There’s more fuzz around AI than ever, but far fewer companies know how to identify where the real value lies. We’ve all seen it: an executive team hears about a flashy AI use case at a competitor and suddenly there’s pressure to “do something with AI.” A pilot is launched. A dashboard is built. The hype fades. The ROI never materializes.
The challenge is that many organizations still treat AI as a feature instead of a strategic capability. They chase what’s trending instead of identifying where AI can truly move the needle.
“AI isn’t valuable because it’s advanced – it’s valuable when it solves something that matters.”
⸻ Laura Stevens
So how can leaders cut through the noise and focus on what will actually make a difference?
Through real-world practice and hard-earned lessons, we established a practical, 5-step approach that helps leadership teams identify high-value opportunities instead of just interesting ones.
Before turning inward, zoom out. Ask:
Adopting an outside-in perspective doesn’t just help ensure your AI strategy is differentiating by spotting white spaces – it also makes it well-informed. It helps you understand the most important AI market drivers, emerging trends, and where the major players are placing their bets. Just as importantly, it allows you to cut through the noise and focus on what really matters.
This isn’t just about scanning the market. It’s equally about building AI fluency at the executive level. Just as leaders need financial literacy to make sound investment decisions, they need a baseline understanding of the AI landscape to make smart strategic calls. Without that, it’s easy to fall for hype or miss the real game.
We’ve seen that many teams start by asking “Where can AI support the business?” While this is a perfectly valid question, recent evolutions in AI, especially the rise of foundation models and more general-purpose capabilities, have opened up an additional lens: AI is no longer just an enabler of business strategy, it has become a driver of business growth. Hence, there is substantial value in not just considering how to improve what you already do, but exploring what new offerings, services, or business models AI now makes possible.
Doing that well opens up a fundamentally different opportunity space, yet it requires a change in mindset: starting not from today’s pain points, but from a vision of what your business could become because of AI. Such a future-back lens helps you ask questions like “If we started from AI’s capabilities – not our current constraints – what could we create that wasn’t possible before?”.
We recommend using both as complementary lenses.
Ask: How can AI help us achieve our current goals faster, better, or cheaper?
This often leads to:
Example: A telco client applied AI to optimize call center staffing – an unsexy but high-impact win.
Ask: If we started from AI’s capabilities, what new offerings or models could we imagine?
This brings into view:
Example: A media company we worked with used AI to simulate content virality before publishing. That idea didn’t come from their strategy deck – it came from asking, what’s possible with AI now that wasn’t before?
After mapping the broader landscape and defining your opportunity lenses, the next step is to bring this down to the functional level. This is where the strategy starts to meet operations. Go function by function – Sales, Marketing, HR, Finance, Operations – and look for the signals of AI opportunity:
This scan helps surface concrete use cases and clusters of opportunity that are grounded in reality, not just ideas, setting the stage for meaningful prioritization.
Tip: Involve people on the ground. They often know where AI could help before leadership does.
Should we scan across all functions or just a few?
Start broad enough to spot big opportunities, but don’t stay wide for long. A light-touch scan across functions gives you a view of where to dig deeper. Then quickly narrow your focus to 2–3 priority domains where AI can make a real difference. This avoids “boiling the ocean” while still ensuring you don’t miss hidden gems.
AI transformation isn’t about exploring everything – it’s about knowing where to start.
AI strategy is 50% picking the right problem, 50% not chasing the wrong one.
Not every shiny idea is worth the chase. Use a simple 2×2 to map:
Don’t treat validation as a final step – make it part of the prioritization process itself.
But keep in mind: feasibility isn’t only about internal capabilities like data quality, tech infrastructure, or talent. It again requires an outside-in perspective. Some spaces may appear attractive – large markets, mature tech – but may be difficult to win due to high entry barriers, capital intensity, or strong incumbents. Others may be constrained by regulation or shifting policy.
Assess feasibility by asking:
This broader view helps ensure you don’t pour time and energy into exciting ideas that aren’t winnable – no matter how technically feasible or high-potential they might seem.
Feasibility and prioritization should be pressure-tested with experts and stakeholders:
This cross-functional input ensures you’re not just chasing ideas that look good on paper, but investing in those that are actionable, relevant, and supported across the business. Refine. Align. Then move forward with the top 3–5 high-value opportunities.
Use this dual-lens canvas to guide the conversation.
Phase 4 (feasibility analysis and prioritization) helps you decide which AI opportunities are worth pursuing, based on value and feasibility. But Phase 5 is about “How to Win” and it goes one level deeper: once you’ve chosen your plays, this step is about spelling out the enablers that will make success possible.
Ask:
Feasibility tells you if something’s worth doing. ‘How to win’ tells you how to make it real.
And here’s an important note: the more disruptive the opportunity, the more critical these enablers become.
But don’t be fooled: even incremental use cases can fail if the foundations aren’t in place. Whether you’re optimizing call centers or building AI-native products, success hinges on your ability to deliver:
If you want AI to be more than a buzzword, stop asking “Where can we use AI?” and start asking “Where can AI deliver disproportionate value?”.
And make sure you’re thinking both from today’s pain and tomorrow’s possibility – with a clear plan to make those opportunities real.
To unlock real value, leaders must ask not just “Where can we use AI?” but “Where can AI deliver disproportionate value—and how will we make it real?”
1. Start outside-in:
2. Explore AI opportunities through two lenses:
3. Run a cross-functional opportunity scan:
4. Score and prioritize (value vs feasibility):
5. Define “How to Win”:
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.
