{"id":6315,"date":"2026-08-31T09:15:45","date_gmt":"2026-08-31T09:15:45","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=6315"},"modified":"2026-08-31T09:15:45","modified_gmt":"2026-08-31T09:15:45","slug":"im-the-dean-of-innovation-and-i-know-that-nobody-can-agree-what-innovation-actually-means","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=6315","title":{"rendered":"I\u2019m the \u2018dean of innovation\u2019 and I know that nobody can agree what \u2018innovation\u2019 actually means"},"content":{"rendered":"
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This is increasingly the real innovation problem inside large organizations. We use one word, \u201cinnovation,\u201d to describe fundamentally different kinds of work. Adopting a technology. Improving a process. Launching a product. Creating a business model. Transforming an organization. Responding to a crisis. Everyone uses the same word and assumes alignment. There isn\u2019t any. <\/p>\n

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I\u2019ve been called the \u201cDean of Innovation\u201d for nearly four decades, although the title started more as a bit of good-natured ribbing than a credential. I was 27 and Vice President of New Ventures at Domino\u2019s Pizza during the company\u2019s explosive growth in the 1980s when I attended a retreat with Tom Peters, the legendary business author. Somehow \u201cDean of Innovation\u201d emerged from the gathering, and my older colleagues at Domino\u2019s found it amusing enough to make it stick. Since then, I\u2019ve worked with much of the Fortune 500, the U.S. military, and more cultural organizations than I can remember. After decades of tours of duty inside organizations trying to make innovation actually happen, I\u2019ve become convinced of one thing: almost nobody means the same thing when they use the word.<\/p>\n

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The result is predictable: resources scatter, expectations conflict, and initiatives are managed with the wrong processes and measured by the wrong metrics. Most companies don\u2019t need more innovation. They need a better way to talk about it.\u00a0<\/p>\n

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For decades, executives and academics have tried to define innovation: incremental or radical, sustaining or disruptive, product or process. A better question is simpler: Innovative compared with what? Innovation is positive deviance from the norm that creates value. The same practice can be revolutionary in one company, incremental in another, and obsolete in a third. For a traditional company with decades of legacy systems, using AI to redesign a core process may represent a major departure from the norm. For an AI-native startup, it may be table stakes. The technology may be similar. The starting points are not. That is why leaders should ask two questions: What norm are we breaking? What new value are we creating, and for whom? Without those answers, \u201cinnovation\u201d is little more than a corporate aspiration.\u00a0<\/p>\n

Nvidia\u2019s extraordinary rise, for example, cannot be explained simply by saying the company innovated. Its graphics processing capabilities were repeatedly extended into new arenas, most consequentially AI computing. That is the real work of innovation: departing from the existing norm in a way that creates new value. And the farther an idea departs from the norm, the harder it becomes to prove that it will work.\u00a0<\/p>\n

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Executives are trained to demand evidence before committing resources. That is generally good management. It can also kill innovation. The paradox is simple: The more genuinely novel an idea is, the less reliable the historical data becomes. A modest improvement to an existing product can usually be modeled. Customers are known. Costs can be estimated. Competitors can be studied. But what happens when a company enters a market that does not yet exist or deploys a technology whose capabilities and economics are changing every few months?\u00a0<\/p>\n

Consider generative AI. In late 2022, no executive could produce a credible five-year ROI model. The technology, costs, competitors, regulations, and use cases were changing too quickly. The companies that waited for certainty did not reduce uncertainty. They simply learned more slowly. This is the data trap. The ideas with the strongest evidence are usually those most similar to what the organization already does. Demand too much proof, and you unintentionally select for the familiar. Innovation gradually becomes optimization.\u00a0<\/p>\n

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Instead of asking, \u201cCan you prove this will work?\u201d ask, \u201cWhat is the cheapest, fastest experiment that will tell us something important?\u201d In established operations, data informs action. In innovation, action often creates the data. Experiments do not validate the plan. They create the knowledge from which a plan can eventually be built.\u00a0<\/p>\n

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Organizations also make the mistake of treating innovation as a single activity. It isn\u2019t. UPS improving route efficiency is largely an optimization challenge. The system exists, data is abundant, and progress can be measured precisely. A company responding to a cyberattack is playing a different game. Speed matters more than perfect information. A legacy company building an AI-enabled service faces uncertainty about customers, pricing, and the business model. Small experiments and staged investments are appropriate. An automaker shifting toward electric and software-defined vehicles faces something larger still: a transformation affecting manufacturing, suppliers, talent, capital, and organizational identity.\u00a0<\/p>\n

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All four require good management. They do not require the same management. Yet companies routinely force them through the same stage gates, funding processes, and metrics. Before choosing the process, leaders should ask: How large is the departure from what we already know? How quickly must we move? How much uncertainty exists? The answers should determine the team, funding, governance, metrics, and pace, not the other way around.\u00a0<\/p>\n

Companies call almost every form of change \u201cinnovation\u201d. Installing a new ERP system. Adopting Microsoft Copilot. Reorganizing a business unit. Digitizing a process. Running a hackathon. These activities may be valuable. But they are not the same kind of work. A useful distinction is simple: Creativity produces possibilities. Innovation develops and tests novel possibilities to create value. Change gets people to adopt and scale what has been chosen. Buying an AI platform is technology acquisition. Getting thousands of employees to use it is change management. Discovering a fundamentally new way to create customer value with it is innovation. A company can succeed at the first two without accomplishing the third.\u00a0<\/p>\n

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This is why counting AI pilots tells us little about whether an organization is becoming more innovative. Automating an existing process may increase productivity. Deploying copilots may help employees work faster. Those may be excellent investments. But adoption is not invention, and efficiency is not necessarily innovation.\u00a0<\/p>\n

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Return to the executive meeting. The CFO believes the AI initiative should reduce costs within 12 months. The head of product wants new sources of revenue. The CIO wants a secure technology platform. The CHRO wants to redesign work. All four may be right. But unless those differences are made explicit, the initiative will eventually be judged against conflicting expectations. The organization does not have an execution problem. It has a language problem.\u00a0<\/p>\n

Leaders often respond by pushing harder for alignment. But in uncertain situations, disagreement is information. One executive may see an efficiency opportunity. Another sees a competitive threat. A third sees a new business model. The goal should not be to eliminate those differences too quickly. It should be to make them visible. That is not dysfunction. That is clarity.\u00a0<\/p>\n

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Executives do not need another elaborate innovation taxonomy. They need a few questions they can use in a meeting, funding review, or conversation with a team. Before approving any innovation initiative, ask five.\u00a0<\/p>\n