AI & The Power of Make-Believe
Introduction
Mr. Rogers’ Neighborhood of Make-Believe captivated generations with its invitation to explore, create, and imagine. Today, that same spirit of make-believe—the capacity to envision what isn’t yet real—holds the potential to transform how we approach business growth. For companies eager to leverage artificial intelligence (AI), tapping into the power of imagination isn't just beneficial; it’s an essential tool enabling you to be customer-centric with less effort and expense. With AI, businesses can build innovative customer experiences, reimagine their business models to drive customer value, and fundamentally reshape their future.
Customer Centricity and Make-Believe
In the early days of our consulting experience, simply focusing on the customer’s perspective was transformative. Small insights, brought forward through direct observations and empathy, could change entire business trajectories. However, as more companies grew familiar with customer-centricity, our consulting role evolved. The work became less about gathering raw insights and more about interpreting the data creatively.
We leaned on a “make-believe "mindset to keep that edge, using our imaginations to create hypotheses we could test rather than try and boil the ocean to find unexpected insights from data. Drawing from frameworks like Clayton Christensen’s "Jobs to be Done, "voice-of-customer (VOC), and design thinking, we embarked on structured “ideation sessions” with clients. These sessions were analytical exercises where we focused on the decisions that needed to be made, not just the insights, and they were creative ones, where we envisioned ourselves as the customer, trying to see our client’s businesses through their eyes.
We used this imaginative work to construct tree hierarchies of potential needs—visions of possibilities—that were MECE (mutually exclusive and collectively exhaustive). This process helped us to identify customer needs and jobs to be done; while hypothetical, it was grounded enough to help our clients launch targeted investigations into the decisions they needed to make to build high-impact business strategies. The challenge, however, was that not every consultant could bring the same depth of creativity to the table. Make-believe effective enough to turn a broad fishing expedition into a targeted investigation is hard to come by, even with some of the best talent working on the problem.
Three Paths in the Neighborhood of Customer Make-Believe
Through this work, we identified three primary paths that most successful customer-centric innovation projects followed. Each pattern required its kind of make-believe:
Customer-Driven Innovation: The first pattern starts with observing a customer's job to be done or need and imagining a “make-believe” world where a product or service directly addressed the customer’s need or jobs to be done to be able to define an opportunity based on the imagined outcomes. This is the most common successful pattern, and most readers will be familiar with it. Many successful startups have outpaced their corporate rivals using this kind of approach. After all, this front-loaded, creativity-driven understanding of the customer gives them a big head start, assuming their observation represents an opportunity—but importantly, it's also a shorter and faster path to creating a winning solution. Because it starts with customer insight, it is also an inductive process without needing to do the same breadth of customer work that would be done if the innovation weren’t leading with that insight. We have done this kind of work helping clients create new businesses, essentially corporately incubated startups, as well as on behalf of prominent clients who have recognized a significant shift in their customer base and realigned entire business units to solve the customer jobs to be done or customer needs that have newly risen to the be a top concern of their customers.
The Technology Pantry: Here, the goal is to repurpose or reimagine technology, giving it new life in the eyes of potential users. In our experience, this approach to make-believe is common in large organizations with abundant technical resources but limited pathways to market. Our role here was to engage creative minds to enter a “make-believe lab,” ideating potential applications and imagining how existing products could be re-envisioned to meet evolving customer needs. While this is usually a longer road to creating a business, it's also a more common problem for large companies with many talented scientists and engineers but few business builders. The challenge of this approach is that it requires more intensive customer engagement, and the ability to enter and utilize our “make-believe lab” once again depends on the team's creativity and analytical skills to structure proper research and solution design. We have had success here, too, working on going to the market for new-to-world materials and helping clients create financial services products. These often feel like innovative go-to-market strategies for known technologies, but underneath these are solution design projects where the key is identifying and chasing areas of opportunity and using repeated customer engagement to refine opportunities to solutions.
Customer Experience Innovation: In this scenario, we helped clients shift from purely transactional relationships to those grounded in genuine value. Through “make-believe” exercises that placed us and our client’s sales reps in their customer’s shoes, we explored ways to transform each sales encounter into an opportunity for connection and value-building. In B2B relationships especially, where deeper relationships yield tangible rewards and opportunities to collaborate to bring differentiated products to market, our make-believe exercises envisioned ways to deliver ongoing value, reducing churn and encouraging loyalty.
Each pattern represents a different path through the customer “make-believe " landscape, where creativity and analysis must intersect. Challenges arise when make-believe hits the limits of our experience or the constraints of our imagination.
Beyond Traditional Research: Embracing the Power of Make-Believe with AI
Over time, the consulting services offered by our former firms became too expensive to continue collecting data, and we started, in some cases, working with clients or even partners they selected to gather the information needed to create these insights. Interestingly, we saw success with clients using individual interviews, focus groups, and customer surveys.
It became clear that the right data collection method depended as much on whether the client offered B2B or B2C solutions as anything else. In other words, the data collection methods only needed to be appropriate enough to engage the right audience. This flew in the face of the training at the beginning of my career, where we prioritized in-depth interviews and observations over other methods. After all, focus groups and surveys were an attempt to be more efficient than being able to go and ask a large number of customers what they thought, how they used solutions, and why or why not they would purchase a solution again.
AI’s arrival has sparked an evolution in this make-believe process. Today, AI tools can speed up survey design, produce focus group questions, and simulate responses, essentially acting as a stimulus for our creativity. However, this is only scratching the surface of AI's potential for customer centricity: What if a team could step into customers' minds not just hypothetically but through insights of an AI-driven “customer twin”?
Creating AI-Powered Customer Twins: A Make-Believe Revolution
“Customer twins”—simulated versions of customers that replicate actual preferences, jobs to be done, and expectations are within our reach. With AI these twins, a business can easily engage in make-believe as a part of the development process, not just through structured interactions. Imagine running a market study without ever needing to contact a single customer, freed from the potential group think and structural dynamics of focus groups, never needing to worry about the format of a question in a survey and able to go back to interviewees as much as you want to refine concepts and ideas. Drawing on data models that mirror actual customer profiles, the business can ask “what if?” in a controlled, confidential, yet highly insightful environment—allowing us to maximize the valuable time we spend with real customers.
AI customer twins also provide feedback and constraints that can help apply our imaginations to problem-solving. Now, teams don’t need to depend solely on individuals' creativity and analytical skills to role-play customers. The AI can generate, test, and refine hypotheses for solutions almost as a member of the team, drawing on real data and experiences to synthesize potential results. Imagine “conversing” with an AI-driven customer twin, receiving instant feedback on new concepts, product features, or market ideas. Then imagine running that interaction a thousand times in minutes with slight variations to the inputs.
For B2C companies, this means faster insights without repeated data collection. For B2B companies, it means sidestepping obstacles like access to busy or hard-to-reach customers. And for all types of businesses, it means a streamlined make-believe process that maximizes our human creativity by providing us with more inspiration.
Conclusion
The future of customer-centric innovation lies with AI-enhanced “make-believe” that allows innovators to converse with twins of their customers or key stakeholders, getting useful feedback on their ideas, far faster. While real customer interactions remain valuable, AI-powered customer twins allow businesses to act faster and more boldly with business ideas, using insights to create richer products and experiences. In this new age of AI and make-believe, the possibilities are only as limited as our willingness to bring AI along as a thought partner in addition to potential efficiency gains. We just need to keep asking, “What if?” as we reimagine what's possible.