--- title: "AI Strategy Roadmap: The 30/60/90 Day Plan (2026)" url: https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90 description: "AI strategy roadmap — a 30/60/90 day plan to move from readiness assessment to first measurable ROI. Includes templates, KPIs, governance, and scaling steps." lang: en --- Alice Labs (https://alicelabs.ai/en/) Services What we do (https://alicelabs.ai/#welcome)About Alice (https://alicelabs.ai/#who-we-are)Case (https://alicelabs.ai/en/case)Insights (https://alicelabs.ai/en/insights)Contact (https://alicelabs.ai/#email-form) AI Strategy How-to Guide Fresh Last reviewed: 16 September 2026 · 21d ago # AI Strategy Roadmap: The 30/60/90 Day Plan ## TL;DR Quick Answer Cited by AI > A 30/60/90 AI strategy roadmap moves an organisation from readiness assessment (day 1-30) to pilot in production (day 31-60) to first measurable ROI and scaling plan (day 61-90). The framework forces three decisions per phase: one milestone, one deliverable, one stakeholder checkpoint. A week-by-week plan to move from readiness assessment to first measurable ROI in 90 days. Built from 100+ Nordic enterprise implementations and Alice Labs' 14-week pilot-to-production median. An AI strategy roadmap is a time-boxed plan that sequences AI initiatives from readiness assessment through pilot deployment to scaled production. The 30/60/90 day variant compresses the planning horizon into three milestones — assessment, pilot in production, and measurable ROI — to drive faster learning than annual planning while leaving room for real-world validation. Time 90 days Difficulty Intermediate Typical cost €50K-€250K (pilot scope dependent) Tools AI readiness assessment template (5-domain maturity scoring), Use-case canvas (problem, owner, data, metric, exit criteria), KPI dashboard linked to a P&L line… ## Before you start - Executive sponsor at C-level with budget authority and time committed - A nominated business owner for the candidate use case - Access to baseline data for the targeted workflow - A cross-functional governance committee (legal, security, data, business) - Clarity that AI is a means to an outcome, not the outcome itself ## What you'll have at the end A working AI pilot in production after 90 days, first measurable ROI documented, a 12-month scaling plan signed off by leadership, and a repeating 30/60/90 cadence established as the heartbeat of the AI programme. Image: Linus Ingemarsson - Author at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Written by Linus Ingemarsson: https://www.linkedin.com/in/linus-ingemarsson/ Image: Eric Lundberg - Reviewer at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Reviewed by Eric Lundberg: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Published May 6, 2026 · Updated September 16, 2026 12 min read ## 9-step process 0/9 complete 1. 01 #### Step 1: Week 1: Run an AI readiness assessment Score the organisation across five domains: data foundation, talent, governance, executive sponsorship, and use-case clarity. Interview at least five stakeholders across business, IT, legal, and data. The goal is a baseline maturity score and a one-page gap analysis — not a 40-slide deck. RAND's 2024 research identifies missing business owner and weak data foundation as top failure causes, so test for both explicitly. 2. 02 #### Step 2: Week 2: Build a shortlist of 1-2 candidate use cases Generate 8-12 candidate use cases through workshops, then filter to 1-2 based on three criteria: clear business owner, available data, and measurable outcome. Avoid moonshots in the first cycle. The winning pilot is usually unglamorous: a workflow that already exists, with a defined cost-per-transaction, that AI can compress by 30-60%. 3. 03 #### Step 3: Week 3: Scope the pilot and define KPIs Write a one-page pilot scope: business problem, AI approach, success metrics, data sources, owners, and exit criteria. Define KPIs that link to a P&L line — cost saved, revenue added, or risk reduced. Unclear metrics is RAND's third top failure cause, so insist on numbers before code is written. 4. 04 #### Step 4: Week 4: Set up governance and EU AI Act classification Classify the pilot under EU AI Act risk categories (Regulation 2024/1689). If the use case touches biometrics, employment, education, essential services, or any Annex III domain, prepare for high-risk obligations from day one. Establish a governance committee with legal, security, data, and business representation. Document the classification rationale. 5. 05 #### Step 5: Weeks 5-7: Build the pilot Three weeks of focused build with a small team: one product owner, one ML or AI engineer, one data engineer, one domain expert. Use existing models and APIs where possible (buy first, build where differentiated). Ship a working end-to-end flow by end of week 7, even if rough. Working software beats slideware at every checkpoint. 6. 06 #### Step 6: Weeks 8-9: Validate the pilot against KPIs Two weeks of structured validation: run the pilot on real data, measure against the KPIs defined in week 3, and collect qualitative feedback from end users. Compare results to the baseline. If KPIs are missed by more than 30%, decide explicitly: iterate, pivot, or stop. The Alice Labs median says 96% of pilots cross this gate — but only because the bar in week 3 was honest. 7. 07 #### Step 7: Weeks 10-11: Deploy to production Two weeks to move from validated pilot to production deployment. Cover integration, monitoring, logging (Article 12 for high-risk systems), human oversight, and rollback procedures. Brief the affected teams, run a soft launch, then expand to full traffic. The Alice Labs Implementation Index 2026 reports a 14-week median from pilot start to production — this is the milestone that proves the roadmap works. 8. 08 #### Step 8: Week 12: Measure impact and write the ROI report One week to compile the 90-day ROI report: KPI deltas, total cost, total benefit, payback period, and learnings. Present to the executive sponsor and governance committee. The format matters less than the discipline of measurement — most organisations skip this step and lose the institutional learning that compounds across pilots. 9. 09 #### Step 9: Week 13: Build the 12-month scaling plan Use the 90-day learnings to scope the next horizon: which use cases scale from the pilot, which new pilots launch in the next 30/60/90 cycle, what governance and platform investment is needed. The 30/60/90 roadmap is not a one-off — it is the repeating heartbeat of a maturing AI programme. ## Key Takeaways - BCG and MIT (2024) found that only about 26% of GenAI investments deliver the value expected — the gap is execution, not technology. - McKinsey's 2024/2025 Global Survey reports 72% AI adoption, but most organisations still lack a structured roadmap from pilot to production. - RAND Corporation (RR-A2680-1, Aug 2024) names three top failure causes: missing business owner, weak data foundation, and unclear metrics — all addressable in the first 30 days. - The Alice Labs Implementation Index 2026 reports a 96% production rate and a median 14 weeks from pilot start to production deployment across 100+ Nordic engagements. - The 30/60/90 horizon is faster than annual planning yet long enough for real learning — it forces three decisions per phase, not a wishlist of initiatives. - EU AI Act (Regulation 2024/1689) Annex III obligations apply from 2 August 2026 — governance work must start at day 1, not after pilot launch. - Stanford AI Index Report 2025 finds 78% of organisations reported using AI in 2024 (up from 55% in 2023), yet only a minority translate adoption into measurable financial impact — the 30/60/90 cadence targets that translation gap. (Stanford HAI, 2025) 01 / 05 Step ## Why 90 Days Is the Right Horizon for an AI Roadmap In short Ninety days is faster than annual planning, so it forces decisions, but long enough for real-world learning to surface. It compresses the planning-to-evidence loop to a single quarter, which is the cadence most executive teams already run on. Annual AI strategy decks age badly. By month six, the model landscape has shifted, the use cases are stale, and the budget is half-spent. A 90-day horizon forces a different posture. The team must pick one or two bets, scope them tightly, and produce evidence by the end of the quarter. Ninety days is also long enough to be honest. A 30-day plan cannot include production deployment. A 12-month plan cannot avoid politics. Ninety days is the smallest unit of real learning. Leadership teams that want a baseline capability score feeding the Day-30 scope decision engage our Alice Labs AI maturity assessment framework (https://alicelabs.ai/en/ai-maturity-assessment)so the first pilot is picked against a measured starting point, not an opinion. BCG and MIT (2024) found that only about 26% of GenAI investments deliver the value expected. The gap is rarely technology — it is the gap between planning ambition and execution discipline. Ninety days closes that gap. Teams whose sponsor is still conflating the AI programme with an in-flight digital transformation should pair this roadmap with our AI transformation vs digital transformation (https://alicelabs.ai/en/insights/ai-transformation-vs-digital-transformation) deep-dive before writing the first 30-day plan — the two operate on different cadences and governance assumptions. 02 / 05 Step ## The 30/60/90 Structure: Three Milestones, Three Deliverables, Three Checkpoints In short Each 30-day block has exactly one milestone, one deliverable, and one stakeholder checkpoint. The constraint forces focus and gives leadership a predictable rhythm to govern the programme. The 30/60/90 framework is not a list of activities. It is a cadence of three decisions. **Days 1-30: Readiness and scope.** Milestone: pilot scope signed. Deliverable: one-page pilot brief. Checkpoint: executive sponsor go/no-go. **Days 31-60: Pilot in production.** Milestone: working end-to-end flow on real data. Deliverable: validated pilot meeting KPIs. Checkpoint: governance committee review. **Days 61-90: Measurable ROI and scale.** Milestone: production deployment plus ROI report. Deliverable: 12-month scaling plan. Checkpoint: board-level sponsor review. The structure is deliberately spare. Add a fourth deliverable per phase and the roadmap collapses into a wishlist. | Phase | Milestone | Primary deliverable | Stakeholder checkpoint | | --- | --- | --- | --- | | Days 1-30: Assess and scope | Pilot scope signed by executive sponsor | One-page pilot brief with KPIs and exit criteria | Executive sponsor go/no-go review | | Days 31-60: Pilot in production | Working end-to-end pilot meeting validation KPIs | Validated pilot with measured baseline vs target | Governance committee review (legal, security, data) | | Days 61-90: ROI and scale | Production deployment and first measurable ROI | 90-day ROI report and 12-month scaling plan | Board-level sponsor review and budget commitment | Source: Alice Labs 30/60/90 Roadmap framework Image: Linus Ingemarsson (https://alicelabs.ai/images/linus-ingemarsson.png) Image: Eric Lundberg (https://alicelabs.ai/images/eric-lundberg.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 03 / 05 Step ## Common Pitfalls — and How to Avoid Them In short RAND Corporation's 2024 analysis of AI failure names three top causes: missing business owner, weak data foundation, and unclear metrics. All three are addressable in the first 30 days if the roadmap forces the right checkpoints. RAND Corporation (RR-A2680-1, August 2024) analysed root causes of AI project failure. Three patterns dominate. **Missing business owner.** When the project sponsor is IT or a centre of excellence, accountability for the business outcome is diffuse. The fix: in week 1, name a single business owner who controls a P&L line the pilot will move. **Weak data foundation.** Teams discover in week 6 that the data they planned to use is incomplete, unlabelled, or behind a system boundary they cannot cross. The fix: in week 1, validate that the baseline data exists and is usable before committing scope. **Unclear metrics.** The pilot ships, but no one agrees whether it worked. The fix: in week 3, write the metric and the target before code is written. Numbers, not adjectives. A fourth pitfall is specific to 2026: skipping EU AI Act classification at the scoping stage. If the use case turns out to be high-risk under Annex III, governance work that should have started in week 4 surfaces in week 11 — and the launch slips. Alice Labs practitioner team ## Want the Alice Labs 30/60/90 Roadmap for your organisation? We run AI strategy engagements built around the 30/60/90 cadence. In 90 days you get a pilot in production, the first measurable ROI documented, and a 12-month scaling plan signed off by leadership. 100+ Nordic enterprise engagements delivered, 96% production rate, 14-week median pilot-to-production. Book a strategy call 04 / 05 Step ## Alice Labs Cases Mapped to the 30/60/90 Timeline In short Real engagements from Ljusgårda, public sector, and media show how the 30/60/90 cadence plays out across very different use cases — and why the Alice Labs Implementation Index 2026 reports a 14-week median. The 30/60/90 cadence holds across industries. Three Alice Labs engagements — all delivered under our enterprise ai strategy (https://alicelabs.ai/en/ai-strategy) practice — illustrate the pattern. **Ljusgårda (consumer / cannabis).** Days 1-30: readiness assessment identified content operations as the highest-ROI workflow. Days 31-60: AI-assisted content pipeline in production with measurable throughput gain. Days 61-90: ROI report and second pilot scoped for customer service. **Public sector engagement.** Days 1-30: readiness assessment plus full EU AI Act Annex III classification (high-risk pathway). Days 31-60: pilot built with governance, logging, and FRIA running in parallel. Days 61-90: production launch with regulator-ready documentation. **Nordic media client.** Days 1-30: shortlisted three candidate use cases, picked one with clear baseline cost. Days 31-60: pilot validated against editorial throughput metrics. Days 61-90: scaled to second newsroom and produced a 12-month plan covering five additional use cases. Across 100+ Nordic engagements the Alice Labs Implementation Index 2026 reports a 96% production rate and a median 14 weeks from pilot start to production. The 30/60/90 roadmap is the framework that produces those numbers. 05 / 05 Step ## From 90-Day Pilot to 12-Month Roadmap In short The 30/60/90 framework is not a one-off — it is the repeating heartbeat of a maturing AI programme. After the first cycle, the next 30/60/90 launches inside the broader 12-month roadmap, with platform and governance investments compounding across pilots. The first 30/60/90 produces evidence. The second produces momentum. By the fourth, the cadence is the operating system of the AI programme. The 12-month roadmap is built from the 90-day learnings, not from a top-down vision deck. Each quarter, the leadership team decides which pilots scale, which new ones launch, and which platform investments unlock the next batch. By month nine, two patterns appear. First, the cost per pilot drops as governance, platform, and data work compounds. Second, the team starts running pilots in parallel — one 30/60/90 cycle does not block the next. By month twelve, the question shifts. It is no longer "can we do AI?" It becomes "which AI bets give the next quarter's return?" That is the moment the programme has matured. For a curated shortlist of vendors that operationalize these patterns, see our basta ai strategi konsulter sverige (https://alicelabs.ai/sv/insights/basta-ai-strategi-konsulter-sverige-2026) listicle. Alice Labs practitioner team Book a Discovery Call ## About the Authors & Reviewers Written by Image: Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Linus Ingemarsson (https://www.linkedin.com/in/linus-ingemarsson/) CEO & Co-Founder, Alice Labs CEO & 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/ Linus Ingemarsson on LinkedIn: https://www.linkedin.com/in/linus-ingemarsson/ Email Linus Ingemarsson: linus@alicelabs.ai Reviewed by September 16, 2026 Image: Eric Lundberg - Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/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/ Eric Lundberg on LinkedIn: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Email Eric Lundberg: eric@alicelabs.ai Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is an AI strategy roadmap? An AI strategy roadmap is a time-boxed plan that sequences AI initiatives from readiness assessment through pilot deployment to scaled production. The 30/60/90 day variant compresses planning into three milestones — assessment, pilot in production, and measurable ROI — to drive faster learning than annual planning. ### Why use a 30/60/90 day framework instead of an annual plan? Annual plans age badly in a fast-moving AI landscape. The 30/60/90 horizon is short enough to force decisions and long enough for real learning. It compresses the planning-to-evidence loop to a single quarter, which matches the cadence most executive teams already run on. ### What goes into the first 30 days of an AI roadmap? Days 1-30 cover readiness assessment, use-case shortlisting, pilot scoping with KPIs, and governance setup including EU AI Act classification. The deliverable is a one-page pilot brief signed by the executive sponsor. The objective is a single, well-scoped pilot — not a portfolio. ### How long should a first AI pilot take? In the Alice Labs Implementation Index 2026, the median pilot-to-production timeline across 100+ Nordic enterprise engagements is 14 weeks. The 30/60/90 roadmap is calibrated against that median: pilot build in weeks 5-7, validation in weeks 8-9, production deployment in weeks 10-11. ### What KPIs should the pilot measure? KPIs must link to a P&L line — cost saved, revenue added, or risk reduced. Examples: cost per transaction processed, time per case resolved, conversion rate uplift, error rate reduction. RAND's 2024 research names unclear metrics as a top failure cause, so insist on numbers and targets before code is written. ### When do EU AI Act obligations apply to a pilot? EU AI Act (Regulation 2024/1689) high-risk obligations under Annex III apply from 2 August 2026. Classification must happen at scoping (week 4), not after launch. If the use case touches biometrics, employment, education, essential services, or any Annex III domain, governance work runs in parallel with the build. ### What is the Alice Labs 30/60/90 Roadmap? The Alice Labs 30/60/90 Roadmap is a proprietary framework used across 100+ Nordic enterprise implementations. It sequences three milestones — readiness assessment plus pilot scoping (days 1-30), pilot in production with KPIs (days 31-60), and first measurable ROI plus scaling plan (days 61-90). The Implementation Index 2026 reports a 96% production rate and a 14-week median pilot-to-production timeline. ### AI for manufacturing operations roadmap for the first 90 days — where should I focus? In manufacturing, the first 30 days should score one production line for AI readiness: OT/IT data availability, sensor coverage, and a named plant owner. Days 31-60 pilot one narrow use case — usually predictive maintenance on a single asset class, quality-inspection vision on one SKU, or throughput forecasting for one shift pattern. Days 61-90 measure OEE delta versus a 90-day baseline and scope the next line. Target a 3-8% OEE lift or 15-30% unplanned-downtime reduction before scaling. ### What is a 30/60/90-day plan to move from AI-assisted to AI-native engineering? Days 1-30: baseline current AI-assisted usage (Copilot acceptance rate, PR cycle time, test coverage) and pick one workflow to redesign around agents — typically code review, test generation, or incident triage. Days 31-60: ship an agentic workflow with human-in-the-loop, targeting a 40-60% reduction in that workflow's cycle time. Days 61-90: measure the delta, remove the human checkpoint where safe, and publish an internal AI-native engineering standard covering evaluation, guardrails, and rollback. ### What happens after the first 90 days? The 30/60/90 cadence repeats. The next cycle launches inside a 12-month roadmap built from the first cycle's learnings. By the fourth cycle the team is running pilots in parallel, platform and governance investments are compounding, and the question shifts from 'can we do AI?' to 'which AI bets give the next quarter's return?' Previous in AI Strategy ### Build vs Buy AI: Decision Framework for 2026 https://alicelabs.ai/en/insights/build-vs-buy-ai Next in AI Strategy ### How to Get Board Buy-In for AI Investment: 5-Slide Briefing https://alicelabs.ai/en/insights/how-to-get-board-buy-in-for-ai ## Further reading - RAND Corporation — Root Causes of Failure in AI (RR-A2680-1, 2024) (https://www.rand.org/pubs/research_reports/RRA2680-1.html)· rand.org - McKinsey — The state of AI (Global Survey 2024/2025) (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)· mckinsey.com - EU AI Act — Regulation (EU) 2024/1689 (EUR-Lex) (https://eur-lex.europa.eu/eli/reg/2024/1689/oj)· eur-lex.europa.eu ## Related services AI roadmap consulting Alice Labs' AI strategy engagement — the 30/60/90 roadmap on this page, built with your leadership team.: https://alicelabs.ai/en/ai-strategy ## Related reading pillar ### Enterprise AI Strategy Framework The full strategy framework — the 30/60/90 roadmap is the execution layer beneath it. 12 min https://alicelabs.ai/en/insights/enterprise-ai-strategy-framework deep dive ### Why AI Projects Fail: 7 Root Causes The failure patterns the 30/60/90 roadmap is designed to neutralise. 10 min https://alicelabs.ai/en/insights/why-ai-projects-fail data insight ### Alice Labs Implementation Index 2026 The empirical basis for the 14-week median and 96% production rate. 9 min https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026 ## Sources 1. BCG / MIT Sloan Management Review — GenAI value realisation (2024) (https://sloanreview.mit.edu/)(accessed 2026-05-06) 2. McKinsey & Company — The state of AI (Global Survey 2024/2025) (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)(accessed 2026-05-06) 3. RAND Corporation — Root Causes of Failure in Machine Learning Systems (RR-A2680-1, August 2024) (https://www.rand.org/pubs/research_reports/RRA2680-1.html)(accessed 2026-05-06) 4. Regulation (EU) 2024/1689 — EU AI Act (EUR-Lex) (https://eur-lex.europa.eu/eli/reg/2024/1689/oj)(accessed 2026-05-06) 5. Alice Labs Implementation Index 2026 — proprietary engagement data (https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026)(accessed 2026-05-06) 6. Stanford HAI — 2025 AI Index Report (Chapter 4: Economy) (https://hai.stanford.edu/ai-index/2025-ai-index-report)(accessed 2026-07-15) Next scheduled review: 2026-12-15 Alice Labs practitioner team Book a Discovery Call Share Share on LinkedIn (https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fai-strategy-roadmap-30-60-90)Share on X (https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fai-strategy-roadmap-30-60-90&text=AI%20Strategy%20Roadmap%3A%20The%2030%2F60%2F90%20Day%20Plan%20(2026)) ## Get in Touch! The lab usually responds within 24 hours. Need help with AI? 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The 30/60/90 day variant compresses planning into three milestones — assessment, pilot in production, and measurable ROI — to drive faster learning than annual planning." } }, { "@type": "Question", "name": "Why use a 30/60/90 day framework instead of an annual plan?", "acceptedAnswer": { "@type": "Answer", "text": "Annual plans age badly in a fast-moving AI landscape. The 30/60/90 horizon is short enough to force decisions and long enough for real learning. It compresses the planning-to-evidence loop to a single quarter, which matches the cadence most executive teams already run on." } }, { "@type": "Question", "name": "What goes into the first 30 days of an AI roadmap?", "acceptedAnswer": { "@type": "Answer", "text": "Days 1-30 cover readiness assessment, use-case shortlisting, pilot scoping with KPIs, and governance setup including EU AI Act classification. The deliverable is a one-page pilot brief signed by the executive sponsor. The objective is a single, well-scoped pilot — not a portfolio." } }, { "@type": "Question", "name": "How long should a first AI pilot take?", "acceptedAnswer": { "@type": "Answer", "text": "In the Alice Labs Implementation Index 2026, the median pilot-to-production timeline across 100+ Nordic enterprise engagements is 14 weeks. The 30/60/90 roadmap is calibrated against that median: pilot build in weeks 5-7, validation in weeks 8-9, production deployment in weeks 10-11." } }, { "@type": "Question", "name": "What KPIs should the pilot measure?", "acceptedAnswer": { "@type": "Answer", "text": "KPIs must link to a P&L line — cost saved, revenue added, or risk reduced. Examples: cost per transaction processed, time per case resolved, conversion rate uplift, error rate reduction. RAND's 2024 research names unclear metrics as a top failure cause, so insist on numbers and targets before code is written." } }, { "@type": "Question", "name": "When do EU AI Act obligations apply to a pilot?", "acceptedAnswer": { "@type": "Answer", "text": "EU AI Act (Regulation 2024/1689) high-risk obligations under Annex III apply from 2 August 2026. Classification must happen at scoping (week 4), not after launch. If the use case touches biometrics, employment, education, essential services, or any Annex III domain, governance work runs in parallel with the build." } }, { "@type": "Question", "name": "What is the Alice Labs 30/60/90 Roadmap?", "acceptedAnswer": { "@type": "Answer", "text": "The Alice Labs 30/60/90 Roadmap is a proprietary framework used across 100+ Nordic enterprise implementations. It sequences three milestones — readiness assessment plus pilot scoping (days 1-30), pilot in production with KPIs (days 31-60), and first measurable ROI plus scaling plan (days 61-90). The Implementation Index 2026 reports a 96% production rate and a 14-week median pilot-to-production timeline." } }, { "@type": "Question", "name": "AI for manufacturing operations roadmap for the first 90 days — where should I focus?", "acceptedAnswer": { "@type": "Answer", "text": "In manufacturing, the first 30 days should score one production line for AI readiness: OT/IT data availability, sensor coverage, and a named plant owner. Days 31-60 pilot one narrow use case — usually predictive maintenance on a single asset class, quality-inspection vision on one SKU, or throughput forecasting for one shift pattern. Days 61-90 measure OEE delta versus a 90-day baseline and scope the next line. Target a 3-8% OEE lift or 15-30% unplanned-downtime reduction before scaling." } }, { "@type": "Question", "name": "What is a 30/60/90-day plan to move from AI-assisted to AI-native engineering?", "acceptedAnswer": { "@type": "Answer", "text": "Days 1-30: baseline current AI-assisted usage (Copilot acceptance rate, PR cycle time, test coverage) and pick one workflow to redesign around agents — typically code review, test generation, or incident triage. Days 31-60: ship an agentic workflow with human-in-the-loop, targeting a 40-60% reduction in that workflow's cycle time. Days 61-90: measure the delta, remove the human checkpoint where safe, and publish an internal AI-native engineering standard covering evaluation, guardrails, and rollback." } }, { "@type": "Question", "name": "What happens after the first 90 days?", "acceptedAnswer": { "@type": "Answer", "text": "The 30/60/90 cadence repeats. The next cycle launches inside a 12-month roadmap built from the first cycle's learnings. By the fourth cycle the team is running pilots in parallel, platform and governance investments are compounding, and the question shifts from 'can we do AI?' to 'which AI bets give the next quarter's return?'" } } ] }, { "@context": "https://schema.org", "@type": "HowTo", "name": "AI Strategy Roadmap: The 30/60/90 Day Plan", "description": "AI strategy roadmap — a 30/60/90 day plan to move from readiness assessment to first measurable ROI. Includes templates, KPIs, governance, and scaling steps.", "totalTime": "12 min read", "step": [ { "@type": "HowToStep", "position": 1, "name": "Week 1: Run an AI readiness assessment", "text": "Score the organisation across five domains: data foundation, talent, governance, executive sponsorship, and use-case clarity. Interview at least five stakeholders across business, IT, legal, and data. The goal is a baseline maturity score and a one-page gap analysis — not a 40-slide deck. RAND's 2024 research identifies missing business owner and weak data foundation as top failure causes, so test for both explicitly.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#week-1-readiness" }, { "@type": "HowToStep", "position": 2, "name": "Week 2: Build a shortlist of 1-2 candidate use cases", "text": "Generate 8-12 candidate use cases through workshops, then filter to 1-2 based on three criteria: clear business owner, available data, and measurable outcome. Avoid moonshots in the first cycle. The winning pilot is usually unglamorous: a workflow that already exists, with a defined cost-per-transaction, that AI can compress by 30-60%.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#week-2-shortlist" }, { "@type": "HowToStep", "position": 3, "name": "Week 3: Scope the pilot and define KPIs", "text": "Write a one-page pilot scope: business problem, AI approach, success metrics, data sources, owners, and exit criteria. Define KPIs that link to a P&L line — cost saved, revenue added, or risk reduced. Unclear metrics is RAND's third top failure cause, so insist on numbers before code is written.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#week-3-scope" }, { "@type": "HowToStep", "position": 4, "name": "Week 4: Set up governance and EU AI Act classification", "text": "Classify the pilot under EU AI Act risk categories (Regulation 2024/1689). If the use case touches biometrics, employment, education, essential services, or any Annex III domain, prepare for high-risk obligations from day one. Establish a governance committee with legal, security, data, and business representation. Document the classification rationale.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#week-4-governance" }, { "@type": "HowToStep", "position": 5, "name": "Weeks 5-7: Build the pilot", "text": "Three weeks of focused build with a small team: one product owner, one ML or AI engineer, one data engineer, one domain expert. Use existing models and APIs where possible (buy first, build where differentiated). Ship a working end-to-end flow by end of week 7, even if rough. Working software beats slideware at every checkpoint.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#weeks-5-7-build" }, { "@type": "HowToStep", "position": 6, "name": "Weeks 8-9: Validate the pilot against KPIs", "text": "Two weeks of structured validation: run the pilot on real data, measure against the KPIs defined in week 3, and collect qualitative feedback from end users. Compare results to the baseline. If KPIs are missed by more than 30%, decide explicitly: iterate, pivot, or stop. The Alice Labs median says 96% of pilots cross this gate — but only because the bar in week 3 was honest.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#weeks-8-9-validate" }, { "@type": "HowToStep", "position": 7, "name": "Weeks 10-11: Deploy to production", "text": "Two weeks to move from validated pilot to production deployment. Cover integration, monitoring, logging (Article 12 for high-risk systems), human oversight, and rollback procedures. Brief the affected teams, run a soft launch, then expand to full traffic. The Alice Labs Implementation Index 2026 reports a 14-week median from pilot start to production — this is the milestone that proves the roadmap works.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#weeks-10-11-deploy" }, { "@type": "HowToStep", "position": 8, "name": "Week 12: Measure impact and write the ROI report", "text": "One week to compile the 90-day ROI report: KPI deltas, total cost, total benefit, payback period, and learnings. Present to the executive sponsor and governance committee. The format matters less than the discipline of measurement — most organisations skip this step and lose the institutional learning that compounds across pilots.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#week-12-roi" }, { "@type": "HowToStep", "position": 9, "name": "Week 13: Build the 12-month scaling plan", "text": "Use the 90-day learnings to scope the next horizon: which use cases scale from the pilot, which new pilots launch in the next 30/60/90 cycle, what governance and platform investment is needed. The 30/60/90 roadmap is not a one-off — it is the repeating heartbeat of a maturing AI programme.", "url": "https://alicelabs.ai/en/insights/ai-strategy-roadmap-30-60-90#week-13-scale" } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "AI Strategy Roadmap: The 30/60/90 Day Plan", "description": "AI strategy roadmap — a 30/60/90 day plan to move from readiness assessment to first measurable ROI. 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