The Argument · Why 94% see no AI impact

The seventy percent problem.

Eighty-eight percent of organizations use AI. Six percent see meaningful EBIT impact from it. The gap is everything other consultancies don’t sell.

Published 88% use AI 6% see real impact BCG 10/20/70 framework McKinsey State of AI 2025
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The thesis

Adoption is solved. Impact is not.

The thesis

The gap between adoption and impact is the seventy percent of AI transformation effort that consulting firms structurally cannot deliver.

Adoption is no longer the question. Impact is. That gap is the seventy percent of AI transformation effort that consulting firms structurally cannot deliver — and that mid-market operators have to figure out for themselves, or hire Mezura.

The evidence

The three numbers that define this problem.

Only 6% of companies see real profit impact from AI. McKinsey surveyed 1,993 organizations across 105 countries in Q3 2025 to find out why. Three numbers — from three independent studies — define the shape of the problem.

88%
of organizations reported using AI in at least one function, up from 78% the previous year. Adoption is solved.McKinsey, State of AI 2025 (surveyed 1,993 organizations across 105 countries, Q3 2025)
6%
attribute 5% or more of EBIT directly to AI — McKinsey’s “AI high performers.” 39% report any measurable EBIT impact at all, and the majority of those say it is less than 5% of EBIT.McKinsey, State of AI 2025
95%
of generative AI projects deliver no measurable ROI to the deploying enterprise. The 5% that do are concentrated in organizations that rebuilt their operating models around AI economics — not those that deployed AI on top of existing ones.MIT NANDA, GenAI Divide (July 2025)

88% adopting, 6% impacting, 95% of projects failing. The technology works. The deployment does not.

The framework

BCG’s 10/20/70 framework.

The framework

Validated repeatedly over a decade: 10% algorithms, 20% technology and data, 70% people and processes. The seventy percent is the work most firms cannot sell.

Boston Consulting Group has spent a decade quantifying where AI transformation value comes from. The answer, reaffirmed in BCG’s January 2026 publication Scaling AI Requires New Processes, Not Just New Tools:

Where AI transformation actually happens
10%Algorithms
20%Technology & data
70%People & processes
A buyer can spend 80% of their AI budget on the 30% of effort that drives value.
BCG, Scaling AI Requires New Processes, Not Just New Tools (January 2026)
10% — Algorithms
Model selection, fine-tuning, prompts, RAG. What AI labs and technical boutiques sell.
20% — Technology & data
Integration, pipelines, vendor-stack management, governance infrastructure. What Big Four firms sell at scale.
70% — People & processes
Workflow architecture, decision rights, incentive structure, governance, operational measurement. What determines whether AI compounds or sits unused.

McKinsey’s 2025 report confirms it from another angle: out of 25 organizational attributes tested for correlation with EBIT impact, fundamental workflow redesign was the single highest-correlated. Only 21% of organizations had done it.

McKinsey, State of AI 2025 · 25 organizational attributes tested for correlation with EBIT impact
The cohort

McKinsey’s 6% cohort: what high performers actually do.

The high-performer cohort was distinguished not by better technology, but by five operating-model choices.

01
Transformative ambition
3.6× more likely than other organizations to pursue transformative change with AI, not incremental productivity gains.
02
Redesigned workflows
More likely to have fundamentally redesigned workflows for AI economics — not bolted AI onto the workflows they already had.
03
Defined KPIs
More likely to track well-defined KPIs for AI deployment, rather than running pilots without an outcome metric.
04
Senior accountability
More likely to have senior leadership directly accountable for AI outcomes, not delegated to a function.
05
Board-level discussion
More likely to make AI deployment a board-level discussion — an operating priority, not an IT line item.
McKinsey, State of AI 2025 (the “AI high performers” cohort)

None of these are technology variables. All of them are operating-model variables. The other 94% are not making mistakes that better algorithms would fix — they are running operating models built before AI economics existed, with AI bolted on top.

The structural reason

Why most consulting firms cannot sell the seventy percent.

The structural reason

Their economics are wrong for it. Big firms sell projects, not outcomes. Boutique firms are paid by the vendors they recommend. Neither has skin in the game on the result.

The 70% does not scale with people. It requires senior judgment applied to specific buyer contexts; it cannot be templated, delegated to associate teams, or sold as a fixed-scope deliverable with confidence — because the work depends on what the operating-model audit reveals, unknown at the time of sale. Mezura’s answer: productize the diagnostic — fixed scope, firm price — and keep the rebuild scoped to what the diagnostic actually surfaces.

The seventy percent requires a structurally different model: senior-only delivery, no vendor partnership economics, skin in the game on outcomes the firm has moved before, and selectivity by design. That model is uncommon. It is what Mezura is.

In practice

What working on the seventy percent looks like.

In one 2024 engagement, a YC-backed career-services company moved its application-to-interview conversion rate from 0.5% to 2.3% — a 4.6× lift, no new product, no new team — by rebuilding four layers of the operating model in a ten-week sprint, using AI-driven workflow automation to strip out the manual drag. Input quality. Decision protocols. Automation handoffs. Incentive alignment. The technology was not the bottleneck. The operating model around it was.

Read the full case study →  ·  See how Mezura’s methodology runs →  ·  See how it works →

One more thing about timing

The wrong variable to wait on.

On timing

The seventy percent is not an AI problem. It is an operating-model problem that AI exposes and amplifies.

The operating models that produce the McKinsey 6% cohort would produce above-market performance with or without AI. AI is the multiplier; the operating model is the base. A buyer who waits for AI to stabilize before fixing the operating model is waiting on the wrong variable.

Next step

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Next step

A free 30-minute call with the founder — a direct read on whether, and where, you’re leaking, and whether the $25,000 Diagnostic is the right next step. No deck, no pitch.

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