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. HBR’s work on generative AI and human creativity gives this argument a useful frame: AI is strongest when it augments imagination rather than replacing judgment.

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 recently become 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. The NIST AI Risk Management Framework and OECD AI Principles are useful guardrails for this kind of exploration because generated scenarios can sound plausible even when they are wrong.

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

AI-generated customer simulations are useful as make-believe in the best sense: they help teams generate hypotheses, explore scenarios, pressure-test concepts, and prepare for better customer research. They should not be treated as proof of actual customer behavior. The value is in challenging assumptions before the team spends time and budget validating the strongest ideas with real customers.

The practical benefit is not that teams can skip evidence. It is that they can enter real research with sharper questions, clearer concepts, and a better sense of what needs to be validated.

Conclusion

The future of customer-centric innovation lies with AI-enhanced “make-believe” that allows innovators to explore ideas with simulated customers or key stakeholders far faster. While real customer interactions remain essential, AI-powered simulations can help teams sharpen hypotheses and pressure-test possibilities before validating the strongest ideas with real customers. 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.