FRESH TAKES ON BIG IDEAS

Here’s something you might not know about gen AI: Most organizations are now using it, but study after study shows that nearly all of them are stuck in the pilot phase. Only a fraction of companies are successfully scaling gen AI across their organizations—and even fewer are experiencing genuine financial impact.

The problem is not the technology itself. Far from it. We’ve created gen AI systems that can reason, create, and even act. Instead, it’s a user experience problem. The technology is new and powerful, yet we’re stuck using search bars and chat boxes bolted onto interactions tailored to a pre-AI era. To put it another way: The slow progress with gen AI is not a technology problem; it’s a design problem. If organizations are to reap the technology’s full potential, they will need to create new kinds of AI experiences—rich and meaningful interactions that employees and customers will enthusiastically embrace.

More than a decade ago, McKinsey research definitively showed that design is about more than ornamentation and sheen. Rather, design is a strategic capability that can be a real driver of engagement—and bottom-line results. It’s time to bring that thinking into the gen AI era.

Think about the last time you used a gen AI tool, either at work or in your personal life. You were probably wowed by the speed with which it responded. Perhaps you were charmed by the bot’s personable, confident voice. But speed is not the same as comprehension, and confident language can mask shallow reasoning. No wonder, then, that so many users tend to oscillate between accepting gen AI outputs uncritically or abandoning tools when they fail to deliver.

This will only change when organizations start designing AI-native experiences that feel like a natural part of the way people actually work. What does that look like? We’ve identified four essential design characteristics that we believe organizations must embrace if they hope to lead in the AI era: clarity, continuity, depth, and collaboration.

First, clarity. Users will never embrace AI tools if their logic remains opaque. Systems should reveal how conclusions are reached and be up front when uncertainty exists. When AI’s reasoning becomes legible, people can engage with it, question it, and use it with confidence.

“Organizational advantages will come from the ability to design AI experiences that people trust, rely on, and choose to use.”

Just as important is continuity. Work does not happen in isolation. Yet many AI systems behave as if every new request is a fresh start. The most successful AI tools, by contrast, are designed to recognize progress across users and tasks, scanning the intelligence and experience of the entire organization, building an understanding of what came before so it can anticipate what comes next.

In this context, depth means automating entire workflows rather than just providing answers. AI tools should draw on a rich array of data sources, while connecting the multistep processes that human users follow instinctively to deliver meaningful, actionable outputs.

Finally, systems must be designed to foster collaboration between AI and its human users. This goes beyond the notion of including a “human in the loop.” The goal is not for people to correct the system after the fact, but for humans and AI tools to interact—steering, revising, and debating—in ways that drive superior outcomes.
 
Building AI tools according to these four principles can generate real results. For example, we recently worked with a leading global organization on an AI-native system to reinvent the way it generated marketing campaigns. Nearly three-quarters of users in a pilot program embraced the tool, resulting in a more than 2 percent boost in sales. In another case, an AI tool designed to provide sales reps with better talking points was adopted by 90 percent of users. And when we revised a tool for hotel managers to reveal reasoning behind its decisions, nearly all of the users began deploying it in their day-to-day activities.

The big takeaway: The organizations that break through will not be the ones that are chasing better models. It will be those that fundamentally rethink the way work happens. Their advantage will come from the ability to design experiences that people trust, rely on, and choose to use. The next frontier of AI will be about reinventing the architecture of collaboration, driven by systems that make intelligence understandable, governable, and usable at scale. It’s a natural extension of what leading organizations have understood for years: Design is not a layer of polish; it’s a key driver of performance.

—Edited by Larry Kanter, senior editor, New York

ABOUT THIS AUTHOR

Chris Smith is a partner in McKinsey’s Southern California office.