{"id":39307,"date":"2026-09-29T11:33:21","date_gmt":"2026-09-29T10:33:21","guid":{"rendered":"https:\/\/www.hult.edu\/blog\/?p=39307"},"modified":"2026-09-29T11:34:07","modified_gmt":"2026-09-29T10:34:07","slug":"when-ai-makes-building-easier-what-you-build-is-more-important","status":"publish","type":"post","link":"https:\/\/www.hult.edu\/blog\/when-ai-makes-building-easier-what-you-build-is-more-important\/","title":{"rendered":"When building gets easier because of AI, the real question is: What should you build?"},"content":{"rendered":"

How customer value creation helps students move from ideas to market while developing the human skills that increasingly matter in business.<\/span><\/i>\u00a0<\/span><\/p>\n

\u00a0<\/span>The process of turning an idea into something tangible is changing remarkably quickly. Students can now use generative AI to brainstorm alternatives, analyze markets, develop concepts, write code, create prototypes and prepare professional-looking presentations in a fraction of the time these activities once required. That is exciting, and I want students to use these tools. At the same time, faster production introduces a less obvious challenge: if it becomes easier to build almost anything, deciding what is worth building becomes considerably more important.<\/span>\u00a0<\/span><\/p>\n

The challenge is becoming visible in software development. Demirer, Musolff and Yang (2026), studying more than 500,000 software developers, find substantial increases in coding activity following the adoption of successive generations of AI coding tools. Yet those gains diminish considerably as work moves from writing code toward completed releases. Their analysis of major software marketplaces shows a similar pattern: new-app creation has accelerated sharply since the rise of agentic coding tools, while usage has not kept pace. They also observe considerably more experimentation along the way, including more code being rewritten, more branches being created and more projects ultimately abandoned.<\/span>\u00a0<\/span><\/p>\n

One example is wearable technology. Bose approached smart eyewear with its Frames product, combining conventional sunglasses with open-ear audio, calls and voice assistance. The product was initially part of a broader vision for audio augmented reality. Bose presented its AR platform as designed for everyday use but wound down the initiative in 2020 after it failed to meet expectations. A Bose spokesperson said at the time that the technology had found specific use cases but not broad daily use. Bose continued developing audio-focused Frames for a period, but its broader augmented-reality ambition had not generated the hoped-for market.<\/span>\u00a0<\/span><\/p>\n

Today, Meta and EssilorLuxottica are approaching essentially the same product category in a different way, integrating cameras, open-ear audio, voice interaction, translation and AI into familiar Ray-Ban and Oakley eyewear. Adoption has been substantially broader: Meta reported millions of Ray-Ban Meta units sold, while EssilorLuxottica reported that sales of its Meta AI glasses exceeded seven million units during 2025. The interesting question is therefore not simply whether the underlying technology has improved, which it clearly has, but whether the value proposition is now better connected to recognizable customer jobs and everyday behavior.<\/span>\u00a0<\/span><\/p>\n

For business educators, that distinction matters. Students today are often very good at generating ideas. Yet when the conversation moves from enthusiasm to market reality, the questions become harder. Who exactly is the customer, not simply as a broad segment or invented persona, but in sufficiently concrete terms that we can understand their situation? What problem or unmet need is important enough to solve? What are customers doing today instead? Why would they change their behavior? And how will value be delivered, captured and measured over time?<\/span>\u00a0<\/span><\/p>\n

These questions expose the distance between a promising idea and a credible market opportunity. Christensen, Hall, Dillon and Duncan (2016) make a related point through the Jobs to Be Done perspective. Organizations may know a great deal about customer characteristics and still fail to understand the progress customers are trying to make in a particular situation. Innovation becomes more useful when we understand the job the customer is trying to accomplish rather than relying primarily on customer categories or product characteristics.<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

This has influenced how I teach. Students can become very good at talking about solutions before determining whether the underlying problem deserves to be solved at all. In my classes, we therefore intentionally spend time in the problem landscape. Students frame assumptions, conduct primary research, seek supporting evidence and repeatedly test whether their interpretation of the customer is correct. I encourage them, somewhat provocatively, to fall in love with the customer problem before even considering solutions.<\/span>\u00a0<\/span><\/p>\n

Generative AI makes that discipline more, not less, important. Large language models are enormously useful for exploring possibilities, identifying questions, locating patterns, challenging assumptions and accelerating research. But plausible output is not the same thing as market evidence. AI can help us think about a customer; it should not prevent us from talking to the customer.<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

From idea generation to customer value creation<\/span><\/b>\u00a0<\/span><\/h2>\n

The broader shift is from treating innovation primarily as idea generation toward treating it as a discipline of customer value creation. Markets do not reward ideas simply because they are interesting or technologically possible. Ultimately, someone must find the resulting value proposition sufficiently relevant to choose it over available alternatives.<\/span>\u00a0<\/span><\/p>\n

A credible value argument therefore has to withstand a demanding sequence of questions:<\/span>\u00a0<\/span><\/p>\n

    \n
  1. Who are we really serving?<\/span>\u00a0<\/span><\/li>\n
  2. What are they trying to accomplish?<\/span>\u00a0<\/span><\/li>\n
  3. What makes the current situation frustrating, costly, risky or otherwise worth changing?<\/span>\u00a0<\/span><\/li>\n
  4. What alternatives and workarounds already exist?<\/span>\u00a0<\/span><\/li>\n
  5. What would make our proposed solution sufficiently better to justify switching?<\/span>\u00a0<\/span><\/li>\n
  6. What capabilities are required to deliver that promise reliably?<\/span>\u00a0<\/span><\/li>\n<\/ol>\n

    \u00a0<\/span>This idea is well established in research on customer value propositions. Anderson, Narus and Van Rossum (2006), for example, argue that effective value propositions should concentrate on the benefits that matter most to target customers and provide credible evidence of the value being claimed, rather than presenting an indiscriminate catalogue of benefits. This remains one reason I view customer value creation not as a narrow marketing topic, but as a bridge between marketing, strategy, entrepreneurship, product management and organizational execution. It connects an understanding of the customer with choices about what a company should offer, prioritize, build capabilities around, take to market and ultimately measure.<\/span>\u00a0<\/span><\/p>\n

    The timing is also relevant from a skills perspective. The World Economic Forum\u2019s\u202f<\/span>Future of Jobs Report 2025<\/span><\/i>, based on responses from more than 1,000 leading global employers representing more than 14 million workers, identifies analytical thinking as the top core skill for employers. Creative thinking, leadership and social influence, and curiosity and lifelong learning also rank among today\u2019s core skills and among the capabilities expected to increase in importance as work continues to change.<\/span>\u00a0<\/span><\/p>\n

    Hult\u2019s own research points in a similar direction. In a survey of 800 U.S. HR leaders and 800 recent graduates working in business roles, employers identified communication, curiosity and willingness to learn, collaboration, creativity and critical thinking as particularly important capabilities for new hires. Communication was cited by 98% of HR leaders, curiosity and willingness to learn by 93%, collaboration by 92%, creativity by 90% and critical thinking by 87%.<\/span>\u00a0<\/span><\/p>\n

    The educational challenge, then, is not simply to tell students that these capabilities matter. It is to create situations in which they must use them to solve meaningful business problems.<\/span>\u00a0<\/span><\/p>\n

    A framework for moving from problem to market<\/span><\/b>\u00a0<\/span><\/h2>\n

    In my teaching and executive education work, I use an eight-part Customer Value Canvas for this purpose. The framework is intended to create a portable value argument that can travel from early exploration to market execution and hold up in conversations with customers, managers and executives.<\/span>\u00a0<\/span><\/p>\n

    The sequence begins with customers and noncustomers, followed by the customer problem or job to be done, and then the solution and value proposition. Only after those foundations come priorities, performance, people, partners and key resources, and Creative Leadership, my term for combining creative thinking with proactive leadership and execution. The wider methodology moves iteratively through Engage, Explore and Execute: deciding which problem is worth pursuing, reducing uncertainty through customer and market learning, and moving what has been validated toward implementation.<\/span>\u00a0<\/span><\/p>\n

    Students and executives work with the one-page canvas shown below. Its purpose is to make the whole value-creation system visible rather than treating customer discovery, strategy, organization and execution as separate conversations.<\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span>Figure 1. Customer Value Canvas: A practical tool for moving from customer problem to market execution<\/span><\/b><\/p>\n

    \"Customer<\/p>\n

    Source: Totterman (2026), Customer Value Creation framework.<\/em><\/p>\n

    I do not ask participants simply to \u201cfill in the boxes.\u201d We begin by treating the content as a set of hypotheses. The first three areas receive disproportionate attention, with five questions at the center of the work:<\/p>\n

      \n
    1. Who exactly is the customer?<\/li>\n
    2. What are they trying to accomplish?<\/li>\n
    3. What is getting in the way today?<\/li>\n
    4. What alternatives or workarounds already exist?<\/li>\n
    5. What would have to be true for our proposed solution to represent a meaningful improvement?<\/li>\n<\/ol>\n

      The distinction between customers and noncustomers is particularly useful because asking whom we are not trying to serve creates discipline. The customer problem is articulated without reference to the proposed solution, and only after that do we begin shaping the value proposition and asking what would make someone change from the status quo.<\/p>\n

      The remaining areas force the idea into organizational reality:<\/p>\n