Five Truths about AI Strategies
AI promises to revolutionize business, but the reality is harsh: most AI strategies never deliver on their promises. The reason is usually the same: organizations don't know how to turn strategy documents into concrete results.
The problem isn't with the AI strategy or the technology, but with how leaders approach AI.
After working with hundreds of organizations, we've identified five decisive factors that separate the winners from those who fail. Understanding these in time can determine whether you create transformation or disappointment.
Truth 1: AI strategy is a journey, not a destination
Building an impressive PowerPoint presentation is the easiest part of the AI journey. The real work begins after that is built, and it is the continuous building of understanding, shared vision, and enthusiasm across the entire organization.
An AI strategy evolves continuously, and it should also be reassessed regularly. Business needs change and technology advances rapidly. If you think of an AI strategy and its rollout as a one-off project, you'll quickly go off the rails.
How to act?
Treat your AI roadmap like a product development backlog, not a schedule set in stone. Hold quarterly AI strategy sprints where you update your AI portfolio to match business goals or, for example, new regulations. This keeps the strategy relevant and adaptable to the situation.
Truth 2: AI can trigger a broader business transformation
The best AI strategies don't just streamline existing processes - they make it possible to do business in an entirely new way. If you see AI only as technology, you lose its greatest potential. Instead, think big. Frame your AI initiatives as part of bigger business goals, or audacious promises.
Examples of genuine AI strategies:
- "Half of our sales will come from digital channels by 2027." This forces a media company to consider how the entire business and its services are transformed from traditional print media to digital, and what AI's role is in that.__
- "Our machines will operate autonomously by the end of the decade." Here AI plays a key role in enabling the shift.
The point is to see AI as an enabler of change, not just a tool for a minor improvement.
Truth 3: A good strategy connects use cases to business value
It isn't enough to prove that AI works technically. It's tempting to get excited about technical features, such as a machine learning model's predictive accuracy or applying the latest algorithm to a business problem.
What matters more is showing how it affects key metrics such as revenue, margin, and customer experience. Every AI initiative must be anchored to a concrete business outcome.
Define concrete goals for every use case:
- What is the metric and the baseline? (e.g., customer churn drops from 12% to 9%)
- What is the economic benefit? (e.g., 6 million euros in profit)
- Who owns the rollout? (e.g., the customer success director owns the budget and the targets)
Start with your most urgent business challenges, not with AI's technical possibilities.
Truth 4: Success depends more on people and processes than on technology
Even the world's smartest AI model won't fix a broken work process or a siloed organization. Many leaders fall into the trap of assuming that the best technology automatically brings success.
The success of AI initiatives depends more on your people and internal processes than on the technology itself. Likewise, cultural debt grows much faster than technical debt.
Successful strategies account for three critical elements:
- Adoption planning: User experience for AI tools and training programs tailored by role.
- Change management: Communication that addresses fears, and leadership setting an example in using AI.
- New ways of working: Redesigning processes with AI, as well as updating job descriptions and metrics.
Truth 5: The strategy must address adoption and change management
Even the finest AI system is worthless if people don't use it or use it incorrectly. It's crucial to consider how new AI-based processes are integrated and how they reshape ways of working. A proactive plan for organizational change is needed.
Avoid the common "last-mile gap," where great technology fails because of poor adoption. Set aside 25-30% of your entire AI budget for change activities such as training, communication, and process renewal.
This isn't just about training people on new tools. It's about a fundamental rethinking of how work is done, how decisions are made, and how success is measured in an AI-supported organization.
A good strategy starts with honest conversations
The path to AI success isn't found in technical specifications or vendor presentations - it's found in honest conversations about your organization's challenges, capabilities, and goals.
These five truths provide the framework for those conversations:
- Embrace the journey mindset instead of chasing quick wins
- Think about broader business transformation, not just automation
- Focus on business value rather than technical features
- Invest in people and processes as much as in technology
- Plan change management from the very beginning
Organizations that understand and act on these truths don't just succeed at adopting AI - they use it to create lasting competitive advantages.
Ultimately, the best AI strategy is the one that actually gets done.
