The Enterprise AI Playbook
Lessons from 51 Successful Deployments
Elisa Pereira, Alvin Wang Graylin and Erik Brynjolfsson
Stanford Digital Economy Lab
Stanford University · April 2026
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Foreword
There is no shortage of predictions and sentiment surveys about artificial intelligence today.
Every week brings new forecasts and debates about whether AI is useful, which jobs will disappear,
which industries will transform, which companies will dominate. But when we speak with
executives actually deploying AI inside their organizations, we hear a different set of questions. Not
what might happen in five years, but what is happening right now. Practical realities, not abstract
frameworks.
This report was born from a simple conviction: the most valuable insights about AI adoption are not
in hypotheticals or predictions. They are in the patterns of those who have already walked the path.
We set out to build something empirical. To document real-world use cases that have actually
delivered business value. To map the practices of organizations that are not just experimenting with
AI but successfully deploying it at scale. We wanted depth. To understand the pitfalls that do not
make it into press releases, the nuances that separate a successful pilot from a failed one, and the
organizational realities that no vendor whitepaper will tell you.
Across 51 enterprise cases over 5 months, we found stories of transformation measured in weeks
and others measured in years. Same technology, same use cases, vastly different outcomes. The
difference was never the AI model. It was always the organization. Its readiness, its processes, its
leadership, its willingness to change and fail.
Our ambition with this research is simple: to offer a practical window into what is actually
happening inside companies as they create value with AI, including detailed company case studies.
The future of work only makes sense when one first understands the present of work.
In the conclusion, we offer some forward-looking insights based on upcoming trends in the AI
space. We hope these findings serve as both a mirror and a map. Reflecting where your
organization might be and illuminating the paths on how you can move forward with confidence.
Elisa Pereira, Alvin Wang Graylin & Erik Brynjolfsson
The Research Team
Stanford Digital Economy Lab
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Contributors
Elisa Pereira
Researcher, Stanford Digital Economy Lab · MSx Candidate, Stanford Graduate School of
Business
Elisa Pereira is a researcher at Stanford's Digital Economy Lab and MSx candidate at the
Stanford Graduate School of Business, with a background in venture capital and hands-on
experience building dozens of enterprise AI solutions across Latin America. Her current
research focuses on measuring the real-world impact of these deployments, identifying
patterns behind successful implementations, and exploring how Latin America can
establish technological sovereignty.
Alvin Wang Graylin
Digital Fellow, Stanford Digital Economy Lab · Stanford University
Alvin Wang Graylin is Digital Fellow at the Stanford Digital Economy Lab, and an author,
serial entrepreneur and technology executive with over 35 years of experience in AI, XR,
cybersecurity and semiconductor industries. He’s currently the chairman of the Virtual
World Society, Senior Fellow at the Asia Society Policy Institute CCA, lecturer at MIT and
advises governments, organizations and corporations on technology transitions. His book,
Our Next Reality, discusses how AI and immersive technology will reshape our world in the
coming decade. His current research is focused on the economics of AI and the associated
governmental policies needed to ensure a smooth transition to a post-labor economic
model.
Erik Brynjolfsson
Director, Stanford Digital Economy Lab · Professor, Stanford University
Erik Brynjolfsson is the Director of the Stanford Digital Economy Lab and the Jerry Yang
and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-
Centered AI (HAI). He is also the Ralph Landau Senior Fellow at SIEPR, professor by
courtesy at the Stanford Graduate School of Business and Department of Economics, and
a research associate at the National Bureau of Economic Research (NBER). One of the
most-cited authors on the economics of information, he has co-authored hundreds of
articles and books, including The Second Machine Age and Machine, Platform, Crowd. He
puts his academic insights to practical use via Workhelix, a company he co-founded to
identify and measure the benefits of AI
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Contents
Foreword
Contributors
The Macro Context
Methodology
Key findings briefly
Chapter 1
Why do AI business cases underestimate real investment?
Chapter 2
How to cross the valley of death between deployment and ROI?
Chapter 3
How much human oversight is optimal?
Chapter 4
What separates sponsors who drive results from those who just approve budgets?
Chapter 5
Where does fatal resistance come from?
Chapter 6
When productivity gains are high, what happens to headcount?
Chapter 7
Where is AI opening doors that were previously closed?
Chapter 8
Is agentic AI generating real value?
Chapter 9
How clean does enterprise data actually need to be?
Chapter 10
Does rigorous security protect the project or kill it?
Chapter 11
When is foundation model choice not a commodity?
Conclusion
Appendix
Measurements and what to avoid
Research Sample
End notes
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The Macro Context
Why enterprise AI implementation matters now
General purpose technologies like AI enable and require significant complementary investments in
process redesign, workforce development, and organizational restructuring. These investments are
largely intangible and poorly measured in national accounts, which means productivity growth is
systematically underestimated in the early years of a new technology and overestimated later,
when the benefits are harvested. Brynjolfsson, Rock, and Syverson (2021) formalized these
observations in a model called the "Productivity J-Curve".[1]
The macroeconomic outcome hinges not on the technology itself but on how organizations deploy
it. We face a “productivity fork”: AI can either augment workers and create new capabilities or
primarily automate existing tasks and cut headcount. The path chosen will shape economic growth
for decades.[2] In particular, automation displaces workers from existing tasks, but the creation of
new tasks in which humans have a comparative advantage can reinstate labor demand. Whether AI
leads to broad prosperity or concentrated gains depends on whether organizations generate
enough new opportunities to offset labor displacement.[3]
Some employment effects are already surfacing. Analysis of high-frequency payroll data covering
millions of . workers finds that early-career workers in AI-exposed occupations have experienced
a 16% relative decline in employment, with software developers aged 22 to 25 seeing a nearly 20%
drop.[5] These "canaries in the coal mine" suggest that some of the labor market disruptions many
anticipated are no longer hypothetical.
These measurement challenges are not merely academic. Standard metrics like GDP systematically
fail to capture the welfare contributions of new and free digital goods. Their GDP-B framework,
which measures consumer benefits rather than production costs, reveals substantial unmeasured
value creation in the digital economy. If aggregate statistics undercount the gains from relatively
simple digital services, they are likely to miss even more of the value that AI creates inside
organizations—precisely the kind of value this report attempts to document.[6] One new non-
monetary benefit that AI agent systems are delivering to software developers is “free time” to think.
While AI agents autonomously build increasingly larger portions of the code, human coders are
allotted more coffee breaks to ponder bigger picture issues. This won’t show up in standard
productivity measurements, but it is a real benefit that changes their daily work for the better.
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The gap between these macro findings and what happens inside organizations is significant. The
economic models describe aggregate effects. The firm-level experiments measure more controlled
settings. Neither captures the messy operational reality of deploying AI across departments,
overcoming resistance, and building the complementary infrastructure that the J-curve framework
identifies as essential. This is the gap our research addresses.
Why this research
Despite billions in enterprise AI spending, a 2025 study from MIT’s NANDA initiative concluded that
95% of generative AI pilot programs fail to produce measurable financial impact.[7] They argued that
the failures stem not from model quality but from poor workflow integration and misaligned
organizational incentives. This is the gap between what technology can do and what organizations
manage to do with it.
In contrast, our objective was to understand the cases where AI was deployed successfully. We
dove deep into companies and analyzed 51 cases where enterprise AI delivered measurable value.
What did these organizations do differently? What did integration actually cost? Where did
resistance come from, and most importantly, how was it overcome?
How we incorporated failure
While this report focuses on implementations that delivered measurable value, we did not study
success in isolation. In every interview, we explicitly asked participants to describe the failures, false
starts, and abandoned pilots that preceded their current results. We asked what they tried first,
why it did not work, and what they changed.
What emerged is not a story of organizations that avoided difficulty. It is a story of organizations
that failed iteratively and built systematic approaches to overcome initial setback. Two thirds of the
companies we investigated had significant failed attempts prior to achieving value creation. The
patterns in this report reflect what these organizations learned from the process as much as what
they achieved through success.
We want to be transparent that this does carry a known limitation: selection bias toward positive
outcomes. Our findings describe what success looks like and what it took to get there; we don’t
claim to provide representative data on how common success is across the broader economy.
“All happy families are alike; each unhappy family is unhappy in its own way.” – Leo Tolstoy
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Methodology
This research is based on in-depth interviews with executives and project leaders who have
deployed AI solutions at scale. We focused exclusively on initiatives that have moved beyond pilot
stage and are delivering measurable business value.
Research Sample Profile
Our 51 case studies draw from 41 organizations, 7 countries, 5 regions, representing over a million
employees. (full list of anonymized companies in the appendix).
Figure 1. Research interview and analysis workflow
Selection Criteria
Four dimensions define the mature AI projects we selected for analysis:
● Operational Stability
System is live, integrated into real workflows, and consistently used in production.
● Sustained Business Adoption
Teams across functions actively rely on the AI system for decision-making over months
(3+ months).
● Quantified Value Creation
Clear business outcomes such as productivity gains, revenue growth, or customer
satisfaction.
● Scalability & Replicability
Can be extended or replicated across teams, geographies, or business units.
Technologies range from data science models (machine learning, deep learning) to agentic workflows.
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Interview Approach
Each case study was developed through at least one structured 60-minute interview per company
following a consistent discussion framework. We supplemented interview data with written
documentation provided by participant companies, including internal metrics/reports, project
plans/reviews, and financial updates. Interviews were conducted between Aug. ‘25 and Feb. ‘26.
Figure 2. Interview approach and supplementary data sources
Scoring Criteria
Each dimension was scored based on documented evidence:
3 = Strong (all criteria met), 2 = Moderate (most criteria met), 1 = Weak (few criteria met).
We required evidence from systems, documentation, or named owners.
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Sample Composition
In terms of business functions, our cases cover a wide range of applications. This diversity allows us
to identify patterns that transcend specific use cases.
Figure 3. Sample composition by business function
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Our sample spans 9 industries, with particular depth in manufacturing, financial services, and
technology. The distribution reflects the current landscape of enterprise AI adoption.
Figure 4. Sample composition by industry
Limitations
This research relies primarily on self-reported data from interview participants. While we
triangulated information where possible and focused on mature initiatives with documented
outcomes, readers should consider potential selection bias toward successful deployments.
Our sample, while diverse, is not representative of all enterprise AI initiatives. The concentration in
technology and financial services reflects early adoption patterns rather than the full universe of AI
deployment.
All data was anonymized and aggregated to protect proprietary information and follow subject
company disclosure policies. Specific company names and identifying details have been removed or
generalized.
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Key Findings Summary
1. Technology is not the hardest part. 77% of the hardest challenges were invisible and intangible
costs: change management, data quality, and process redesign. 61% of successful projects included
at least one prior failure, whose costs never appear in the final ROI.
2. Timeline variance is organizational, not technical. Similar use cases took weeks at one company
and years at another. The difference was executive sponsorship, existing organizational 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. This may, in part, reflect different types of tasks addressed.
4. Executive sponsorship is about actions, not approval. Effective sponsors clear blockers weekly,
bridge business and technical teams, and tie AI adoption to corporate OKRs. Most critically, they
create a culture that gives permission to fail.
5. Staff functions are the most frequent source of resistance, but some parts may become
enablers after buy-in. Legal, HR, Risk, and Compliance were the most frequent source of resistance
at 35%, ahead of internal end-users at 23%.
6. Headcount reduction is common but not inevitable. Headcount reduction was the largest
outcome in 45% of the deployments, but alternatives (hiring avoided, redeployment, no reduction)
accounted for 55%. Broader labor market data suggests entry level roles in AI exposed occupations
are already declining.
7. Revenue from AI is real, but still rare, and follows three patterns. Personalization that converts,
speed that wins deals, and internal tools repackaged as products. A small subset of cases also shows
AI enabling work that was previously impossible.
8. Agentic AI works, but most firms have not used it, yet. Agentic implementations showed 71%
median productivity gains versus 40% for high-automation but represented only 20% of cases.
Agentic AI isn’t a new UI; it’s a redefinition of the role of humans and machines in the workflow.
9. Messy data is not a blocker if you design around it. LLMs fixed many of the data problems they
were supposed to struggle with. Store everything, connect it, and let the models do the cleaning.
10. Security enables more than it blocks. Security was not a project killer in any of the cases we
studied. Requirements that were initially barriers later enabled projects to handle sensitive data.
11. Model choice is a commodity for many use cases. For 42% of implementations, model choice
was fully interchangeable. Companies don’t always need the best available AI models. The durable
advantage is in the orchestration layer, not the foundation model.
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Chapter 1
Why do AI business cases underestimate
real investment?
The hidden costs that determine success or failure
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Published Findings
Scaling requires heavy non-model investment. McKinsey's research identifies that high-performing
AI organizations (those attributing >5% of earnings before interest and taxes [EBIT] to AI) are
significantly more likely to invest in "rewiring" business processes and data products rather than
just model deployment.[8]
"Proof of Concept Factories" represent sunk costs. Accenture estimates that 80-85% of companies
are stuck in a "Proof of Concept Factory" stage, where they conduct experiments but achieve low
returns and low scaling success rates.[9]
Data foundations are a major line item. Strategic scalers are far more likely to possess a large,
accurate data set (61% vs 38% for non-scalers) and invest heavily in data quality, management, and
governance frameworks.
The Productivity J-Curve implies hidden investment. Earlier research found that for every $1 of
tangible tech investment, companies spend up to $10 on intangibles (process redesign, reskilling,
organizational transformation), initially depressing productivity before gains are realized.[1]
What We Found
77% of the hardest challenges practitioners faced were invisible costs: change management, data
quality, and process redesign, not technical issues. Technology was consistently described as the
easiest part. The true cost of a successful deployment usually includes at least one failed attempt
(see Finding 2), and the bulk of investment goes to everything except the model.
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Finding 1
77% of the hardest challenges are "invisible costs"
When we asked practitioners, "what was the hardest thing to fix?", the answers reveal where AI
budgets actually go.
Figure 5. Hardest challenges in AI implementation
"All the hard work is in process documentation and data architecture. If you can do those two
things, everything else is quite simple."
- Executive, Telecom Company
" Technology wasn't the bottleneck - organizational adoption was the failure point."
- Executive, Professional Services Company
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Finding 2
61% had a failed AI project before their current success
These failed experiments represent sunk costs that never appear in the "successful" project's ROI:
Had previous AI failure(s) 61%
No previous failures 39%
These failed experiments are sunk costs that may never appear in the successful project's ROI but
were often essential to it. The failures share a pattern: teams treated AI as a technology project
instead of a process and change management project. First attempts failed when applied to broken
workflows, when led by technical teams without business ownership, or when organizations
assumed the model would fix problems that required redesigning the work itself.
"This was actually the second time they looked to AI for the recruiting process. It failed initially
because they didn't account for bias, and they thought AI would just fix processes instead of
requiring process redesign."
- AI Project Lead, Professional Services Company
The technology was consistently described as the easiest part.
"The more you invest in your data, the better you can get out of these AI solutions."
- Manager, Technology Company
"The problem isn't the models."
- Executive, Professional Services Company
The implication for budgeting: the true cost of a successful AI deployment usually includes at least
one failed attempt, and the bulk of the investment goes to everything except the model.
DRAFT
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CASE STUDY
Invoice Processing at a Logistics Company
How they overcame invisible costs
The Company
A $1B+ US-based logistics company managing a large fleet of refrigerated trailers. The company
receives +100k invoices annually from vendors across the country performing maintenance on
trailers - everything from tire changes to sensor replacements.
The Challenge
The volume and variation of invoices created a significant operational burden. Seven full-time
employees were dedicated exclusively to this task: consolidating invoices, matching them to
internal templates, validating the work, entering data into the enterprise resource planning (ERP)
system, and generating client invoices.
"They get all these invoices in different channels, including fax. They might get phone calls. A lot of
these repair shops, middle of nowhere, they just dial in and say, hey, we did this repair. So they
might be phone calls, they might be emails, they might be all types of ways that they get this
information."
- Senior Executive, Technology Services Company
DRAFT
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The Invisible Work
Process Simplification: thousands of templates reduced to hundreds. Years of accumulated invoice
templates were redundant and inconsistent. This cleanup was required before any AI could work.
"We very quickly realized that the 750 templates don't make any sense and most of them are
repetitive. Nobody really did a review on this."
- Senior Executive, Technology Services Company
Data Annotation: Subject matter experts (SMEs) reviewed thousands of AI outputs. They
validated AI-generated invoices on top of their daily work, explaining every mistake to improve the
model.
Executive Sponsorship: President involved in weekly check-ins. This removed bottlenecks and
ensured buy-in from the operations team.
"The president was checking in every week - what is the progress, where are we, what are the
bottlenecks? Then the rest of the team also engaged."
- Senior Executive, Technology Services Company
Knowledge Transfer: Two junior IT staff embedded from day one. Daily stand-ups, weekly and
monthly reviews. No black box - the company could operate the system independently.
The Solution
In simple terms, the company built a system that automatically reads invoices regardless of how
they arrive, understands their content, and enters the data directly into the company's financial
system, eliminating the need for manual processing.
The technical implementation used Azure Document Intelligence with Azure OpenAI Service,
combining optical character recognition (OCR) parsing with large language model (LLM)-based
semantic mapping. The system ingests invoices from multiple channels (email, fax, phone
transcriptions), parses and extracts data using OCR, maps invoice content to the simplified template
taxonomy, and writes validated data directly to MS Dynamics D365.
DRAFT
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The Results
Headcount
7 → 2 full-time equivalents (FTEs)
Accuracy
85%
Processing time
< 24 hours
Time to production
8 weeks
Value created
> $1M
Key Lessons
"It always starts with the people. There are people, process, and technology - and I know it's in that
order even though I'm representing a technology company. The technology was the easiest part.
We basically used a lot of open-source and off the shelf stuff."
- Senior Executive, Technology Services Company
"Look guys, 80% is perfect for us. We can take these folks, we can just put them in the other
bottleneck. I understand that you can keep improving and at one point the model is going to be
95%, but we don't care. What we care is immediate cost saving and getting rid of these backlogs."
- President, Logistics Company
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Chapter 2
How to cross the valley of death between
deployment and ROI?
What separates weeks from years in similar use cases
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Published Findings
Intentional timelines beat "move fast." Accenture concludes that successful AI scalers are 65%
more likely to set 1–2-year timelines to move from pilot to scale. Contrary to the "move fast" ethos,
they are more intentional about the time required to scale responsibly.[9]
High performers redesign workflows, not just deploy tools. McKinsey reports that top performers
are nearly three times more likely to fundamentally redesign workflows as part of their AI efforts.
55% of high performers redesigned workflows around AI versus only 20% of other companies.[11]
Most companies are stuck in pilot mode. While 88% of organizations use AI in at least one
function, only one-third have begun to scale their AI programs at the enterprise level. Two-thirds
remain in testing or proof of concept phase.
What We Found
Similar use cases can take weeks or years depending on the organization. We identified three
factors that consistently accelerate projects - executive sponsorship, existing foundations, and end-
user willingness - and four that slow them down. Every successful project in our sample used an
iterative approach.
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Finding 1
The range is dramatic: from weeks to years for similar use
cases
A large fintech used an AI coding agent to migrate millions of lines of legacy extract, transform, load
(ETL) code to a modern architecture. The project took weeks. A technology company redesigned
their customer support system with AI and launched in six months. A major bank attempting the
same customer support use case reports that projects take multiple years.
"Within weeks of the AI agent's launch, we identified a clear opportunity to accelerate the
migration at a fraction of the engineering hours."
- Executive, Fintech
"It takes us multiple years just to even stand one of these things up."
- Executive, Financial Services
The same use case, the same AI models, vastly different timelines. The insight here is not a
median or average. It is that organizational context matters more than the technology itself.
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Finding 2
Three factors consistently accelerate time to value
Acceleration Factor Frequency
Executive Sponsorship 43%
Building on Existing Foundation 32%
End User Willingness 25%
Building on Existing Foundation. Projects that leveraged existing infrastructure or platforms moved
significantly faster. One technology company built their sales copilot in months because they had
already developed an AI platform for customer support.
"We launched the first MVP [minimum viable product] in April. Because we finished the customer
support project early, we went on to build this one."
- Executive, Technology Company
End User Willingness. When users genuinely want the solution, adoption friction disappears. In
healthcare, hospital systems adopted ambient AI transcription despite unclear ROI simply because
physicians were desperate for relief. With existing processes, after a full day of work, they were
forced to spend hours documenting their daily activities.
"The state of current medical practice is so bad, and the doctors were so burnt out that the hospital
systems were willing to try anything as a Hail Mary just to see if it made a difference."
- Executive, Healthcare AI Company
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Finding 3
Four factors consistently slow projects down
Delay Factor Frequency
Learning Curve and Iteration 25%
Data Quality and Preparation 21%
Regulatory and Compliance 21%
Process Documentation Gaps 21%
Data quality was a recurring theme. Regulatory constraints created structural delays in financial
services, where compliance requirements extend timelines regardless of technical readiness.
"Majority of customers don't do a good job maintaining their knowledge bases."
- Executive, Software Company
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Finding 4
Every successful project used an iterative approach
100%
used iterative approach
Of cases where we could identify the development methodology, all used an iterative approach.
None used traditional waterfall planning. The pattern was consistent: start small, learn, expand.
"Think of it as like a layered cake. We built one process, documented it, then built that layer of the
agent, then the second feature and the third feature on top of it."
- Executive, Logistics Company
"Probably 90% of the pilots and tests fail, but then we iterate on those until we find them and it
grows and grows."
- Executive, Food Delivery Company
DRAFT
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CASE STUDY
Recruiting at a Translation Services Company
How they crossed the valley of death
Professional Services | Recruiting | Mid Market
The Company
Their recruiting process had become their biggest cost sink and a strategic bottleneck to business
scalability: slow candidate intake, high turnover, difficulty staffing niche languages and dialects, and
inconsistent screening quality limited how fast the company could grow.
The First Attempt - and Why It Failed
This was the company's second attempt at AI for recruiting. The first failed for two reasons: they did
not account for bias in their screening algorithms, and they assumed AI would fix broken processes
without addressing the underlying workflow problems.
"They thought AI would just fix processes instead of also stepping back and making sure everything
was working as expected."
- Executive, Professional Services
DRAFT
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What Changed the Second Time
Three things were different:
First, the CEO took ownership rather than delegating to the CTO. The project had executive
visibility and weekly check-ins that cleared bottlenecks quickly.
Second, they fixed the process before applying AI. They mapped the entire recruiting workflow
and identified where the real pain points were.
Third, they targeted genuine pain. The recruiters were not mildly inconvenienced. They were
burdened by a stream of applications that just overwhelmed the team each day, and it kept
compounding.
"This was a painkiller for those guys. It wasn't 'Hey, this would be great.' It was 'I'm drowning.'"
- Executive, Professional Services
The Solution
The team built an AI powered recruiting pipeline with hyper-personalized screening by language
and dialect, automated first-round video interviews with bias-mitigated evaluation, and a feedback
loop connecting hiring outcomes back to screening criteria. The system learned which candidate
signals predicted success.
DRAFT
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The Results
Time to build
~1 month
Time per role
3 hrs → 3 min
Intake efficiency
+83%
Screening efficiency
+79%
Candidate conversion
+75%
Key Lessons
The same company, the same function, the same goal - but radically different outcomes. The first
attempt failed. The second took one month and delivered 83% efficiency gains. The difference was
not the technology.
Fix the process before applying AI. AI amplifies whatever process it is applied to. If the process is
broken, AI makes it worse faster.
Target real pain. Adoption was easy because users were desperate for relief. The recruiting team
did not need to be convinced. They needed to be rescued.
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Chapter 3
How much human oversight is optimal?
Examining human involvement across AI implementations
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Published Findings
Enterprise and individual usage patterns differ. Anthropic's Economic Index finds that 52% of
individual usage involves human AI collaboration versus 45% full automation. Enterprise
application programming interface (API) usage shows the inverse: 77% automation. This suggests
enterprises deploy AI differently than individuals, but the optimal balance remains unclear.[10]
Structured human oversight correlates with success. McKinsey reports that 65% of AI high
performers have defined human-in-the-loop processes to determine how and when model outputs
need human validation, versus only 23% of other organizations — nearly a threefold difference.[11]
What We Found
Escalation-based operating models (AI handles 80%+ autonomously, humans review only
exceptions) delivered the highest productivity gains with a median of 71%. This partly reflects task
selection: the escalation model is typically applied to high volume, recoverable tasks, while approval
and collaboration models serve regulated or high stakes work. The level of human oversight
depends on error tolerance, regulatory requirements, and task complexity, and is often a strategic
design choice rather than a limitation.
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Finding 1
Moderate human oversight is associated with the highest
productivity gains
We classified each case on a three-point scale based on the level of human involvement:
Human-in-the-Loop (HITL)
Level
Description
Escalation AI handles 80%+ autonomously; humans review only exceptions or sample
≤20%
Approval AI does the work; human reviews and approves every output before action
Collaboration Human and AI work together continuously on each task
Figure 6. Human oversight models across AI deployments
"90 or 95% are now fully automated by an agent. If someone says their food didn't arrive or
something went wrong with their order, 90 to 95% of those are completely automated."
- Head of AI, Food Delivery Company
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Finding 2
The optimal oversight level varies by function
The appropriate level of human oversight (HITL – human in the loop) depends on error tolerance,
regulatory requirements, and task complexity.
Function Typical HITL Level Avg. Gain
IT Operations Escalation 90%
Customer Support Escalation 71%
Claims Processing Escalation 50%
Field Service Approval 80%
Clinical Documentation Approval 66%
Coding Collaboration 54%
In customer support, a technology company achieved 82% ticket deflection by redesigning
workflows around AI first resolution. In clinical documentation, physicians must approve every AI
generated note because these are legal documents. In coding, engineers shifted from writing code
to reviewing AI generated changes.
"Rather than engineers completing an entire migration task, they could just review the changes,
make minor adjustments, then merge their PR [pull request]."
- Head of Engineering, Latin American Fintech
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Finding 3
When human oversight is the obvious choice
Human oversight is not a sign of AI immaturity. In many contexts, it is the strategically correct
design choice. Four patterns emerged where human involvement creates clear value:
Zero error tolerance. When a single mistake costs more than thousands of correct outputs, human
review is essential. Marketing content for major brands, legal documents, and customer facing
communications fall into this category.
"I cannot run a campaign with an error. I cannot run a large campaign that will reach millions of
customers with uncertainty."
- Head of Strategy, Enterprise AI Company
Regulatory requirements. In healthcare, finance, and other regulated industries, human review is
legally mandated regardless of AI capability. The question is not whether AI can do the work, but
whether regulators will accept AI doing the work.
"The doctor reviews it, approves it, and then it gets sent back to the EMR [electronic medical
record]. Doctors must still review every note due to legal requirements."
- Executive, Healthcare AI Company
Enterprise risk management. Large organizations prefer human-in-the-loop solutions even when
full automation is technically feasible. The perceived risk of autonomous AI outweighs the efficiency
gains.
Continuous improvement. Human reviewers identify patterns in AI errors that feed back into
model improvement. This feedback loop accelerates learning in ways that fully automated systems
cannot match.
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CASE STUDY
Marketing Content at a Financial Services Company
How they calibrated human oversight
Financial Services | Marketing | Enterprise
The Company
A financial services company faced a content bottleneck. They had customer data enabling hyper
personalization but could not generate content fast enough to leverage it. Traditional agency
workflows took seven weeks per campaign.
The Solution
They deployed an AI platform that generates multi-channel content while maintaining brand
consistency. The team chose an 80/20 model: AI handles 80% of the generation, humans provided
20% refinement and quality assurance. As the technology matures and learnings from experience
grows, the percentage offloaded to AI will eventually go towards 100%.
How Human Oversight Enabled Success
This split was deliberate. Enterprise marketing cannot tolerate errors on customer facing content.
"To run at the enterprise level, you need 80% technology and 20% humans refining. The AI industry
has not yet reached the level where you can nail that final 20%."
- Head of Strategy, Enterprise AI Company
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The human layer served three functions:
Brand protection against errors that would damage years of brand building.
Edge case handling for unusual combinations requiring judgment.
Feedback loop where reviewers identify patterns that improve AI outputs.
The Results
Time to market
7 weeks → 6 hours
Click through rate
2x improvement
Production efficiency
>80% reduction in time
Key Lessons
Human oversight is not a tax on productivity. The 80/20 model delivered % reduction in time
to market while maintaining zero error tolerance.
The oversight level should match the stakes. The company views the 20% human component as
transitional, expecting to reduce it as AI improves, but they started with what worked rather than
waiting for perfect automation to arrive.
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Chapter 4
What separates sponsors who drive results
from those who just approve budgets?
The activities that define effective executive sponsorship
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Published Findings
High correlation with performance. McKinsey reports that AI high performers are more likely
to agree that senior leaders demonstrate ownership and commitment to AI initiatives.[11]
Champion profile matters. Accenture finds that Strategic Scalers are typically championed by a
Chief AI, Data, or Analytics Officer, while struggling firms rely on a lone champion within
technology.[9]
Intentionality over presence. Scalers drive AI anchored in C-suite objectives; proof of concept
factories lack connection to strategic imperatives.
The above research establishes correlation but does not address what sponsors actually do that
makes the difference.
What We Found
Active Steering (weekly check-ins, proactive blocker removal) is the most common pattern among
successful projects. But the seven cases that achieved organization-wide transformation all reached
Strategic Integration: the sponsor made AI adoption a corporate Objective and Key Result (OKR)
tied to bonuses, not just a project to support. When we looked beyond what sponsors did to how
they led, a consistent pattern emerged: the most effective sponsors created conditions where
teams could fail, learn, and try again without career consequences. The key wasn’t the executive
themselves, but that they created a corporate culture that encouraged experimentation, demanded
collaboration, designed in accountability and nurtured a safe environment where initiative was not
punished.
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Finding 1
Active Steering is common, but Strategic Integration drives
transformation
We classified sponsor engagement on a four-point scale:
Level What It Means
1 Passive Approval Approved budget, delegated entirely, little ongoing involvement
2 Periodic Oversight Monthly reviews, removes blockers when escalated, reactive
3 Active Steering Weekly check-ins, proactively removes blockers, involved in decisions
4 Strategic Integration AI in corporate OKRs, incentives tied to adoption, culture change
Engagement Level %
Periodic Oversight (Level 2) 12%
Active Steering (Level 3) 58%
Strategic Integration (Level 4) 29%
.
Active steering works for projects within a single function. But the seven cases that achieved
organization-wide transformation all reached strategic integration: the sponsor made AI adoption a
measure of organizational success, not just a project to support.
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Finding 2
Four activities define what effective sponsors do
Activity Cases What It Looks Like
Resource Allocation 59% Dedicated budget, people, infrastructure for AI
Strategic Integration 49% AI connected to business objectives and OKRs
Org Communication 32% Messaging AI importance across the organization
Blocker Removal 20% Actively clearing obstacles before escalation
Resource allocation is table stakes. What separates effective sponsors is what they do beyond
budgets: connecting AI to business objectives, communicating its importance across the
organization, and most critically, actively clearing obstacles before teams had to escalate.
"The president was on top of it, checking in every week: what is the progress, where are we, what
are the bottlenecks? Which was helpful because then the rest of the team also engaged."
- Senior Executive, Technology Services Company
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Finding 3
Business plus Tech co-sponsorship unlocks cross-
functional projects
Eight cases showed co-sponsorship between business and technical leaders was what made the
difference. At a professional services company, the first AI-push attempt was CTO-led and just failed
to gain traction. The second try succeeded when the CEO and the Head of Talent drove it together
with the CTO:
"The org had to know this was a CEO-led thing, this wasn't just the CTO. When AI is tech-led and
tech-first, it does not work or it rarely works."
- Executive, Professional Services Company
The CEO provided a strategic mandate. The Head of Talent defined incentives and success metrics.
The CTO owned implementation. Each brought something the others lacked.
At a telecom company, success came from finding a leader who bridged both worlds:
"The biggest enabler is that we hired a senior vice president of AI who had a deep understanding of
the process and would map it out in detail. But he also had a deep understanding of artificial
intelligence. That's our number one issue: we lack people who understand the process AND
understand the AI and can put the two together."
- Executive, Insurance Company
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Finding 4
Effective sponsors give teams permission to fail
Chapter 1 reported that 61% of successful projects included a prior failure. But failure only converts
into learning under specific conditions. When we examined how sponsors handled setbacks, three
strategies separated organizations where failure accelerated the next attempt from those where it
led to abandonment.
Sponsor continuity through failure. In every case where we could identify whether the same
executive sponsored both the failed and successful attempts, the answer was yes. At a technology
company, the executive who built and then scrapped the first platform personally led the redesign
six months later. At a semiconductor manufacturer, early AI initiatives stalled because engineering
built solutions without coordination. The same AI leader who oversaw those failures escalated to
the CEO and drove the second wave to production. When sponsors change after failure,
institutional memory walks out the door: what not to do, which stakeholders to involve, where the
real bottlenecks are. And most importantly, it sends a message to everyone that failure is a career
risk.
Controlled scope as a failure strategy. 73% of implementations started small deliberately, and 63%
framed their pilots explicitly as experiments. This is not timidity. It is a political calculation. Small
pilots fail cheaply. Cheap failures do not end careers. One professional services company had failed
twice on prior technology implementations. The sponsor accepted 80% accuracy as good enough to
move forward, treating imperfection as a starting point rather than a flaw. Starting with an
achievable bar gave the team room to iterate without the pressure of delivering a finished product
on the first attempt.
Feedback loops instead of launch dates. The most effective approach to failure was not tolerating
it after the fact but designing to handle it in advance. At a semiconductor manufacturer, the shift
between the failed and successful attempts was making continuous user feedback and iteration a
first-class priority in the solution lifecycle, rather than treating each deployment as a finished
product.
The common thread: in none of the cases we examined was anyone punished for a failed AI
initiative.
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CASE STUDY
Field Service at a Semiconductor Company
How they achieved effective executive sponsorship
Hardware Manufacturing | Field Service | Enterprise
The Company
A semiconductor manufacturer producing solid-state drives for enterprise customers. The company
has multiple departments with different levels of technical requirements: Engineering and IT at the
front, Operations and Finance in the middle, Legal and HR at the back.
The Problem
When enterprise customers reported issues, field service engineers needed to gather technical data
before diagnosis. Product specs, test libraries, data sheets, engineering logs lived in five or six
different repositories owned by different teams. The service-level agreement (SLA) for data
gathering alone was 40 hours.
"Documents, different data sheets, different test libraries, and it all is not centralized. Each of
[them] is owned by different teams. When you have a sighting, all this has to come together."
- AI Leader, Manufacturing Company
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The First Attempts Failed
Earlier AI initiatives built LLM-based agents for data analysis. They worked in demos but not in
production. The problem was not technical. Engineering built solutions for their own use cases
without coordination. There were no shared standards, no accountability for adoption.
What the Sponsor Did
The AI leader recognized that departmental sponsorship was not enough. He escalated to the CEO
through three specific actions:
1. Established AI champions in every department. Engineering and IT adopted quickly. But Legal,
HR, and other non-technical departments lagged. The sponsor created champions in each
department to drive peer-to-peer adoption.
"What I saw was within an organization there are different levels … So, we got AI champions in
each department."
- AI Leader, Manufacturing Company
2. Made AI adoption a corporate OKR. When peer pressure was not enough, the sponsor escalated
to the CEO and made AI adoption part of how the company measures success.
3. Created visible leadership commitment through AI demo days. CEOs present at demo days,
giving recognition to teams driving adoption. This signaled that AI was a strategic priority, not an IT
experiment.
"We had AI demo days with rewards being given and CEOs presenting prizes. That recognition and
pride coming from the people who are doing the work are actually pushing the momentum
forward."
- AI Leader, Manufacturing Company
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The Solution
With organizational support in place, the team built a multi-agent framework for the field service
bottleneck. When a customer issue came in, agents pulled data from all repositories automatically.
"What the agent framework does is it goes into five or six different areas that it needs to look for
information tied to this customer, tied to this issue, tied to this engineering area, and then it pulls it
in."
- AI Leader, Manufacturing Company
The Results
Data gathering time
40+ hours → < 1 hour
Issues with complete data
0% → 95%+
Product testing cycle
20% reduction
Key Lesson
Departmental AI initiatives hit a ceiling when they require cross-functional adoption. Tying AI
adoption to corporate OKRs and bonuses broke through resistance that standard communication
and training could not.
"AI is a mindset change, it's nothing more than that. It is actually completely change-management
driven."
- AI Leader, Manufacturing Company
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Chapter 5
Where does fatal resistance come from?
Understanding where pushback originates and how to overcome it
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Published Findings
End user adoption is a major barrier. Accenture lists lack of employee adoption as one of the top
challenges for AI implementations.[9]
Leadership engagement varies. McKinsey notes that 33% of high performers have senior leaders
actively driving adoption, compared to significantly fewer in the general pool.[11]
Workforce composition matters. Anthropic found that US states with higher concentrations of tech
workers have higher AI adoption, suggesting resistance may be higher in non-technical
workforces.[10]
The above research does not isolate middle management as a distinct source of resistance, nor does
it address the nature of resistance: is it fear of replacement, lack of skills, or poor tooling?
What We Found
Staff functions (Legal, HR, Risk, Compliance) were the most frequent source of resistance at 35%,
not the AI end users. Each source resists for different reasons: C-level demands measurable proof of
ROI, staff functions worry about process risks and blame, end users distrusted system inconsistency,
and frontline workers feared replacement. Each group required a different solution.
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Finding 1
Staff functions, not end users, are the most frequent source
of resistance
Figure 7. Sources of resistance to AI adoption
The conventional wisdom focuses on end user resistance, but staff functions were the most
frequent blockers. Legal departments worried about liability. HR worried about change
management. Risk and compliance teams worried about regulatory exposure. These functions have
organizational authority to slow or stop projects regardless of executive support.
"What I saw was within an organization there are different levels of AI maturity. Engineering and
IT want to push forward. Other organizations, maybe Legal, are holding back."
- AI Leader, Manufacturing Company
IT functions are a notable exception: rather than blocking, they more often serve as enablers,
providing the platform infrastructure and data pipelines that allow business units to move faster.
"Middle management most resistant, while senior management and junior employees were more
accepting."
- Executive, Retail Company
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Finding 2
Each source resists for different reasons and requires
different solutions
Staff functions worry about risk. At a large bank, past regulatory issues made risk teams extremely
cautious. The solution is mandates, not persuasion. When AI adoption affected compensation
through corporate OKRs and they don’t need to take the blame for potential failure, Legal and HR
found ways to enable rather than block. When given a role in governance rather than simply told to
approve, staff functions frequently shifted from blocking to actively supporting deployment.
"I spent almost all of my time on risk and controls where everyone is very afraid to do anything."
- Executive, Large Bank
C-Level demands ROI proof. CFOs require clear financial justification before approving AI
investments. The solution is measured pilots that demonstrate value before asking for broader
investment.
"Hospital C-suite executives need direct line-item impact on balance sheet to justify software
purchases."
- Executive, Healthcare AI Company
End users distrust inconsistency. Users accustomed to deterministic systems struggle with AI
variability. The solution is expectation setting: users need to understand that AI outputs require
review and that "good enough" performance on routine tasks frees time for higher-value work.
"We have to start by setting realistic expectations. Part of it is to change the thought process and
shift the paradigm a little bit."
- Executive, Consulting Firm
Frontline workers fear replacement. This is the most discussed concern but appeared in only two
cases. The fear is real but addressable. The solution is showing a concrete path forward: what work
disappears, what work remains, and how roles evolve.
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CASE STUDY
Security Operations at a Technology Services
Company
How they overcame team resistance
Technology Services | Security Operations | Mid-Market
The Company
A technology services company with a six-person Security Operations Center (SOC) processing
approximately 1,500 security alerts per month. The majority were false positives requiring manual
triage.
The Problem
The team was drowning in alerts. With limited capacity, analysts could only investigate high-priority
alerts thoroughly. Lower-priority alerts received minimal coverage. The work was mechanical:
triage, classify, escalate or close. Analysts spent most of their time on repetitive tasks rather than
the judgment-intensive investigation that required their expertise.
The Solution
The team deployed an AI system that automated alert triage. The AI handled initial classification
and false positive filtering, processing alerts in seconds rather than hours. It escalated only alerts
requiring human judgment to analysts.
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Resistance
When leadership proposed the AI solution, the expected concern was job security. With a six-
person team and AI capable of replacing most of the workload, the risk of resistance was real.
How the Sponsor Overcame Resistance
The Head of Technology had full mandate and no dependencies on other teams. He bought into the
solution and ran the implementation as a dedicated program.
First, the context did most of the work. The team was already overwhelmed and failing to cover
lower-priority alerts. This was not a team performing well that AI would disrupt. This was a team
that could not keep up. AI was positioned as relief, not replacement.
Second, the division of work was intuitive. AI took the mechanical triage that consumed most of
their time: classification, false positive filtering, routine escalation. Analysts kept the judgment-
intensive work that required expertise.
Third, the sponsor framed freed capacity as a path up, not out. The extra bandwidth would go to
higher-value activities that the team had never had time to pursue. The message was specific: AI
replaces the hiring the company would otherwise need to do, not the people already there.
"You have to have a roadmap for the people. What's in it for the individual? They should see their
life gets easier. And because it gets easier, that extra bandwidth is now employed for other
activities which skill them up."
- Executive, Technology Services Company
The reframe was specific: AI replaces the hiring the company would otherwise need to do, not the
people already there.
"AI is not replacing the person you have. AI is replacing the person you don't need to hire. The
person you have can now do two or three or four people's work."
- Executive, Technology Services Company
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The Results
Alerts processed
1,500 → 40,000/mo
Alert coverage
High-priority → 100%
Team capacity required
6 → FTEs
Freed capacity redeployed
FTEs
No one was laid off. The FTEs of freed capacity were redeployed to threat hunting, security
architecture, and capability development.
Key Lesson
Fear of replacement dissolves when the path forward is concrete. The sponsor showed exactly what
work would disappear (mechanical triage), what work would remain (expert investigation), and
what new work would emerge (capability building). The team moved from resistance to advocacy
once they saw AI as liberation from drudgery rather than threat to employment.
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Chapter 6
When productivity gains are high, what
happens to headcount?
Firing, reallocating, or freezing hiring?
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Published Findings
Expectations of decrease. McKinsey found that 32% of respondents expect their organization's
workforce to decrease in the next year due to AI, while 43% expect little change and 13% expect an
increase.[11]
Service Operations hit hardest. In the past year, 39% of respondents in Service Operations and 30%
in Manufacturing reported a decrease in employees due to AI.
Deskilling versus upskilling. Anthropic's analysis suggests AI covers higher-education tasks,
potentially leading to deskilling for some roles and upskilling for others.[10]
A recent Anthropic paper investigated theoretical
AI job exposure to actual observed AI coverage
adopted in the workplace, and showed that
although in some fields, the exposure can be up
to 90%, but actual current adoption is
significantly lower. [23] (see figure) This would help
to explain why the current impact on
unemployment may not be as high as many had
feared yet. But as adoption expands, the outlook
may become much bleaker.
The above research captures expectations and aggregate trends but does not link specific high-
productivity projects to actual headcount decisions.
What We Found
Reduction is the most common outcome at 45%, but not the majority. The combined alternatives
(hiring avoided, no reduction, redeployment) account for 55% of cases in aggregate. Three distinct
strategies emerge: accelerate rather than cut, redeploy to higher-value work, or reduce headcount
directly. The technology does not dictate the outcome. Revenue-generating applications more often
led to redeployment or acceleration, while cost-reduction applications more often led to direct
cuts.
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Finding 1
Reduction is the most common outcome, but not the
majority
Figure 8. Headcount impact of AI deployments
Reduction is the largest single category but represents less than half of outcomes. Today,
companies are still finding ways to capture AI productivity without eliminating positions. This may
shift over time as productivity gains grow and social norms evolve.
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Finding 2
Three distinct strategies emerge
Strategy 1: Accelerate rather than cut. Some companies explicitly chose to reinvest productivity
gains into growth rather than cost reduction.
"There was debate on whether AI should reduce headcount. The CEO and COO leaned toward cost
reduction; I pushed to use gains to accelerate the roadmap due to a large backlog."
- CTO, Education Technology Company
The productivity gains went into shipping more features faster, not into reducing engineering
headcount.
Strategy 2: Redeploy to higher-value work. Other companies moved people from automated tasks
to work that required human judgment. At a technology consulting company, AI automated 80% of
invoice processing. Rather than cutting the team, they moved people to the next bottleneck:
Strategy 3: Reduce headcount directly. Some companies cut staff. At a private equity (PE)-owned
company, an 88% productivity gain in coding led to reducing the development team from seven to
three.
The choice depends on strategic context. Growth-stage companies tend toward acceleration. Cost-
focused ownership (PE, turnaround) tends toward reduction. Technology does not dictate the
outcome.
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CASE STUDY
Engineering at an Education Technology Company
How they chose acceleration over headcount cuts
Education Technology | Engineering & Content | Enterprise
The Company
An education technology company with thousands of employees, including +200 in technology and
+100 engineers. The company provides continuing education and professional certification courses
across regulated industries.
The AI Implementation
The CTO implemented a three-pillar AI strategy: productivity tools for engineering, customer
experience improvements, and AI-differentiated products. The engineering team ran a six-month
pilot with GitHub Copilot and Cursor.
"Outcome: 20 to 30% reduction in time and effort for engineers early on, with upside expected as
prompts and confidence improved."
- CTO, Education Technology Company
On the content side, the company re-architected production so that subject matter experts became
human-in-the-loop reviewers rather than drafters. AI drafted content; SMEs refined it. This
generated millions in cost savings.
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The Headcount Debate
With documented productivity gains and cost savings, the leadership team faced a decision: use the
gains to reduce headcount or reinvest them.
The positions were clear. The CEO and COO, under PE pressure to show returns, leaned toward cost
reduction. The CFO was initially unconvinced that AI would generate net savings. The CTO argued
for acceleration.
The Decision
For engineering, the company chose acceleration over cuts. The rationale was strategic: the
company had a large product backlog. Shipping features faster would generate more revenue than
cutting the team that ships features...for now.
"Savings were used to accelerate the roadmap, not reduce staff."
- CTO, Education Technology Company
For content production, the savings were captured and reinvested:
"We captured real savings in 2025 budgeting by reducing certain departmental budgets and
reinvesting in AI. The biggest savings driver: SME content production re-engineering."
- CTO, Education Technology Company
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The Results
Engineering productivity
20-30% time saved
Development costs
Millions in savings
Engineering headcount
No reduction
Savings reinvested in
AI development
Key Lesson
Productivity gains create a strategic choice, not an automatic outcome. The same gains that could
justify headcount cuts can also justify accelerating the roadmap. The decision depends on whether
the company prioritizes near-term cost reduction or long-term growth.
A Forward-Looking Caveat
The findings above are drawn from backward-looking data: what organizations chose to do with AI-
driven productivity gains through early 2025. The pattern of redeployment and acceleration that
dominates our sample may not persist as AI capabilities improve and economic pressures intensify.
Research from the Stanford Digital Economy Lab and Anthropic provides early evidence that
broader labor market shifts are already underway. Brynjolfsson, Chandar, and Chen (2025),
analyzing high-frequency payroll data from ADP covering millions of . workers, found that early-
career workers (ages 22–25) in AI-exposed occupations experienced a 16% relative decline in
employment since late 2022. A complementary Anthropic study found no systematic increase in
unemployment to date, but identified that hiring of younger workers has already slowed in AI-
exposed fields, and that actual AI deployment remains a fraction of theoretical capability,
suggesting the labor market impact is still in early stages. [5] [23]
This matters because the redeployment and hiring-avoidance strategies documented in our sample
are characteristic of an early adoption phase, when organizations are still learning what AI can do.
As implementations mature, models improve, and cost pressures mount, the distribution of
outcomes is likely to shift. The 45% reduction rate we observed may represent a floor, not a ceiling.
Companies that today choose acceleration over cuts may face different calculus when the next
generation of models arrives. The canaries are singing.
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Chapter 7
Where is AI opening doors that were
previously closed?
How enterprises move from efficiency to new revenue, new capabilities, and strategic
advantage
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Published Findings
Revenue growth is the aspiration, not the reality. Deloitte’s 2026 survey of 3,235 leaders found
that 74% of organizations hope to grow revenue through AI, but only 20% are doing so today. Only
34% are using AI to deeply transform their business through new products, services, or reinvented
business models.[21]
High performers pursue growth, not just efficiency. McKinsey’s 2025 State of AI survey found that
while 80% of organizations set efficiency as an AI objective, the companies seeing the most value
also set growth or innovation as objectives[11]. Yet only 6% of organizations report EBIT impact
above 5% from AI, and most revenue gains remain concentrated in marketing and sales, strategy,
and product development.[11]
Published research shows that growth via AI is widely aspired to but rarely realized.
What We Found
Most implementations are measured as cost savings. But the highest returns came from companies
that pointed AI at revenue: personalizing offers for each customer instead of segments, closing
deals in hours instead of weeks, and packaging internal tools as products sold to clients. Others
went further and used AI to do work no one had attempted before, like migrating legacy codebases
or building sales intelligence in markets where no structured data existed.
As agentic systems changed the workflow of engineers and product managers, they will increasingly
be liberated from the drudgery of manual coding and development tasks. This will give them more
time to focus on higher value work, experimentation and collaborative innovation. In time, the
novel applications and ideas that emerge from this change in working model could yield significant
dividends.
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Finding 1
New revenue from AI is real, but rare, and follows three
patterns
Most implementations in our sample are measured as productivity or cost reduction. But a subset
shows direct, quantified revenue impact. What distinguishes these cases is not the technology. It is
that someone measured the revenue side, not just the cost side. The revenue mechanisms fall into
recognizable patterns.
Personalization that converts.
A retail firm deployed AI to personalize marketing emails at scale, combining a machine learning
recommendation engine with generative AI content. In the first month, they measured a 40%
increase in purchase intent and a 20% increase in actual purchases. The AI did not change the
product. It changed which product each customer saw.
“The only thing this did was it gave them better emails to send.”
— Executive, Retail company
“60% opened the email, 40% went to the site, and probably 20% purchased something.”
— Executive, Retail company
A food delivery company serving millions monthly orders moved from group-based segmentation
(500 customers per segment) to individual personalization. The previous approach was not slow. It
was structurally incapable of operating at this granularity.
“Instead of like taking three weeks to create 50 campaigns, now you can have a campaign for each
person.”
— Executive, Food Delivery Company
An enterprise content platform measured a 200% increase in click through rates for AI generated
campaigns. Time to launch dropped from seven weeks to six hours. Both are efficiency metrics on
the surface, but the volume and precision they unlock translate directly into revenue.
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Speed that wins deals.
An insurance services company found that AI powered contract drafting turned speed into a
competitive weapon. Contracts that previously took weeks were delivered in four hours. The result
was not just efficiency. They won deals that would have been lost.
“They were drafting a contract that was really perfect, overseen by a lawyer, in four hours. In the
past, it would have taken weeks and they might have lost the contract.”
— Partner Value Creation, Private Equity Firm (owner of the operating firm)
In a market where speed of response determines who gets the deal, four hours versus four weeks is
not an efficiency gain. It is a different competitive position for small and medium businesses.
“SMEs can respond much better to this leverage, and they can actually be the winners of this
revolution.”
— Partner Value Creation, Private Equity Firm (owner of the operating firm)
In semiconductor manufacturing, the same pattern emerged at a different scale. Reducing testing
cycles by 20% and cutting customer issue resolution from 40 hours to under one hour changed how
the company competed for enterprise accounts.
“When time to market for a product shrinks, it’s not 5 million, 10 million in savings. It’s hundreds of
millions of dollars in savings.”
— Executive, Semiconductor Manufacturer
From insight to product.
Some companies discovered that their internal AI capabilities could become revenue sources. A
consulting firm that had built an analytics platform for marketing attribution realized the AI layer
could generate predictive recommendations and simulate campaign outcomes. The firm is now
launching this as a product offering and expects to “double” revenue from the platform.
“Then as we go towards productizing the simulator, then yes, probably doubling the revenue is
what we would expect.”
— Executive, Consulting Firm
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A technology services company went further. After building an AI invoice processing solution for
internal use, they packaged it and began selling it externally. A professional services firm’s internal
AI platform now serves new customers, becoming the foundation for service lines.
“We actually packaged it up and took some versions to some of our clients. One of the top three
largest consulting companies on the planet is using it.”
— Executive, Technology Services Company
What these cases share is not a common technology stack or industry. It is that someone asked a
question beyond “how do we reduce cost?” and measured the answer.
“ROI is king. If you can show that in your sales cycle, that is immediately going to get you where
you need to go. I’ve tried to sell efficiency with other things throughout my career and it is really
difficult.”
— Founder, Healthcare AI Company
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Finding 2
AI is enabling work that was never on the roadmap
Beyond revenue, a separate group of cases shows AI making entirely new work feasible. Not faster
versions of existing processes. Work that no one planned or budgeted because it was considered
impossible.
Rewriting what was considered technically impossible. A fintech with over 100 million customers
needed to migrate millions of lines of legacy code to a modern architecture. The traditional
estimate was 18 months with over 1,000 engineers. With AI coding agents, business units began
completing migrations in weeks.
“Rather than engineers having to work across several files and complete an entire migration task
100%, they could just review the changes, make minor adjustments, then merge their PR.”
— Executive, Fintech
An insurance firm found that AI could rewrite legacy systems from scratch faster than refactoring
them. A project originally quoted at 5,000 hours with a team of seven, scheduled for completion in
2027, was finished in 600 hours with a team of three. This opened a strategic question the firm had
never considered:
“Do you buy a customer base and then try and retool that? Could you start from scratch and go
disrupt a company by building their technology?”
— Executive, Insurance firm
Building market intelligence where none could exist. In healthcare markets with insurance, sales
teams buy claims data to know exactly which providers prescribe what, in what volume. That is the
standard playbook. But medical aesthetics is entirely cash pay. There are no claims, no centralized
registries, no structured datasets. Territory intelligence for this market was not expensive or slow. It
was impossible. A healthcare AI company changed that by building a system that scrapes public
sources, assembles provider profiles, and scores prospects by estimated procedure volume and
growth potential. For the first time, sales reps have a qualified pipeline in a market that never had
one. The company is now expanding the platform to serve other manufacturers.
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Turning operations into data assets. A robotic inspection company is generating historical datasets
from consistent AI powered inspections that enable predictive analytics and incident forensics.
Competitors cannot replicate this data without years of similar deployment. The inspections started
as an efficiency play. The data they produce is becoming an entirely new asset.
These cases share a common trait: they were not on the roadmap before AI made them possible.
No team was asked to do this work faster. AI wasn’t just used to improve efficient, emergent
solutions made themselves visible. The work itself was new and solved a problem they didn’t realize
existed.
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CASE STUDY
Customer Relations at a Call Center
How AI turned a traditional call center into a growth engine
Call Center Services | Customer Relations | Mid-Market
The Company
A call center as a service (CCaaS) company providing traditional call center services to enterprise
customers: answering calls, routing inquiries, and managing ticket queues. In a market increasingly
defined by AI native competitors, the company’s value proposition was under pressure.
The Problem
The CCaaS market was shifting. Enterprise customers were beginning to expect AI powered
capabilities as standard, not as a premium add-on. AI native startups could offer intelligent routing,
automated resolution, and real-time analytics from day one. The company’s traditional model, built
on seat-based pricing and human agents, faced two threats simultaneously: competitors could
deliver more value per interaction, and the underlying pricing model was eroding as AI reduced the
number of seats customers needed.
“One of the issues is that it has an impact on the SaaS model because it is reducing the number of
seats. So you need to find a new way to price it.”
— Partner Value Creation, Private Equity Firm
The challenge was not operational efficiency. It was strategic relevance.
The Solution
The management team embedded agentic AI directly into the company's product offering. Rather
than using AI to make human agents faster, they redesigned the service so that AI could resolve
tickets end to end, not just take calls or answer questions, but actually close issues.
The technical approach used an agentic AI framework that could orchestrate the full resolution
process. This went beyond copilot-style assistance into autonomous task completion.
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The Results
New project wins
20+ attributed to AI
Market position
Top 4 in AI for CX
Customer acquisition
Winning new logos
AI in every deal
No project without AI
Competitive repositioning
Traditional CCaaS → Benchmarked against AI na ves
What AI Unlocked Beyond Cost Reduction
The same technology stack that could have been used to reduce headcount or lower cost per ticket
instead repositioned the company in its market. Three things changed that had nothing to do with
efficiency.
First, the company started winning deals it could not have competed for before. AI capabilities in
the product became a differentiator. Thirty new projects were won not because the company was
cheaper, but because it was more capable than competitors still operating on traditional models.
Second, the company’s competitive set changed. An independent technology assessment ranked
the company among the top four for AI capabilities in customer relations. The other three were AI
native companies. A traditional call center was now benchmarked against startups, not against
other incumbents.
Third, the pricing model began to shift. Instead of selling seats, the company could sell outcomes. AI
capabilities became the product, not a cost reduction tool applied to the old product.
“Basically, what we see is that this [AI] is an approach that helps us win new deals.”
— Executive, Call Center Services Company
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Key Lesson
AI deployed for efficiency saves money. AI deployed into the product changes the competitive
position. The difference is not the technology. The question is not "how do we reduce cost?" but
"how do we win deals we could not win before?" This company asked the second question, and
thirty new projects later, the answer was clear.
“I strongly believe that mid-sized companies and small size companies are very well positioned to
win the AI revolution if you provide them the right capabilities. Decision making is taken much
faster. They don’t have that much legacy systems. They didn’t know what to do with unstructured
data, and now they can use it. And they lack resources, and the resources can get augmented with
AI.”
— Partner Value Creation, Private Equity Firm (owner of the operating firm)
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Chapter 8
Is agentic AI generating real value?
Where autonomous AI works and where simpler approaches win
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Published Findings
High hype, low scale. McKinsey reports that 62% of organizations are experimenting with AI agents,
but only 23% are scaling them. Scaling is limited to one or two functions, most commonly IT and
knowledge management.[11]
Emerging value in niches. OpenAI reports that enterprise AI adoption is accelerating unevenly by
sector, with technology (11×), healthcare (8×), and manufacturing (7×) showing the fastest year-
over-year growth, while finance and professional services operate at the largest absolute scale.[12]
Reliability limits. Anthropic warns that success rates decline as task complexity increases. Their
data suggests API success rates drop below 50% for tasks requiring approximately hours of
human effort.[10]
Agentic Capability is growing exponentially.
METR, an independent AI evaluation organization,
measures the length of software tasks that frontier
models can reliably complete autonomously. Their
research shows this metric had been doubling
approximately every seven months since 2019, but
in recent months it has accelerated. As of early
2026, the most capable models can now reliably
complete tasks without human intervention that
would take a human expert approximately 15
hours.[22]. (see Figure) This trajectory suggests that
the set of enterprise tasks suitable for agentic AI will
expand massively in the near term. These are benchmark capabilities; real-world deployment still
depends on integration, permissions, and exception handling.
What We Found
Agentic implementations are currently a minority at 20% of cases. Most likely due to the reality that
enterprise AI agent frameworks only emerged into the popular zeitgeist in 2025. But even with such
immature scaffolding, agentic AI is delivering higher median productivity gains (71% vs 40% for high
automation) in functions with high volume, clear success criteria, and recoverable errors. As these
systems mature and use cases broaden, we expect the advantages of agentic AI to accelerate. It’s
important to note that agentic AI isn’t a just new way to access AI, it’s a redefinition of the role of
Task completion time horizon of frontier AI models on software
engineering tasks, 2019 to 2026
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humans and machines in the workflow. Companies started treating AI as an extension of the team,
not just a tool, guided and supervised by humans but increasingly capable of acting on their behalf
and amplifying human capabilities beyond what one would have expected purely from looking at
the team headcount.
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Finding 1
Agentic AI is in production, but most implementations use
simpler approaches
Level Definition %
Agentic AI takes autonomous actions, completes multi-step tasks end-to-end without human
intervention
20%
High
Automation
AI handles >80% of work autonomously, humans review only exceptions or final
outputs
34%
Human-in-Loop AI and human work together, human reviews or approves each output before action 46%
Agentic implementations are the minority. The majority of successful enterprise AI uses simpler
approaches: high automation with exception handling or human-in-loop collaboration. This does
not mean agentic AI fails. It suggests many use cases do not yet require full autonomy to deliver
value, and likely the bigger blockers to agentic AI adoption are the lack of technology maturity and
limited deployment experience of the workforce.
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Finding 2
Agentic AI delivers higher productivity but with wider
variance
Agentic implementations show the highest median productivity gain at 71%. The highest gains came
from field service where a multi-agent framework gathered data across repositories automatically.
Human-in-loop shows 22% median, appropriate for document review and clinical documentation
where human judgment is essential.
As agentic frameworks mature over time, we expect an increasing percentage of use cases to fall
into the full autonomy category. In the coding space, that trend is already increasingly clear.
Examples of coding agents (Claude Code, OpenAI Codex, etc.) running for days autonomously
delivering tens or hundreds of thousands of lines of working code is not uncommon in recent
months.
This level of autonomous capability will not only increase productivity, but it will also redefine roles
in the organization. Team members with limited or no technical experience will soon be able to
build and deploy complex projects just by having a natural language conversation with the toolset,
as they had in the past with the development team lead. This level of capability won’t be restricted
to the software development realm alone. It’s foreseeable that such workflows will extend into
financial, accounting, consulting services and other data focused sectors rapidly. The macro labor
implications of this on the wider economy can be dramatic when adoption widens.
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Finding 3
Successful agentic implementations share common
characteristics
The ten agentic cases clustered in specific functions (procurement, field service, security operations,
coding, and customer support triage), but what matters more than the function is what these
implementations have in common: High volume, repetitive tasks. Security operations processing
thousands of alerts. Procurement handling hundreds of decisions. Customer support triaging
tickets. The volume justifies the investment in building autonomous systems.
Clear success criteria. Alert is valid or not. Procurement decision is correct or not. Ticket is resolved
or not. The AI can evaluate its own outputs against objective criteria.
Recoverable errors. A missed alert can be caught later. A wrong procurement recommendation can
be overridden. A failed ticket resolution escalates to a human. Errors are costly but not
catastrophic.
Data access across systems. Agentic AI requires the ability to query multiple systems, gather
information, and take actions. Implementations that succeeded had invested in data infrastructure
and API access.
"Don't just apply AI to your existing processes. That's a mistake. We're redesigning our workflow
and that's what makes us successful."
- Head of Operations, Technology Company
DRAFT
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CASE STUDY
Procurement at Supermarket Chain
How they built agentic AI that delivers real value
Retail | Procurement | Mid-Market
The Company
A regional supermarket chain with approximately two dozen stores. Unlike market leaders with
substantial margins and massive procurement power, this company operated at roughly half the
industry benchmark with minimal negotiating leverage against suppliers.
The Problem
Supermarket economics are unforgiving. Margins are thin, waste is constant, and stockouts lose
customers permanently. The company faced three interconnected challenges:
First, waste. Perishable goods expiring on shelves, seasonal products ordered in wrong quantities,
and promotional items that did not sell.
Second, stockouts. Empty shelves do not just lose one sale. They lose the customer who drives to a
competitor and may not come back.
Third, procurement timing. A human buyer made decisions based on gut feeling, supplier
relationships, and whatever data they could manually compile. They could not possibly optimize
thousands of SKUs across 25 stores.
DRAFT
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The Solution
The company deployed an AI system that replaced the human procurement function entirely. The
system does not assist humans or generate recommendations for review. It makes purchasing
decisions autonomously.
The architecture has three components: a data platform that pulls inventory, sales, and supplier
data from multiple systems; demand forecasting models that predict sales at the store and stock
keeping unit (SKU) level; and an autonomous procurement agent that decides what to buy, when to
buy it, and from which supplier.
"They replaced the human procurement guy with an AI tool that is buying. Telling them what to
buy. And so again they have the supermarket full of stuff and the stock is optimized."
- Project Lead, Retail company
What Makes It Agentic
This implementation crosses the line from automation to agentic AI in three ways:
First, it replaced a human function, not just a task. The AI took over the procurement role entirely,
determining what to buy, when to buy it, and how much, across all stores simultaneously. A human
buyer making the same decisions could not optimize at this scale.
Second, it connects multiple decision steps that were previously handled by intuition. A single
procurement decision requires predicting demand, checking current inventory, factoring in supplier
lead times, and balancing waste against stockouts. The system handles this chain continuously
across every product in every store.
Third, it operates across multiple systems without human orchestration. The AI pulls inventory
data, supplier catalogs, and sales history from different sources, processes them together, and
outputs purchasing decisions. Before, a human buyer was the integration layer.
DRAFT
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The Results
Waste reduction
40%
Stockout reduction
80%
EBITDA margin
Doubled
"The market leader has much higher margins. These guys are super small. But they do almost as
well, and their procurement power is zero compared to the big players. But what they have is that
they don't have waste."
- Project Lead, Retail Company
Key Lesson
This case illustrates that even for more traditional sectors, when agentic AI is applied properly, it
creates real value: tasks too complex for rules but too repetitive for humans, with clear success
criteria and recoverable errors. Thousands of SKUs across dozens of stores required continuous
optimization no human could perform. The AI could evaluate its own performance because
outcomes were measurable: did the product sell, expire, or run out? For a small retailer competing
against giants, agentic AI turned intelligence into a substitute for scale.
Sample Limitations and Future Outlook
Our findings on agentic AI should be interpreted with an important caveat: agentic technology was
still emerging during our data collection period (August 2024 to January 2025). Only 20% of the
implementations in our sample involved agentic workflows, and most organizations were
experimenting rather than scaling. The limited sample of agentic cases reflects the state of the
technology at the time, not its long-term trajectory.
That trajectory is likely to be transformative. Foundation models and agentic frameworks are
improving rapidly in their ability to reason, plan multi-step workflows, and recover from errors.
DRAFT
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These are precisely the capabilities that define agentic AI. As these models advance, the share of
enterprise use cases suitable for agentic approaches will grow substantially. Tasks that today
require structured automation with human oversight may increasingly be handled by autonomous
agents capable of navigating ambiguity and making context-dependent decisions.
The 71% median productivity gains we observed in agentic implementations, compared to 40% for
high automation, suggest that when agentic AI is applied to the right use cases, the impact is
significantly larger. As technology matures and the conditions for successful deployment become
better understood, we expect agentic implementations to represent an increasingly dominant share
of enterprise AI value creation. The patterns documented in this chapter capture the early innings
of a trend that will likely reshape how organizations think about the boundary between human and
machine work.
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Chapter 9
How clean does enterprise data actually
need to be?
Why the real data challenge is access and storage, not cleanliness
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Published Findings
Clean data is a scaler's advantage. Among Strategic Scalers, 61% possess a large, accurate data set,
compared to just 38% of companies stuck in Proof of Concept. Strategic Scalers are adept at "tuning
out data noise" to focus on priority domains like financial, marketing, and customer data.[9]
Data products are key. McKinsey notes that high performers are more likely to have created
"reusable, business-specific data products."[11]
Unstructured data tolerance. OpenAI reports that enterprise use of structured workflows (Custom
GPTs and Projects) grew 19× year-to-date, now handling approximately 20% of all enterprise
messages. This suggests organizations are succeeding by building access layers to existing data
rather than requiring perfect data structures before deploying AI.[12]
The above research establishes that clean data correlates with AI success. It does not quantify how
messy data can be while still yielding results.
What We Found
Only 6% of implementations had data that was fully ready for AI. But in the majority of cases where
data challenges existed, LLMs were part of the solution, not just the consumer of clean data, but
the tool that made messy data usable. Models unlocked previously inaccessible data in 88% of
cases, processing voice transcripts, scanned documents, legacy code, and scattered knowledge
bases that no prior technology could handle. A caveat: because this sample focuses on successful
implementations, it likely underrepresents cases where data quality proved insurmountable. The
finding reflects what is possible with deliberate design, not a universal guarantee.
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Finding 1
LLMs are not just consuming data. They are fixing it!
The conventional narrative assumes AI needs clean data to work. Our data tells a different story.
Only 6% of implementations had data that was fully ready for AI deployment. The vast majority
faced data challenges ranging from moderate to severe. Yet in most of those cases, LLMs were
part of the solution to the very data problems they were expected to struggle with.
Figure 9. Data quality challenges across deployments
This is a fundamental shift. Previously, unstructured data required human analysts to impose
structure before any analysis could happen. Now, 91% of our implementations successfully
processed unstructured data, including voice transcripts, scanned documents, images, chat logs,
and legacy code, that would have been unusable two years ago. In 88% of cases, LLMs unlocked
data that was previously inaccessible, not because it did not exist, but because earlier approaches
(OCR, rules engines, manual tagging) couldn’t process it at the accuracy and scale required.
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" We've had partners tell us, hey, it would have taken us two months to clean this up, and you guys
flagged all the data issues within a day."
- VP of AI, Professional Services Firm
The types of data made newly accessible span the full range of enterprise information.
Voice and conversation data. Ambient transcription in healthcare made doctor-patient
conversations accessible to coding teams for the first time. Call center transcripts became sources
of real-time coaching and quality assessment. Previously, coding teams had no window into clinical
decisions. Now they have the full conversation.
"With an ambient transcription technology, now that person does have access to everything that
was discussed as part of that person's medical care. The auditability and traceability of medical
care is now much more possible with this technology
- Executive, Healthcare Company
Scattered documents and knowledge bases. A semiconductor manufacturer reduced data
gathering more than 10 times by deploying a multi agent framework that pulls information from
five or six different repositories automatically. The data was technically available but practically
inaccessible due to organizational silos and the sheer time required to assemble it manually.
"Documents, different data sheets, different test libraries - it all is not centralized. Each of it is
owned by different teams."
- VP of Engineering, Semiconductor Manufacturer
Visual and multimodal data. Field technicians can now photograph equipment and receive instant
AI generated repair instructions. Retail procurement systems process scanned paper forms, emails,
and Excel spreadsheets that previously required armies of manual data entry clerks.
" You can take a photo of it and AI will instantly give him a detailed description of that device and
how to fix it.
- VP of AI, Telecom Company
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Finding 2
Process documentation and access matters more than data
perfection
Of the implementations where we could assess the data architecture, 59% had data scattered
across multiple systems owned by different teams. Only 16% had fully centralized data. Yet success
did not require centralization. It required access.
Organizations that built integration layers—whether APIs, RAG architectures, or multi agent
frameworks—to connect scattered data performed as well as those with centralized data stores. In
the pre-LLM world, enterprises had to structure and centralize data before extracting value. Today,
RAG architectures and knowledge base connectors work with messy data if the retrieval layer
