AI initiatives often fail for reasons that have little to do with technology.

The tools work.
The data is well organised and sufficiently clean.
The business case is sound.

Execution still falters because change management is treated as a downstream activity, addressed only once decisions have already been made.

In AI-enabled strategies, this approach is increasingly costly and reduces return on investment (ROI).

In one professional services firm, I have seen first hand how the lack of early change management is slowing down ROI. They lost 6 months in the benefits realisation. Line managers (the key stakeholders) were debating the approach at the planning stage, without a clear process to resolve their differences. The AI program was becoming increasingly politicised as each division wants their own preferences and objectives to be prioritised. I advised them on how they can set up a process and governance structure to get this agreement, and fortunately the program is back on track.

 

Why AI intensifies the change challenge

AI does not simply optimise existing ways of working. It reshapes them.

It changes:
• How decisions are made
• How work is prioritised
• How performance is measured
• How authority is distributed

These shifts affect people’s roles, identity, and confidence. When change considerations are introduced late, resistance is not a reaction to AI itself. It is a reaction to exclusion.

Recent research from Gartner indicates that AI-driven change initiatives fail more often when employee engagement begins after key design decisions are locked in, rather than during the strategy formation phase (Gartner, 2024).

 

Late engagement creates invisible execution risks

When change management starts late, organisations encounter predictable problems.

People comply but do not commit.
Workarounds appear.
Adoption remains superficial.

These issues rarely show up in dashboards, but they slow execution significantly.

McKinsey’s 2025 research on AI transformations shows that organisations that delay engagement until rollout face slower adoption and lower realised value, even when technical implementation is successful (McKinsey, 2025).

Execution risk accumulates quietly.

 

Change management is a strategy design discipline

Effective AI change management starts before plans are finalised.

It focuses on:
• Who will be affected
• How work will change
• What concerns need to be addressed early
• Where capability and confidence gaps exist

This input improves strategy quality. It surfaces constraints, tests assumptions, and highlights where execution support will be required.

Change management is not about persuasion. It is about informed design.

 

Participation builds execution readiness

When people are involved early, change feels different.

Participation:
• Builds understanding of intent
• Creates shared ownership
• Reduces uncertainty
• Accelerates learning

Gartner research on participative change approaches shows higher adoption rates and lower levels of change fatigue when employees are involved in shaping change, rather than reacting to it. They describe this as “open source change management” (Gartner, 2023). I have written extensively on this describing it as “open strategy”, “culture-friendly” approaches or “culture intelligence” (more specific than emotional intelligence).

In AI initiatives, employee participation also improves the quality of implementation decisions.

 

Leadership visibility matters more than messaging

AI change initiatives place new demands on leaders.

Employees look to leaders for cues about:
• How seriously AI will affect their roles
• Whether learning is genuinely supported
• How mistakes will be handled

MIT Sloan research highlights that visible leadership involvement is a key factor in successful AI adoption, particularly where uncertainty is high and experimentation is required (MIT Sloan, 2025).
Leadership behaviour communicates priorities more clearly than any change plan.

 

Embedding change into execution governance

Change management is sustained through governance.

Governance provides:
• Clear decision-making pathways
• Mechanisms for feedback and learning
• Accountability for adoption outcomes
• The ability to adjust approaches over time

Without governance, change efforts lose momentum once initial enthusiasm fades.
AI-enabled strategies require governance structures that keep change visible throughout execution, not just during launch.

 

From managed change to execution momentum

AI magnifies the consequences of how change is handled.

Late engagement slows execution.
Early involvement accelerates it.

Organisations that treat change management as part of strategy execution, rather than a communication exercise, move faster with fewer disruptions.

AI does not reduce the need for change management. It raises the stakes.

PS More Ways to Accelerate your Strategy

Looking to move your strategy forward? Lisa Carlin works with selected clients directly to go from strategy to plan to result. Email Lisa.

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