Eight Reasons Why AI Transformation Efforts Fail
In 2024, during a session at MAICON—an AI conference specifically designed for marketing leaders—I heard Jasper AI’s Director of AI Transformation, Jessica Hreha, highlight the ongoing relevance of management guru John Kotter’s work.
Nearly three decades earlier, Kotter had published a seminal piece in Harvard Business Review titled “Leading Change: Why Transformation Efforts Fail”.
In it, he documents his observations of over 100 companies undertaking major transformation initiatives.
Kotter ultimately identified eight key reasons these transformation efforts faltered, reasons that have proven remarkably resilient—and applicable even today.
What struck me from Hreha’s presentation was her insight that Kotter’s principles aren’t just relevant historically; they’re precisely the same reasons why AI transformations often fail as well.
After diving deeper into Kotter’s work myself, and leading multiple organizations through their own AI journeys, I can now say that I wholeheartedly agree with Hreha.
The consistent patterns and pitfalls Kotter outlined nearly thirty years ago provide key insights for organizations navigating the rapid, complex world of AI today.
With that in mind, here’s how Kotter’s original insights can be adapted specifically for AI initiatives.
- Not Establishing a Great Enough Sense of Urgency
Kotter’s Insight: Change only happens when people feel the need to move right now, not later. Complacency is a transformation killer.
Adapted for AI: Clearly communicate why adopting AI isn’t just a nice-to-have but an immediate necessity. Highlight the real risks your organization faces by not keeping pace with technological change.
- Not Creating a Powerful Enough Guiding Coalition
Kotter’s Insight: Successful change needs a coalition of committed, influential leaders who champion the vision.
Adapted for AI: Set up a focused, cross-functional AI Council, supported by executives, to champion AI projects. This group ensures alignment, breaks through bureaucracy, and drives strategic momentum.
- Lacking a Vision
Kotter’s Insight: Without a clear and compelling vision, transformation efforts scatter and lose impact.
Adapted for AI: Craft a clear, relatable vision for your AI initiatives. Clearly articulate how AI aligns with and enhances your organization’s broader strategic goals, ensuring everyone understands and supports it.
- Undercommunicating the Vision by a Factor of Ten
Kotter’s Insight: Most organizations drastically under-communicate their transformation vision, expecting people to just get it.
Adapted for AI: Over-communicate your AI vision through every available channel. Embed it in daily conversations, routine meetings, and regular updates to keep your teams informed and engaged.
- Not Removing Obstacles to the New Vision
Kotter’s Insight: Genuine change requires proactively identifying and eliminating barriers.
Adapted for AI: Address and remove specific roadblocks to AI adoption, such as legacy systems, data silos, skill gaps, or resistance within the organization. Be proactive and transparent in overcoming these hurdles.
- Not Systematically Planning for, and Creating, Short-Term Wins
Kotter’s Insight: Celebrate incremental victories to build momentum and confidence in long-term transformations.
Adapted for AI: Identify and celebrate short-term successes in your AI projects, like successful pilots or measurable improvements. These quick wins build enthusiasm, credibility, and sustain motivation.
- Declaring Victory Too Soon
Kotter’s Insight: Declaring transformation complete prematurely can halt momentum and lead to regression.
Adapted for AI: Treat early AI successes as milestones, not the finish line. Continue refining, scaling, and integrating AI initiatives to achieve lasting impact and avoid slipping back into old habits.
- Not Anchoring Changes in the Corporation’s Culture
Kotter’s Insight: Lasting change becomes embedded when new behaviors and attitudes become “just the way we do things.”
Adapted for AI: Embed AI deeply into your organizational culture. Regular training, clear connections between AI initiatives and performance improvements, and strategic leadership succession planning will ensure AI practices become second nature.
What This Means
The connection between Kotter’s work on why transformations fail and why AI transformations fail is incredibly clear, but I suppose it makes sense since what transformation ultimately brings is change.
And we know change is hard, for both people and organizations, and AI is catalyzing change on both fronts simultaneously.
The way we do work is changing, and work itself is beginning to change.
Strategic Recommendations to Ensure AI Success:
We know now that there are steps organizations can take to make their AI transformations efforts easier, and, ultimately, successful.
- Build Strong Governance: Clearly structured AI Councils, charters, and policies enable strategic alignment and nimble decision-making.
- Secure Executive Sponsorship: Active involvement from senior leaders ensures resources, prioritization, and visibility for AI efforts.
- Invest in Continuous Learning: Ongoing education and skill-building programs help your team stay adaptive as AI evolves.
Your Next Steps
If you’re thinking about beginning your AI transformation journey, you can use Kotter’s insights and my translation as a checklist of sorts when reviewing your current AI transformation efforts.
By aligning your strategy with Kotter’s time-tested principles, you can set your organization up not just for a successful transformation, but for sustained competitive advantage.
If you’re looking for help, contact me today.
