Consumers fall into five distinct categories based on their attitudes toward and engagement with generative AI
If you want to transform how your business engages with consumers, you need to understand how they think about generative AI. Bain & Company’s latest research draws a clear line through five consumer types: the enthusiasts who lead adoption, the dabblers who test the water, the cautious who stand on the sidelines, the rejectors who stay out entirely, and a small segment that doesn’t fit neatly into any bucket.
These are not just personality types. They’re behavioral indicators. Each group brings unique expectations and barriers. For most companies, this isn’t about reaching just the early adopters anymore. It’s about recognizing the worries of the skeptics, people who want transparency, control, and real value. You have to address those concerns head-on, not dismiss them. This segmentation gives leadership teams a practical roadmap for tailored implementation. Marketing, product development, and customer support teams should stop using the same playbook for everyone. It doesn’t work.
As a leader, if you’re overlooking this segmentation, you’re already behind. You can’t build trust or scale adoption without clarity on how different users engage with the technology, or why they don’t. Knowing your users means optimizing how you position AI, how you roll it out, and what barriers you remove along the way.
According to Bain–Dynata’s Generative AI Consumer Survey (December 2024), only 35% of consumers claim to have used generative AI. But 10% of respondents didn’t fit any specific category, which means we’re still early. Ignore these nuances, and you miss where the market is headed.
Usage among current generative AI users is increasing due to expanded functionality and perceived value
Across the board, people who’ve already started using generative AI are doubling down. They’re not just experimenting anymore, they’re integrating it into daily work and life. The shift is happening because the tools are getting more useful. Users are leaning into what AI can do, not just what it promises. Think productivity gains, faster decision-making, and quicker access to relevant information. That’s what’s driving engagement.
Enthusiasts, those using AI at higher frequency, are pushing the boundaries. They’re applying AI to solve actual problems: writing better, understanding faster, communicating more clearly. Dabblers, on the other hand, are still testing what’s possible, using AI for creativity, learning, or just fun. Both groups show there’s momentum, and momentum matters more than early hype. But it’s the enthusiasts who are most valuable to watch. They’re the ones unlocking the next wave of commercial use cases.
If you want real returns from your AI strategy, look where people are already seeing value, not just where the tech is shiny. We’ve seen this pattern before: initial experimentation, followed by deeper, sustained use once real-world value becomes obvious. You’re either building products and processes that scale with that reality, or you’re stuck reacting to it.
Bain’s December 2024 survey makes it simple: usage among AI users is growing. Six months ago, they used it less frequently than today. This is where the shift starts. You can’t lead a market if your organization isn’t paying attention to how these behaviors evolve.
The adoption of generative AI may be accelerated by the integration of wearable AI devices
AI is not staying on the screen. It’s moving with the user. Bain’s research points to a clear link between the most engaged adopters of generative AI and the rise in wearable AI devices, glasses, pendants, rings. These aren’t concepts. They’re shipping, and they’re gaining traction.
Right now, about 5% of U.S. adults are using some form of AI wearable. That number is expected to jump to 20% by the end of 2025. This growth matters because wearables make AI more ambient and accessible. You don’t have to think about using AI, it becomes a part of how you interact with information, with systems, with people.
This opens the door for broader integration. As generative AI becomes embedded in hardware, companies have new ways to engage customers and employees in real time, in physical space. That’s a major shift. The user interface is changing, and it’s changing fast. It also means businesses can’t afford to design AI around old platforms. If you’re not looking at wearables as part of your strategy, you’re designing for the wrong interface.
Executives should focus on this convergence point, where generative AI and personal hardware intersect. It’s early, but clear patterns are emerging. That’s where market leadership begins. The companies that figure out how to serve the wearable-native segment will be positioned to influence the next behavior curve.
According to Bain’s projections, U.S. wearable AI adoption will quadruple from 5% to 20% by late 2025.
Many consumers remain cautious or disengaged from generative AI
Most executives look at AI adoption through the lens of potential. But just as important is understanding resistance. And right now, that resistance is driven by trust, or the lack of it.
According to Bain’s December 2024 research, nearly half of nonusers have never tried generative AI. Another 18% tried it and stopped. Among those lapsed users, half tested it out of curiosity and then dropped off. Why? Because they didn’t see clear value. Others backed away due to concerns about privacy, skepticism over how personal data is used, or discomfort with ceding control.
This isn’t just user hesitation; it’s a signal. Consumers want transparency. They want to know what data is collected, how it’s stored, and who has access. They also want to remain in charge. Solutions that feel intrusive or overly automated create friction, not adoption.
For leadership teams, the takeaway is simple: building trust has to be designed into the product. It can’t be solved later through PR campaigns. Companies that can prove data security, provide opt-in control, and clarify how value is delivered will pull users off the sidelines. Those that ignore this opportunity will see usage stall just as the market scales.
The gap is real. But it’s also addressable. Trust and control aren’t obstacles, they’re requirements. And the companies that meet them will lead the transition from experimentation to sustained customer engagement.
Bain–Dynata’s December 2024 survey found that 18% of nonusers are lapsed users, with half abandoning generative AI after initial curiosity-driven use. Among broader nonusers, privacy and data control concerns were among the top reasons for avoidance.
A significant number of consumers interact with generative AI unknowingly through embedded services in daily digital interactions.
Many consumers are already using generative AI, they just don’t realize it. That matters. It shifts how we think about adoption, awareness, and product strategy. AI is embedded in everyday experiences: chatbots, spelling corrections, smart recommendations, voice assistants. These aren’t fringe use cases. They’re mainstream, and they’re powered by AI.
According to Bain’s December 2024 survey, a large portion of consumers who claim they don’t use generative AI are actually interacting with it through these integrated tools. They’re accepting AI-written sentence suggestions, getting personalized results in apps, and engaging with AI through support channels, all without calling it AI use.
This matters for executives. Real adoption is higher than people report. That disconnect is strategic. It tells us that an increasing percent of the market is already comfortable letting AI handle specific tasks, as long as it’s unobtrusive and useful. It also tells us that labeling something “AI” isn’t required to drive engagement. What matters is that it works and that it solves a problem.
Here’s the key insight: If you’re building consumer-facing solutions, designing for seamless AI is more effective than pushing flashy features. The more naturally AI blends into the user experience, the faster familiarity and ease of use spread. That’s how you scale.
For decision-makers, this also signals a shift in how to communicate value. AI doesn’t have to be positioned as a separate layer. It can be the invisible engine that powers a better product, faster, smarter, more responsive. That’s how trust builds. Not through press releases, but through performance. Over time, this consistent low-friction exposure changes perception. It shifts resistant users from passive exposure to conscious adoption.
Bain–Dynata’s December 2024 survey confirms widespread unintentional interaction with AI, especially through digital assistants, auto-generated writing suggestions, and smart recommendation tools. These functions reflect a more significant undercurrent of AI-enabled engagement than headline numbers suggest.
Key executive takeaways
- Understand consumer segmentation to drive adoption: AI consumers fall into five distinct groups, each with unique behaviors and levels of interest. Leaders should tailor go-to-market strategies and customer engagement based on these segments to remove adoption barriers and unlock value.
- Track engagement shifts to guide product strategy: Use among current generative AI users is rising, especially for productivity and learning. Decision-makers should invest in high-utility use cases and monitor evolving behaviors to align products with user demand.
- Unlock growth through wearable integration: AI adoption is poised to accelerate with the rise of wearable tech, projected to grow from 5% to 20% U.S. penetration by 2025. Leaders should prioritize seamless AI experiences in hardware to stay ahead of interaction shifts.
- Address trust and control to reach nonusers: Privacy concerns and a desire for self-reliance keep many consumers from adopting generative AI. Companies must build in transparency, clear consent mechanisms, and visible value to bridge the trust gap and convert skeptics.
- Leverage passive usage as a growth lever: Many consumers already interact with AI unknowingly through embedded tools like chatbots and smart suggestions. Executives should focus on refining these low-friction experiences to build familiarity and gradual acceptance.
