Most management teams say they are pursuing AI transformation. In reality, many are implementing AI productivity tools; simply bolting LLM functionality into existing processes to yield small, incremental gains.

The distinction matters. A business targeting a 30% productivity gain requires a fundamentally different strategy from one pursuing a 10x improvement in how work gets done. They are different investment cases, different organisational changes and different timelines.

Across Mayfair’s portfolio and through Boost! portfolio networking events, a consistent pattern is emerging: businesses often optimise for the first while presenting the second to boards and investors. The first step in building an effective AI strategy is understanding which one you are actually trying to achieve.

What is the difference between AI productivity and AI transformation?

AI productivity means adding tools to existing workflows, meaning employees write emails faster, reports take less time and analyses are produced more efficiently. These gains are real and, in many cases, material. But they are not transformation, which means redesigning those workflows entirely. They are not asking how to make an existing process faster, but whether it should exist in its current form at all.

That distinction surfaced repeatedly at a recent Boost! event attended by operators, executives and technology leaders from across the Mayfair portfolio, and it shapes everything that follows.

This creates two distinct strategies:

Strategy 1: Tool adoption. AI is introduced into existing workflows to help people work faster. Typical gains include shorter reporting cycles, more efficient analysis, faster content creation and reduced administrative burden. Many businesses achieve meaningful results through this approach alone.

Strategy 2: Workflow transformation. The organisation maps how work moves through the business, identifies bottlenecks and decision points, and rebuilds processes around AI-native workflows. The question changes from ‘how do we make this process 30% faster?’ to ‘would we design this process the same way if we were starting today?’ This is where the potential for 10x improvement emerges.

Workflow transformation requires deeper organisational and individual mindset change. Roles, governance structures and operating models all come under scrutiny, which is why relatively few businesses progress beyond productivity gains.

Why do most AI projects fail to deliver value?

The failure mode is rarely the technology. Most AI programmes underperform because organisations automate existing work rather than redesigning workflows.
Research discussed at the event showed that the overwhelming majority of AI initiatives fail to achieve their expected return on investment. Many organisations become trapped in “pilot purgatory”: an endless cycle of experiments and proofs of concept that never reach production scale.

The common causes are structural: no clear ownership, no success metrics, fragmented data and teams that treat AI as an isolated experiment rather than a business transformation programme.

There is also a systems constraint problem. Improving one stage of a process often exposes bottlenecks elsewhere. A software team that doubles development velocity through AI-assisted coding may find that product management or release governance becomes the new constraint.

What do the highest-performing AI adopters do differently?

According to BCG research discussed at the event, businesses leading in AI adoption achieve 3.5x higher total shareholder returns and 1.6x greater EBIT performance than peers. They are not winning because they have chosen better models. They are winning because they have built stronger organisational foundations.

Three characteristics consistently separate leaders from laggards:

Organisational alignment: AI is owned by business leaders, not treated as an IT initiative. There is clear accountability for outcomes and value creation.

Data readiness: AI cannot compensate for poor information foundations. Successful adopters address data quality, accessibility and governance early.

Execution discipline: The best performers move quickly from experimentation to production, with mechanisms to test, learn and scale while maintaining appropriate oversight.

How should management teams get started?

Start by identifying two or three workflows that matter. Map how information moves through each one, the handoffs, bottlenecks and decision points. Then ask a more fundamental question: if this workflow were being designed today, with AI available from the outset, would it look the same?

That question shifts the conversation from tools to transformation. For most management teams, it is the moment the difference between 30% and 10x becomes visible.

Through Mayfair’s Boost! programme, portfolio management and operations teams continue to share practical examples of AI adoption, workflow redesign and organisational change. The most consequential AI decision facing a management team today is not which tool to deploy, it is which outcome to pursue.