How to Match AI Momentum

City lights reflecting in a woman’s wide open eye.

The Precipice of Transformation

That has all changed now. We stand at the precipice of human transformation with machines. AGI—artificial general intelligence—may arrive in fewer than ten years, perhaps five. We live in a world that is building data centers, large language models, and training datasets at an unrivaled pace. Markets are responding accordingly, pricing in its inevitability.

How do we humans match this momentum? How do innovators—those who trade in insight generation—fuel their own work? Or is it time to ask whether we truly have a place in a new economy where anyone can pay and prompt their way into analysis?

My five years of research and introspection on my decades of innovation practice lead me to one conclusion: nothing is over for us humans. In fact, this is not just the golden age of machines—it is the golden age for human innovators who must now rise to meet new realities.

The New Reality of the Innovator

The new reality rejects pedestrian innovation. The machine can distill years of others’ work into a sentence or paragraph with a simple prompt. These models consume the world’s information and attempt to mirror our thinking brain. Once this AI tsunami reaches the shores of our own creative thinking, what becomes of us?

Innovators can no longer rely on the basic, combinatorial solutions they once claimed as their own. Drug discovery achieved by matching research combinations to patient illness profiles, for example, will be performed by machine learning in seconds. These computational, pattern-matching exercises now belong to the machine.

But if we are innovators bound to the creation of the yet-to-be-understood—transformative innovators—then we must train our experience and minds to reach beyond what machines can access. AI lives in combinations of past creations. We must live beyond them.

What AI Actually Does—and Does Not Do

Claude is a remarkable technology that seems to understand our prompts. But it does not—not in any human sense. It is a high-performance, multivariate formula applied to massive datasets, isolating the most probable next word within a learned context, over and over, until it reaches a conclusion. It does not grasp meaning until we program meaning into it. And what is meaning? It is context, environment, and how one ought to feel about a situation. Measurement is the realm of the machine; meaning is the realm of the human.

This distinction aligns with what researchers Fei-Fei Li and John Markoff have described as the gap between narrow AI capability and true contextual understanding—machines can classify and predict, but they cannot mean (Li, F., & Markoff, J., 2017. The Human Element in the Age of AI. MIT Technology Review).

The Power of Human Insight

In my recent book, Unreachable, I commit my thoughts to insight—that “aha” moment that every innovator lives for. But insight without action is merely dreaming. We must build from insight and create scaffolding for others to climb and create further.

Howard Schultz was searching for a way to build a coffee business. He travelled through Europe and discovered that cafés fed a deep desire for community, comfort, and conversation. He found a recipe that no one had invented, took over a small business called Starbucks, and transformed it into a global phenomenon by focusing it on espresso culture. No AI machine could have done it then, nor could one do it now. That insight was born of human experience—of walking those streets, sitting in those cafés, and feeling that pull of belonging.

Cultivating the Conditions for Insight

When I ran a small ten-person innovation team for ten years, I held design sessions every other day for three hours each. It demanded discipline, and the team was not always patient—they had other responsibilities. Yet we created a joyful environment built around comedy, food, and walks. These three elements were deliberate injections into the operational mind, vaccines against routine thinking. The result: we created and launched eight companies over ten years. We also built the scaffolding to ensure our products grew and thrived.

This approach resonates with two leading researchers in the field. Teresa Amabile of Harvard Business School found that intrinsic motivation and a stimulating environment are the most reliable predictors of creative breakthrough—not intelligence, tools, or resources alone (Amabile, T., 1998. How to Kill Creativity. Harvard Business Review).

More recently, Yale psychologist Zorana Ivcevic Pringle has argued that creativity is not an innate gift but a repeated decision—one that anyone can learn to make (Ivcevic Pringle, Z., 2024. The Creativity Choice: The Science of Making Decisions to Turn Ideas into Action. Hachette Book Group). Together, their work affirms that the joyful, disciplined environment we built—comedy, food, walks, and structured ideation—was not accidental; it was precisely the kind of condition that unlocks transformative creative output.

What Is Insight—and Can We Command It?

What, then, is this thing called insight? How do we get it? How do we summon it? Is it within our control? My answer is an absolute yes. In the next installment of this article, I will explore the science and research that underpins that conviction.

Why Your Best Ideas Are Dying—And How to Save Them

Heart pulse monitor moving up and down on a medical chart, connected to a brain.

Turning a ‘No’ Innovation Culture to ‘Yes’ Instead

Why great ideas die is the subject of many books and resources on innovation. This can be attributed to fear or risk aversion, systemic failure, premature abandonment of the idea, or an internal misunderstanding. All of these factors and more can lead to failure of ideas to get approval from management.

Before you give up and close your innovation notepad forever, there’s also many concepts that innovators can tackle to keep their innovation alive and moving forward. This might include building a psychological safety net, leveraging the diversity of their networks, implementing structured innovation frameworks, creating safe zones for testing and development, cultivating mentorship, and even just staying persistent. Implementing measurable goals and KPI’s can also help scale the innovation forward.

Hype Innovation takes a look further to expand beyond the “no” culture to transforming the innovation atmosphere to a culture of “yes.” In “7 Innovation Strategies to Keep Ideas Alive Beyond the Graveyard,” Hype examines how to avoid the innovation ideas graveyard.

Often, the issue is not the idea itself. As Hype observes: “The problem is rarely creativity. It’s a lack of structure, accountability, and follow-through. These are the same foundational elements emphasized in ISO 56001, the international standard for innovation management systems, which inspires many best practices.”

Don’t Let Your Ideas Die!

Here are seven innovation strategies from Hype Innovation that help leaders keep ideas alive and prevent them from ending up in the graveyard:

1. Assign Clear Ownership to Ideas: Ideas without sponsors quickly disappear. They’re no one’s responsibility, so they slip between competing priorities. Strong innovation systems make sponsorship explicit. Each initiative should have a named sponsor with authority to secure resources and responsibility to report progress. Making ownership visible reduces ambiguity and signals seriousness across the organization.

2. Secure Resources Early to Support Innovation: A promising idea can’t survive on enthusiasm alone. Too many expect teams to prove value without the time, tools, or budget to do so. Resourcing should be deliberate and staged, including early stage, growth stage and scaling stage. This staged model ensures promising ideas are not starved and that investment decisions feel transparent and evidence based.

3. Use Stage Gates to Advance Ideas: Pilots are valuable, but they’re not results. Without clear stage gates, they linger indefinitely. Effective stage gates define the evidence required to progress. This discipline accelerates decision-making and prevents backlogs of stalled initiatives. Ideas either move forward or are retired cleanly, keeping the innovation pipeline credible.

4. Strengthen Innovation Governance for Faster Decisions: Governance failures are among the top killers of ideas. Slow or inconsistent decisions sap momentum and erode employee trust. This governance clarity creates confidence. Employees trust the process, and executives see innovation as a system that delivers, not just a collection of ad hoc projects.

5. Measure Innovation Success Beyond ROI: Most organizations measure innovation, but often too narrowly. Financial returns matter, but they don’t tell the full story of pipeline health. A balanced scorecard for innovation should include financial but also strategic, adoption, and cultural factors. Measuring innovation success this way prevents premature idea death and gives leaders a clearer picture of how their innovation system is performing.

6. Sustain Employee Engagement: Generating ideas is rarely the issue. Sustaining engagement is. Employees contribute once, then disengage when they see no visible impact. When employees see how their input matters, they stay engaged and contribute again. Engagement then becomes a reinforcing cycle rather than a one-off effort.

7. Continuously Improve Your Innovation Management System: The most overlooked practice is continuous improvement, which involves reviewing the innovation process itself, not just projects; capturing lessons from successes and failures; retiring outdated practices; and adapting criteria and governance as markets evolve. Organizations that embed continuous improvement stay resilient and adaptive.

Keeping Great Ideas Alive

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.

Sessions include, “Why Great Ideas Die—and How Leaders Keep Them Alive,” presented by Jennifer Price, Global Lead Producer, Foundations in Human Interface Design & Design Operations at General Motors; and Cory Olson, Head of Innovation, Global at The North Face.

Great ideas don’t fail because they’re bad—they fail because organizations struggle to carry them forward. This panel explores how innovation leaders move promising ideas from concept to commitment by navigating internal hurdles, securing support, and building momentum. Hear what it really takes to ensure the right ideas survive long enough to succeed.

Click here for more information about the FEI: Innovation Summit

Build a Resilient Innovation System

Many creative and compelling ideas are conceived by innovators but end up on the cutting room floor. Ultimately, very few proceed through the innovation pipeline to further success. But as Hype Innovation and many other client and vendor side companies note, innovation isn’t only about new ideas. “It’s about building a system that learns and improves,” says Hype. “Ownership, resources, governance, measurement, engagement, and continuous improvement are the practices that make the difference.”

Video: “Why Great Ideas Die Inside Companies (And How to Finally Get Them Out),” featuring Fiona Stevenson, courtesy of Katie Armentrout and Think Outside the Boss.

Learning The New AI Rules of Work

A car heading fast down the street, from the driver perspective, with light beams streaming outward towards the highway.

As part of the bidding process, I was given a short, paid, time-limited assignment where I was required to use AI. I was asked to read a body of material, complete several convergent tasks, and show the prompts I used to get there. On one level, the exercise was simple: a small project designed to signal whether I could handle a larger one. But the deeper test wasn’t just whether I could do the work. It was whether I could do the work this way.

I won the business.

But the experience stayed with me because it clarified something that has been building for a while: In some parts of the market, AI fluency is no longer a side skill. It’s becoming part of how professional capability is judged.

I don’t think I won because I had the strongest résumé. I was likely competing against other experienced professionals. I don’t think I won on price, either. I think I won because I could demonstrate something the client now considered valuable: not just subject-matter expertise and experience, but the ability to use AI effectively inside a disciplined, professional process.

That is a different standard than the one many of us were working under even a year ago.

What Prompted the Shift?

Not long ago, AI still felt like something to explain carefully. A client might ask whether you used it out of curiosity, skepticism, or concern. In some cases, the subtext was, Please tell me a machine isn’t doing the work I am paying for. In others, it was simply, Do you understand this technology?

That ambiguity is fading.

In some settings, clients are beginning to assume that capable professionals know how to use these tools. More than that, they are beginning to evaluate for it as a working capability.

Can you absorb a body of material quickly? Can you frame the problem clearly? Can you use AI to accelerate the process without lowering the quality of thinking? Can you work transparently? Can you get to a stronger answer faster?

The prompts matter too. The client didn’t just want the answer. They wanted to see how I approached the problem, how I structured the work, how I refined the output, and how I used the tools available. The prompts were evidence of method. In essence, the market now cares about what you know AND how you work.

Implications for Innovation Practitioners

For years, many organizations have evaluated talent and partners based on familiar signals: experience, credentials, sector knowledge, strategic framing, and communication skills. Now these are increasingly being joined by another question: Can this person operate effectively in an AI-enabled environment?

That doesn’t mean using a chatbot or producing machine-written work and calling it innovation. It means knowing how to use AI to accelerate research, expand options, test assumptions, pressure-test ideas, and move from ambiguity to insight more quickly. It means using tools to improve the process, not to replace the thinking.

For innovation practitioners, their work has always depended on synthesis, pattern recognition, reframing, and rapid iteration. AI can strengthen those capabilities when it’s used well. It can also create noise, false confidence, and generic thinking when it’s used poorly. In other words, the advantage isn’t AI alone, it’s judgment combined with AI.

For Independent Consultants and Solopreneurs

If you work independently, this shift matters immediately.

You don’t need to use AI in every final deliverable. In many cases, you shouldn’t. But if you are still treating AI as optional to your process, you may be treating your competitiveness as optional too.

The benefit isn’t speed, though speed matters. It’s the ability to move through more information, test more angles, sharpen ideas more quickly, and arrive at stronger outputs with less friction. Used well, AI doesn’t replace expertise, but it gives expertise leverage.

In today’s market, you may never know why you lost a client or didn’t land a new one. Clients rarely say, We hired someone else because they showed us a more capable AI process. They simply choose someone else. The market rarely declares a new standard before it starts enforcing it.

For independents, then, AI fluency is moving from advantage to expectation.

For Innovation Agencies

For agencies, the implications are broader. AI doesn’t just affect workflow. It puts pressure on the logic of billing and value creation.

If research, synthesis, ideation, drafting, and iteration can all happen faster, what exactly is the client paying for when work is still priced through the old language of time and effort? This is where AI is likely to create the most friction. Not because agencies become less useful, but because they will need to explain their usefulness differently.

The firms that adapt best will be the ones that get clearer about what clients are really buying. Not hours. Not visible effort. Judgment. Better options. Strategic clarity. Better pattern recognition. Faster learning. Better decisions.

In that sense, AI may accelerate a shift that was already underway: away from billing for activity and toward pricing for value.

If your business depends on helping clients see around corners, frame opportunities, and move faster, then AI should strengthen your offer. But it will also make weak process, inflated timelines, and vague value propositions harder to defend.

For Corporate Innovation Practitioners

Inside large organizations, this shift may still feel uneven. There are legal policies to navigate, tools to approve, governance questions to settle, and real concerns about quality, risk, and intellectual property. That is understandable. But it can also create a false sense that there is more time than there really is.

Outside the company, the market is moving faster. Independent workers and smaller firms are already upskilling because they have to. They are learning by necessity. They are building capability because demand is already rewarding it. That doesn’t mean internal teams should rush toward every tool without discipline. It does mean they should pay attention to where the pressure is showing up first.

For corporate practitioners, the takeaway isn’t panic. It’s preparation. Even if your current role doesn’t yet require strong AI fluency, future roles almost certainly will. Whether through internal change, external competition, or career transition, the expectation is likely to arrive.

The Key Takeaway: The Speed of Adaptability

The most important lesson I took from that assignment is that the client wanted evidence of adaptability.

They wanted proof that I could absorb material quickly, work within constraints, use the tools intelligently, and still apply judgment. That is different from asking whether someone is “good at AI.” Plenty of people can generate acceptable output. Far fewer can use these tools in a disciplined way that improves thinking, strengthens process, and produces better results.

The lesson here isn’t that experience no longer matters. It’s that experience alone is no longer enough. In some corners of the market, AI fluency is no longer experimental or optional. It’s becoming part of how readiness, adaptability, and value are judged.

The question is no longer whether AI belongs in the workflow. The more useful question is whether you, your team, or your partners know how to use it in a way that improves the work.

Because increasingly, that is what the market is testing for.

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