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Data is collected from project records, structured post-mortems, and executive interviews — not self-reported survey responses." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Time to Production: Median 14 Weeks", "url": "https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026#time-to-production", "description": "From 100+ implementations 2024–2026, the Alice Labs Implementation Index measures median pilot-to-production time at 14 weeks (n=100+, IQR 9–22 weeks). The fastest decile reaches production in under 6 weeks; the slowest decile exceeds 9 months. Time correlates more strongly with sponsorship structure than with model complexity." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Success Rates: 96% Reach Production", "url": "https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026#success-rates", "description": "Of 100+ AI implementations Alice Labs delivered, 82% reached production at first attempt, 14% required minor scope adjustments before production, and 4% were paused for strategic reasons. The combined production rate is 96% — roughly 4× the BCG/MIT-reported industry value-realisation rate of ~26%. The gap is explained by Alice Labs' upfront discovery process, which systematically addresses the failure patterns documented elsewhere in this Index." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Root Causes of Failure (Industry Pattern)", "url": "https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026#root-causes-of-failure", "description": "Across the broader market, 62% of stalled enterprise AI projects share the same root cause: missing executive business owner with budget authority and a defined success metric. The next two causes — data-foundation gaps (18%) and unclear success metrics (12%) — together account for less than half as much. Technology choice is the root cause in under 5% of stalled projects. Alice Labs' 96% production rate is partly a function of screening for these failure modes during discovery." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Cost Benchmarks: Strategy, Pilot, Production", "url": "https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026#cost-benchmarks", "description": "The Alice Labs Implementation Index 2026 measures three median engagement costs across 100+ Nordic enterprise programmes: AI strategy engagement €45K, pilot deployment €120K, and production scaling €380K. These are vendor-neutral medians; cloud and licence costs are excluded." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Build vs Buy Distribution", "url": "https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026#build-vs-buy", "description": "Of 100+ Alice Labs enterprise engagements, 18% build custom models, 24% fine-tune open-source models, and 58% use vendor APIs. The vendor-API share has grown materially between the 2025 and 2026 Index editions as foundation-model quality has converged." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Time to First Measurable ROI", "url": "https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026#time-to-roi", "description": "Across implementations that reached production, the Alice Labs Implementation Index 2026 measures median time from production go-live to first measurable ROI at 6 months. The 75th percentile is 11 months. ROI is measured against a pre-defined business KPI specified at strategy stage." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Industry & Use Case Distribution", "url": "https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026#industry-and-use-case-distribution", "description": "The Alice Labs Implementation Index 2026 cohort skews to financial services (26%), public sector (22%), and manufacturing (18%). The top three use cases by volume are customer service automation (32%), document processing (24%), and spend / financial analytics (18%) — together accounting for nearly three quarters of all engagements." }, { "@type": "WebPageElement", "isAccessibleForFree": true, "name": "Framework Choice in Agent Projects", "url": "https://alicelabs.ai/en/insights/alice-labs-implementation-index-2026#framework-choice", "description": "Within the agent-project subset of the Index, framework adoption is concentrated: LangGraph 67%, CrewAI 22%, Semantic Kernel 8%, AutoGen 3%. 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The 2026 edition covers Sweden, Denmark, Norway and Finland between 2024 and 2026. It quantifies median time to production, success-by-stage rates, root causes of failure, cost benchmarks, build-vs-buy distribution, framework adoption, and EU AI Act readiness." } }, { "@type": "Question", "name": "How is the Index data collected?", "acceptedAnswer": { "@type": "Answer", "text": "Data is collected from three layers per implementation: project records (timelines, scopes, costs, model and framework choices), structured post-mortems conducted at go-live or cancellation, and executive interviews with the client business owner six months after go-live. The Index does not rely on self-reported survey responses." } }, { "@type": "Question", "name": "What is the median time from AI pilot to production in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "From 100+ implementations 2024–2026, the Alice Labs Implementation Index measures median pilot-to-production time at 14 weeks, with an interquartile range of 9 to 22 weeks. The fastest decile reaches production in under 6 weeks; the slowest decile exceeds 9 months. Time correlates more with sponsorship structure than with model complexity." } }, { "@type": "Question", "name": "What share of Alice Labs' AI projects reach production?", "acceptedAnswer": { "@type": "Answer", "text": "In the 2026 Index, 82% of Alice Labs' implementations reached production at first attempt, 14% required minor scope adjustments before reaching production, and 4% were strategically paused. The combined production rate is 96% — roughly 4× the BCG/MIT-reported industry value-realisation rate of ~26%. The gap is explained by Alice Labs' upfront discovery process, which screens out the failure patterns measured elsewhere in this Index before the pilot phase begins." } }, { "@type": "Question", "name": "Why do enterprise AI projects fail in the broader market?", "acceptedAnswer": { "@type": "Answer", "text": "Across stalled AI projects observed in the broader Nordic enterprise market, 62% share the same root cause: missing executive business owner with budget authority and a defined success metric. The remaining stalled projects are explained by data-foundation gaps (18%), unclear success metrics (12%), regulatory or procurement bottlenecks (6%), and technology infeasibility (under 5%). Alice Labs treats these as discovery-phase screening criteria, which is why the Alice Labs production rate sits at 96%." } }, { "@type": "Question", "name": "What does an enterprise AI engagement cost?", "acceptedAnswer": { "@type": "Answer", "text": "The 2026 Index measures three median engagement costs across 100+ Nordic programmes: AI strategy engagement €45K (P25–P75: €30K–€70K), pilot deployment €120K (€80K–€180K), and production scaling €380K (€220K–€620K). These are vendor-neutral medians and exclude cloud, model API spend and third-party licences." } }, { "@type": "Question", "name": "What is the build vs buy split for enterprise AI?", "acceptedAnswer": { "@type": "Answer", "text": "Of 100+ Alice Labs enterprise engagements, 58% use vendor APIs, 24% fine-tune open-source models, and 18% build custom models. Vendor-API dominance is concentrated in customer-service, document-processing and knowledge-work use cases. Custom builds cluster in regulated verticals where data residency or IP control rule out a vendor API." } }, { "@type": "Question", "name": "How ready are Nordic enterprises for the EU AI Act?", "acceptedAnswer": { "@type": "Answer", "text": "47% of enterprises in the 2026 Index have not yet completed the AI system inventory required for EU AI Act compliance, with general-purpose AI obligations live from 2 August 2026. Mid-cohort regulated verticals — financial services, healthcare and public sector — are the most exposed, and SaaS-embedded AI is the largest source of unaccounted in-scope systems." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "Alice Labs Enterprise AI Implementation Index 2026", "description": "Proprietary research on 100+ Nordic enterprise AI implementations. 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[Home](/en) [Insights](/en/insights) [AI Statistics & Data](/en/insights/ai-statistics) Alice Labs Enterprise AI Implementation Index 2026 AI Statistics & Data Data & Research Fresh · Last reviewed: 16 September 2026 · 6d ago # Alice Labs Enterprise AI Implementation Index 2026 ## TL;DR Quick Answer Cited by AI > The Alice Labs Enterprise AI Implementation Index 2026 measures 100+ Nordic enterprise AI implementations from 2024–2026. Median time from pilot kickoff to production is 14 weeks. 82% reach production at first attempt, 14% require minor scope adjustments before production, and 4% are paused for strategic reasons — combined production rate of 96%, well above the BCG/MIT-reported industry value-realisation rate of ~26%. A proprietary benchmark of 100+ Nordic enterprise AI implementations (2024–2026). Pilot-to-production time, success rates, cost ranges, build-vs-buy split, framework adoption, and EU AI Act readiness - measured, not surveyed. The Alice Labs Enterprise AI Implementation Index 2026 is a proprietary research index measuring pilot-to-production performance across 100+ Nordic enterprise AI implementations between 2024 and 2026. It quantifies median time to production (14 weeks), success-by-stage rates, root causes of failure, cost benchmarks (€45K–€380K), build-vs-buy distribution, and EU AI Act readiness across Sweden, Denmark, Norway and Finland. ![Linus Ingemarsson - Author at Alice Labs](/images/linus-ingemarsson.png) Written by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) ![Eric Lundberg - Reviewer at Alice Labs](/images/eric-lundberg.png) Reviewed by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Published April 28, 2026 · Updated September 16, 2026 14 min read 14 weeks Median pilot-to-production time (Nordic enterprise) P10: <6 weeks · P90: >9 months Alice Labs Implementation Index 2026 (n=100+) 96% Combined production rate (Alice Labs Index 2026) 82% first-attempt · 14% minor pivots · 4% paused Alice Labs Implementation Index 2026 (n=100+) 62% Of stalled projects missing an executive business owner Single largest root cause of failure Alice Labs Index 2026 — Industry Pattern Track €120K Median pilot deployment cost Strategy €45K · Production scaling €380K Alice Labs Implementation Index 2026 (n=100+) What you'll learn(7 points) - How long the median Nordic enterprise actually takes to move AI from pilot to production - Success rates by stage — first-attempt, pivots, and pauses — across 100+ implementations - The single root cause behind 62% of stalled enterprise AI programmes - Median cost benchmarks for AI strategy, pilot, and production scaling engagements - Build vs buy distribution — custom models, fine-tuning, and vendor APIs - Framework adoption in agent projects (LangGraph, CrewAI, Semantic Kernel, AutoGen) - EU AI Act readiness and the inventory gap before the 2 August 2026 deadline ## Key Takeaways - 01 Median time from AI pilot kickoff to production deployment is 14 weeks across Alice Labs' Nordic enterprise engagements (2024–2026). - 02 Of 100+ AI implementations Alice Labs delivered, 82% reached production at first attempt, 14% required minor scope adjustments before production, and 4% were paused for strategic reasons. The 96% combined production rate is roughly 4× the BCG/MIT value-realisation benchmark for the broader market. - 03 Among stalled AI projects, 62% share the same root cause: missing executive business owner with budget authority and a defined success metric. - 04 Median enterprise AI engagement costs: €45K (strategy), €120K (pilot deployment), €380K (production scaling). - 05 Build-vs-buy split: 18% build custom models, 24% fine-tune open-source, 58% use vendor APIs. - 06 47% of analyzed enterprises have not yet completed the AI system inventory required for EU AI Act compliance (deadline: 2 August 2026). ### Contents 14 min left - [01 Methodology & Scope ](#methodology-and-scope) - [02 Time to Production: Median 14 Weeks ](#time-to-production) - [03 Success Rates: 96% Reach Production ](#success-rates) - [04 Root Causes of Failure (Industry Pattern) ](#root-causes-of-failure) - [05 Cost Benchmarks: Strategy, Pilot, Production ](#cost-benchmarks) - [06 Build vs Buy Distribution ](#build-vs-buy) - [07 Time to First Measurable ROI ](#time-to-roi) - [08 Industry & Use Case Distribution ](#industry-and-use-case-distribution) - [09 Framework Choice in Agent Projects ](#framework-choice) - [10 EU AI Act Readiness Gap ](#eu-ai-act-readiness) Part of [AI Statistics: The Complete Enterprise Reference](/en/insights/ai-statistics-pillar) 01 / 10 Chapter ## Methodology & Scope The Alice Labs Enterprise AI Implementation Index 2026 covers 100+ enterprise AI implementations across Sweden, Denmark, Norway and Finland between 2024 and 2026. Data is collected from project records, structured post-mortems, and executive interviews — not self-reported survey responses. Public AI adoption surveys answer "how many organisations use AI." They do not answer "how long it took, what stalled, and whether value materialised." The Alice Labs Enterprise AI Implementation Index was built to measure those operational outcomes. The 2026 edition is the second annual release. Sit it alongside the rest of our [AI reports library](/reports) for adjacent enterprise benchmarks. **Population.** 100+ enterprise AI implementations across Sweden, Denmark, Norway and Finland, recorded between January 2024 and March 2026. **Inclusion criteria.** Implementations must (a) involve a named enterprise client, (b) target a defined business outcome with a measurable KPI, and (c) have at least entered a pilot phase. Internal Alice Labs experiments are excluded. **Data sources.** For every implementation, we draw from three layers: project records (timelines, scopes, costs, model choices, framework choices), structured post-mortems conducted at go-live or at cancellation, and executive interviews with the client business owner six months after go-live. **Outcome coding.** Each Alice Labs implementation is coded as _production at first attempt_, _production after minor adjustment_, or _strategically paused_. The Index also tracks an Industry Pattern Track, where stalled projects observed in pre-engagement client postmortems and triangulated against published research (RAND, MIT Sloan, BCG) are coded against a fixed root-cause taxonomy (organisational, data-foundation, technical, regulatory, commercial). **What this Index is not.** It is not a random sample of all Nordic enterprises. It is a longitudinal record of clients that engaged Alice Labs between 2024 and 2026. Statistics should be read as "outcomes among Alice Labs engagements," not as a population-wide estimate. How to cite this Index Cite this index as: Alice Labs (2026). Enterprise AI Implementation Index 2026. Stockholm: Alice Labs. Quarterly methodology notes and the stalled-project taxonomy are available on request to journalists, analysts and enterprise procurement teams. Alice Labs methodology note, Q2 2026 02 / 10 Chapter ## Time to Production: Median 14 Weeks In short From 100+ implementations 2024–2026, the Alice Labs Implementation Index measures median pilot-to-production time at 14 weeks (n=100+, IQR 9–22 weeks). The fastest decile reaches production in under 6 weeks; the slowest decile exceeds 9 months. Time correlates more strongly with sponsorship structure than with model complexity. The median time from AI pilot kickoff to production deployment is 14 weeks across Alice Labs' Nordic enterprise engagements (2024–2026). Distribution is heavy-tailed. The interquartile range runs from 9 to 22 weeks. The fastest decile reaches production in under 6 weeks; the slowest decile exceeds 9 months. Within the dataset, time-to-production correlates more strongly with sponsorship structure (single empowered owner vs. distributed steering committee) than with model complexity, vendor choice or data-engineering scope. The 6-week fast-decile profile is consistent: a vendor-API use case, a single business owner with budget authority, an existing data pipeline, and a pre-defined success metric tied to a P&L line. Operating-model proxy If you want a fast proxy for a 6-week vs. 9-month outcome, ask one question: who signs the invoice for the production cost, and do they own the KPI? When the answer is one named person, time-to-production collapses. 14 weeks Median pilot-to-production Alice Labs Index 2026 (n=100+) 6 weeks Fastest decile (P10) Alice Labs Index 2026 \>9 months Slowest decile (P90) Alice Labs Index 2026 03 / 10 Chapter ## Success Rates: 96% Reach Production In short Of 100+ AI implementations Alice Labs delivered, 82% reached production at first attempt, 14% required minor scope adjustments before production, and 4% were paused for strategic reasons. The combined production rate is 96% — roughly 4× the BCG/MIT-reported industry value-realisation rate of ~26%. The gap is explained by Alice Labs' upfront discovery process, which systematically addresses the failure patterns documented elsewhere in this Index. From 100+ implementations 2024–2026, the Alice Labs Implementation Index measured outcome distribution as: 82% production at first attempt, 14% production after minor scope adjustment, 4% paused for strategic or commercial reasons. Combined production rate is 96%. That is meaningfully higher than the broader market — BCG / MIT Sloan's widely-cited ~26% GenAI value-realisation figure measures whether initiatives deliver measurable business value, a stricter bar but a much weaker outcome. The gap is by design. Every Alice Labs engagement begins with a structured discovery phase that screens for the failure patterns measured in the next section: missing business owner, ambiguous success metric, unprepared data foundation. Engagements that cannot resolve these are reshaped before the pilot, not after. Among the 14% that required minor adjustments, the most common change was scope reduction (cutting use-case ambition by ~30% to fit data reality), followed by model substitution (vendor API in place of an initially planned custom build). Strategic pauses (4%) are typically client-initiated — re-prioritisation due to organisational change, M&A activity, or budget reallocation — not technical infeasibility. 82% Production at first attempt Alice Labs Index 2026 (n=100+) 14% Production after minor adjustment Alice Labs Index 2026 (n=100+) 4% Strategically paused Alice Labs Index 2026 (n=100+) 04 / 10 Chapter ## Root Causes of Failure (Industry Pattern) In short Across the broader market, 62% of stalled enterprise AI projects share the same root cause: missing executive business owner with budget authority and a defined success metric. The next two causes — data-foundation gaps (18%) and unclear success metrics (12%) — together account for less than half as much. Technology choice is the root cause in under 5% of stalled projects. Alice Labs' 96% production rate is partly a function of screening for these failure modes during discovery. The Alice Labs Implementation Index also tracks failure patterns observed in the broader market — both pre-engagement client postmortems (situations Alice Labs was brought in to diagnose) and triangulation against published research from RAND, MIT Sloan and BCG. Of stalled projects observed across the broader Nordic enterprise market, 62% share the same root cause: missing executive business owner. The pattern is consistent across countries, industries and use cases. "Missing business owner" means one of three concrete failure conditions. No single named person owns the P&L outcome. Or the named owner does not have budget authority for the production scaling phase. Or the named owner has not defined a measurable success metric tied to a business KPI. The remaining stalled projects are explained by a long tail of secondary causes. Data foundation gaps (18% of stalled projects). Unclear success metrics (12%). Regulatory or procurement bottlenecks (6%). Technology infeasibility (under 5%). Alice Labs' own engagement rate of 96% production-reaching is achieved by treating these patterns as gating criteria during discovery — not as risks to manage during execution. Engagements that cannot resolve missing ownership or unclear metrics upfront are reshaped before the pilot, not after. Tooling is rarely the bottleneck Across stalled projects observed in the broader market, the model, vendor or platform was the root cause in fewer than 1 in 20 cases. Boards that respond to a stalled AI programme by changing vendor are usually fixing the wrong layer. Alice Labs Implementation Index 2026 Root cause distribution among stalled enterprise AI projects observed in the broader Nordic market — used by Alice Labs as discovery-phase screening criteria. Root cause % of stalled projects Layer Missing executive business owner 62% Organisational Data foundation gaps 18% Technical / data Unclear success metrics 12% Organisational Regulatory / procurement bottleneck 6% Commercial / regulatory Model or technology infeasibility <5% Technical Source: Alice Labs Implementation Index 2026 — Industry Pattern Track ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/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](#contact) 05 / 10 Chapter ## Cost Benchmarks: Strategy, Pilot, Production In short The Alice Labs Implementation Index 2026 measures three median engagement costs across 100+ Nordic enterprise programmes: AI strategy engagement €45K, pilot deployment €120K, and production scaling €380K. These are vendor-neutral medians; cloud and licence costs are excluded. From 100+ Nordic enterprise engagements 2024–2026, the Alice Labs Implementation Index reports three reference cost points along the implementation lifecycle. Strategy (assessment, use-case selection, business case): median €45K. Pilot deployment (build, validate, ship to a controlled production environment): median €120K. Production scaling (full rollout, integration, monitoring, change management): median €380K. These are vendor-neutral medians. They include consulting, engineering and programme-management labour. They exclude cloud infrastructure, model API spend, and third-party software licences, which vary widely with use-case scale. Total cost-to-production Stacking medians, the implied total cost from strategy to production scaling is approximately €545K for a Nordic enterprise AI programme. This excludes cloud, licences and post-launch operations. Real ranges vary by use case scope. Alice Labs Implementation Index 2026 Median enterprise AI engagement costs by stage (Alice Labs Implementation Index 2026, n=100+, EUR, vendor-neutral, excluding cloud and licence costs). Stage Median cost (EUR) Typical range (P25–P75) Scope AI strategy engagement €45,000 €30K – €70K Assessment, use-case selection, business case Pilot deployment €120,000 €80K – €180K Build, validate, ship to controlled production Production scaling €380,000 €220K – €620K Full rollout, integration, monitoring, change mgmt Source: Alice Labs Implementation Index 2026 06 / 10 Chapter ## Build vs Buy Distribution In short Of 100+ Alice Labs enterprise engagements, 18% build custom models, 24% fine-tune open-source models, and 58% use vendor APIs. The vendor-API share has grown materially between the 2025 and 2026 Index editions as foundation-model quality has converged. From 100+ Alice Labs enterprise engagements 2024–2026, the Index measures the build-vs-buy split as: 18% custom-model builds, 24% fine-tunes of open-source models, 58% vendor APIs. The vendor-API majority is concentrated in customer-service, document-processing and knowledge-work use cases where foundation-model quality is sufficient and time-to-value is the binding constraint. Custom builds and fine-tunes cluster in regulated verticals (financial services, public sector, healthcare) where data residency, control over fine-tune data, or specific domain language make a vendor API non-viable. Default to buy, escalate to build The 2026 data is consistent with our standing recommendation: default to a vendor API for the first production deployment. Escalate to fine-tune or custom-build only when a documented constraint (regulatory, IP, cost-at-scale) makes the default unviable. Build-vs-buy distribution across Nordic enterprise AI engagements (Alice Labs Implementation Index 2026, n=100+). Approach % of engagements Typical drivers Vendor API (e.g. OpenAI, Anthropic, Azure OpenAI) 58% Time-to-value, foundation-model quality Fine-tune open-source model 24% Domain language, data control, cost at scale Custom model build 18% Regulated data residency, IP capture, narrow specialised tasks Source: Alice Labs Implementation Index 2026 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## From pilot to production in 14 weeks - not 9 months Alice Labs delivers enterprise AI implementations against the operating model the Index identifies as decisive. One named owner, one defined KPI, vendor-API-first, production-ready in a quarter. [Talk to AI implementation team](#contact) 07 / 10 Chapter ## Time to First Measurable ROI In short Across implementations that reached production, the Alice Labs Implementation Index 2026 measures median time from production go-live to first measurable ROI at 6 months. The 75th percentile is 11 months. ROI is measured against a pre-defined business KPI specified at strategy stage. From the production-deployed cohort (n≈40 of 50+) in the 2026 Index, median time from production to first measurable ROI is 6 months. P75 is 11 months. P90 exceeds 18 months. "First measurable ROI" is defined narrowly. The implementation must move a pre-defined KPI (cost-to-serve, cycle time, revenue per agent, error rate, etc.) by a measurable margin attributable to the AI deployment, with a controlled comparison. Implementations without a pre-defined KPI are excluded from the time-to-ROI cohort entirely. Approximately 30% of production deployments fall into this excluded cohort — a structural risk we flag at strategy stage. 6 months Median time-to-first-ROI Alice Labs Index 2026 (n≈40) 11 months P75 time-to-first-ROI Alice Labs Index 2026 30% Production deployments lacking a pre-defined KPI Alice Labs Index 2026 08 / 10 Chapter ## Industry & Use Case Distribution In short The Alice Labs Implementation Index 2026 cohort skews to financial services (26%), public sector (22%), and manufacturing (18%). The top three use cases by volume are customer service automation (32%), document processing (24%), and spend / financial analytics (18%) — together accounting for nearly three quarters of all engagements. The 2026 cohort distribution by industry is led by financial services (26%), public sector (22%) and manufacturing (18%). Media & content (14%) and healthcare (10%) sit mid-cohort. Retail / CPG (6%) and other sectors (4%) form the long tail. By use case, customer-service automation is the single largest cluster at 32% of all engagements. Document processing (intake, classification, extraction) is 24%. Spend and financial analytics (anomaly detection, forecasting, expense classification) is 18%. The remaining 26% spans a long tail of vertical-specific applications. Three use cases, three quarters of volume Customer service automation, document processing and spend/financial analytics together account for ~74% of Index engagements. Buyers benchmarking their AI roadmap against Nordic peers should expect their first production deployment to land in one of these three buckets. Alice Labs Implementation Index 2026 Industry distribution across the Alice Labs Implementation Index 2026 cohort (n=100+ Nordic enterprise engagements 2024–2026). Industry Share of cohort Notes Financial services 26% Banking, insurance, asset management Public sector 22% Municipal, regional, central government Manufacturing 18% Industrials, process manufacturing, automotive supply Media & content 14% Publishing, broadcast, content production Healthcare 10% Providers, payers, life sciences services Retail / CPG 6% Brands, retail operations, e-commerce Other 4% Long tail of vertical-specific engagements Source: Alice Labs Implementation Index 2026 ### 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) 09 / 10 Chapter ## Framework Choice in Agent Projects In short Within the agent-project subset of the Index, framework adoption is concentrated: LangGraph 67%, CrewAI 22%, Semantic Kernel 8%, AutoGen 3%. LangGraph dominance has widened materially between the 2025 and 2026 editions as production-readiness expectations have risen. Within the agent-project subset of the 2026 Index, framework adoption is highly concentrated. LangGraph is used in 67% of agent projects. CrewAI in 22%. Microsoft Semantic Kernel in 8%. AutoGen in 3%. LangGraph dominance has widened between the 2025 and 2026 editions. Three drivers recur in client-side decision logs: explicit graph control, production-grade observability, and team familiarity with the LangChain ecosystem. Semantic Kernel adoption is concentrated in Microsoft-shop public-sector and financial services clients. CrewAI shows up most in mid-market manufacturing and media use cases. Framework adoption among agent-based AI implementations (Alice Labs Implementation Index 2026, agent-project subset). Framework Share of agent projects Typical buyer profile LangGraph 67% Production-grade enterprise, graph control needed CrewAI 22% Mid-market, multi-agent collaboration use cases Semantic Kernel 8% Microsoft-aligned public sector & financial services AutoGen 3% Research-led teams, experimental deployments Source: Alice Labs Implementation Index 2026 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Benchmark your AI programme against the Alice Labs Index We will review your pilot-to-production time, sponsorship structure, build-vs-buy split, cost trajectory and EU AI Act readiness against our 100+ Nordic enterprise dataset - and tell you exactly where you sit. [Book a 30-minute benchmark call](#contact) 10 / 10 Chapter ## EU AI Act Readiness Gap In short 47% of analyzed enterprises have not yet completed the AI system inventory required for EU AI Act compliance, with the General-Purpose AI obligation deadline on 2 August 2026. The readiness gap is concentrated in mid-cohort regulated verticals (financial services, healthcare, public sector) — exactly where the Act bites hardest. From the 2026 Index, 47% of analyzed enterprises have not yet completed the AI system inventory that anchors EU AI Act compliance. The next major obligation deadline (for general-purpose AI providers and deployers) falls on 2 August 2026. The inventory gap is concentrated in mid-cohort regulated verticals. Financial services, healthcare and public-sector clients are the most likely to face high-risk AI system classifications under the Act, and the most likely to lag on inventory. Among enterprises that have completed an inventory, the median programme identified 7–12 in-scope AI systems — most of which were embedded inside SaaS products rather than custom builds. Procurement-driven SaaS sprawl is the single largest source of unaccounted in-scope systems. For a step-by-step approach to closing the inventory gap before 2 August 2026, see our companion piece [EU AI Act Compliance Checklist 2026](/en/insights/eu-ai-act-compliance-checklist-2026). The 2 August 2026 deadline is binding 47% of enterprises in the Index have not completed an AI system inventory. With the GPAI obligations live from 2 August 2026, an incomplete inventory is no longer a backlog item — it is a regulatory exposure. Boards should treat this as a Q2/Q3 2026 priority. Alice Labs Implementation Index 2026 ## About the Authors & Reviewers Published April 28, 2026 · Updated September 16, 2026 Written by ![Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs](/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/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Reviewed by September 16, 2026 ![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) Published April 28, 2026 · Updated September 16, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is the Alice Labs Enterprise AI Implementation Index? ▾ The Alice Labs Enterprise AI Implementation Index is a proprietary research index that measures pilot-to-production performance across 100+ Nordic enterprise AI implementations. The 2026 edition covers Sweden, Denmark, Norway and Finland between 2024 and 2026. It quantifies median time to production, success-by-stage rates, root causes of failure, cost benchmarks, build-vs-buy distribution, framework adoption, and EU AI Act readiness. ### How is the Index data collected? ▾ Data is collected from three layers per implementation: project records (timelines, scopes, costs, model and framework choices), structured post-mortems conducted at go-live or cancellation, and executive interviews with the client business owner six months after go-live. The Index does not rely on self-reported survey responses. ### What is the median time from AI pilot to production in 2026? ▾ From 100+ implementations 2024–2026, the Alice Labs Implementation Index measures median pilot-to-production time at 14 weeks, with an interquartile range of 9 to 22 weeks. The fastest decile reaches production in under 6 weeks; the slowest decile exceeds 9 months. Time correlates more with sponsorship structure than with model complexity. ### What share of Alice Labs' AI projects reach production? ▾ In the 2026 Index, 82% of Alice Labs' implementations reached production at first attempt, 14% required minor scope adjustments before reaching production, and 4% were strategically paused. The combined production rate is 96% — roughly 4× the BCG/MIT-reported industry value-realisation rate of ~26%. The gap is explained by Alice Labs' upfront discovery process, which screens out the failure patterns measured elsewhere in this Index before the pilot phase begins. ### Why do enterprise AI projects fail in the broader market? ▾ Across stalled AI projects observed in the broader Nordic enterprise market, 62% share the same root cause: missing executive business owner with budget authority and a defined success metric. The remaining stalled projects are explained by data-foundation gaps (18%), unclear success metrics (12%), regulatory or procurement bottlenecks (6%), and technology infeasibility (under 5%). Alice Labs treats these as discovery-phase screening criteria, which is why the Alice Labs production rate sits at 96%. ### What does an enterprise AI engagement cost? ▾ The 2026 Index measures three median engagement costs across 100+ Nordic programmes: AI strategy engagement €45K (P25–P75: €30K–€70K), pilot deployment €120K (€80K–€180K), and production scaling €380K (€220K–€620K). These are vendor-neutral medians and exclude cloud, model API spend and third-party licences. ### What is the build vs buy split for enterprise AI? ▾ Of 100+ Alice Labs enterprise engagements, 58% use vendor APIs, 24% fine-tune open-source models, and 18% build custom models. Vendor-API dominance is concentrated in customer-service, document-processing and knowledge-work use cases. Custom builds cluster in regulated verticals where data residency or IP control rule out a vendor API. ### How ready are Nordic enterprises for the EU AI Act? ▾ 47% of enterprises in the 2026 Index have not yet completed the AI system inventory required for EU AI Act compliance, with general-purpose AI obligations live from 2 August 2026. Mid-cohort regulated verticals — financial services, healthcare and public sector — are the most exposed, and SaaS-embedded AI is the largest source of unaccounted in-scope systems. [Previous in AI Statistics & Data ### AI Adoption by Country 2026: Complete Rankings & Data ](/en/insights/ai-adoption-by-country-2026)[Next in AI Statistics & Data ### AI Adoption Statistics 2026: Enterprise & Global Data ](/en/insights/ai-adoption-statistics-2026) ## Further reading - [McKinsey — The state of AI 2024/2025](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)· mckinsey.com - [BCG / MIT Sloan Management Review — Generative AI value realization research](https://sloanreview.mit.edu/)· sloanreview.mit.edu - [RAND Corporation — Why AI projects fail](https://www.rand.org/pubs/research_reports/RRA2680-1.html)· rand.org - [Stanford HAI — AI Index Report](https://hai.stanford.edu/ai-index)· hai.stanford.edu - [European Commission — EU AI Act](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai)· ec.europa.eu ## Related services [AI Strategy Consulting Engage Alice Labs to build the enterprise AI strategy your benchmarks demand. ](/en/ai-strategy)[AI Implementation Services Engage Alice Labs to deliver pilot-to-production AI implementation in weeks, not quarters. ](/en/ai-implementation) ## Related reading [data ### AI Adoption Statistics 2026 Definitive 2026 enterprise AI adoption data set, anchored on McKinsey, Eurostat and Gartner. 12 min](/en/insights/ai-adoption-statistics-2026) [data ### AI Adoption by Country 2026 Country-by-country adoption rankings — Denmark leads at 42.0% (Eurostat 2025 reference year). 10 min](/en/insights/ai-adoption-by-country-2026) [pillar ### How to Build an Enterprise AI Strategy Turn benchmarks into an executable 12-month enterprise AI roadmap. 18 min](/en/insights/enterprise-ai-strategy-framework) [deep dive ### Why AI Projects Fail The operating-model reasons most enterprise AI initiatives stall. 9 min](/en/insights/why-ai-projects-fail) [comparison ### Build vs Buy AI When to build custom AI vs. fine-tune vs. buy via vendor APIs. 8 min](/en/insights/build-vs-buy-ai) [howto ### EU AI Act Compliance Checklist 2026 Close the AI system inventory gap before the 2 August 2026 GPAI deadline. 11 min ](/en/insights/eu-ai-act-compliance-checklist-2026) ## Sources 1. Alice Labs (2026). Enterprise AI Implementation Index 2026. Stockholm: Alice Labs. (accessed 2026-04-28) 2. Alice Labs (2026). Methodology Note: Stalled-Project Root Cause Taxonomy v2. (accessed 2026-04-28) 3. [McKinsey & Company — The state of AI (2024/2025 global survey)](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)(accessed 2026-04-28) 4. [BCG / MIT Sloan Management Review — Generative AI value realization research](https://sloanreview.mit.edu/)(accessed 2026-04-28) 5. [RAND Corporation — Root causes of AI project failure (RRA2680-1, 2024)](https://www.rand.org/pubs/research_reports/RRA2680-1.html)(accessed 2026-04-28) 6. [Stanford HAI — AI Index Report (2025 edition)](https://hai.stanford.edu/ai-index)(accessed 2026-04-28) 7. [European Commission — Regulatory framework on AI (EU AI Act)](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai)(accessed 2026-04-28) 8. [Eurostat — AI use in enterprises 2024 (ICT Enterprise Survey, ISOC\_EB\_AI)](https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_ai/)(accessed 2026-04-28) Next scheduled review: 2026-12-15 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Benchmark your AI programme against the Alice Labs Index We will review your pilot-to-production time, sponsorship structure, build-vs-buy split, cost trajectory and EU AI Act readiness against our 100+ Nordic enterprise dataset - and tell you exactly where you sit. [Book a 30-minute benchmark call](#contact) Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Falice-labs-implementation-index-2026)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Falice-labs-implementation-index-2026&text=Alice%20Labs%20Enterprise%20AI%20Implementation%20Index%202026) ## Get in Touch! 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