You’ve just concluded an exciting AI POC in your team, with fantastic results. You navigated it carefully through various stakeholders—ops gave positive feedback, IT approved, the AI risk team gave a green signal, finance okayed the projected savings, and leadership was aligned. It’s a clear win, and you’re convinced it will make your team future-ready.
Then you scale the solution. And suddenly, there’s no visible impact. Usage drops, your dashboard turns red, and you’re left wondering what went wrong.
Was the tool overhyped? Was the team not ready? Maybe the POC data was flawed?
The answer is simpler: You added AI but didn’t subtract anything.
Welcome to the missing piece that determines whether your AI initiative delivers real business impact—behavior design.
The Pattern We Keep Missing
The AI Revolution That Isn’t Happening
You’ve just concluded an exciting AI POC in your team, with fantastic results. You navigated it carefully through various stakeholders—ops gave positive feedback, IT approved, the AI risk team gave a green signal, finance okayed the projected savings, and leadership was aligned. It’s a clear win, and you’re convinced it will make your team future-ready.
Then you scale the solution. And suddenly, there’s no visible impact. Usage drops, your dashboard turns red, and you’re left wondering what went wrong.
Was the tool overhyped? Was the team not ready? Maybe the POC data was flawed?
The answer is simpler: You added AI but didn’t subtract anything.
Welcome to the missing piece that determines whether your AI initiative delivers real business impact—behavior design.
The Pattern We Keep Missing
Think of AI notetakers—we all have them now. But have they really made meetings more effective? Do you review your AI notetaker’s notes? Has it changed your habits?
This example plays out frequently. It’s surprising more leaders haven’t noticed the pattern.
Consider an insurance claims team at a National Health Insurance provider. They wanted to improve learning outcomes. They deployed an AI-enabled training bot. The bot analyzed documents and made data access easier. Despite much easier access to learning, claims associates didn’t adopt it. They stuck to their earlier approach—asking an SME.
What went wrong? Probably the same thing as your scaling attempt. Leaders assumed better technology would naturally create better outcomes. This didn’t happen. AI layered on existing workflows is like fresh paint on damaged walls. You haven’t fixed the underlying issues first.
We don’t automatically change behavior when better tools arrive. As creatures of habit, our habits run deeper than efficiency desires. This happens even when we know change improves our lives.
The Invisible Architecture of Work
Most AI implementations fail because they ignore the invisible architecture that governs how we get things done, adopt new ways of working, or change our old ways. This architecture isn’t code, process, or policy—it’s made of habits, expectations, and the subtle social norms that determine how we run meetings, how we learn, and what we consider when making changes in our work lives.
When leaders introduce AI tools without examining this invisible architecture, they create “technological layering”—new systems stacked on top of old ways of working. The outcome is usually more complexity, not less.
So consider what you will subtract from work when you introduce a new AI tool in your team, and observe how readily they adopt the new approach.
When Adding More AI Isn’t the Answer
After the introduction of their AI-powered learning bot, the insurance company found that while the bot could answer questions and provide information much faster than the old SMEs who previously solved the claims team’s problems, training outcomes didn’t improve. Knowledge retention stayed similar to the old system, and most still saw training as a checkbox exercise rather than genuine skill development.
The problem wasn’t the AI—it was that they had automated a fundamentally flawed learning experience. The bot made it easier to access training that people consumed passively but didn’t truly absorb or apply. Just like your scaling challenge, the team got sophisticated new technology added to their workday without subtracting the physical training or SME access—both underlying behavioral patterns that continued to coexist with the new tool.
The Transformation That Worked
During this time, worxogo piloted a behavior-focused automation program. Instead of just adding AI capability to the team, they replaced how the team received refresher training. They eliminated the earlier method of physical training followed by long-form tests that claims agents took, replacing it with short-burst, nudge-enabled micro-learning deployed by worxogo‘s Nudge coach.
The outcome in learning was dramatic. Adoption went up, with over 90% of the team embracing the new way of learning. More importantly, employees applied updated knowledge in their work within days rather than weeks, and this showed up in team performance metrics. The same AI-powered content that had produced mediocre results became genuinely transformative—not because the technology changed, but because it replaced an existing learning pathway with a new approach enabled by behavior-aligned coaching.
The Behavior Paradox and questions we dont ask
This story reveals something crucial about human psychology and organizational change. The team’s success wasn’t just about having better technology—it was about understanding that combining behavior change with new technology works better than simply adding technological fixes to existing processes. As AI tools increasingly enter our work lives, maybe we should focus less on how impressive the tool is and more on how effectively it can change employee behavior. What parts of our existing processes should we eliminate before the AI tool arrives? What existing team behaviors—not just work steps, but actual behaviors—should we consider changing?
The Real Revolution
The companies that figure this out won’t just have AI tools. They’ll have something more valuable: they’ll become places where AI seamlessly coexists with human workers, augmenting abilities, establishing new behaviors, and delivering real impact.
The question isn’t whether your AI tool works. The question is whether you’re ready to subtract the old ways of working to make room for the new.
Conclusion
Keep your team behaviors in mind to get the value you seek from Ai deployments. Without subtracting a series of tasks / behaviors from a team’s workflow, don’t expect AI will solve the problem you want to resolve. worxogo’s Nudge coach builds Insurance team habits and improves customer outcomes in Claims, Customer Support and many other teams.
