Research Summary

The AI Implementation Gap

Why 95% of AI projects fail, and what the 6% of winners do differently. A synthesis of research from McKinsey, MIT, Gartner, BCG, Deloitte, Accenture, PwC, Forrester, and RAND.

95%

of AI pilots deliver no measurable return

MIT, 2025

94%

of companies fail to see meaningful returns from AI

McKinsey, 2025

74%

struggle to scale AI beyond proof of concept

BCG, 2024

80%

of AI projects fail (double the rate of traditional IT)

RAND, 2024

The Scale of the Problem

Despite unprecedented investment (US companies spent over $300 billion on AI in 2025), the vast majority of organisations are failing to capture meaningful business value. MIT researchers call this the "GenAI Divide": high adoption, low transformation.

"Companies are pouring $30-40 billion into generative AI, yet 95% of enterprise pilots deliver zero measurable return."

MIT NANDA Report, 2025

Why AI Projects Fail

Leadership Misunderstanding

Executives have inflated expectations fuelled by impressive demos. Poor communication between business and technical teams leads to misaligned goals.

Source: RAND Corporation

Bolting AI Onto Existing Processes

Most companies try to add AI to existing workflows instead of redesigning how work gets done. This is the single biggest differentiator between success and failure.

Source: McKinsey

Poor Data Quality

Insufficient data volume, quality issues, and lack of governance lead to inaccurate models and biased outputs.

Source: Gartner, RAND

Skills Gap

81% of IT professionals think they can use AI, but only 12% actually have the skills. Only 22% of employees receive sufficient AI training.

Source: IDC, McKinsey

Lack of Patience

Most AI projects require 2-4 years for satisfactory ROI, not months. CFOs uncomfortable investing for indirect, future value abandon projects prematurely.

Source: Deloitte, RAND

Knowledge Stays With Individuals

When the AI champion leaves, the knowledge goes with them. Without structured rollout, capability never becomes organisational.

Source: Various

94%

of companies fail to see meaningful returns from AI

McKinsey, 2025

What the 6% Do Differently

McKinsey defines "AI high performers" as organisations that attribute 5% or more of EBIT to AI use and report significant value. Only 6% of companies meet this threshold. Here's what makes them different.

More Likely to Redesign Workflows

High performers don't bolt AI onto existing processes. They fundamentally rethink how work gets done. This is the #1 success factor out of 25 attributes tested.

McKinsey, 2025

3.6x

More Likely to Pursue Transformation

Winners aim for transformational, enterprise-level change, not just incremental efficiency improvements.

McKinsey, 2025

22%

Receive Sufficient Training

Only 22% of employees receive sufficient AI training. The rest are left to figure it out alone, leading to inconsistent adoption and wasted potential.

McKinsey, 2025

Stronger Leadership Commitment

High performers have consistent, transparent support from top leadership, not just initial enthusiasm.

McKinsey, 2025

The Key Insight

Success isn't about having the best technology. It's about how you integrate AI into your workflows, processes, and people.

Out of 25 attributes tested by McKinsey, workflow redesign has the biggest effect on an organisation's ability to see EBIT impact from AI. Companies that fundamentally rework their processes when deploying AI are 3x more likely to succeed than those who bolt AI onto existing workflows.