Why Leaders Need to Think Beyond AI Use Cases
Companies are experimenting with AI, but that doesn’t necessarily mean they’re seeing meaningful business results.
In my first Substack Live, I explored a challenge I’m seeing in conversations with business leaders: different executives often have very different ideas about what AI should accomplish.
A CTO might focus on technology and infrastructure. A COO might see opportunities to improve operations and efficiency. A CHRO might be thinking about how roles, training, performance, and the employee experience need to change.
These are all important perspectives. The problem is that they don’t automatically add up to a shared business strategy.
Without that alignment, companies risk remaining in what I call AI dabbling, where individuals and teams experience productivity improvements, but the business as a whole struggles to capture measurable value.
Leadership Alignment Comes Before Technology
One of the biggest opportunities for leadership teams is developing a shared understanding of AI and the broader technology landscape.
That doesn’t mean every executive needs to become a technical expert. But leaders should understand enough about emerging technologies, including different types of machine learning, to recognize what may be possible beyond today’s popular AI tools.
It’s also worth looking three to five years ahead. The technology available today is only part of the picture.
More importantly, leadership teams need to agree on the business result they’re trying to achieve.
Improving efficiency or increasing revenue is a starting point, but those goals are still too broad on their own. Leaders need to identify where the opportunity exists, what needs to change, and how those changes will translate into value.
Stop Adding AI to Business as Usual
For years, businesses have adopted software that helps them execute existing processes more efficiently.
Salesforce, for example, provides tools for managing the sales process, from an initial lead through closing a deal.
AI introduces a different possibility.
Rather than simply purchasing software to improve an existing workflow, businesses can increasingly explore building systems around their specific needs.
This changes the role of leadership.
Instead of asking how AI can make an existing process faster, leaders should be asking what that process would look like if they were designing it today.
What steps could disappear entirely? Where could AI take on work previously performed by employees? Where would people and AI collaborate? And where does human judgment remain essential?
Simply accelerating an existing process can create new bottlenecks, increase pressure on employees, and amplify problems that were already there.
The opportunity isn’t just to make business as usual faster. It’s to reconsider what business as usual should look like.
And even when AI performs more of the work, people remain accountable for the decisions, customer experience, and business results.
Think Bigger Than One Use Case, but Start Small
Another distinction I explored was the difference between selecting an individual AI use case and redesigning a larger customer or operating experience.
A single use case might improve one task.
Redesigning an experience requires understanding how multiple activities, decisions, technologies, and people work together to produce an outcome.
That doesn’t mean transforming the entire company at once.
I recommend choosing one high-value domain, such as operations, sales, finance, technology, or HR, and identifying an experience within it that could produce meaningful business value.
From there, consider multiple ways that experience might work differently using AI and other technologies.
Your organization’s data is an important part of this conversation. Understanding what data you already have, how it connects, and where it needs improvement can help reveal opportunities that aren’t obvious when you start by comparing software products.
Don’t Start Change Management Before You Know What’s Changing
I also discussed why organizations sometimes begin talking about change management too early.
It’s difficult to prepare employees for a transformation when leaders haven’t defined what the future way of working will actually be.
Changes to work affect more than efficiency. They influence responsibilities, employee expectations, and how people identify with their contributions.
Rather than immediately introducing a major organizational change, businesses can test alternative ways of working through smaller pilots.
Those experiments help leaders understand what works, what doesn’t, and what adjustments are necessary before expanding.
The goal is to learn before committing the entire organization to a new approach.
One Leadership Meeting You Can Run
If you’re leading a business or working with an executive team, here’s an exercise I recommended during the Live:
Choose one customer or operating experience. Focus on an experience within a business domain that has meaningful potential for improvement, rather than picking an isolated AI task.
Agree on the business result. Define what should improve for customers and the organization, whether that’s revenue, efficiency, speed, or another measurable outcome.
Explore how the work could be redesigned. Consider the roles of people, AI, existing systems, and organizational data. Don’t begin by choosing a product.
Develop two to four possible ways of working. Challenge assumptions about how the experience currently operates instead of automatically preserving every existing step.
Test before scaling. Use smaller experiments to understand what creates value, what needs adjustment, and what employees will need to succeed.
This gives leaders something more useful than a collection of AI ideas. It gives them a shared direction and a way to begin testing it.
From Incremental Improvement to Business Innovation
In the Live, I also shared observations from my work with companies, including opportunities for double-digit improvements in efficiency, revenue, or a combination of the two.
The timeline varies depending on the size and complexity of the organization. Smaller businesses may be able to make meaningful changes within months, while larger organizations may need a year or longer.
These results aren’t automatic. They depend on the opportunity, the quality of implementation, and whether the organization can sustain the new way of working.
But the broader point is that AI creates an opportunity to think beyond the incremental improvements businesses have traditionally pursued.
It asks leaders to become innovators, not just operators.
And there’s a reason to start exploring now.
Businesses that wait until competitive pressure or declining performance forces them to change may have fewer options and less room to experiment.
Starting while the business is healthy gives leaders the space to question existing assumptions, test possibilities, and make thoughtful decisions about the future.
Continue the Conversation
The question I want to leave leaders with is this:
If you were building your business today, knowing what AI and emerging technologies can make possible, what would you design differently?
That’s a much more valuable conversation than simply asking which AI tools your company should adopt next.
If you’re exploring how to move from scattered AI activity to measurable business value, I’d love to connect.
Learn more about my work at Work Redesigned, or reach out to me on LinkedIn or here on Substack.
