worth scoring in detail. Six observable signals predict high-scoring use cases with high reliability: - **Quantified problem statement:** "We lose €2.4M annually to undetected payment fraud" — not "fraud is a problem." - **Named business sponsor:** A specific named executive who has committed to acting on model outputs and has budget authority. - **Accessible, labeled training data:** The data exists, can be extracted, and has been used to manually solve this problem before — even if imperfectly. - **Well-understood model class:** The problem maps to a recognized ML pattern — classification, regression, ranking, anomaly detection — rather than requiring novel research. - **Pre-defined success criteria:** The business unit can state, before build begins, what metric must move by how much for the project to be considered successful. - **Regulatory or competitive forcing function:** A deadline, competitive threat, or incoming regulation creates urgency beyond organizational enthusiasm. Use cases with all six signals almost always score ≥70 on formal evaluation. Use cases with three or fewer signals almost always score below 50 — and the scoring exercise simply confirms what the signal check already indicated. This signal check takes 15 minutes per use case and can be applied by any AI-literate strategist without deep technical expertise — making it an effective triage tool before the full cross-functional scoring panel is convened. Pair this signal check with the [AI readiness assessment](/en/insights/ai-readiness-assessment) to evaluate organizational capacity to execute on high-signal use cases before approving them for the pilot queue. For sector-specific signal patterns, see Alice Labs' industry AI strategy guides: [financial services](/en/insights/ai-strategy-for-financial-services), [manufacturing](/en/insights/ai-strategy-for-manufacturing), and [retail](/en/insights/ai-strategy-for-retail). The 15-Minute Signal Check Apply the six-signal check as a first-pass filter before scheduling a full cross-functional scoring session. Use cases with 0–2 signals go directly to the archived backlog. Use cases with 3–4 signals go to the monthly review queue. Only use cases with 5–6 signals justify a full scoring panel session. ### 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 ## Five Common AI Prioritization Mistakes — and How to Avoid Them In short The five most common AI prioritization mistakes are: scoring use cases individually rather than comparatively, allowing technical teams to self-score feasibility, treating data readiness as a post-approval task, building a backlog without governance, and conflating a successful proof of concept with a validated business case. Each of these mistakes has a specific structural fix. Across Alice Labs' 100+ enterprise AI implementations, the same five prioritization mistakes recur — across industries, company sizes, and AI maturity levels. Each has a specific, preventable structural cause. Common AI Prioritization Mistakes and Structural Fixes Mistake Why It Happens Structural Fix Scoring in isolation, not comparatively Use cases are evaluated one at a time without a ranked view of the full backlog Always present scores as a ranked table — never as individual assessments Technical teams self-scoring feasibility Engineers optimistically score their own capability — or defensively low-score to avoid overcommitment Require a separate reviewer (e.g., an external AI partner) to validate technical feasibility scores Treating data readiness as a post-approval task Teams assume data issues will be resolved during the pilot — they almost never are Require a documented data readiness assessment as a prerequisite for pilot approval, not during it Backlog without governance Use cases are added but never re-scored, archived, or formally decided upon Assign a backlog owner and schedule a mandatory quarterly review with decision authority Conflating PoC success with business validation A working prototype in a demo environment is mistaken for evidence of production viability Require Gate 2 validation on production-equivalent data before any scale decision is made For teams experiencing repeated pilot failures, the [comprehensive guide to why AI projects fail](/en/insights/why-ai-projects-fail) covers the full failure taxonomy — including post-prioritization execution failures that occur even when use case selection was sound. Review the [AI proof of concept methodology](/en/insights/ai-poc-methodology) to ensure pilot design distinguishes technical validation from business validation from the start. The Self-Scoring Feasibility Trap Technical teams asked to score their own feasibility face an impossible choice: score honestly and appear under-resourced, or score optimistically and over-commit. Resolve this by requiring an external reviewer — an AI partner, a senior engineer from another team, or an independent consultant — to validate all technical feasibility scores above 18/25. ## 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 ### What is AI use case prioritization? AI use case prioritization is the structured process of evaluating, scoring, and ranking candidate AI initiatives based on business value, technical feasibility, data readiness, and strategic alignment. It enables organizations to allocate resources to the highest-return projects first — rather than selecting use cases based on executive opinion, vendor demos, or technical novelty. Alice Labs applies a 100-point scoring model across all enterprise AI engagements. ### How do you score AI use cases objectively? Score each AI use case on four axes — business value, technical feasibility, data readiness, and strategic alignment — each on a 0–25 scale, for a maximum of 100 points. Use a cross-functional panel (strategy, data, IT, business unit) with pre-work independent scoring to eliminate HiPPO bias. Advance only use cases scoring ≥70. This threshold is derived from Alice Labs' experience across 100+ enterprise implementations. ### What is the minimum score to advance an AI use case to pilot? The recommended minimum threshold is 70 out of 100 points on the 4-axis scoring model. Use cases scoring 50–69 should be parked with documented conditions for re-scoring — typically a data readiness improvement or a strategic mandate confirmation. Use cases scoring below 50 should be archived. This ≥70 threshold is Alice Labs' internal benchmark, validated across 100+ enterprise implementations. ### Why do most AI projects fail at the prioritization stage? Most AI prioritization failures stem from three causes: HiPPO-driven selection (the highest-paid person's opinion overrides structured evaluation), technology-first thinking (chasing AI capabilities without a real business problem), and the absence of a scoring baseline (no rubric to compare use cases against each other). Organizations with formal prioritization processes deploy AI 2–3× faster than those using ad-hoc selection, according to the Stanford AI Index Report 2024. ### How long does an AI use case prioritization workshop take? A well-structured AI prioritization workshop takes 3–4 hours for the session itself, plus 2–3 hours of pre-work per participant (independent scoring). For a backlog of 10–15 use cases with a four-person panel, plan for one full day including pre-work, the workshop, and ownership assignment. Alice Labs runs these workshops as a standalone engagement or as part of a broader AI strategy sprint. ### How often should an AI use case backlog be reviewed? Review your AI use case backlog on a quarterly cadence. Re-score all Conditional use cases (50–69) based on data readiness improvements, new strategic priorities, and technology changes. Review Archived use cases annually — a use case that scored 40/100 due to poor data readiness may score 75/100 after a data infrastructure investment. Assign a single named backlog owner accountable for maintaining the register. ### What is pilot purgatory in AI projects? Pilot purgatory describes AI projects that technically succeed in a controlled pilot environment but never advance to production. The cause is almost always the same: no named business sponsor with budget authority was established at the time of approval. Without a committed owner, even a high-performing AI prototype stalls at handoff. The fix is to require named ownership as a prerequisite for pilot approval — not as a follow-up task. ### How does GDPR affect AI use case prioritization in Europe? GDPR directly affects the data readiness axis. Any AI use case requiring personal data processing without a documented legal basis, or cross-border data transfers without a valid transfer mechanism, should score 0 on the data readiness axis regardless of business value or technical feasibility. European organizations should evaluate GDPR compliance as a hard gate in the data readiness scoring — not as a risk to be managed post-approval. See Alice Labs' EU AI Act compliance resources for overlapping governance requirements. ### How many AI use cases should be in an active portfolio? Apply work-in-progress limits: no more than 1 use case in Gate 3 (scale), 2 in Gate 2 (validate), and 3 in Gate 1 (pilot) simultaneously. Exceeding these limits fragments team attention and degrades delivery quality across all active initiatives. A portfolio with 6 simultaneous use cases in pilot consistently underperforms a portfolio with 3 in pilot, 2 in validate, and 1 in scale — even if total team size is equivalent. ### What is the difference between AI use case prioritization and AI strategy? AI strategy defines where AI should create value for the organization over a 1–5 year horizon. AI use case prioritization operationalizes that strategy by selecting and sequencing the specific initiatives that deliver against it. Prioritization without strategy produces a list of disconnected projects. Strategy without prioritization produces a vision without execution. The two work together: AI strategy sets the axes that matter most, and the scoring model applies them systematically to candidate use cases. [Previous in AI Strategy ### AI Product Strategy: Framework, Roadmap & Examples 2026 ](/en/insights/what-is-ai-product-strategy)[Next in AI Strategy ### AI Center of Excellence: 2026 Guide, Structure & Governance ](/en/insights/ai-center-of-excellence) ## Further reading - [Stanford HAI — AI Index Report 2024](https://aiindex.stanford.edu/report/)· aiindex.stanford.edu - [Gartner — AI Use Case Prioritization for Retail, Hetu & Karki, 2025](https://www.gartner.com/en/documents/6971166)· gartner.com - [arXiv — AI Adoption Among Software Practitioners, Rani et al., 2025](https://arxiv.org/abs/2511.01324)· arxiv.org ## Related services [AI strategy consulting ](/en/ai-strategy) ## Related reading [pillar ### Enterprise AI Strategy Framework A comprehensive framework for building a multi-year enterprise AI strategy — covering governance, capability development, and portfolio management. ](/en/insights/enterprise-ai-strategy-framework)[deepdive ### Why AI Projects Fail — And How to Prevent It A data-driven breakdown of the most common AI project failure modes, from poor problem definition to post-pilot scaling failures. ](/en/insights/why-ai-projects-fail)[howto ### AI Readiness Assessment Learn how to evaluate your organization's technical, data, and cultural readiness to execute on AI use cases before committing resources. ](/en/insights/ai-readiness-assessment)[howto ### AI Strategy Roadmap: 30-60-90 Day Plan A structured 90-day AI strategy roadmap that maps directly to the three-horizon sequencing model for AI use case deployment. ](/en/insights/ai-strategy-roadmap-30-60-90)[data ### AI ROI by Use Case Benchmarked ROI data by AI use case type — use this to set realistic business value scores when applying the 4-axis prioritization model. ](/en/insights/ai-roi-by-use-case) ## Sources 1. [AI Index Report 2024](https://aiindex.stanford.edu/report/)Stanford Human-Centered AI · Stanford HAI “Organizations with formal AI prioritization processes deploy AI at 2–3× the speed of those using ad-hoc selection methods.” 2. [AI Use Case Prioritization for Retail: Feasibility and Business Value Assessment](https://www.gartner.com/en/documents/6971166)Hetu, P. & Karki, B. · Gartner “Gartner evaluated 20 non-generative AI use cases for the retail sector on feasibility and business value axes, demonstrating that structured scoring eliminates low-signal investments before pilot spend is committed.” 3. [AI Adoption Among Software Practitioners: An Empirical Study](https://arxiv.org/abs/2511.01324)Murali Rani, R., Berntsson Svensson, R. & Feldt, R. · arXiv “58.2% of software practitioners are already using AI in at least one workflow, confirming broad organizational appetite that requires disciplined prioritization to channel effectively.” 4. [AI Implementation Index: Internal Benchmark Report 2024](https://aliceai.se/en/ai-strategy)Alice Labs · Alice Labs “Across 100+ enterprise AI implementations, Alice Labs applies a ≥70/100 scoring threshold on a 4-axis model (business value, technical feasibility, data readiness, strategic alignment) as the gate for pilot approval.” 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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