Imagine an Executive team discussing the most urgent strategic priority – artificial intelligence. The CEO wants AI to become a new engine of growth. The CIO wants to deploy copilots. The COO sees opportunities to automate processes. The head of product wants AI-enabled offerings. The CHRO is concerned about how AI will impact people and reshape work. While everyone agrees that AI is the future and the company needs to innovate, unfortunately, everyone leaves the meeting with different perspectives !
This is increasingly the real innovation problem inside organizations – especially large enterprises. Why ? Because we use one word “ innovation ” 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. While everyone uses the same word and assumes alignment, it’s typically not the case !
In the way of background, I’ve been called the “ Dean of Innovation ” for nearly 40 years. I was 27 and VP of New Ventures at Domino’s Pizza during the company’s explosive growth in the 1980s and attended a retreat with Tom Peters, the legendary business author. Since then, I’ve worked with much of the Fortune 500, the U.S. military, and other organizations. After decades of experiences with enterprises trying to make innovation happen, I’ve observed – almost nobody means the same thing when they use the word “ Innovation “ !
Because of this – expectations conflict, strategies and tactics differ, resources aren’t aligned, and initiatives are managed with the wrong processes and/or measured by the wrong metrics. To help get past these issues and make innovation more rewarding, the need is for context – a better way to talk about it – to get all stakeholders on the same page.
Stop Asking – ‘ Is It Innovative ? ‘
For decades, executives and academics have tried to define innovation – incremental or radical, sustaining or disruptive, product or process. A better question is “ How are we being Innovative compared with what ? “ to respect innovation needs to be a positive deviance from the norm that creates value. Interestingly 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’s probably table stakes. While 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 good answers to these questions, “ innovation ” is little more than a corporate aspiration, academic exercise, or innovation theater !
For example, Nvidia’s extraordinary rise 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 recognize the farther an idea departs from the norm, the harder it becomes to prove that it will work !
Avoid The Data Trap
Executives are trained to demand evidence before committing resources. While it is generally a good management practice, it can also kill innovation ! The paradox is simple – The more genuinely novel an idea is, the less reliable the historical data becomes ! In contrast, 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 makes or enters a new market or deploys a technology whose capabilities and economics change frequently ?
Consider Generative AI in late 2022, no Executive could produce a credible 5-year ROI model. The technology, costs, competitors, regulations, and use cases were new and changing too quickly. Interestingly, the companies that waited for certainty did not reduce uncertainty ! They simply learned more slowly and entered the data trap ! In addition, they didn’t recognize the changing nature of risk – which over time, has dire consequences.
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.
Instead of asking “ Can you prove this will work ? ” ask “ What is the cheapest, fastest experiment that will tell us something important ? ” and recognize –
- 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 be built.
Realize Innovation Is Not One Game
While organizations typically make the mistake of treating innovation as a single activity, it isn’t. For example, UPS improving route efficiency is largely an optimization challenge because the system exists, data is abundant, and progress can be measured precisely. In contrast, a company responding to a cyberattack is in a different game with speed being more important than perfect information. In another example, a legacy company building an AI-enabled service faces uncertainty about customers, pricing, and the business model. In this case, small experiments and staged investments are appropriate. Another scenario is an automaker shifting toward electric and software-defined vehicles faces something larger still – a transformation affecting manufacturing, suppliers, talent, capital, and organizational identity.
While all four scenarios require good oversight, they do not require the same thinking, processes or 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 this initiative a 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.
Most Corporate ‘ Innovation ’ Is Actually Something Else
Companies call almost every form of change “ innovation ” – Installing a new ERP system / Adopting Microsoft Copilot / Reorganizing a business unit / Digitizing a process / Running a hackathon. While these activities may be valuable, they are not the same kind of work ! To explain – Creativity produces possibilities, while Innovation develops and tests novel possibilities to create value. Change gets people to adopt and scale what has been chosen. While buying an AI platform is a “ technology acquisition “, getting many people to use it is ‘change management ’ for discovering a fundamentally new way to create customer value and making Innovation more rewarding.
With this insight, recognize counting AI pilots tells us little about whether an organization is becoming more innovative. As well, automating an existing process may increase productivity, and deploying copilots may help employees work faster. While they may all be excellent investments, recognize adoption is not invention, and efficiency is not necessarily innovation !
The Danger of False Alignment
Returning 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 to mitigate risk with change and/or have a secure technology platform. The CHRO wants to redesign work and minimize disruption. While these views have merit, unless those differences are made explicit, the initiative will eventually be judged against conflicting expectations and inappropriate metrics. To avoid these issues, recognize the organization does not have an execution problem – there is a language problem – to get past the perception differences.
While leaders often respond to challenges by pushing harder for alignment in uncertain situations, different views and disagreement is inevitable. For example, one Executive sees the potential to improve efficiency, another sees a need to mitigate a competitive threat, and a third sees a new business opportunity. The goal should not be to ignore or eliminate those differences too quickly – but to make them visible. That is not dysfunction – it’s about bringing clarity to the situation.
A 5-Question Language for Innovation
Executives do not need another elaborate innovation strategy or taxonomy. They need to ask good questions in a meeting, funding review, or conversation with a team. Before approving any innovation initiative, ask –
1. What norm are we breaking ?
Is it a norm inside our company, our industry, the technology, or customer expectations ? If you cannot identify the norm being broken, you may simply be improving what already exists – which is really “ optimization “.
2. What new value are we creating, and for whom ?
Innovation is not new or a novelty. “ We need an AI strategy ” is no more useful than saying “ we need an internet strategy ” was 25 years ago. The technology is not the strategy. The question is – What becomes possible now (that was not possible before) ? If the value cannot be identified, the initiative may be innovation or technology theater. As well, assess if the initiative is within an acceptable level of the organization risk tolerance.
3. How big is the departure from what we already know ?
An incremental improvement may involve familiar customers, technologies, and economics. A new business model may call all three into question. Small departures can often be planned. Large departures need to be learned. Match the size of the investment and risk tolerance to the amount of knowledge available and the objectives to be achieved.
4. How fast do we need to move ?
While speed is not always a virtue, in certain situations it’s critical (ie: a cybersecurity breach) since time is of the essence (ie: having a remedy in minutes or within hours) to mitigate or minimize damage or risk. On the other hand, a new business model may take years to mature. The question is not “ How can we move faster ? ” It needs to be – “ What is the appropriate speed for this project or type of uncertainty ? “ As well, determine how speed affects risk.
5. What is the next experiment ?
Not – What is the 5-year plan ? Not – When can we scale it ? Rather, ask – “ What is the next experiment “ ? A good experiment answers an important question at an acceptable cost and risk. Will customers use it ? Can the technology or innovation work under real conditions ? Will someone pay for it ? What assumption, if wrong, would cause the idea to fail or risk to be unacceptable ? The purpose is not to prove that the team was right – but learn what is true. The real failure is spending 2 years building something that could have been disproven in two weeks !
Change the Questions, Change the Conversation
Instead of asking –
- “ Is it innovative ? ” ask “ What norm are we breaking ? ”
- “ What is the ROI ? ” ask “ What must we learn before investing more ? ”
- “ What is the plan ? ” ask “ What is the next experiment ? ”
- “ Are we aligned ? ” ask “ Where do we see the problem differently ? ”
- “ How quickly can we scale ? ” ask “ Have we created enough value to scale yet ? ”
These questions do not eliminate uncertainty or risk, but increase awareness and make the situation more manageable by recognizing –
- An optimization effort should be accountable for results.
- An experiment should be accountable for learning.
- A crisis response should be accountable for speed.
- A transformation should be accountable for building capabilities.
When leaders use the same language and metrics for all four, they create confusion.
When they distinguish among them, they can better manage each more intelligently.
Innovation Is Something You Navigate
As AI accelerates technological, thinking, and process changes – more initiatives will be labeled innovative. More companies will launch pilots, labs, and strategic initiatives – with more money being committed before leaders agree on how to look at things or what kind of problem they are actually trying to solve. The companies that succeed will not necessarily be those with the most ideas or higher risk tolerance, but the ones that can distinguish –
- optimization from invention
- adoption from experimentation
- evidence from assumptions
- plans from learning
And know when to –
- create data
- request data
- move quickly
- be patient
- experiment
- scale
Innovation has always been difficult to define because it is not one thing. It changes between and within organizations, by industry, objectives, strategies, technology, risk, perspective, etc. That is why the search for a perfect definition has always been problematic. Innovation does not need another definition – but leaders using a language that fast-track understanding and people getting on the same page.
Sept 14, 2026 – by CAIL and Jeff DeGraff, Clinical Professor of Management and Organizations at the University of Michigan’s Ross School of Business
CAIL Innovation commentary info@cail.com www.cail.com 905-940-9000