The 1-Hour Test That Shows Where AI Will Actually Pay Off

A few months ago, I sat with the leadership team of a global retailer that had invested heavily in an AI recommendation engine for its customer service operation. On paper, the rollout looked like a success. Response times improved, recommendations became more accurate and executives celebrated another milestone in their AI strategy.

Then I spent time beside the customer service representatives who relied on the system every day. They bounced between four different screens to complete a single interaction and copied information from one application into another because the tools failed to communicate with each other. Every recommendation added another step, which meant many employees simply ignored the AI.

Sure, the technology worked, but the experience fell short.

That visit reinforced a lesson I have learned throughout my career, from helping launch Amazon Web Services (AWS)’s first database product to advising organizations through digital transformation. AI delivers its greatest value when it fits naturally into how people already work. Every extra click, every unnecessary screen, and every added layer of complexity reduces the value the technology promises to deliver.

Start with people instead of platforms

Many AI initiatives begin with a product demonstration instead of a business problem. Leaders become excited by impressive features, purchase a platform and expect employees to adjust. Months later, adoption stalls and executives wonder why the investment produced so little business value.

The strongest transformations begin in a very different place. They start with curiosity about the people doing the work every day. Before selecting technology, leaders should observe employees as they navigate systems and serve customers. Every unnecessary click, duplicate entry and manual workaround becomes an opportunity for AI to remove friction instead of creating another layer of complexity.

I saw this firsthand while working with a logistics company that wanted to automate dispatching. Before building a single model, the leadership team spent days shadowing dispatchers during real shifts. They discovered that many decisions depended on context and timing that lived outside the software. That insight shaped an AI system that supported experienced dispatchers instead of competing with them.

Watch for the early warning signs

Many organizations believe adoption equals success. Employee training sessions fill quickly and dashboards show strong activity. Those numbers create confidence in the AI rollout, even while business performance remains unchanged. The better signal comes from observing behavior.

When employees create spreadsheets outside the systems, keep handwritten notes or invent shortcuts, they are revealing friction in the workflow. Those workarounds represent valuable feedback because people naturally move toward whatever helps them accomplish meaningful work with less effort.

Another warning sign appears when people struggle to explain how the AI helps them make better decisions. A useful system builds confidence because employees understand its contribution to the outcome. When the technology feels disconnected from everyday work, trust fades and enthusiasm eventually disappears.

Measure business outcomes instead of activity

One financial services organization asked me to evaluate an AI system that automatically categorized incoming customer complaints. The technology performed exactly as designed. Every issue received an accurate internal label within seconds. Leadership celebrated impressive processing speed. Yet, customers experienced something very different.

People with urgent concerns waited alongside routine requests because the AI organized work around internal categories instead of customer urgency. The system achieved technical accuracy while missing the outcome that mattered most.

Once the organization redesigned the workflow around customer urgency, response times for high-priority issues improved dramatically. The underlying technology changed very little. The results improved because the design centered on the customer experience instead of internal processes.

That experience reflects one of the most important leadership questions in AI: Ask whether the person doing the work becomes better or faster at delivering the business outcome.

Activity matters far less than measurable improvement. When outcomes improve, adoption usually follows.

Build around the experience

The strongest AI initiatives improve life for everyone involved. Customers receive faster, better service. Employees accomplish meaningful work with less frustration. Leaders gain measurable business results that extend far beyond adoption metrics.

That shift begins with a simple question. Instead of asking what the technology can do, ask what people need to accomplish. The answer usually leads toward simpler workflows and better outcomes. AI becomes part of the work instead of another task employees have to manage.

Technology almost disappears when it serves people well. Employees stop thinking about the software because their attention returns to solving customer problems, making better decisions and creating value. That is where long-term competitive advantage begins.

A simple test every leader can use

Before approving another AI investment, spend one hour sitting beside someone whose work will change because of the technology. Skip the polished executive demonstration and observe a normal workday instead. The lessons from that single hour often prove more valuable than weeks of planning meetings.

As you watch, look for three things.

1. Where does work slow down?

Notice every pause, every extra click and every moment someone leaves one system to search for information somewhere else. Those interruptions reveal where productivity quietly disappears.

2. Where are people creating workarounds?

Watch for spreadsheets, handwritten notes, sticky notes or manual processes employees have created to get the job done. Those workarounds point directly to opportunities where AI can simplify the experience.

3. Where can AI remove friction instead of adding it?

Look for opportunities where technology can reduce steps, eliminate repetitive work or help employees make faster decisions. The best AI investments usually improve the work people already do instead of asking them to learn an entirely new way of working.

Once you’ve completed that exercise, spend the next month focused on execution.

• Audit where AI already exists across your organization.
• Map every point where employees experience friction.
• Prioritize improvements that simplify everyday work before expanding into new use cases.
• Measure success by business outcomes rather than system activity.

Small improvements inside existing workflows often produce greater business impact than ambitious projects built around impressive technology.

A few months ago, I sat with the leadership team of a global retailer that had invested heavily in an AI recommendation engine for its customer service operation. On paper, the rollout looked like a success. Response times improved, recommendations became more accurate and executives celebrated another milestone in their AI strategy.

Then I spent time beside the customer service representatives who relied on the system every day. They bounced between four different screens to complete a single interaction and copied information from one application into another because the tools failed to communicate with each other. Every recommendation added another step, which meant many employees simply ignored the AI.

Sure, the technology worked, but the experience fell short.