Innovation theatre happens when organizations focus on the activities that look like innovation—the workshops, the demo days, the sticky notes—without generating the business value that true innovation requires.
Harry Laplanche, Global AI Strategy and Transformation at Panasonic Well, reveals battle scars from multiple angles—as a pod leader, a portfolio manager, and now overseeing AI transformation.
Systems exist to avoid the "theatre trap," offering a path for innovators to use AI not just as a tool, but as a strategic partner in enforcing discipline.
To check the full session recording, upgrade to Premium
What Is Innovation Theatre?
Spot the gap between performance and progress
There are hackathons, workshops, and demo days—but few live pilots in customer environments. Strategy decks showcase huge market sizes and fast growth rates, yet validated unit economics and CAC-to-LTV ratios remain absent. Thirty-slide status updates make everything sound great, while fierce strategic discussions with senior leaders rarely happen.
"You're seeing rituals around tools, frameworks, canvases, sticky notes, but you're not seeing rapid customer feedback loops," Harry explains.
PR tours, road shows, and press demos generate external buzz, but meaningful working models and conversations with the core business are nowhere to be found. Ultimately, innovation theatre are all those activities that look like innovation but really generate no business value.
The workshops and decks are valuable—they're necessary components of innovation, but they must be accompanied by customer validation, rigorous economics, and strategic alignment.
Why Innovation Theatre Happens: The Flywheel
Moving beyond the symptoms, innovation theatre often follows a specific flywheel pattern.
It starts with hype
A new technology launches, and everyone is thrilled. CEOs put out mandates: We will be using 90% AI-powered workflows. We're going to change our workforce dramatically. We're going to become a tech company. Everyone's rushing to build new innovation teams, invest in innovation and really “go, go, go."
The Missing Middle
This is where the innovation groups come in. "We have a huge, ambitious target: deliver billions in revenue, transform the company. However, we have no map to get there." There is no innovation strategy nor metrics to measure how the team is working or if they are moving closer to the goal.
Fear and Insecurity Set In
Performance starts happening over progress in the need to meet lofty and sometimes unrealistic stakeholder expectations. Innovators create demos and statuses that hide the hard truths because they are trying to show they can do the job. It is a hard, messy, difficult job, and it doesn't always look good on the surface.
The Hard Truths Come Out
Eventually, the difficulties of innovating come to light. The business value of innovation and the long tail required to realize it become apparent. The hype dies down, while initiatives lose momentum as focus goes back to the core business.
"It's not because the people are wrong. It's because the systems are designed around the wrong things."
When you start with hype, when you start with tech, when you start with what other people think… it leads to this slippery slope.
What Went Wrong at a Fortune 500
At a conservative Fortune 500 company, Harry founded a transformation team where every classic theatre cue was present. "We were all about the tech. It was a magic wand to solve all of our problems and all of society's problems, but we had no innovation strategy."
The team executed the fundamentals: deep strategic market analysis, customer discovery interviews, robust build/buy/partner modeling, and stakeholder buy-in across the organization. They acquired a company and launched a new business—outcomes that would signal success for a startup.
Corporate innovation demands different rigor. The critical failure? Future problems identified never proved relevant enough today to drive corporate-scale impact. Business-critical assumptions went unidentified and unchallenged—for the team and senior leaders.
"We were looking for why this business could work instead of why it wouldn't work."
Here's the trap: when very senior leaders stake their reputations on a project, telling them they might be wrong becomes nearly impossible. The pressure to perform overrides the discipline to validate.
Therefore, it is important to flip the instinct. "When everyone needs your innovation to succeed, that's when your bar has to go up for what's good enough." When stakeholder expectations are highest, your standards must become more rigorous rather than more lenient.
Designing Systems to Avoid the Pitfalls
In a subsequent role, Harry was brought on to design a new innovation organization for a growth engine. Here, he saw similar cues of hype, but with a different flavor. It wasn't about technology; it was about talent. There was an idolization of talent—the idea that "these people know how to innovate so they're going to do it."
To avoid the theatre trap, Harry and his team focused on self-enforcing discipline.
"Stage Gate funding was a key function for us to put accountability on ourselves and our innovation system, because if we waited for other people to do it, it would be too late."
Firstly, they designed a robust end-to-end innovation process and mapped projects at the system level. Then, they went through every single assumption underneath the project categories to rank them, understand the most impactful and uncertain ones, test them, and update the map. It was effective, but insanely difficult and time-consuming. This reality created the opening for a new approach; this is where AI comes in.
AI as a Strategic Copilot
Reduce the burden of rigor without sacrificing discipline
AI lessens the load of the hard work that innovation requires and is viewed as a strategic copilot, or a second brain that helps understand business outcomes and workflows.
Translating this philosophy into practice, here is Harry's practical workflow for using AI to enforce innovation discipline:
Building a Knowledge Base
Start by spinning up a project folder in a secure AI environment like Claude Enterprise, then turn on voice recording and talk for 30 minutes straight, dumping everything—every assumption, every constraint, every half-formed idea about your project.
Upload every relevant company document you can access to give the AI maximum context, then prompt it to make sense of this gibberish and identify your gaps. Use those identified gaps to generate deep research prompts, which you can then run through tools like Perplexity, AlphaSense, or CB Insights to triangulate your data from multiple sources. Set up automated scanning to keep your knowledge base continually refined and current.
The goal is simple: "Take the 98% you don't need to think about, keep it in a folder and ask the AI the one thing you need to do right now."
Prioritizing the Next Action
Don't let AI generate a long list of priorities—instead, have an actual conversation with it to work through your decision-making process.
Run through examples together until you've built a "golden data set" that acts as your North Star for what good looks like. Use this dataset to score and prioritize your work so you can focus exclusively on the single highest-leverage task at any given moment. The full context stays preserved in AI while your attention stays laser-focused on what matters most right now.
Designing and Running Experiments
Let AI help you craft your experiments and build your stimuli, but take it a step further by bringing it directly into your live validation sessions.
Keep your AI window open during actual customer calls so you can use it to generate real-time probes, follow-up questions, and critical thinking prompts that you might not catch in the moment.
Calibrating and Storytelling
Run your transcription, analysis, and synthesis through AI immediately after completing your experiments to capture insights while they're fresh. This way, you’ll be able to do qualitative research at a quantitative scale.
Connect your findings back to your knowledge base and ask the AI to contextualize what this means for your overall project direction. Update your strategic plan based on these learnings, then request a slide outline whenever you need to share results. This approach keeps your narrative current and compelling without the manual synthesis overhead that traditionally bogs down innovation teams.
When the Organization Pushes Back
In conservative organizations, the instinct is to soften your ideas to survive the politics. According to Harry, the opposite works better: use the conservatism to hold yourself to a higher standard. Four moves make that possible:
Predefine Success Criteria: Establish the criteria for what makes a good innovation before any project comes through. Once people are emotionally invested in specific initiatives, rational assessment becomes nearly impossible. Agreeing upfront on what constitutes "good enough" removes the emotion and politics from individual decisions and makes it about the standard, not the person.
Evaluate Comparatively: Evaluate multiple projects simultaneously against the predefined bar, with clear exit criteria at each stage gate. This transforms the conversation from "why are you killing my project?" to "which of these projects best meets our agreed standards?" — depersonalizing failure and making resource allocation a strategic choice rather than a personal judgment.
Point to the Data: When a project meets the criteria established upfront, it advances. When it doesn't, the data makes the case. This gives armor against political pressure because the criteria everyone already agreed to is making the call.
Celebrate the Shutdowns: Be the first to call out when something isn't good enough and shut it down — then treat it as a win. When you celebrate killing projects that don't meet the bar, you signal that the real failure is continuing to fund bad bets, and you create permission for others to do the same.
The End of Excuses
The goal is to trade the "Theater Loop"—driven by hype and the missing middle—for a "Responsible Innovation Loop."
Understanding why innovation theatre happens allows you to build AI-powered systems and workflows that enforce responsible innovation by design.
"It's up to us as the innovators, to self enforce the discipline to avoid theatre, because it's hard to manage an innovation group when you're not an innovator."
Apply these principles by building a knowledge base and using AI to maintain systemic context, so you can focus on the one thing you need to do right now. This ensures you're not just performing rituals, but generating actual business value.
