Poetic License: Inspiring the Innovation Mindset

A jumble of block letters on a game board with Carpe Diem spelled out and standing out.

Poetry Lessons on Leadership

Bringing the principles of spoken words into corporate innovation centers can provide actionable lessons. In fact, poetry has been used in academic settings for decades as an educational tool to develop leaderships skills. At the FEI: Innovation Summit, the audience will get to experience this firsthand in a session by Andrew Embry, Senior Director- Insights Innovation and Capabilities, Lilly MR HIT Team at Eli Lilly and Company (see the session information below).

In “How to use poetry in the workplace,” an article by the London School of Economics, author Anne Theunissen explores how poetry can help to understand a worker or teams’ personal and emotional workplace experiences, and the managerial and leadership practices that underlie them. This can be seen through the perspective of a researcher, who might use poetry as a form of qualitative data collection and analysis as well as a different way of writing to better understand and express workers’ experiences, according to Theunissen.

She writes that, “Poetry allows researchers to capture what was said in between the lines instead of confining themselves to the use of literal interview quotes. This offers a possibility to express highly sensitive experiences that may be difficult to convey or too painful to talk about in standard, more literal forms of communication.”

In addition to research and feedback, poetry can also be used effectively by management and leadership. The article notes, “One of the areas in which poetry can be of use according to researchers, is in refining critical thinking and stimulating creativity. Managers often deal with dilemmas, uncertainty and complex decision-making processes. As poetry draws on metaphors, ambiguity, imagination and symbolism, it evades simplistic thinking and promotes a mindset in which a multiplicity of interpretations and potential pathways is embraced. Scholars argue that these qualities are particularly useful for agile, long-term strategizing.”

Poetry may also help foster emotional intelligence and empathy in others, and help a leader foster that connection, creativity, and curiosity in a team of innovators, for example. “Being attentive to workers’ emotions is an essential managerial quality, and scholars hold that engaging with poetry, which provides a deep insight into people’s personal experiences and inner lives, invites empathy and a better understanding of the feelings of others,” says Theunissen.

Bringing Empathy as a Practical Tool

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, “Lessons in Change Management from a Spoken Word Poet in Working in Corporate Innovation,” will be presented by Andrew Embry, Senior Director- Insights Innovation and Capabilities, Lilly MR HIT Team at Eli Lilly and Company.

Most change efforts fail not because the idea is wrong, but because no one takes the time to understand the people being asked to change. In this interactive session, spoken word poet and innovation leader Andrew Embry blends poetry and change management to make empathy a practical, actionable tool — not a soft skill. Through creative exercises like perspective-taking and writing from someone else’s point of view, you’ll learn how to intentionally shape what people think, feel, and do. Expect an engaging, slightly uncomfortable experience that challenges corporate norms, invites authenticity, and equips you with a simple practice to apply before your next initiative: understand first, then design.

Click here for more information about the FEI: Innovation Summit

Closer to the Heart: Inspiring Action & Trust

While the London School of Economics article looked at poetry through a somewhat academic perspective, spoken word artist Sekou is just as powerful in his expression of how the spoken word can impact leadership.

In his article, “Spoken Word Poetry: A New Tool for Leadership,” Sekou writes, “Leadership has evolved. It’s no longer about issuing directives or managing tasks—it’s about inspiring action, building trust, and fostering connection. In today’s world, leaders must communicate with authenticity and passion. Spoken word poetry, a powerful and unexpected tool, offers a fresh way to elevate leadership and amplify impact. By combining storytelling, rhythm, and vulnerability, it transforms leaders into catalysts for change.”

This view speaks to the heart of developing innovation in the face of uncertainty, whether through a leader or team perspective. It also creates accessibility for the leader, notes Sekou, who is creating an experience with spoken words, fostering trust, collaboration, and the humanization of leadership.

“Spoken word poetry thrives on its ability to make complex ideas relatable and emotional,” writes Sekou. “This art form can revolutionize leadership communication. Imagine speaking to your team not just with facts, but with a rhythm that reflects their struggles, triumphs, and aspirations. Spoken word allows leaders to frame challenges as opportunities and visions as shared missions. It’s a way to lead not just with your mind, but with your heart.”

Video: “Facing Disruption by Sekou Andrews,” courtesy of Sekou World.

Signal-to-Action Discipline: Why Most Innovation Scanning Programs Fail

A traffic signal hanging in the air with a right turn highlighted.

The problem is not a lack of information.

The problem is a lack of signal-to-action discipline.

After working with organizations across multiple industries, I have observed a recurring pattern: companies invest heavily in understanding the future but far less in deciding what to do about it. The result is an ever-growing library of insights and an underwhelming number of strategic actions.

Innovation scanning is not a research activity. It is a strategic decision system.

Don’t Let Your Signals Die

Here are eight observations that separate organizations that generate insight from those that create competitive advantage:

1. The Bottleneck Is Not Finding Signals. It Is Converting Signals into Action.

Most organizations can identify emerging trends, technologies, and disruptions. What they struggle with is deciding what to do about them.

As scanning capabilities improve, awareness increases. However, the number of actual adoption decisions often remains unchanged. Companies become better informed without becoming more decisive.

Scanning creates awareness. Decision-making creates competitive advantage.

2. Strategy Must Define the Scan. Not the Other Way Around.

Many organizations begin by searching broadly and then attempting to determine strategic relevance afterward.

Successful innovators reverse the process. They first define the business decision that needs to be made and then seek the information required to support that decision.

Without strategic boundaries, teams often produce lengthy lists of interesting possibilities that never survive contact with operational reality.

Define the business decision before funding the search.

3. Your Next Breakthrough Will Likely Come from Outside Your Industry.

Many companies spend most of their scanning effort focused on direct competitors.

Yet some of the most valuable innovations originate elsewhere. Materials innovations from healthcare, sensing technologies from automotive, advances in battery science, artificial intelligence, and manufacturing automation frequently create opportunities far beyond their original applications.

Your competitors are not your only source of disruption.

4. More Information Makes Decisions Harder, Not Easier.

Executives rarely suffer from a lack of information.

They suffer from a lack of clarity.

Many innovation programs generate detailed reports and extensive analyses. While useful for researchers and analysts, these outputs often fail to support executive decision-making.

Leaders need clear signals, concise recommendations, and explicit choices.

5. Not Every Emerging Signal Deserves Investment.

One of the most important disciplines in innovation management is distinguishing technical possibilities from business readiness.

A promising scientific breakthrough may deserve monitoring, but if it has not yet been demonstrated in its intended environment, it may still belong in the category of research rather than innovation.

Strategic discipline requires knowing the difference between what could happen and what can create value today.

6. Single-Source Scanning Misses Most of the Opportunity.

No single source contains the full truth.

Customers, suppliers, startups, universities, regulators, venture investors, adjacent industries, and trade events all provide unique perspectives on the future.

Organizations that rely on only one or two information sources often develop blind spots. Diverse inputs create stronger insights and better decisions.

One source provides perspective. Multiple sources create insight.

7. The Most Difficult Leadership Lesson Is Learning to Say No.

Many organizations maintain large portfolios of partially funded initiatives because no one owns the decision to stop them.

The result is predictable: resources become fragmented, leadership attention is diluted, and execution slows.

A portfolio without kill decisions is not a strategy. It is inventory.

Great innovation leaders understand that focus is often more valuable than breadth.

8. Scanning Can Be Outsourced. Competitive Advantage Cannot.

External partners can help identify technologies, monitor markets, and surface emerging opportunities.

What they cannot do is make strategic decisions on behalf of the organization.

Adoption requires leadership alignment, investment decisions, organizational commitment, and execution capability. Those responsibilities remain internal.

Buy the intelligence. Own the transformation.

Build Stronger Signal-to-Action Discipline

The future will not belong to the organizations with the most information.

It will belong to the organizations that consistently identify the right signals, make timely decisions, allocate resources effectively, and act with conviction.

Innovation scanning is not about producing reports. It is about creating better decisions.

Ask yourself: If your organization doubled its innovation intelligence budget tomorrow, would it generate better decisions or simply more information? The answer may reveal your greatest opportunity for competitive advantage.

AI Can Simulate the Consumer, But It Still Can’t Replace Knowing One

A computer-like background, network with circuits and data points.

The interface looked polished. The feature list was long. There were workflows, dashboards, automations, and enough functionality to make the product feel more mature than it was. He told me he had built the whole thing with Claude over a weekend — and he wasn’t a software engineer.

He was excited, understandably. A few years ago, getting this far would have required engineers, money, and time. Now he had a working prototype before Monday.

But no one in retail had used it yet. And he had never worked in the industry himself. He was building for a user he did not really know, in a context he had not lived, based largely on what he imagined the problem to be.

That moment captured the promise and the problem of this stage of AI. More people can now move from idea to artifact. But artifacts are not the same as evidence of need.

Building Has Been Democratized

AI is democratizing software creation. A founder, designer, operator, marketer, or domain outsider can now create software that would once have required a technical team. That expands who gets to participate in the creation economy and lowers barriers, broadens participation, and speeds up experimentation.

While AI expands who can build, it does not automatically expand the judgment required to decide what should be built.

Of course, the underlying lesson is not new. Lean Startup told founders to test assumptions before scaling. IDEO helped popularize human-centered design. Steve Blank told entrepreneurs to get out of the building. For years, the discipline of innovation has warned against falling in love with the solution before understanding the problem.

But AI changes the stakes. The old danger was building the wrong thing after spending too much time and money. The new danger is building the wrong thing quickly, beautifully, and with enough synthetic evidence to feel right.

From Minimum Viable Product to Minimum Valuable Problem

If a founder can now describe an idea to a coding assistant and get back functioning software that looks convincing, it can obscure the fact that the underlying customer understanding is thin.

This shifts a central question for innovation. Increasingly, the issue is not whether a team can produce a minimum viable product. As Nick Coster and others comment, it’s whether they have found a minimum valuable problem: a customer need with enough urgency, context, and consequence to warrant a solution.

AI can help teams get closer to that answer. It can, for example, summarize customer reviews, analyze support tickets, scan forums, identify complaints, map competitors, synthesize interview transcripts, generate personas, and surface patterns in consumer behavior. Used well, AI can make discovery faster and broader.

Increasingly, AI can also simulate the consumer. Synthetic panels and AI-generated personas can test concepts, pricing, positioning, and potential responses. BCG has argued that these tools can make consumer insight faster and more scalable. Recent work from Ipsos and a Cornell-hosted arXiv study on synthetic purchase-intent modeling suggests a similar pattern: synthetic consumers can approximate aggregate purchase-intent patterns and support early concept screening, but they remain weaker as substitutes for real customer contact, especially when the question depends on individual context, workflow, trust, or lived experience.

That makes these tools useful for hypothesis generation, but not validation.

Leaders still need to know when the signal is shallow, biased, overfit to existing behavior, or culturally tone-deaf. A generated persona is not a person. A summary of complaints is not the same as watching someone struggle through a real workflow. A pattern in online reviews is not the same as understanding the moment when a customer feels frustration, hesitation, embarrassment, distrust, or urgency.

That judgment is what I think of as appropriateness. Appropriateness is not just whether a product solves a stated problem. It is whether the solution fits the customer’s reality. Does it match how they already behave? Does it ask them to change too much? Does it create new anxieties? Does it feel trustworthy? Does it arrive at the right moment? Does it respect the social, emotional, operational, economic, or cultural context in which the problem occurs?

In retail, for example, a workflow that looks efficient in a demo may fail on a store floor because associates are busy, systems are fragmented, managers are overloaded, and no one has time to adopt another dashboard. The same pattern shows up in other industries: a tool may be technically useful and still fail because it misunderstands trust, workflow, incentives, or timing. These considerations are the difference between software that exists and software that gets used.

Build Quickly, but Build from Contact

The implication is not that teams should return to bloated research cycles, endless strategy decks, or months of analysis before action. AI should make innovation faster. But speed should be used to learn, not simply to launch.

For example, AI can be used to scan the landscape, generate hypotheses, identify patterns, and simulate responses. Then use human judgment to test whether those patterns reflect a real problem in a real context. Talk to customers. Watch behavior. Understand constraints. Look for workarounds. Ask what people have already tried. Study what they ignore, not just what they request. Then build quickly — but build from contact.

The founder on the Zoom call had accomplished something remarkable. He had created functioning software without being a software developer. That suggests a future in which more people can move from idea to artifact, and where technical barriers no longer decide who gets to participate in innovation.

But it also showed the limit of the moment. He had built an app for retail before he had built understanding with retailers.

“Build it and they will come” was always an unreliable theory of innovation. AI makes it even more cautionary because it makes building feel deceptively easy and insight feel artificially complete. Today’s question is whether we have earned enough understanding to know why anyone would care.

Click here for more columns by Gail Martino; if you enjoy this content, please consider connecting with Gail Martino on LinkedIn.