AI Autopsy Report

01 / 20

GLOBAL GAUNTLET AI

THE UNCOMFORTABLE TRUTH

The AI Autopsy Report Why 87% of Enterprise AI Projects Fail—And What Dead Initiatives Can Teach the Living

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

02 / 20

THE PROBLEM

GARTNER 2024 RESEARCH

87% OF AI PROJECTS NEVER MAKE IT TO PRODUCTION

That's not a failure rate.

That's an extinction event.

Source: Gartner, McKinsey, MIT Sloan Research 2024

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

03 / 20

WHY THIS MATTERS

The AI Graveyard Is Full of Good Ideas 1

Every failed AI project had executive sponsorship

2

Every one had budget approval

3

Every one had talented teams

4

Every one had "strategic alignment"

"They died anyway."

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

04 / 20

ROOT CAUSES

The 5Causes of Death After analyzing 150+ failed implementations:

1

Data Delusion — Building on a foundation of garbage

2

Scope Creep Syndrome — Trying to boil the ocean

3

Sponsorship Abandonment — Executive AWOL

4

Integration Avoidance — The "demo trap"

5

Change Resistance — People problems, not tech problems

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

05 / 20

CAUSE OF DEATH #1

Data Delusion THE #1 KILLER OF AI PROJECTS

What They Say

What It Means

"We have tons of data"

"We have tons of messy, siloed, unlabeled data"

■■ REALITY CHECK

76% of AI project time is spent on data preparation. Most teams budget 10%.

The Fix: Audit data BEFORE greenlighting any AI initiative. No clean data = no project.

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

06 / 20

CAUSE OF DEATH #2

Scope CreepSyndrome HOW "WHILE WE'RE AT IT" KILLS PROJECTS

Original Scope

$500K

After "Quick Additions"

$2.3M

Final Spend (Before Cancellation)

$4.1M

The Fix: Define a 90-day MVP with ONE measurable outcome. Protect it with your career.

Average scope creep: 4.6x original budget

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

07 / 20

CAUSE OF DEATH #3

Sponsorship Abandonment THE EXECUTIVE VANISHING ACT

WEEK 1

WEEK 12

WEEK 24

"Top priority"

"Let's revisit next quarter"

"What AI project?"

Daily standups

Monthly check-ins

No contact

Unlimited budget

"Stay within limits"

Frozen budget

The Fix: Require bi-weekly exec steering meetings with documented decisions.

68% of failed AI projects lost executive engagement by month 3

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

08 / 20

CAUSE OF DEATH #4

TheDemo Trap WHEN "IT WORKS IN THE LAB" MEANS NOTHING

"The demo was amazing. The production rollout was a disaster."

— Every failed AI project sponsor

Demo Reality

Production Reality

Perfect data, controlled environment, hand-picked scenarios

Messy data, legacy systems, edge cases everywhere

The Fix: Require production path before demo approval. No integration plan = no demo.

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

09 / 20

CAUSE OF DEATH #5

Change Resistance THE HUMAN FIREWALL

What users actually say:

→ "I wasn't consulted" — Lack of ownership → "This threatens my job" — Fear of obsolescence → "It doesn't fit how we work" — Workflow disruption → "The old way is faster" — Learning curve fatigue

The Fix: Involve end users from Day 1. Their buy-in is the project. Everything else is PowerPoint.

70% of AI failures are attributed to organizational factors, not technology

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

10 / 20

REAL-WORLD AUTOPSY

CASE STUDY

The $12M Chatbot Catastrophe How a Fortune 500 company burned $12 million on an AI chatbot that was worse than the $50K solution they already had.

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

11 / 20

AUTOPSY DETAILS

Timeline of a $12M Failure PHASE

WHAT HAPPENED

COST

Q1

Vendor selection (3 months, 47 meetings)

$400K

Q2-Q3

Custom development ("enterprise-grade")

$4.2M

Q4

Integration nightmare discovered

$2.8M

Y2 Q1-Q2

Pivot to "hybrid approach"

$3.1M

Y2 Q3

Project cancelled

$1.5M (wind-down)

What they got:

A chatbot that answered 23% of questions correctly (vs. 71% for their existing system).

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

12 / 20

THE 13% THAT SURVIVE

THE SURVIVORS

What the 13% Do Differently They don't have better technology.

They don't have bigger budgets.

They have better discipline.

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

13 / 20

FRAMEWORK

TheVITALFramework 5 QUESTIONS BEFORE ANY AI PROJECT

V alue — Can we quantify the ROI in dollars within 90 days?

I

ntegration — Do we have a production path, not just a demo?

T eam — Are the right humans committed (not just assigned)?

A ssets — Is our data clean, labeled, and accessible?

L eadership — Will the sponsor stay engaged weekly?

Score yourself:

5/5 = Go. 4/5 = Fix first. 3 or below = Don't start.

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

14 / 20

SURVIVAL TACTIC #1

The90-Day Rule IF IT CAN'T SHOW VALUE IN 90 DAYS, IT WON'T

■ Doomed Projects

✓ Successful Projects

"We'll see ROI in 18-24 months after full deployment"

"We'll prove $X value in 90 days with a limited pilot"

The math is simple:

→ Executive attention span: ~90 days → Budget cycle pressure: Quarterly → Technology half-life: ~6 months

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

15 / 20

SURVIVAL TACTIC #2

Integration First BUILD THE PIPES BEFORE THE PRODUCT

"Demo teams build features. Production teams build pipes."

Before writing a single line of AI code, answer:

→ How does data flow into this system? → How do outputs flow into existing workflows? → Who owns the handoff points? → What happens when the AI is wrong?

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

16 / 20

SURVIVAL TACTIC #3

TheHuman Metric MEASURE ADOPTION, NOT ACCURACY

96% ACCURACY MEANS NOTHING IF 0% OF USERS ADOPT IT

Vanity Metric

Success Metric

"Our model has 96% accuracy"

"73% of target users use it daily"

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

17 / 20

YOUR ACTION PLAN

ThePre-MortemChecklist RUN THIS BEFORE EVERY AI PROJECT

■

Data audit complete (not just "we have data")

■

90-day MVP defined with single success metric

■

Executive sponsor committed to weekly check-ins

■

Integration plan reviewed by IT/Security/Compliance

■

End users involved in design (not just informed)

■

Kill criteria defined (when to stop)

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

18 / 20

THE BOTTOM LINE

AI doesn't fail because the technology is bad AI fails because we keep making the same human mistakes

The autopsy reports are all the same.

The question is: will you read them before your project dies?

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

19 / 20

KEY TAKEAWAYS

3 Things to Remember 1 87% fail for predictable reasons Data, scope, sponsorship, integration, people. Fix these first.

2 90 days or bust If you can't prove value in 90 days, you probably never will.

3 Adoption > Accuracy A 70% accurate tool that people use beats a 99% accurate tool that sits idle.

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay

20 / 20

LET'S CONNECT

WANT MORE?

I've killed and saved AI projects worth

$4B+ Google-Fitbit ($2.1B)

Intuit M&A

Philips Healthcare

Thermo Fisher

DM me "AUTOPSY" for the full Pre-Mortem Checklist Or connect at bit.ly/jjshay

JJ Shay | M&A Executive × AI Builder

bit.ly/jjshay