Why AI success depends on effective change management

AI is here to stay, but corporations are still figuring out how to extract the most value from it. The GenAI Divide: State of AI in Business 2025 (from MIT’s Project NANDA) found that approximately 95% of AI projects delivered no measurable return on investment for enterprises. AI is a leadership, strategy, and execution challenge that will shape organisational performance for years to come.

But why is it such a challenge? We’ve identified three common questions that many business leaders are facing around artificial intelligence, and figuring out the answers is no longer theoretical:

1. Where can AI deliver material business value within our organisation in the next 12 to 24 months?
2. Do we have a coherent AI strategy that aligns investment, governance, and operating model change, or are we still running disconnected experiments?
3. Are our leaders equipped to secure the cross-functional buy-in required to turn AI ambition into sustained execution?

This is not the time for businesses to stick their proverbial head in the sand when it comes to AI.

Recent research underlines the urgency. Gartner’s Top Strategic Technology Trends for 2026 identifies AI as a central enterprise priority, shifting from isolated pilots to organisation-wide strategy, governance, and operating model change (Gartner). McKinsey’s State of AI global survey shows AI adoption is now widespread, with competitive advantage driven by how effectively organisations translate AI ambition into execution (Mckinsey). The clear implication is that inaction is no longer neutral. It represents a strategic risk.

So, in all likelihood, your competitors are already assessing what AI can bring to their business.

The difference between AI success and failure lies in how leaders respond to each of these questions.

Where can AI deliver material business value within our organisation in the next 12 to 24 months?

There is no one-size-fits-all answer. The pace of change is such that any definitive list of tools would be outdated almost immediately.

At a high level, however, there are several core capabilities that can be augmented by AI across most organisations:

1. Drafting, summarising and refining written content such as reports, proposals, and internal communications
2. Analysing large datasets to identify trends, risks and opportunities
3. Automating routine administrative and operational tasks
4. Supporting decision-making through forecasting, scenario analysis, and modelling
5. Enhancing customer support through first-line query handling and triage
6. Assisting software development through code generation, testing, and documentation

The impact is already visible. Organisations are using AI to shorten decision cycles, improve forecasting accuracy, and reduce operational friction.

Even where AI is not formally implemented, employees are using it informally. Without clear governance, this introduces exposure around data security, compliance, and decision quality, which leads directly to the next question.

Do we have a coherent AI strategy that aligns investment, governance, and operating model change, or are we still running disconnected experiments?

Once potential value is identified, it must be translated into a credible AI strategy. Effective strategies typically address five core areas:

1. Clear business objectives linked to measurable outcomes
2. Governance and guardrails defining acceptable use, risk, and accountability
3. Prioritisation of use cases based on value and feasibility rather than novelty
4. Capability development, including skills, data readiness, and operating processes
5. Measurement and review to ensure AI-enabled decisions deliver real impact

Without this structure, AI initiatives remain fragmented and projects struggle to scale beyond experimentation.

Once we formalise a strategy, execution emerges as the primary challenge, leading us to our final query.

Are our leaders equipped to secure the cross-functional buy-in required to turn AI ambition into sustained execution?

Without stakeholder buy-in, even the best AI strategy will fail. AI creates genuine uncertainty within organisations. It is a significant disruptor that requires changes to ways of working and, in some cases, role expectations. Resistance is a natural response, particularly where there is perceived risk to job security or professional relevance.

Delivering an AI strategy is therefore, in practice, a major change management exercise. It demands the ability to influence, align, and sustain engagement across functions and seniority levels. Organisations lacking these capabilities often stall between intent and impact.

Where simulation meets AI

This is why we have developed a new storyline for our Influence change management business simulation. It challenges participants to secure cross-departmental adoption of a new AI strategy while navigating the real-world tensions, trade-offs, and opportunities leaders face.

And if you are wondering whether we used AI in developing this simulation, the answer is yes. We identified where it could add value, formed a clear strategy, and ensured our stakeholders understand how and why we are changing.

If you'd like to learn more about this simulation or explore how it can support AI strategy execution in your organisation, please get in touch with us.