The Human Problem of AI Strategy: Why Behavioural Alignment (Not Data) Is the Biggest Bottleneck

Over 80% of AI projects fail to scale not for technical reasons, but human ones. (McKinsey & BCG 2024)

🧩What Misalignment Actually Looks Like

Data scientists optimise for precision while product teams need speed. Executives demand clarity but insights stay buried in dashboards.Frontline teams ignore the output and default to old instincts. Without structured processes governance teams struggle to surface and address bias or ethical risks

The result? AI becomes a siloed oracle: powerful, underused and disconnected from the decisions that matter.

🔄Why This Keeps Happening

Most AI strategies obsess over technical alignment: Is the model accurate? Is the dataset clean? Are the outputs explainable?

What gets overlooked is behavioural alignment:how people across teams actually interpret and act on AI insights. As Forbes notes, successful AI adoption requires a double alignment strategy that addresses both technical and human dimensions.Yet organisations still treat AI as a tool to manage, not a collaborator to integrate. They assume that if you build it correctly adoption will follow. 

💸The Real Cost

This misalignment isn't abstract it's expensive. Behavioural friction shows up as project delays, budget overruns and wasted CAPEX on models that never reach production. If teams fail to turn AI insights into coordinated action, even the best technology will just sit unused.

🔀A Different Approach

🔹Design Insight Sprints. Replace passive dashboards with facilitated sessions where cross-functional teams interpret outputs together and turn data into shared strategy. E.g A 90-minute sprint where product, data science , SME's and Human centred design practitioners collectively answer: What decision does this enable? and Who needs to act differently?

🔹Create Decision Assets. Turn insights into transparent artefacts that document how decisions were made and challenged. This might look like a one-page brief that captures: the question asked, the data consulted, competing interpretations and the rationale for the chosen path. These become organisational memory not just outputs.

🔹Train for Strategic Inquiry. Teach teams to ask What problem is this solving? and Is this the best approach? not just "What does the model say?" This shifts AI from answer-machine to thinking partner.

🔹Map Behavioural Friction Early. Before committing capital, run participatory workshops to identify where collaboration breaks down. Surface the unspoken tensions between teams before they become bottlenecks.

If You are Designing AI Strategy for 2026

Start with behaviour, not infrastructure.I am currently working on mapping insight sprint and decision artefacts that bridge this gap. If you are tackling similar challenges particularly in scaling AI beyond pilots. How are you approaching these challenges?