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Why AI Pilots Often Stall

AI creates value when organisations redesign ownership, validation, data practices, and workflows around the technology.

On this page

  • From experiments to scaled impact
  • Human validation and clear ownership
  • Data practices and operating models
Preview for Why AI Pilots Often Stall

Introduction

Business AI adoption beyond pilot projects is not mainly a question of buying a better model. It is the organisational work of turning AI from a promising experiment into a governed, measured and repeatable way of working. Pilots often stall because they are tested in clean, narrow conditions: a small team, a controlled dataset, a friendly workflow and limited consequences if the output is wrong. Scaling is different. It requires clear ownership, human validation rules, reliable data pipelines, integration into daily systems, risk controls and a reason for employees to change how work actually gets done. McKinsey’s 2025 global AI survey describes the move from pilots to scaled impact as still “a work in progress” for most organisations, even as AI use has become widespread. It also finds that higher performers are more likely to redesign workflows, assign senior ownership and define when model outputs need human validation. [McKinsey & Company]mckinsey.comthe state of aithe state of ai

Overview image for Business Adoption

From Experiments to Scaled Impact

A pilot proves that AI can do something useful in a limited setting. Scaled adoption proves that the organisation can keep getting value from it when the work becomes messy, repeated and accountable. That difference is why many companies can point to impressive demos while still struggling to show profit-and-loss impact. The 2025 MIT NANDA report, widely discussed for its finding that 95% of enterprise generative AI pilots were not delivering measurable return, framed the issue less as model failure and more as an enterprise integration problem: tools did not learn from workflows, adapt to context or fit how people actually made decisions. [MLQ AI]mlq.aiv0.1 State of AI in Business 2025 Reportv0.1 State of AI in Business 2025 Report

The most useful question for leaders is therefore not “Which AI tool should we try?” but “Which business process are we willing to redesign?” A customer-service chatbot that only answers frequently asked questions may reduce a few tickets. A redesigned service workflow might use AI to classify the request, retrieve the right policy, draft a response, flag uncertainty, route exceptions to a specialist and update the knowledge base after resolution. The value comes from the whole operating loop, not the model alone. BCG’s 2025 work on the AI impact gap similarly argues that changing how people work and focusing on the right value pools is the bridge between ordinary adoption and business advantage. [BCG Global]bcg.comclosing the ai impact gapclosing the ai impact gap

Scaled AI also needs a portfolio discipline that many pilots avoid. Organisations need to separate low-risk productivity aids from high-impact decision systems, and then fund them differently. A writing assistant for internal summaries may need light-touch guidance and monitoring. An AI system used in credit, hiring, clinical triage, pricing or legal review needs stronger validation, audit trails, escalation rules and risk acceptance. NIST’s AI Risk Management Framework is useful here because it treats AI governance as a lifecycle process: organisations should govern, map, measure and manage AI risks continuously rather than treat risk review as a one-off sign-off before launch. [NIST AI Resource Center]airc.nist.govAI Resource Center AI RMF CoreAI Resource Center AI RMF Core

The “pilot purgatory” pattern is also a measurement problem. Many early projects are evaluated by output quality in isolation: whether the answer looks good, whether the summary is readable, whether the code compiles. In production, the metric must move closer to the business process: shorter handling time, fewer rework cycles, higher conversion, reduced error rates, faster onboarding, better compliance evidence or improved employee capacity. A large field experiment in online retail found that generative AI enhancements increased sales in some consumer-facing workflows, but effects varied by use case, which is a reminder that AI value is not automatic even when the same broad technology is used. [arXiv]arxiv.orgarXiv Generative AI and Firm Productivity: Field Experiments in Online RetailarXiv Generative AI and Firm Productivity: Field Experiments in Online Retail

Business Adoption illustration 1

Human Validation and Clear Ownership

The phrase “human in the loop” is often too vague to be useful. In a real operating model, the organisation must define who checks what, when, against which standard and with what authority to override the AI. A lawyer reviewing an AI-drafted clause, a nurse checking an AI-generated patient note and a procurement manager approving an AI-ranked supplier list are not performing the same kind of oversight. Each needs different evidence, training and escalation rights. McKinsey’s 2025 survey highlights defined processes for deciding when model outputs need human validation as one of the practices that distinguishes AI high performers. [McKinsey & Company]mckinsey.comthe state of aithe state of ai

Clear ownership matters because AI systems cut across normal organisational boundaries. A model may be bought by IT, configured by a data team, used by operations, monitored by risk, and judged by customers or regulators. If nobody owns the business outcome, the system becomes a shared experiment with no accountable operator. Morgan Stanley’s public description of its firmwide AI team illustrates the opposite pattern: AI is treated as an organisation-wide capability with unified stewardship, human-centred design, data protection and controls rather than as scattered tool adoption by isolated teams. [Morgan Stanley]morganstanley.comOpen source on morganstanley.com.

Ownership also has to include negative outcomes. If an AI tool drafts a poor client email, leaks sensitive information into the wrong workflow, recommends a biased decision or silently degrades after a data change, the organisation needs a known incident path. NIST’s guidance stresses that governance should connect AI policies to existing organisational governance and risk controls, align with data governance, define human roles and responsibilities, and document risk mapping and measurement processes. Those requirements sound procedural, but they are what turn AI from a clever assistant into an accountable business system. [NIST AI Resource Center]airc.nist.govAI Resource Center GovernAI Resource Center Govern

A practical ownership model usually includes three layers. The business owner is responsible for the value case and the process redesign. The technical owner is responsible for integration, monitoring and performance. The risk or governance owner is responsible for acceptable use, controls, documentation and escalation. Without all three, AI tends to fail in predictable ways: technically impressive tools with no adoption, popular tools with no risk controls, or compliant tools that never change the economics of the work.

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Data Practices and Operating Models

AI pilots often succeed on hand-picked documents, cleaned spreadsheets or a narrow set of historical examples. Production systems have to deal with incomplete records, inconsistent labels, changing policies, access restrictions, duplicated customer identities and data that lives across old enterprise systems. The OECD’s 2025 report on AI adoption in firms makes this point directly: before adopting AI, firms need digital technologies that systematically gather data from business processes and interactions, because AI applications depend on an accurate digital representation of the business. [OECD]oecd.orgThe Adoption of Artificial Intelligence in FirmsThe Adoption of Artificial Intelligence in Firms

This is why data governance becomes a business adoption issue, not just a technical hygiene task. AI systems need to know which information is current, which source is authoritative, which users are allowed to see which records, and how sensitive data should be handled. Retrieval-augmented generation, where an AI tool draws on approved internal documents instead of relying only on its general training, can reduce some errors, but it does not remove the need for good document ownership, permissions and version control. If the underlying policy library is stale, fragmented or poorly tagged, AI can make the wrong answer easier to produce at scale.

Operating models must also decide whether AI capability is centralised, embedded or federated. A central team can set standards, manage common platforms and reduce duplicated risk. Embedded teams understand local workflows and can redesign work where value is created. A federated model tries to combine both: central governance and shared infrastructure, with business units responsible for practical deployment. McKinsey’s “six dimensions” for capturing AI value — strategy, talent, operating model, technology, data, and adoption and scaling — reflect this broader point: scaled impact comes from coordinated organisational capability, not a single technical asset. [McKinsey & Company]mckinsey.comthe state of aithe state of ai

The operating model also has to manage shadow AI. When employees lack useful approved tools, they may turn to consumer services or informal workarounds. BCG’s 2025 survey of more than 10,600 workers across 11 countries found that AI use had gone mainstream, but also warned that employees without the right tools may find alternatives themselves, creating frustration, security risks and fragmented efforts. That is a strong argument for governed access to usable tools rather than a purely restrictive policy that pushes experimentation underground. [BCG Global]bcg.comai at work momentum builds but gaps remainai at work momentum builds but gaps remain

Why Workflow Redesign Beats Tool Roll-Out

A tool roll-out asks employees to add AI to the work they already do. Workflow redesign asks what work should change now that AI can draft, classify, retrieve, compare, summarise or simulate parts of the process. The second approach is harder, but it is where durable value usually appears. It may remove unnecessary handoffs, change approval thresholds, create new quality checks, or shift human effort from first-draft production to exception handling and judgement.

Consider a sales team. Giving every salesperson a generic AI writing assistant may save time on emails. Redesigning the workflow could connect AI to customer history, product rules, approved language, pricing boundaries and next-best-action guidance, while requiring human approval for commitments or discounts. The result is not “AI replaces salespeople”; it is a different division of labour between automated preparation and human relationship management. The same pattern applies in finance, HR, operations, legal and software development.

This is also where employee trust becomes decisive. People are more likely to adopt AI when it improves a real pain point and when they know what they remain responsible for. They are less likely to trust it when it adds another screen, produces hard-to-check output or threatens to make them accountable for errors they cannot detect. The OECD’s 2026 work on AI and skills argues that most workers will not need advanced AI programming skills; instead, AI raises the importance of digital skills, data interpretation, problem-solving, creativity, innovation and managerial capability. [OECD]oecd.orgfull reportfull report

The strongest adopters therefore invest in workflow literacy, not just prompt training. Prompting matters, but a useful prompt in a broken process is still a patch. Teams need to understand where AI is allowed to help, where it must not decide, which outputs require evidence, and how to report failure. In regulated or high-stakes work, the organisation also needs to preserve records of what was generated, what was changed and who approved the final action.

Business Adoption illustration 2

The Governance Shift From Permission to Assurance

Early AI governance often focused on permission: who may use which tool, for what task, with what data. Scaled adoption requires assurance: how the organisation knows the system is still performing acceptably after deployment. That means monitoring for drift, reviewing errors, updating data sources, testing for bias or security vulnerabilities, and retiring systems that no longer meet the business or risk case. NIST’s AI RMF describes risk management as continuous and lifecycle-based, with governance infused through mapping, measuring and managing risks. [NIST AI Resource Center]airc.nist.govAI Resource Center AI RMF CoreAI Resource Center AI RMF Core

Generative AI makes this harder because outputs can be fluent without being correct. A model can summarise a document elegantly while missing an exception, draft a confident answer from outdated policy, or produce code that works in a simple case but fails under edge conditions. A 2025 systematic literature review on generative AI in enterprise architecture found benefits in ideation, documentation and decision support, but also highlighted risks including opacity, bias, contextually incorrect outputs, privacy concerns, compliance issues and the need for professional oversight. [arXiv]arxiv.orgOpen source on arxiv.org.

Assurance also means treating third-party AI systems as part of the enterprise risk surface. Many organisations will not build foundation models, but they will buy AI-enabled software, connect it to internal data and let it affect customer or employee experiences. NIST’s governance guidance explicitly includes currently deployed and third-party AI systems within formal AI risk management policies. This matters because outsourcing the tool does not outsource accountability for how it is used in a business process. [NIST AI Resource Center]airc.nist.govAI Resource Center GovernAI Resource Center Govern

A useful maturity test is simple: can the organisation explain why a deployed AI system exists, what data it uses, what decision or workflow it affects, who owns it, how it is monitored, when a human must intervene, what failure looks like and when it should be changed or switched off? If the answer is no, the organisation may have adoption activity, but it does not yet have scaled adoption.

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What Scaled Adoption Looks Like in Practice

Scaled business AI adoption usually has several visible features. First, the use cases are tied to business outcomes, not novelty. Second, the AI is embedded in systems where people already work. Third, validation is explicit rather than improvised. Fourth, data ownership is clear. Fifth, governance is strong enough to enable use rather than merely block it. These features explain why the same technology can produce very different results in two companies.

Morgan Stanley offers a useful public example because its AI programme has been presented as a firmwide operating shift rather than a one-off chatbot launch. The company has announced AI tools for research access and knowledge retrieval, including AskResearchGPT for investment banking, sales and trading, and research staff, while also describing a firmwide AI function focused on stewardship, controls and data protection. [Morgan Stanley]morganstanley.commorgan stanley research announces askresearchgptmorgan stanley research announces askresearchgpt

The lesson is not that every company should copy a bank’s tooling. It is that scaled adoption depends on matching AI to an information-rich workflow, wrapping it in controls, and making it part of how professionals work. In wealth management or investment research, the valuable task is not simply generating text. It is helping staff find, distil and apply approved knowledge faster while preserving human judgement and compliance expectations.

There is also a caution in the evidence. Stanford’s 2025 AI Index reported that organisational AI use had accelerated sharply, with 78% of organisations reporting AI use in 2024, up from 55% the previous year. But broad use does not mean broad transformation. The gap between “we use AI somewhere” and “AI has changed our operating model” is precisely the gap that business leaders need to manage. [Hai Production]hai-production.s3.amazonaws.comhai ai index report 2025hai ai index report 2025

Business Adoption illustration 3

Decisions That Move AI Beyond the Pilot

Moving beyond pilots is a management discipline. The important decisions are concrete:

Choose fewer, better use cases. Prioritise workflows where the value is measurable, the data is available, the risk is understood and the process owner is willing to redesign work. Avoid spreading effort across dozens of weak experiments.

Define validation before launch. Decide which outputs can be accepted automatically, which require sampling, which require expert review and which should never be delegated to AI. Validation should reflect the risk of the decision, not the excitement around the tool.

Make data readiness part of the business case. If a project depends on clean policies, customer records, product data or case histories, the cost of improving that data is part of the AI investment, not an unfortunate side task.

Assign accountable owners. Every production AI system should have a business owner, a technical owner and a risk owner. Shared enthusiasm is not a substitute for operational responsibility.

Measure process outcomes, not demo quality. Track cycle time, error rates, rework, customer experience, cost-to-serve, conversion, compliance evidence or employee capacity. A model that produces impressive text but does not improve the workflow is still a stalled adoption.

Plan for change management. Training should cover not only how to use the tool, but how work changes, what remains human-owned, what evidence must be checked and how concerns are raised. Recent legal-industry analysis from Reuters makes the same broader point: generative AI adoption can create fragmented transformation and change fatigue unless organisations manage implementation through structured change, governance, training and stakeholder engagement. [Reuters]reuters.comThe article underscores that generative AI introduces complex legal, operational, and ethical challenges requiring holistic, interdiscipl…

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The Real Meaning of Business AI Adoption

Business AI adoption beyond pilot projects means treating AI as a change in organisational capability. Models matter, but they are only one layer. The durable value comes from redesigned workflows, accountable ownership, human validation, reliable data practices, employee trust and continuous assurance. That is why two companies can buy similar tools and get different outcomes: one adds AI to existing friction, while the other changes how work is owned, checked and improved.

The practical takeaway is clear. AI creates value when it becomes part of a managed operating system, not when it remains a laboratory experiment or a scattered collection of individual productivity hacks. The companies that progress beyond pilots are not necessarily the ones with the most ambitious announcements. They are the ones that can answer, in operational detail, what the AI is for, who is responsible for it, how it is validated, what data it depends on, how people use it and how the organisation knows whether it is still worth running.

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Endnotes

  1. Source: mckinsey.com
    Title: the state of ai
    Link: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

  2. Source: mlq.ai
    Title: v0.1 State of AI in Business 2025 Report
    Link: https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

  3. Source: bcg.com
    Title: closing the ai impact gap
    Link: https://www.bcg.com/publications/2025/closing-the-ai-impact-gap

  4. Source: bcg.com
    Title: ai at work momentum builds but gaps remain
    Link: https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain

  5. Source: airc.nist.gov
    Title: AI Resource Center AI RMF Core
    Link: https://airc.nist.gov/airmf-resources/airmf/5-sec-core/

  6. Source: nist.gov
    Link: https://www.nist.gov/itl/ai-risk-management-framework

  7. Source: arxiv.org
    Title: arXiv Generative AI and Firm Productivity: Field Experiments in Online Retail
    Link: https://arxiv.org/abs/2510.12049

  8. Source: airc.nist.gov
    Title: AI Resource Center Govern
    Link: https://airc.nist.gov/airmf-resources/playbook/govern/

  9. Source: oecd.org
    Title: The Adoption of Artificial Intelligence in Firms
    Link: https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/05/the-adoption-of-artificial-intelligence-in-firms_8fab986b/f9ef33c3-en.pdf

  10. Source: oecd.org
    Title: the adoption of artificial intelligence in firms f9ef33c3 en
    Link: https://www.oecd.org/en/publications/the-adoption-of-artificial-intelligence-in-firms_f9ef33c3-en.html

  11. Source: bcg.com
    Title: 26june2025 beyond ai adoption full potential
    Link: https://www.bcg.com/press/26june2025-beyond-ai-adoption-full-potential

  12. Source: oecd.org
    Title: full report
    Link: https://www.oecd.org/en/publications/ai-and-skills_f843b352-en/full-report.html

  13. Source: arxiv.org
    Link: https://arxiv.org/abs/2510.22003

  14. Source: hai.stanford.edu
    Title: 2025 ai index report
    Link: https://hai.stanford.edu/ai-index/2025-ai-index-report

  15. Source: reuters.com
    Link: https://www.reuters.com/legal/legalindustry/strategic-change-management-age-generative-ai–pracin-2026-06-15/
    Source snippet

    The article underscores that generative AI introduces complex legal, operational, and ethical challenges requiring holistic, interdiscipl...

  16. Source: hai.stanford.edu
    Title: ai index
    Link: https://hai.stanford.edu/ai-index

  17. Source: nist.gov
    Link: https://www.nist.gov/

  18. Source: nvlpubs.nist.gov
    Title: ai.100 1
    Link: https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf

  19. Source: nvlpubs.nist.gov
    Title: AI.600 1
    Link: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf

  20. Source: nist.gov
    Title: ai risk management framework 2nd draft
    Link: https://www.nist.gov/document/ai-risk-management-framework-2nd-draft

  21. Source: nist.gov
    Title: ai risk management framework initial draft
    Link: https://www.nist.gov/document/ai-risk-management-framework-initial-draft

  22. Source: nist.gov
    Title: A I RMF RFI Comments
    Link: https://www.nist.gov/system/files/documents/2021/09/15/ai-rmf-rfi-0070.pdf

  23. Source: airc.nist.gov
    Link: https://airc.nist.gov/airmf-resources/airmf/0-ai-rmf-1-0/

  24. Source: nist.gov
    Link: https://www.nist.gov/speech-testimony/balancing-knowledge-and-governance-foundations-effective-risk-management-artificial

  25. Source: nist.gov
    Title: A I RMF 2nd Draft Comments
    Link: https://www.nist.gov/document/ai-rmf-2nd-draft-comments-raymond-sheh-karen-geappen-james-dietman

  26. Source: nist.gov
    Title: 1st draft ai rmf comments ferdi van engelen
    Link: https://www.nist.gov/document/1st-draft-ai-rmf-comments-ferdi-van-engelen

  27. Source: nist.gov
    Link: https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence

  28. Source: airc.nist.gov
    Link: https://airc.nist.gov/airmf-resources/playbook/manage/

  29. Source: mit.edu
    Link: https://www.mit.edu/

  30. Source: bcg.com
    Title: ai adoption puzzle why usage up impact not
    Link: https://www.bcg.com/publications/2025/ai-adoption-puzzle-why-usage-up-impact-not

  31. Source: bcg.com
    Title: ai will reshape more jobs than it replaces
    Link: https://www.bcg.com/publications/2026/ai-will-reshape-more-jobs-than-it-replaces

  32. Source: bcg.com
    Title: are you generating value from ai the widening gap
    Link: https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap

  33. Source: bcg.com
    Title: Artificial Intelligence
    Link: https://www.bcg.com/capabilities/artificial-intelligence

  34. Source: bcg.com
    Title: ai transformation is a workforce transformation
    Link: https://www.bcg.com/publications/2026/ai-transformation-is-a-workforce-transformation

  35. Source: bcg.com
    Title: how companies can prepare for ai first future
    Link: https://www.bcg.com/publications/2025/how-companies-can-prepare-for-ai-first-future

  36. Source: bcg.com
    Title: why ai change is actually a people change
    Link: https://www.bcg.com/publications/2026/why-ai-change-is-actually-a-people-change

  37. Source: bcg.com
    Link: https://www.bcg.com/capabilities/artificial-intelligence/generative-ai

  38. Source: mckinsey.com
    Title: the state of ai how organizations are rewiring to capture value
    Link: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-how-organizations-are-rewiring-to-capture-value

  39. Source: mckinsey.com
    Title: the state of ai how organizations are rewiring to capture value final
    Link: https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf

  40. Source: oecd.org
    Title: 795de142 en
    Link: https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/governing-with-artificial-intelligence_398fa287/795de142-en.pdf

  41. Source: oecd.org
    Title: 7376c776 en
    Link: https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/emerging-divides-in-the-transition-to-artificial-intelligence_eeb5e120/7376c776-en.pdf

  42. Source: oecd.org
    Title: 426399c1 en
    Link: https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/12/ai-adoption-by-small-and-medium-sized-enterprises_9c48eae6/426399c1-en.pdf

  43. Source: oecd.ai
    Title: ai and data governance workshops
    Link: https://oecd.ai/en/dashboards/policy-initiatives/ai-and-data-governance-workshops

  44. Source: scale.com
    Title: hitl ep13 ai evals in practice
    Link: https://scale.com/blog/hitl-ep13-ai-evals-in-practice

  45. Source: youtube.com
    Title: The AI Opportunity in the Enterprise: What Industry Leaders See That Others Miss
    Link: https://www.youtube.com/watch?v=Tn-HH0sY8d8
    Source snippet

    MIT NANDA - The GenAI Divide: State of AI in Business 2025...

  46. Source: youtube.com
    Title: MIT NANDA
    Link: https://www.youtube.com/watch?v=FY6M9LBZBF4
    Source snippet

    From AI Pilot to Enterprise ROI: How Adobe's Engineers Drive Adoption at Scale...

  47. Source: morganstanley.com
    Link: https://www.morganstanley.com/about-us/technology/artificial-intelligence-firmwide-team

  48. Source: morganstanley.com
    Title: morgan stanley research announces askresearchgpt
    Link: https://www.morganstanley.com/press-releases/morgan-stanley-research-announces-askresearchgpt

  49. Source: hai-production.s3.amazonaws.com
    Title: hai ai index report 2025
    Link: https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf

  50. Source: imd.org
    Title: Morgan Stanley
    Link: https://www.imd.org/entity-profile/morgan-stanley-ai-maturity-2025/

  51. Source: celent.com
    Link: https://www.celent.com/en/insights/531525129

Additional References

  1. Source: businessatoecd.org
    Link: https://www.businessatoecd.org/hubfs/Business%20Perspectives%20on%20Advancing%20AI.pdf

  2. Source: youtube.com
    Title: From AI Pilot to Enterprise ROI: How Adobe’s Engineers Drive Adoption at Scale
    Link: https://www.youtube.com/watch?v=TYZXu3–sZ4
    Source snippet

    How to Do Change Activation for AI Adoption...

  3. Source: youtube.com
    Link: https://www.youtube.com/watch?v=yIJJ6kvP3aY
    Source snippet

    The AI Opportunity in the Enterprise: What Industry Leaders See That Others Miss...

  4. Source: linkedin.com
    Link: https://www.linkedin.com/posts/abhishekratna_mckinseys-state-of-ai-in-2025-report-activity-7396010152161808384-7nK8

  5. Source: medium.com
    Link: https://medium.com/%40natishalom/95-of-ai-projects-fail-heres-why-that-s-a-good-thing-8a5936ebdea8

  6. Source: reddit.com
    Link: https://www.reddit.com/r/agi/comments/1mvg6pp/mit_report_95_of_generative_ai_pilots_at/

  7. Source: linkedin.com
    Link: https://www.linkedin.com/posts/raghavananand_mit-report-95-of-generative-ai-pilots-at-activity-7363268574611722240-FlkB

  8. Source: facebook.com
    Link: https://www.facebook.com/McKinsey/posts/ai-is-everywhere-but-most-companies-are-still-stuck-in-pilot-modethe-issue-isnt-/1479023967026955/

  9. Source: linkedin.com
    Link: https://www.linkedin.com/posts/christopher-martin-bcg_the-widening-ai-value-gap-activity-7379504659876724736-P42S

  10. Source: linkedin.com
    Link: https://www.linkedin.com/posts/benjamin-rehberg_the-widening-ai-value-gap-activity-7379155605183287296-imzC

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