Originally published on CIO and co-authored with Steve Wunker
You’re not missing AI’s potential because it’s hiding — it’s because you’re looking too closely. Step back and see the whole system, not just the tasks.
In the famous parable of the blind men and the elephant, each person touches a different part of the animal and comes to a wildly different conclusion. One feels the trunk and thinks it’s a snake. Another touches the leg and swears it’s a tree. They’re convinced they’re right, even though they’re entirely wrong.
This is how many companies are approaching AI.
They’re grabbing at a single task or workflow and drawing conclusions about what AI can or can’t do. These partial experiments feel rational. They’re safe. They offer a taste without the risk of a full meal. But they miss the elephant.
The real opportunity is not in tasks. It’s in systems
Task automation is real and valuable. But it’s also the tip of the iceberg. The deeper opportunity lies in rethinking how work is organized, why it flows the way it does and where AI can collapse inefficiencies across the entire system.
Consider how factories first changed from steam power to electricity. Factory owners replaced individual machines, as electric power offered many practical advantages. Yet it took decades before electricity enabled the true breakthrough: the assembly line.
AI is similar; the potential breakthrough lies in rethinking the system. Except that, now, we can’t wait decades to make the change.
Too many leaders are focused on micro-efficiencies. They ask, “What task can we speed up?” instead of, “What value do we deliver, and how might AI let us deliver it in a fundamentally better way?”
The first question gives you incremental improvement. The second reveals the elephant.
Why you’re missing the big picture
AI opportunities often stay hidden for three reasons:
1. Work is fragmented
Most organizations operate in silos: departments, regions and roles. AI pilots are launched within those same silos. So the insights stay narrow, and leaders rarely see how a revelation from one domain could benefit another.
At one healthcare provider we assessed, the marketing department could measure in extreme detail how many digital ads had been clicked on, using AI to hone its campaigns. But there was no connection between patients registering for a service and understanding how the patient was acquired, so the most important measure of ad effectiveness remained unknown. The opportunity existed across the system, but no one was looking at the system.
2. Success metrics are too narrow
Most AI pilots are judged by traditional KPIs: time saved, accuracy improved, costs reduced.
These are valid, but they miss transformational value, such as enabling entirely new customer experiences or reshaping how products are developed.
If you only evaluate AI through a “cost efficiency” lens, you’ll miss where it could generate revenue, open new markets or change the economics of service delivery.
3. Strategy and tech are disconnected
In many firms, AI lives in IT. Strategy lives in the C-suite. As a result, organizations fail to connect the dots between the company’s long-term goals and the places where AI could meaningfully accelerate them.
You can do better. At the insurance giant Travelers, for example, AI wasn’t simply dropped into existing workflows. Managers rethought how claims got processed, how exceptions were handled and how frontline employees interacted with both customers and machines.
IT was essential to the change process, but the transformation was framed as a business rather than a technology imperative. That created systemic gains.
Start with a systemwide view
To overcome the AI elephant problem, organizations need to broaden their aperture. That starts with asking different questions:
- What parts of our value chain are most constrained by human capacity?
- Where do handoffs create friction, rework or delays?
- What insights exist in one part of the company that could benefit another?
- What capabilities — like forecasting, summarization or scenario modeling—are underutilized across teams?
Then look outward:
- How is our business ecosystem changing?
- How could AI help us play a more central role in the industry network?
This is where the real transformation happens. AI doesn’t just automate work; it shifts how work is designed. And that shift requires managers to zoom out before they zoom in.
Practical moves to see the elephant
So how can leaders begin to shift their focus from isolated tasks to systemic transformation? Here are a few steps to get started:
1. Build cross-functional use case teams
Bring together people from operations, tech, customer experience and strategy to map pain points across the value chain. Then ask: Where could AI fundamentally reshape and resolve them?
Financial services firm Capital One does this, organizing teams around customer journeys to think holistically about how to make customer experiences top-notch.
2. Add a strategic lens to AI evaluations
Don’t just ask, “Did this model reduce time-on-task?” Ask, “Could this change how we define roles, structure teams or compete in the market?”
Take the meal kit company HelloFresh. AI enabled HelloFresh to not just optimize kitchen operations but to evolve from a Cheesecake Factory-style binder of menu options to a handful of super-tailored recommendations based on customers’ previous choices and meal ratings.
3. Encourage managers to spot lateral opportunities
After a successful pilot, challenge your middle managers to find two adjacent functions that could also benefit. Make this part of how success is defined: not just implementation, but diffusion.
An insurance company, for instance, moved its consumer-facing chatbot capability into claims management to make sense of the many disparate threads of input into a claims file.
4. Align AI projects with strategic priorities
AI can be exciting in isolation. But it gains power when tied to a clear goal, like entering a new segment, improving customer retention or speeding up product launches. Anchor every project to a “why” that leadership cares about.
For example, at a marketing department we worked with, one priority for all teams was whether AI sped up the time from campaign request to launch.
You can’t solve what you can’t see
AI is a systems tool pretending to be a task tool. That’s the paradox.
Its greatest strength is its ability to find patterns, streamline coordination and surface insights across silos. But organizations won’t benefit unless they look broadly in the first place.
The elephant problem is a vision issue. Companies need to train their leaders to stop grabbing at trunks and tails and start seeing the full opportunity in front of them.
Because the real competitive advantage won’t come from doing today’s work faster. It will come from rethinking what work should look like altogether.
