Many companies start their AI journey with bottom-up excitement – but without top-down direction, they quickly get stuck. This post explains why real AI impact requires clear leadership, focus, and intent.
Start with direction, then invite creativity.
Over the past months, I’ve seen a growing number of companies kick off their AI journey with the best of intentions – and a familiar pattern. They start by launching broad AI awareness campaigns, hosting inspirational sessions for employees, and encouraging bottom-up ideation of use cases. The idea is simple: generate excitement, unlock creativity, and let innovation bubble up from every corner of the organization.
And while that may sound like a healthy starting point, there’s one problem: without exception, these companies get stuck.
They quickly run into a wall of scattered efforts, unclear priorities, and a growing disconnect between aspiration and execution. The use case pipeline grows, but little gets delivered. The initial excitement fades, and the AI team is left wondering what went wrong.
Let’s be clear: bottom-up energy is not the issue. In fact, it’s essential. Some of the most valuable AI ideas will come from people closest to the work – those who understand the friction, inefficiencies, and untapped opportunities in daily processes.
But bottom-up initiatives without top-down direction are like a ship with no compass. You may have motivated people and smart ideas, but without a clear sense of direction, you’re left with a collection of local experiments rather than a cohesive AI strategy.
“If you don’t know where you’re going, any road will take you there.”
⸻ Lewis Carroll
Worse, when you start from the bottom, you often start from what already exists: current processes, current teams, current structures. And that almost guarantees that your AI efforts will reinforce the status quo.
Instead of rethinking how the business operates, you end up digitizing inefficiencies, adding AI to broken processes, or optimizing around outdated models. The result? Incremental improvements at best, not transformation.
Without clear ambition, prioritization, and sponsorship from leadership, bottom-up ideas rarely move past the concept phase. What you get is proof-of-concept purgatory: a graveyard of AI pilots that never scaled – not because the technology didn’t work, but because the organization didn’t know what it wanted to achieve or change.
Many organizations lean on the narrative of democratizing AI: giving everyone the tools, the inspiration, and the opportunity to experiment. And while this democratization mindset is important for adoption and upskilling, it cannot replace leadership direction.
AI is not just another innovation initiative – it is a fundamental shift in how decisions are made, how value is created, and how work is done. That shift requires strategic intent. It demands leadership that is not only aware of AI but willing to shape how it’s used and why.
“AI is not just about innovation. It’s about transformation. And transformation doesn’t happen by accident – it needs ownership”
⸻ Laura Stevens
When leadership is absent, AI becomes disconnected from the business strategy. Teams run in different directions. Prioritization becomes impossible. And the burden of driving impact falls to people who don’t have the authority to change systems, reallocate budgets, or push through resistance.
So what’s the alternative?
Start at the top – with a clear sense of what AI should do for your organization. That doesn’t mean locking strategy in the boardroom and excluding others. It means defining ambition and direction before opening the floodgates to ideas.
Once there is clarity on where to play and how to win, the rest of the organization can engage in a much more meaningful way. Suddenly, AI ideation is not about throwing spaghetti at the wall and seeing what sticks. It’s about solving well-defined problems that matter to the business.
“People don’t resist change. They resist change without purpose.”
⸻ Daniel Pink
Another trap I often see: companies assigning AI innovation to a small group of early adopters or enthusiasts. Again, these individuals may be passionate, skilled, and creative. But without executive sponsorship, they’re set up to fail.
Why? Because AI rarely fits neatly into existing processes. It challenges how decisions are made. It requires changes in infrastructure, roles, and ways of working. And those changes require authority, not just energy.
‘’Transformation is a team sport – but leaders hold the strategy playbook.”
⸻ Laura Stevens
Champions play a key role in AI transformation – but they cannot carry it alone. Their ideas need to be backed by leadership that is willing to make space, remove blockers, and invest beyond the pilot phase.
This isn’t a call to abandon bottom-up involvement. Quite the opposite. The best AI transformations combine top-down clarity with bottom-up energy and creativity. Leadership sets the direction, frames the priorities, and creates the conditions for AI to thrive. Employees then bring those priorities to life with grounded, high-impact use cases.
This simple matrix shows why the sweet spot lies in combining high energy with clear focus: that’s where true AI transformation happens.
And that order matters. If you start with inspiration before intention, you risk overwhelming the organization with noise. Start with clarity, and you’ll turn energy into progress.
AI is not a grassroots movement. It’s a strategic capability. Treat it as such. If you want AI to deliver real business value, don’t start by asking, “Who has a great idea?” Start by asking, “What are we trying to achieve – and are we ready to lead it?”
Energy + Direction = AI that delivers.
Laura Stevens, PhD, is the Managing Director of Data & AI, bringing a unique blend of strategic vision, analytical expertise, and leadership acumen. With a background in neuropsychology, business consulting and organizational transformation, she has successfully navigated a career spanning academia, consulting, and industry leadership. As a former VP Data & AI in an international organization, Laura has led large-scale Data & AI teams covering data science, machine learning, data engineering, data governance, and visualization. She is passionate about leading organizations through their data & AI transformation.
