The Problem With Technology Entrepreneurship

• Because AI-powered applications are so easy to bring to market, founders are tempted to skip the research and iteration phases that determine whether customers will actually adopt the product.
• When technology entrepreneurship students are introduced to complex, ambiguous situations, they will learn to focus first on the problem they must solve, not the technology they can build.
• Instructors can assess learning by requiring students not only to propose ideas, but also to justify them in terms of feasibility, scalability, and ethical implications.

 
In an AI-driven world, business schools are under increasing pressure to teach technology entrepreneurship in a way that is more relevant, practical, and future-facing. A school’s instinctive response is often to double down on tools—AI platforms, prototyping software, and emerging technologies. But this raises a fundamental question: Are we starting in the right place?

Ask most people what technology entrepreneurship education looks like, and they will describe pitch competitions, app prototypes, and sessions on the latest tools. Technology sits at the center of the education process. That makes sense until you speak to founders whose ventures did not work.

A familiar pattern emerges from these conversations: Technically capable teams built products that no one adopted because the entrepreneurs never fully understood the market, the community, or the human problem they were trying to solve. The technology worked, but the venture did not.

In practice, success is more likely to flow from widespread adoption than technological breakthroughs. But founders can only achieve adoption if they understand the markets they are entering, the problems they are solving, and the likelihood that consumers will want the solutions they are building.

These are not novel insights. Market research, problem identification, and iterative development are well-established foundations of any viable venture. The question worth asking is why, in technology entrepreneurship specifically, these foundations are so frequently bypassed.

The Real Problem Is Not the Technology

The answer may lie in the seductive appeal of the technology itself. The assumption is that capability creates demand. This is what might be called the technology-first trap: the belief that a technically impressive solution will find its own problem, and that building is a substitute for understanding.

This tendency is visible across the history of technology ventures. The Segway was heralded before launch as a product so compelling it would redesign cities. But while the technology was extraordinary, the problem it was solving for everyday consumers was never clearly established. Similarly, Google Glass was technically sophisticated but insufficiently grounded in an understanding of how and why real people would integrate it into their lives.

This pattern is arguably more acute in technology entrepreneurship than in other forms of new venture creation because the tools themselves accelerate building. It has never been easier or faster to construct a working prototype, deploy a product, or bring an AI-powered application to market. That speed is valuable, but it also removes some of the friction that once forced founders to think carefully before committing resources. When building is hard, the cost of skipping the research phase is obvious. When building is easy, the temptation to treat the prototype as the research can override judgment.

In 1962, Everett Rogers demonstrated that the diffusion of innovation is governed not by technical merit alone, but by a cluster of adoption-enabling properties: whether a new technology is compatible with existing practices, whether its benefits are visible, and whether it can be tried before commitment. More technically capable solutions routinely lose out to those that are easier to adopt.

The hardest step is not building the technology; it is getting people to use it by demonstrating clear value. This is the chasm that claims most technology ventures.

Clayton Christensen explored this idea further in his 1997 book, The Innovator’s Dilemma. His research showed that disruptive technologies frequently underperform incumbent solutions on conventional benchmarks at the moment of breakthrough. Instead, they succeed by finding underserved adopter segments—a demand-side rather than a supply-side phenomenon. The competitive advantage, in other words, was never primarily technical.

Historical cases make the point with uncomfortable clarity:

• VHS displaced the technically superior Betamax format largely because it offered longer recording times, better studio licensing, and wider retail distribution.
• During the development of the internet, the TCP/IP framework supplanted the more rigorously specified OSI protocol stack because it had been embedded in Unix systems that were distributed to universities, thereby generating an adoption base before competing standards could consolidate.
• The persistence of the QWERTY keyboard in the face of demonstrably more efficient alternatives illustrates how adoption lock-in can render technical optimization effectively irrelevant.

In each case, the technology that won was not necessarily the best, but it was the one people used. And behind each of those winning technologies was a deeper, often unglamorous process of understanding users, testing assumptions, and adapting accordingly.

As Geoffrey Moore argues in his 1991 book Crossing the Chasm, the hardest step is not building the technology; it is getting people to use it by demonstrating clear value, not just capability. This is the chasm that claims most technology ventures. It emerges because building the technology consumed the attention that should have been directed at researching the market.

Experience Alone Is Not Enough

Entrepreneurship research underscores the fact that successful founders tend to be those who listen carefully, test early, and adapt. In his 2011 book The Lean Startup, Eric Ries describes this process as validated learning, the systematic pursuit of adoption pathways. In this approach, founders don’t pursue technical development until they determine whether a given innovation can be absorbed into existing social and economic systems. In other words, they do not start with the technology and then find a problem to solve.

This truth has prompted business schools to ask an uncomfortable question: If much of our teaching still begins with tools, platforms, and possibilities, are we unintentionally reinforcing the wrong starting point?

In response, many schools have expanded their use of experiential learning through live projects, simulations, and field-based work. Yet it is entirely possible for students to be busy, engaged, and even enthusiastic without necessarily developing a clearer way of thinking. And in technology entrepreneurship, the activity of building can feel like progress while quietly substituting for the harder work of researching markets and solving problems iteratively.

If much of our teaching still begins with tools, platforms, and possibilities, are we unintentionally reinforcing the wrong starting point?

At the same time, if “learning by doing” is to translate into insight, any hands-on learning experience needs to be followed by reflection and conceptual thinking.

What seems to make the difference is how an activity is positioned within the learning journey. When students encounter complex situations before they’re introduced to frameworks, their relationship with theory changes. Concepts such as user-centered design, stakeholder analysis, and sustainability are no longer abstract ideas. Rogers’ adoption curve, Christensen’s demand-side logic, and Moore’s chasm arrive as tools and explanations for something the student has already encountered.

This approach, sometimes described as civic immersion, is something I have explored in the context of technology entrepreneurship teaching and regional engagement.

Students Meet Real-World Ambiguity

If entrepreneurship students are going to learn to recognize meaningful problems and think through the consequences of potential solutions, they must be presented with live, unresolved, ambiguous challenges that require them to carefully weigh assumptions and trade-offs. They cannot shortcut that process by building something, because there is nothing yet to build.

At Bournemouth University Business School in the U.K., MBA students in a technology entrepreneurship unit recently had an opportunity to work on an ambiguous problem at the start of their class. They joined an evolving civic initiative linked to the legacy of computer scientist Alan Turing, which took place at the home of the proposed Turing Centre in Sherborne, Dorset.

What students encountered was not a neat case study but an unfinished project with competing stakeholder priorities, questions around funding, and uncertainty about what the initiative might become. To work through that ambiguity, teams developed proposals around business models, positioning, fundraising, and long-term sustainability. They knew they were not seeking a single “right answer” but looking for one possible operating plan.

What was striking was how quickly the conversation shifted. Students who initially gravitated toward technological solutions began to ask more grounded questions. Who is this initiative really for? What problem is being solved? Why would anyone engage with this?

Entrepreneurship students must be presented with live, unresolved, ambiguous challenges that require them to carefully weigh assumptions and trade-offs.

These are, of course, the questions that any good entrepreneur should ask first. The design of the learning experience revealed how naturally those questions get displaced when technology is at the center of an enterprise and how readily they resurface when students are immersed in a problem they cannot simply build their way out of.

It was in this context that a chief technology officer from an AI venture joined one of the in-class sessions. What could have been a discussion about systems and architecture became one about users, assumptions, and the discipline of testing ideas rather than becoming attached to them. The message was clear: Technology should follow need, not lead it.

Assessment Is Where This Holds or Unravels

Even where teaching is thoughtfully designed, the intended learning can come undone at the point of assessment. If students are asked to reflect on their experiences, there is a tendency for the narrative of what happened to replace a deeper analysis of what it means. The work can become descriptive rather than critical.

Assessment will be most effective when students are required not only to propose ideas but to justify them. They should be asked to consider feasibility as well as creativity, scalability alongside intent, and ethical implications overall. Crucially, they should be able to show evidence that they understand the market and have gathered stakeholder insights. They should be able to give credible accounts of why a real group of people would adopt their proposed products.

These requirements are particularly important in technology entrepreneurship, where questions of bias, transparency, and societal impact are embedded within the ventures themselves. Assessment, in this sense, does more than evaluate performance. It signals what counts as good thinking.

Beyond the Technology

The most important lesson for technology entrepreneurship education is also the easiest to overlook. Designing the technology is far less consequential than understanding people, working through uncertainty, and translating possibility into something that others find genuinely valuable.

This makes research and iteration more important, not less. Educating students to treat market insight as the starting point rather than an afterthought may be one of the most practically valuable things a business school can do.

Business schools have long known that market research, problem identification, and iterative development are the foundations of viable entrepreneurship. The challenge specific to technology entrepreneurship is that the technology itself can make those foundations feel optional. Business schools must make them feel indispensable again.

When business schools design classes to include these foundational elements of entrepreneurship, students will not simply build what is possible—they will make sound judgments about what is worth building in the first place.