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Value Creation with AI

Why 95% of AI pilots stall –and what the 5% do differently. The Enterprise AI Playbook: Lessons from 51 Successful Deployments – Stanford Digital Economy Lab (Elisa Pereira, Alvin Wang Graylin, Eric Brynjolfsson) April 2026

51 Deployments

41 Organisations, 7 Countries and >1M Employees

77% faced non-techical challenges

Sponsorship, data cleanup, change management

61%

Successful projects with at least one past failure.

A country’s brand is an economic asset — not a vanity metric.

The Enterprise Playbook: Lessons from 51 Successful Deployments

Stanford Digital Economy Lab


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Why It Matters

Most current AI conversations still focus on forecasts and sentiment surveys. This report is different: it studies only the deployments that actually worked, and asks what those organisations did differently. The answer is not complex, and for leaders that is encouraging.  The technology is not the challenge; disciplined execution is the key to success.

The gap between a convincing demo and a working business capability is dependent almost entirely on organisational work.  This means redesigning processes around the tool rather than bolting it onto the existing process.  It requires strong and visible senior sponsorship who make adoption an accountable objective.

Broad coverage

51

Deployments

9

Industries

14

Functions

Coverage across cases, industries and functions.

What the findings suggest:

You don’t need a hyperscaler budget or deep technical expertise to win in this space. You need the focus and discipline the big players often lack. Pick one workflow, redesign it end-to-end around AI, provide strong sponsorship with accountability, and design for humans in the loop, but not as a requirement for every sign-off. Budget for invisible but critical costs like data cleanup, change management and at least one failure along the way.

AI is extending its impacts across industries and functions. Real benefits are achievable provided there is strong, visible senior sponsorship, effective change management and a willingness to accept failures as part of the journey.

Three implications

1

Technology is not the hardest part. 77% of the hardest challenges were invisible and intangible costs: change management, data quality, and process redesign.

2

Timeline variance is organisational, not technical. Similar use cases took weeks at one company and years at another. The difference was executive sponsorship, existing organisational processes, and end-user willingness.

3

Escalation-based models were associated with better results. Escalation-based models (AI handles 80%+ autonomously, humans review exceptions) delivered 71% median productivity gains versus 30% for approval models.

Our Take

The report shines new light on the state of AI adoption across industries, functions and geographies.  The message is clear–start with a focus on business value, not the shiny new technology.  Invest in process redesign, sponsorship, change management and data integrity.  While everyone can access the same technology, the gap between leaders and laggards is growing.

Attribution

This is ReedKnapp’s independent commentary on a third-party study ––”The Enterprise Playbook: 51 Successful Deployments”, the work of Stanford Digital Economy Lab (Elisa Pereira, Alvin Wang Graylin and Eric Brynjolfsson)

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What to ask now

Which process offers the most value and least risk?

Do we have the internal  capability to deliver successfully?

What is the objective and timing to produce the desired outcome? 

What to ask now

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