Redesigning Innovation Around AI

Male chef cooking in the kitchen, surrounded by a lot of kitchen tools and ingredients.

Generating New Ideas

Your session at FEI will focus on redesigning innovation around generative AI at Newell Brands. Just to give the audience some context, InnoGEN is an internal approach that Newell has been developing. But there are lessons that apply to any corporate innovation audience as they experience this new frontier. What were your pain points in the traditional innovation process that kind of led Newell to redesign around AI?

Gurr relates, “One thing I think we sometimes forget in the age of ChatGPT, is that GenAI first showed up in creative work with image generation. And back in early 2022, I was working in the design org at Newell, and we first started seeing some of the evidence around then of what these AI image generators could do. It genuinely felt like we were seeing the future and thankfully, we moved quickly. We created a dedicated team around that. We set up a research lab and a training program. We made some progress, our leadership challenged us to take that mindset from our design practice into our innovation practice.”

“It wasn’t that our old innovation process was broken by any means,” adds Gurr. “It was was working well, but AI made us question things that we’d accepted as norms in that space. We were used to slow information sharing, long convergence cycles in sessions, and, of course, walls and walls of sticky notes. Today, now using AI tools, we can often explore up to ten times the number of ideas in the same amount of time. The ideas are further developed as well. We don’t just have a handful of hand drawn sketches or notes on stickies. We’ll have high fidelity images, concept statements, sometimes even prototypes and videos. It really changes the quality of the conversation that we can have after those sessions.”

The speed of development has been pretty impressive with AI. How did you move InnoGEN from this initial concept to something broader in the organization?

“Honestly, we expected some resistance initially, particularly within the creative teams,” he says. “But I think because we started the conversation early and we gave people access to the tools along with the training, that really let them experience the potential for themselves, and they got excited about it as much as we did. It also helps at Newell that we have great support from leadership for AI adoption. We’re fortunate to have strong advocacy from our CEO on down.”

“But we still had to earn some confidence mainly with our cross functional partners, and we involved them from the beginning, gaining some early wins. We found that the insights team would help bring in the right consumer understanding to the sessions. R&D were great, helping define technical boundaries and constraints. Then, of course, brand was key in keeping us aligned to business objectives. A couple of key partners were the legal and IT teams that helped us navigate risks, tool selection, IP, gray areas, all as we were figuring out the space together. It was that combination of participation, evidence on our part, and then the leadership support that was really critical.”

Learning Faster

Everybody’s talking about balancing AI with human judgment and intuition, keeping humans in the loop. How did Newell Brands approach this?

“Clearly, we use AI heavily. It’s a great asset for synthesizing trends, market signals, consumer insights, helps visualize, all those factors that I’ve touched on,” says Gurr. “Where we’re really cautious with AI is that we don’t ask it to autonomously generate the ideas itself without any human contribution. We could easily have it spin up hundreds of ideas in just a few minutes, but we don’t think that necessarily leads to better outcomes or better quality innovations. We think that human role of intuition, lived experience, and judgment, it’s something that the AI can easily miss or not easily replicate.”

“Think about the way people experience and feel. They’ll often recognize discomfort, contradictions, or opportunities in situations, sometimes even before they can fully explain it. I’d probably think of it in terms of the AI being there to help generate, challenge, and expand our thinking, but it’s really down to the people to then direct those findings, interpret all the information they’re getting back, and choose and own the outcomes.”

We’ve talked about the speed of AI. Did it lead to better innovation quality in terms of the initiatives?

“It did actually, and that that was our hypothesis right from the start,” says Gurr. “We thought we could leverage the speed that it brought to the process to actually reinforce the quality that we were delivering at the same time. We’re not just using AI to make the old process faster. We’re actually using it to buy back thinking time. It lets us explore more territories, create richer stimulus, and visualize ideas much earlier in the process. So that means that teams are basically spending less time on mechanical tasks, more time thinking deeply about the problems or the opportunities in front of them, and more time reacting and improving the outcomes that they’re putting together.”

“Of course, once an idea becomes more tangible, people can challenge it more quickly. Consumers can respond to it more easily. Engineers can test it and iterate faster. Externalizing the ideas in a richer sense more quickly is really important. But it’s not that we just get to the answer faster. It’s that we learn faster. I think that’s the key thing.”

A Bridge to Insights

Let’s dive into AI and the workflow a little bit. You mentioned insights and how that can help generate the speed of insights. Can you just share with us a little bit about the exploration and the concept development?

“When we approach AI usage now, a lot of our focus is inevitably around the context. The context of the project, the session we’re running, how we can preserve that context availability to the AI tool we are leveraging. Obviously, AI becomes much more powerful when it has access to all the information that the team has access to. It’s easier said than done. We work hard to bring insights, technical reports, creative work, project knowledge closer together in one place, and that means that we can do things with that knowledge that just weren’t practical before.”

He adds, “The synthesis is faster. We can do things like take some of those insights, maybe our segmentation data, and build interactive personas so the people that are ideating can get the voice of the consumer live as they’re working through their thinking. We can scan social conversations if we want to bring in more live dialogue, search through large databases of innovation and insights, and try and find some synergies between those. And then, of course, there’s a creative side that we’ve spoken about in terms of bringing those ideas to life, and it’s not just that we can create great written evidence, great visual assets. We can tell stories now through video, generative video, super quickly and easily, which is relatable to leadership.”

“We’re even doing work with generative 3D CAD now, so that gives us a faster bridge into producing physical tangible artifacts as well,” he says. “It just compresses that whole upstream process and lets us reach down further into the development teams a lot quicker.”

Making Innovation Inroads

Newell Brands has made great inroads with innovation and AI. What about innovation teams that are just starting out with AI? What are a few lessons that you would share?

“I think there’s a handful of things, and I don’t think there’s necessarily a right or wrong order to do them in,” says Gurr. “But a few I would highlight, the IT infrastructure as we talked about is really key. We need to make sure that the tools are safe and they’re available to people. That touches on some of the legal implications with those as well. So those two groups need to be tightly connected.”

“Secondly, it helps to have all that data and knowledge organized,” Gurr continues. “I just spoke a bit about context there. But AI is really only as useful as the information that you give to it, so spending some time on that is important. We found a lot of value as an FEI team in terms of codifying how our team works. A lot of our process is documented. It’s templatized. Our roles are defined. Key decision points in the process are documented as well. Because of that, we’ve been able to give that operating model to our AI, and that means it acts a lot less like a search engine and more like a collaborator. It’ll understand how the team works, what should happen next, and that’s obviously really useful.”

“Lastly, this is something we did from day one, try and build a culture of experimentation. It’s important that people feel empowered and enthusiastic to try out the tools and work with them. The biggest barrier and I think this is still true even in our organization. The biggest barrier to AI adoption is really human inertia. It’s not the pace the technology moves at, but it’s the pace that it diffuses into the organization.”

Gurr adds, “It’s simple things. People have busy working days. Everybody’s got a lot on their plates. So if learning AI is something that they must squeeze in around everything else, then, of course, adoption is bound to be slow. If you’re serious about it, you’ve got to find a way to give people the time and permission to explore it.”

FEI Summit Spotlight: Ideation Multiplier

The FEI: Innovation Summit will be held October 5-6, 2026, at The Colorado Convention Center, Denver. The summit will be co-located with TMRE. The session, “Innovation X: AI as Ideation Multiplier,” will be presented by Chris Gurr, FEI Pod Leader at Newell Co.

What happens when you redesign innovation around generative AI? At Newell Brands, it created a faster, more connected approach to idea development. Chris Gurr will share how InnoGEN, an award-winning internal approach, helped teams strengthen synthesis, expand creative exploration, and move from early signals to actionable concepts with greater speed and confidence. By accelerating learning, leveraging digital teams and winning leadership advocacy, the approach helped teams rethink how insights, exploration, and concept development work together at the Front End of Innovation. This session will unpack the model, the adoption journey, and the practical ways FEI teams can apply these tools today to improve innovation quality, increase throughput, and unlock bigger ideas faster.

Click here for more information about the FEI: Innovation Summit

Be Cause: How Causes Animate Innovation

A butterfly landing on a flower.

I’ve spent decades studying this pattern, first as the child of a man who lived it, then as an executive who tested it inside an $11 billion healthcare transformation, and now as someone who coaches leaders trying to find their way back to it. The pattern has a name. It’s called cause.

Mission Is Given. Cause Is Taken.

Most organizations run on mission statements. They are useful, necessary even — but they are also borrowed. A mission is handed down from a board, a founder, a strategy offsite. It tells you what to do. A cause is different. It is chosen. It is personal. It doesn’t tell you what to do — it tells you who you are, and it doesn’t expire when the quarter ends.

Consider the distinction in practice: “Grow shareholder value” is a mission. “Make sure no one on my team goes home feeling unseen” is a cause. One is a transaction. The other is a transformation. Missions can be copied from a competitor’s website. Causes cannot be copied — they have to be lived into.

This distinction matters more now than ever, because we are building organizations alongside machines that are extraordinarily good at executing missions and completely incapable of holding a cause. Artificial intelligence can optimize a task, but it cannot ache for an outcome. It doesn’t lie awake wondering whether the world will be better because it existed. That ache — that specific, human restlessness — is where innovation is actually born. Not in the strategy deck. In the ache.

A Question That Undoes You

Years ago, I attended a lecture by Rajmohan Gandhi, grandson of Mahatma Gandhi. I’d written a manuscript steeped in his grandfather’s philosophy and hoped, somewhat shamelessly, to get an endorsement. I lost my nerve, retreated to the refreshment table, and ran straight into him getting tea.

We ended up at dinner with his family that night. Riding a wave of self-importance, I fetched water glasses for the table, feeling rather proud of myself. Then he turned to me and asked, simply: “So, tell me — what do you do for the world?”

I fumbled. I inflated my title. I said things that sounded impressive and meant nothing. In today’s terms, he prompted me, and I hallucinated.

That question stayed with me long after the discomfort faded, because it exposed something most professional life is engineered to hide: how easy it is to build an entire career — polished résumé, optimized LinkedIn profile, steady climb — without ever answering what would not exist if you did not exist. That is the question underneath every act of real innovation. Not “what can I build,” but “what am I here to change.”

The Butterflies Only Land When You Stand Still

Causes don’t usually arrive as thunderclaps. They arrive the way a Maori chief once described to me when I asked her what she does when insight visits her, unannounced, mid-thought. “You stand still,” she said, startled by her own answer.

Most of us don’t. We are tuned to our phones, our notifications, our next task — systems engineered to hold our attention, not to earn our reflection. The causes that could animate our work are moving past us constantly, and we are too busy performing productivity to notice them landing.

Sara Blakely noticed hers while selling fax machines door to door, irritated by ill-fitting hosiery under white pants. She had no retail background, no textile expertise, and five thousand dollars. What she had was a specific, personal irritation she refused to let go of. That refusal became Spanx. The innovation wasn’t the product. The innovation was the standing still long enough to take the cause seriously.

Building the Scaffolding

My father, a bank clerk with only a high-school education who was nonetheless fluent in six languages and quietly fought for the underrepresented his entire life, never explained his philosophy outright. He lived it — riding the bus in a starched shirt, crossing picket lines to stand with employees against his own bosses, meditating after long days instead of complaining through them. He and my mother didn’t have evidence that the future they imagined for their children was possible. They built toward it anyway, not because it was rational, but because it was theirs.

That is what a cause requires of an innovator: not a business case, but a willingness to be transformed before you attempt to transform anything else. Listen for the suffering and the unmet potential around you. Document it. Stay close to the dream before logistics arrive to talk you out of it. Ask the question that undoes you — what would I be willing to lose something for? — and then build the scaffolding to get there, using every tool, including AI, as an instrument of that purpose rather than a substitute for it.

Machines will keep getting better at execution. They will never get better at aching. That ache — chosen, personal, undying — is still the most reliable engine of innovation we have.

Click here for more columns from Mohan Nair.

Disrupting the Status Quo

A row of light bulbs, all dark, except one shining bright in the center.

The Heart of the Innovation Process

Questioning the status quo is a leadership principle that moves straight to the heart of innovation decision-making. It can be an essential element to innovation leadership, forming key teams, and building the right innovative culture.

As a UC Berkeley ExecEd article on “The Power of Questioning the Status Quo,” puts it, “It allows us to explore different perspectives, weigh different opinions, and let common goals and truths (rather than our assumptions or fears) guide us toward a better future.”

In fact, this principle stands as one of the foundations of the university’s Haas School of Business: “We thrive at the epicenter of innovation. We make progress by speaking our minds even when it challenges convention. We lead by championing bold ideas and taking intelligent risks.”

Berkeley further posits that questioning the status quo is a mindset, one that can be trained and developed over time. These characteristics show a tendency to excel at recognizing the vital elements of a current situation, exploring multiple perspectives, and taking the necessary actions to affect change:

  • Observant: Being observant doesn’t come naturally to everyone. Those who are more innately observant than others tend to spot problems more easily, recognize outdated habits, and quickly identify when particular strategies are no longer working. They can also acquire helpful information and apply in new ways—in their relationships, careers, and businesses.
  • Agile-Minded: Agile-mindedness means being capable of approaching situations with a blank slate, remaining open to all possibilities, and entertaining diverse viewpoints and thinking processes. This quality allows us to recognize opportunities that may not be immediately apparent and are essential for creativity and bringing new solutions to old problems. It also helps our brains process information and remain flexible.
  • Courageous: Courage is a quality that provides us with inner strength, allowing us to perform the often-uncomfortable tasks necessary to make change happen—for ourselves or others. It empowers us to ask hard questions, engage in difficult conversations, and seek the truth—no matter how inconvenient.

FEI Summit Spotlight: Challenging the Status Quo

The FEI: Innovation Summit will be held October 5-6, 2026, at The Colorado Convention Center, Denver. The summit will be co-located with TMRE.

The session, “Challenging the Status Quo,” will be presented by Cordell Hardy, R&D Vice President, Food North America at Cargill; Cristina Garcia-Cevigon, SVP, Growth Strategy, Portfolio & Innovation at MARS; and Rick Robinson, Vice President and General Manager, AgeTech Collaborative at AARP.

Breakthrough innovation requires challenging assumptions and taking smart risks. In this panel, leaders discuss how they design experiments, test bold ideas, and manage innovation portfolios without putting the business at unnecessary risk. Learn how companies push beyond the status quo while maintaining strategic discipline.

Click here for more information about the FEI: Innovation Summit

Driving Degrees of Change

Questioning the status quo is a powerful capability that may have certain inherent characteristics, as Berkeley detailed above, but nevertheless it is likely exhibited differently with each individual, environment, and experience. That being said, Berkeley offers a general framework including:

  • Identify an Opportunity or Need: Is something not working, or might it soon become an issue if not changed? Is there an opportunity to make something better?
  • Ask Questions: To better understand the issue, it’s crucial to do your research and ask questions to get at the heart of the matter, understand the context, and challenge assumptions.
  • Create a Change Statement: Once you know more about the situation, a clear, straightforward ‘change statement’ (identifying what you want to change) will help you remain focused.
  • Explore Ideas and Weigh Options: Next, come up with ideas to address the issue. It might help first to ask, “How might we…” (insert the actionable part of the change statement).
  • Gather Allies: Partner with others who can contribute to and support this change.
  • Propose Change: Clarify your pitch, prototype your solution (if possible), and communicate it to your team and stakeholders.
  • Implement Change: Implementing change might be a simple feat or lengthy and involved, depending on the chosen solution and degree of change.

Getting Out of Your Comfort Zone

As Frog/Capgemini Invent notes in its article, “The Key to Innovation: Challenging the Status Quo,” this is a process or journey that at times can be uncomfortable. It may sound deceptively simple, but first one has to define the status quo at their organization, identify the old way of doing things and move forward in a new way.

“The first step in that process is identifying what the team or organization is actually doing–not what you say you do or what your customers think you do, but what you actually do,” advises Frog. “What are the tools, processes and products that make the organization tick? This constellation of factors is the status quo that you will need to interrogate and disrupt if you want to innovate. Once you’ve taken stock of your own status quo, the most important question is: Why do we do these things in these ways? The point of asking the question is not necessarily to burn the status quo to the ground completely, but to identify the elements within it that are preventing innovation.”

This may also mean challenging your own leadership qualities (and to be prepared for others to question your values and beliefs).

As Frog notes, “Innovation is not easy, and many organizations fail because they cannot shift their status quo. But if you feel prepared to challenge your own decisions and beliefs for the benefit of the organization, you are ready to begin the journey toward innovation.”

Video: “How status quo bias makes change difficult,” courtesy of Sullivan & Stanley.

Leveling Up Questions to Elevate Innovation Answers

A background with questions in a jumble, with one big question mark front and center.

Jha introduces the “question ladder,” a five-level framework ranging from simple information retrieval to transformational questioning that challenges existing paradigms. Using a case study of the Ford F-150, she demonstrates how reframing a product brief from mechanical improvements to understanding user lifestyle needs can lead to significant design innovations. The session concludes with practical advice for leaders on fostering a culture of curiosity and using “question storming” to drive strategic breakthroughs.

An innovation strategist, Jha has led innovation teams and executive education for organizations like Ford, Cisco, and Harvard. Her work explores a central question: How do we expand human capacity to think, create, and make better decisions in an age of expanding intelligence?

Prapti Jha (In Her Own Words):

I’d love to present a new point of view on the work you’re doing within the expanding intelligence ecosystem we’re navigating today.

The Power of a Child’s Question

Let’s start with a story. In 1943, during a family vacation in Santa Fe, a four-year-old named Jennifer asked her father as he snapped her photo, “Daddy, why can’t I see the picture right away?” Of course, in 1943 photos required darkroom development—a lengthy, complex process. But that simple question led to the invention of the Polaroid camera. Jennifer’s father was Edwin Land, inventor of the instant camera.

Here’s what strikes me about this story: How many questions does a four-year-old normally ask? Countless. Yet as adults, the number of genuine questions we ask drops to nearly zero. I’m talking about genuine, open-ended, curious questions—not operational ones like, “When is the report due?” or “What’s the timeline on this?” Somewhere along the way, as we grow up, we stop asking curious questions. And that’s the shift that costs us. It costs us our creativity, innovation, and personal growth.

My first challenge to you: start asking more questions. Reclaim the volume you once had as a child. Ask about things that sometimes make you feel slightly embarrassed because you assume everyone else knows the answer—they probably don’t. Embracing a questioning mindset is a game changer, especially in the attention economy we inhabit.

Differentiating Your Thinking

But there is a second piece to this questioning imperative. One pattern I’ve noticed—particularly pronounced in this era—is that the teams that truly break through are never the ones with the most information. They are the ones asking a fundamentally different question than everyone else in their category. This leads to my core belief: in a world where execution is getting cheaper and faster, thinking becomes the differentiator.

Having answers isn’t the starting point anymore. Thinking is the differentiator, and questioning is the operating system for that thinking. It decides where your thinking is directed.

Let’s examine what’s been scarce across different eras. In the industrial age, the advantage went to whoever could produce at scale. In the knowledge era that followed, it was information—advantage went to whoever could access and organize it. That’s the age most organizations and professionals were designed and trained for. That’s why we have dashboards, reporting layers, and research functions as our day-to-day reality.

Now, when answers are essentially free thanks to artificial intelligence and other resources, questions become the new scarcity. Yes, you can generate hundreds of questions with any AI tool in seconds, but it’s still a human skill to know which ones actually matter.

From Question Storming to Strategic Selection

So where do you start? With question storming—generating volume first. Ask as many questions as possible. Go wide. Include the obvious ones because that’s where your brain naturally begins. Use AI to push you wider.

Whether you’re an individual contributor, working as a team, or a leader facilitating conversation, starting with questions opens up the canvas far more than any other approach. But here’s the critical part: don’t stop there. If you just create a questions list, you’ve only done the part that machines excel at. The next step is determining which questions deserve the quarter, the budget, your time, your career, your commitment, or your best people. That’s the part that will stay human for a really long time.

Here’s my most important message: in this era of expanding intelligence, generation is cheap. Selection is the job. That is becoming harder due to information overload and data of questionable accuracy, which makes judgment and selection even more crucial.

Creating a Culture of Curiosity

There are two moves when it comes to questioning: generation and selection. The first is volume—asking more questions. Ask the one that worries you might make you look less smart, because it won’t. Most of the time, other people in the same meeting also want to ask it but are too embarrassed. So create that culture of curiosity and questioning. If you’re a leader, scaffold and encourage it. If you’re leading a project, bring that attitude—it helps your team adopt the mindset and helps leaders notice your contribution differently.

But eventually, the volume plateaus. The question shifts to: which of these really deserves our time? That’s the second move—selection. Most of us have never thought about a method for this. How do we know which question is important? I’d love to share a map I use to understand where a question sits and how to prioritize it: the question ladder.

The Question Ladder: Five Levels of Inquiry

Every question you or your team asks sits on exactly one of these levels. To be clear, we need all five levels. This isn’t a ranking where top is good and bottom is bad—it’s a map of altitudes so we can see where we’re standing and whether that’s where the work actually needs us.

Level 1: Information — Retrieving facts. You can’t skip this layer, but understand it’s now fully automated. It’s meaningful for a team’s weekly work, but this work is being repriced—and not in humans’ favor, because it’s getting much cheaper and faster.

Level 2: Clarification — Sharpening a fuzzy question into a precise one. This is the difference between a useful number and a misleading one. Machines largely have this capability with some human intervention, but it still sits below what I call the “AI waterline”—meaning AI can already do it well or will very soon.

Level 3: Insight — This level asks “why.” AI is a genuine partner here, not an automator. It can spot patterns across hundreds of transcripts or data sets that would take us enormous time. It can propose why something is happening, but it cannot believe in one explanation or decide which is true. That’s still human judgment.

Level 4: Choice — This is about strategy—forcing a choice, helping people say “this, not that.” Strategy is fundamentally about what not to do. The test is whether your question helps you choose between two things. AI can lay out trade-offs beautifully and model opportunity cost, even play skeptic if you assign it that role. But it has no stake in the decision. That’s where humans still shine and where your value comes in.

Level 5: Transformation — This level attacks the frame itself. It questions what everyone has agreed on and stopped questioning. This can be dangerous because it can make six months of work look like you’ve been measuring the wrong thing—which is exactly why we rarely ask it. And exactly why it’s the most valuable question in today’s work. A lot of AI-generated work comes out looking so polished that we forget to ask: Is this really what we’re solving for? For example, instead of asking, “Why isn’t adoption going up?” ask “Is adoption even the right measure of value creation?” That completely reframes your thinking.

Elevating Your Questioning: The Ford F-150 Case Study

Let me show how climbing this ladder can shape a real product. This is one of my favorite case studies—how to design a better pickup truck.

The Ford F-150 has long been the bestselling pickup truck in North America and the most important product in Ford’s portfolio. When the team was asked to make the F-150 better for the next launch cycle, the brief seemed strategic but was actually a level one question.

Think about how you’d make a pickup truck better. Everywhere I’ve presented this, people say the same four things: more horsepower, more cargo, better towing capacity, and better fuel efficiency. These aren’t wrong—but they’re all level one answers to level one questions. AI can produce these in seconds with cost estimates attached.

The team reframed the brief entirely: “How do people use their trucks today?” This open-ended question moved beyond level one. As the team observed truck drivers actually using their vehicles—not just surveying them—they discovered something invisible: different users (moms with kids, small business owners) spend a large part of their life in their truck. It’s both a working space and a living space.

The reframed question became: How might we make the F-150 evolve to meet customers’ evolving needs and aspirations, both for current and future customers? Notice “evolving needs and aspirations”—not just horsepower or mechanics.

This led to breakthrough features: seats that fall fully flat so drivers could rest between jobs, and a storable gear shifter that creates a flat surface across the console—a workspace for laptops or lunch.

With the original brief—”design a better truck”—we’d never have arrived at these solutions. We’d have stayed in that room generating conventional answers about horsepower. With AI now in the picture, it can give you magnificent, well-cited, conventional answers. We might feel we’re doing great work. But the reframe was the work in this example. The reframe is the work we as innovators and leaders must do.

Leaping Forward with Questions

The question is the job. What question are we solving for, and how can we reframe it for the better?

Here’s what’s critical to remember: AI expands, you select. In innovation and research, we work across three phases: signals, insights, and bets. We scan for signals, look for insights, then decide where to place our bets. That’s how businesses function.

For signals, AI can scan more than we ever could. For insights, AI can generate five findings from the same data. For bets, it can generate more options than we typically have appetite for. All of that is real and useful.

But here’s what humans must bring at each stage:

At signals: relevance. There are countless signals—which ones actually matter to us? Filtering signal from noise is our selection work.

At insights: assumptions. What would have to be true for this reading to hold? What assumptions are we making based on our context, organization, people, and customers?

At bets: conviction. This is profoundly human—AI can’t replicate it. What will actually be at stake? We don’t want generic lists. We need to understand the nuances of people, organizational culture, and how things have moved previously. The same bet can function very differently in different organizations. Understanding those nuances remains deeply human.

Three Layers of Human Value

As we work across signals, insights, and bets, we bring three irreplaceable layers:

Context — What’s true for your organization that no model has access to. Even custom models can’t capture every nuance.

Judgment — Knowing which assumptions are load-bearing in your organization.

Ownership — Carrying the consequences of decisions over time.

If you skip relevance, you get information overload—signals and trends that overwhelm rather than inform. If you skip assumptions and judgment, you get the average view: well-written and researched, but something everyone else would say. If you skip conviction, you’re hedging small bets nobody’s responsible for—making incremental changes instead of leap changes.

This framework helps you orient where you are, what you bring, and how you work with expanding intelligence. The next time AI hands you something, ask: “Is it expanding? Am I supposed to be selecting? And if selecting, what am I selecting on?” This helps you ask the right questions for the right resource, context, and outcomes.

Let’s Talk Turkey

Let me end with a story. A mother and her daughter were cooking their first Thanksgiving dinner together. Before the turkey went into the oven, the mother chopped off the legs, placed them on top of the turkey, and put it in the oven. The little girl asked, “Mom, why did you do that?” The mother, exhausted from preparing dinner all day, shrugged and said, “That’s how my mom used to do it.”

Still puzzled, the little girl later asked her grandmother, “Grandmom, why do you chop off the turkey legs before putting it in the oven?” Again, her grandmother shrugged: “That’s how my mom used to do it.”

The little girl, still without an answer, eventually found her great-grandmother and asked, “Great-grandmom, why do you chop off the turkey legs and put them on top before putting the turkey in the oven?” The great-grandmom replied, “Well, dear, back when I cooked Thanksgiving dinner, my oven was really tiny and the whole turkey didn’t fit. So I had to chop off the legs, put them on top, and then fit it in.”

How many turkey legs are we chopping off just because “that’s how it’s always been done”?

What will be your next transformational question that changes the trajectory of your team or organization?

Aligning R&D With the Business Innovation Playbook

Hammering a nail into a wood deck, blue sky in the background.

I’ve spent nearly 20 years working in R&D. As an engineer myself, I love to solve problems and develop new products. Engineers solve problems like a hammer looking for a nail to pound into place. The question is whether they are solving the right problem for the right reasons. R&D isn’t academic. Solutions have to scale and show commercial viability, solving real problems for real people.

Innovation is what keeps industries and companies alive. It is also hard to get right, and most attempts do not survive. Getting a technology to perform just right, making sure the business case holds up, and confirming the market is ready all require commercial focus and high-quality technical development.

So here is the engineer’s playbook I use, for myself and my team, for developing commercially viable solutions.

  1. Cross-functionally charter innovation projects. This creates business and technical alignment.
  2. Give the technical teams face time with customers. This means inviting engineering, R&D, and front-end innovation team members to customer meetings.
  3. Lead so that failure produces learning instead of blame. This keeps teams willing to take real risks.

Developing a Project Charter

Customer needs should be at the forefront of every project. That doesn’t mean we stop trying new ideas in the lab. A new discovery could be a breakthrough to a solution that solves an industry problem. Many great inventions were accidental discoveries. The way to ensure alignment between the lab and the business is a project charter. The charter lets us all agree on the priorities and objectives.

A project charter stems from a culture of being business minded. The charter’s purpose is to capture the high-level business case and the technical requirements. As a cross-functional alignment tool, the charter template forces the “path to money” question at the outset. It defines who the solution is for, what they need, and what commercial success looks like. The charter document also has a clear statement of work.

Once we know the business case and who the work is for, then there needs to be clarity on the targets. Engineers need objectives and targets to work towards, otherwise you are asking them to boil the ocean. These targets are only established through commercial and technical collaboration and dialogue.

Leadership begins with teaching and coaching engineers how to write robust project charters. Leaders can train first, and then ask critical questions that ensure alignment between the work and the business objectives.

Keeping Customers at the Forefront

I’ve had many meetings with customers over my career. I’ve also had my commercial colleagues tell me how nervous they were about bringing along a technical person. One wrong thing said could derail years of relationship building.

It is at these customer meetings that valuable insight is gained. The technical person gets to hear firsthand the customers’ needs and challenges. They also get to ask critical questions that can change the trajectory of their work.

The best meetings my team has been part of were the ones where the customer brought a cross-functional team to talk through their needs with mine. The commercial and technical aspects of the need were discussed openly.

Early in my career, I was invited to present a new product concept to buyers at two big box retailers. I learned what they were looking for, which was a way to differentiate themselves from each other, but that maintaining price point was more critical due to an economic downturn. That was not something I would have picked up secondhand, and it led to the project being paused for several years.

Train engineers on the rules of the road in these customer-facing meetings. Give them boundaries for the things that they are allowed to say and are not. Give them context for the customer relationship.

Sharpening Leadership Through the Innovation Journey

The innovation journey is fraught with challenges and failures. Leaders support their teams by helping them stay focused and know what resources they have available. When tough times come, leaders work with their team to navigate the complexity. Leaders can pull their team out of the minutiae and guide them towards success. My method is to ask thought provoking questions, help the team define objectives and pathways, and steer rather than direct.

My experience is that the best leaders celebrate both the wins and the decisions to quickly cancel projects that are not meeting the business case. Helping technical team members understand the rationale for stopping a project helps them to think with their business hat on, which trains them to ask, “what is the path to money?”

Engineers tend to fall in love with their ideas and solutions. Leadership is about helping them understand the reason for moving on to the next venture.

My approach is to talk through the rationale. It is a learning experience for all involved. As a junior engineer I saw projects die and never understood why. I would have appreciated more insight from both the business and technical leadership in a post-mortem analysis. When leaders take a hypothesis-driven approach to innovation, the “failure” is actually a data-driven decision. Innovation is about managing risks and learning, not blaming individuals or teams for failure.

Understanding How to Bring Value

Engineers who understand how to bring value to customers and the business become a powerful asset. They learn where to place the nails before hammering them into place. They also get comfortable with pulling a nail out and relocating it. Business-minded engineers solve both the technical and business case. Training, teaching, and engaging engineers in the business side of innovation brings higher value to your customers and your company.

What is one action that your company can take today that will enhance your R&D and technical team’s delivery?

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