Ep. 4: Why AI Adoption Isn’t an AI Problem

Most AI strategy reports focus on models, metrics, and monetisation and McKinsey’s State of AI 2024 is no exception, but between the charts and case studies, it quietly surfaces something deeper: adopting AI means redesigning workflows. Redesigning workflows means reshaping human behaviour and that’s where behavioural scientists can contribute.

The podcast episode is a quick overview of the report’s findings and this companion post makes the behavioural case for why it matters. If you work in behaviour change, systems thinking, organisational design, or decision science, this is your signal: AI adoption is a human transformation just as much it’s a technical one.

Most AI strategy reports focus on models, performance metrics, or return on investment, and McKinsey's State of AI 2024 report follows this familiar template. However, a closer reading through a behavioural lens suggests that the core challenges organisations face are not primarily technical. They relate to how people work, adapt, and respond within changing systems.

One of the report's clearest findings is that companies seeing the greatest value from AI are not necessarily those deploying the most advanced tools. Instead, they are the ones that have made deliberate changes to how workflows are structured. This suggests that the effort required to benefit from AI lies less in model sophistication and more in the design of day-to-day processes. From a behavioural perspective, that involves a set of tasks that go beyond the scope of engineering or data science.

This post accompanies a recent podcast episode summarising the report. It aims to situate that summary within a broader reflection on how behavioural science could play a more central role in AI adoption. The uptake of generative tools is often framed in terms of technical integration. Yet the work of integration is largely human. It involves shifts in habits, assumptions, and informal decision rules—areas where behavioural science has long experience.

Among the report's findings, one in particular stands out: workflow redesign is the only organisational factor that consistently predicts positive economic outcomes from AI adoption. Despite this, relatively few companies report having changed how work is actually done. Most have added tools into existing systems without rethinking the underlying tasks or responsibilities.

From a behavioural science point of view, this is a familiar pattern. New systems are introduced with the expectation that they will change behaviour, yet little attention is paid to the conditions that shape how people actually behave. If workflows are not adapted to account for changes in decision dynamics, information flow, or accountability, the effects of new tools may remain limited.

The idea of workflow redesign can therefore be seen not just as an operational measure but as a form of behavioural intervention. It draws attention to the routines, defaults, and incentives that guide behaviour. In the context of generative AI, this includes how outputs are evaluated, how iteration is managed, and how responsibility is distributed.

One common framing of generative AI is that it reduces friction in creative or cognitive tasks. Tasks that once required considerable time or skill can now be completed more quickly. However, this reduction in effort does not necessarily translate into overall ease. Instead, the point at which effort is required may shift.

The report notes variation in how organisations handle the review of AI-generated outputs. In some cases, outputs are consistently checked; in others, they are used with minimal oversight. This inconsistency points to a broader ambiguity around quality assurance, decision authority, and accountability. These are not technical questions. They are questions about how work is defined and how decisions are distributed.

When generation is easy, evaluation often becomes harder. This is particularly true in contexts where outputs are numerous and where each appears superficially plausible. The work of discerning what is appropriate or trustworthy becomes a new form of labour—one that is often under-specified in organisational processes.

The implementation of generative systems often focuses on surface-level functionality. Tools are assessed based on speed, usability, or output quality. Yet many of the problems that emerge after deployment relate to how those tools are used in context. Behavioural science offers methods for examining that context, identifying where friction occurs, and designing environments that better support decision-making.

For instance, new workflows often generate cognitive demands that are not visible in formal process maps. These include repeated judgement calls, switching between exploratory and evaluative modes, and managing ambiguity about completion. Identifying and addressing these dynamics requires methods that go beyond performance metrics. It involves observing behaviour, analysing choice environments, and adjusting design accordingly.

Behavioural scientists can also contribute to the development of prompts, feedback structures, and interface cues that shape user expectations and attention. These interventions may seem minor in isolation, but they play a central role in how decisions unfold. The goal is not to optimise individual actions in isolation, but to shape the broader environment in which those actions take place.

There is a tendency in technology adoption discourse to equate deployment with integration. A tool is made available; therefore, it has been adopted. However, availability does not guarantee effective use. Nor does it ensure that new practices will emerge to support long-term impact.

Behavioural science can help clarify the gap between access and actual uptake. It draws attention to the small frictions, misunderstandings, and habit loops that may prevent tools from being used in the ways intended. It also offers strategies for identifying which behaviours are most critical for success, and for supporting those behaviours in practice.

In this context, the McKinsey report functions less as a diagnosis and more as a mirror. It reflects back the fact that many organisations are treating AI integration as a technical project, when its challenges are often behavioural. The process of redesigning workflows, aligning incentives, and clarifying responsibility is one that sits well within the behavioural scientist's domain.

If generative systems change how decisions are made, they also change what kinds of support are needed. Behavioural science, with its focus on context, attention, and decision dynamics, is well-placed to contribute to this work. The opportunities may not always be framed as behavioural problems, but they are rooted in questions about how people engage with tools, environments, and each other.

The podcast accompanying this post offers a short overview of McKinsey's findings. This piece is intended as a complement—less a summary than a suggestion that the behavioural dimensions of AI adoption deserve more attention.