Catalyze AI Success: The Power of Dedicated Innovation Capacity

A Tale of Two Organizations: The Hidden Key to AI Innovation

In the past year, I've witnessed a fascinating phenomenon unfold across various organizations embracing generative AI. Picture this: two companies, strikingly similar in their approach to AI adoption. Both have engaged senior leadership, conducted extensive training, and identified numerous opportunities for AI integration. On paper, they're identical twins in the AI race.

Yet, the outcomes couldn't be more different.

One organization is thriving, with AI-powered innovations transforming their operations and driving tangible results. The other? Stuck in a quagmire of unrealized potential and mounting frustration.

What's the difference?

It's not technology. It's not ideas. It's not even enthusiasm.

The key differentiator is something far more fundamental: the capacity to realize and implement the ideas that have been identified.

This capacity - the ability to turn AI insights into real-world applications - is what I call the "innovation realization gap." And bridging this gap is the critical challenge facing organizations in the AI era.

The Dream Weaver: A Model for Innovation Capacity

This concept of dedicated innovation capacity isn't new, but it's more crucial now than ever. Let me take you back to a pre-AI example that beautifully illustrates this principle.

Will Guidara, the restaurateur behind Eleven Madison Park - once named the world's best restaurant - introduced a role he called the "Dream Weaver." This person's sole responsibility? To help the staff bring their innovative ideas to life.

Guidara recognized that having an insight isn’t the same as implementing an insight; often, the folks on the front lines, with great ideas for improving the customer experience, lacked the bandwidth to realize their dreams.

So he created the Dream Weaver.

The results were transformative. From creating a winter wonderland for a family from Spain to orchestrating a couple's first dance right in the restaurant, the Dream Weaver turned fleeting ideas into unforgettable experiences. More importantly, it ignited a culture of innovation that permeated every aspect of the restaurant.

"I've never worked in a team of people more engaged in the work," Guidara said.

I have personally witnessed how creating capacity can catalyze innovation. One of the unforgettable memories of my adventures in building innovation capacity in organization occurred during my consulting work with Fairchild Semiconductor. After a cohort of teams had presented a handful of potentially impactful concepts — the moment many organizations “declare victory,” only to watch potential wither on the vine over the new few months — the COO, Vijay Ullal, stood up and said, “Really great work, folks. Who’s going to lead it?”

After a beat, one of the managers in the room spoke up. “I’d love to,” she said.

“Great,” said Vijay. “What do I need to clear from your plate so you can give these ideas the bandwidth they deserve?”

The months that followed were a uniquely innovative and profitable period in the tech giant’s history.

The lesson? When you create dedicated capacity for innovation, whether in hospitality or high tech, you don't just realize individual ideas - you unleash a torrent of creativity across your entire organization.

AI's Dream Weaver: The Great Aggregator

Fast forward to today's AI-driven world, and we see the same principle at work - with even greater potential for impact.

Take the Portland Trail Blazers, for instance. When faced with the challenge of integrating AI across their organization, they didn't just provide training and hope for the best. Instead, they created their own version of the Dream Weaver - a role that David Long, their VP of Digital and Innovation, dubbed the "great aggregator."

Long's role goes far beyond understanding AI. He’s become the bridge between technology and practical application, between ideas and implementation. He’s aggregated insights from across the organization, identified pain points, and facilitated AI experimentation.

The result? Tangible AI-driven solutions like "Betty Budget," a custom GPT that simplified budget code navigation, and an AI assistant that analyzes customer survey results and flags negative responses for immediate action.

In both cases, the role wasn’t just about bringing ideas to life; it was about giving the entire organization permission to innovate without the usual friction of competing priorities. Just as the Dream Weaver made servers’ ideas actionable, David Long ensured that AI didn’t remain an abstract tool but an integrated part of the workflow.

This is what creating capacity for innovation looks like in the AI era. It's not just about having ideas - it's about having the dedicated resources to turn those ideas into reality.

Universal Application: Lessons from Innovation Leaders

The need for dedicated innovation capacity isn't unique to restaurants or sports teams. It's a universal principle that applies across industries, especially when it comes to emerging technologies like AI.

On my podcast with Danish entrepreneur Henrik Werdelin, "Beyond the Prompt," I've had the privilege of discussing this very topic with some of the brightest minds in innovation. Their insights reinforce the critical importance of creating space for experimentation and implementation:

Jenny Nicholson emphasized the need for "time to play" with AI tools. Without this dedicated time, even the most promising ideas can wither on the vine. Kevin Kelly, co-founder of Wired magazine, stressed the importance of "failing productively." This echoes the Trail Blazers' approach of becoming "great aggregators" of both successes and failures in AI implementation. Russ Somers highlighted how successful AI adoption often comes down to having the bandwidth to experiment, fail, learn, and try again.

In the world of AI, experimentation isn’t a luxury, it’s a necessity. The speed of technological change means that organizations must be constantly testing, iterating, and refining their approach to keep up.

These conversations underscore a crucial point: in the rapidly evolving landscape of AI, creating capacity for innovation isn't just beneficial - it's essential for survival and growth.

Creating Innovation Capacity in Your Organization

So, how can you create this capacity in your own organization? Here are some practical steps:

1. Designate Your "Dream Weaver": Identify someone who can serve as the bridge between ideas and implementation. This person should have the authority to allocate resources and the skills to facilitate experimentation. Don’t know who this person is? A good rule of thumb is they’re the person who’s already experimenting, pushing others to try out new tools.

2. Allocate Time and Resources: Set aside dedicated time for AI experimentation. This could be a certain percentage of work hours or specific “innovation days,” but I’d advise against being overly-prescriptive. Learn to “embrace the useless.” If you aren’t sure where to start, begin with an AI Recess.

3. Create Safe Spaces to Fail: Foster an environment where failed experiments are seen as valuable learning opportunities, not setbacks. Emphasizing the importance of a breadth of experiments in bending the odds in your favor.

In my own AI journey, I’ve learned that the fastest way to unlock new ideas is by trying, failing, and iterating. My own experimentations with AI—initially clunky, accompanied by many “scraped knees”—have now become key drivers of my consulting work with organizations like the Trail Blazers.

4. Encourage Cross-Pollination: Facilitate communication between different departments. Often, the best ideas come from unexpected places, or the collisions between far flung departments. Steve Jobs valued such unexpected interactions so much that he designed Pixar’s space to foster collisions.

5. Implement Rapid Prototyping: Develop a system for quickly testing and iterating on AI-driven ideas.

Overcoming Challenges

Of course, creating this capacity isn't without its challenges. You might face resistance from those who see it as "unproductive" time or struggle to quantify its immediate ROI. Here's how to address these concerns:

1. Start Small: Begin with pilot programs that demonstrate quick wins.

2. Measure Impact: Track not just successful implementations, but also lessons learned and ideas generated.

3. Share Stories: Regularly communicate the outcomes of your innovation efforts, both successes and instructive failures.

4. Lead by Example: As a leader, participate in the experimentation process yourself.

The Bottom Line: Create Capacity or Fall Behind

The tale of two organizations I opened with isn't just an isolated anecdote - it's a preview of the future. In the AI era, the gap between organizations that create capacity for innovation and those that don't will only widen.

The choice is clear: create this capacity now, or risk falling irretrievably behind.

Call to Action: Your Next Steps

1. Assess Your Current Capacity: Take a hard look at your organization. Do you have dedicated resources for AI experimentation and implementation? Have they been unleashed?

2. Identify Your Dream Weaver: Who in your organization could step into this crucial role?

3. Start Small, But Start Now: Begin with a pilot program. Allocate resources, set clear goals, and give your team the freedom to experiment.

4. Learn and Iterate: Remember, the goal isn't perfection, but progress. Learn from each experiment and continuously refine your approach.

5. Share Your Journey: Be open about your successes and failures. The more we share, the more we all learn. We’ve got a remarkable community of practice if you’re interested in learning as a member of a dynamic learning cohort.

As we stand on the brink of an AI-driven future, the organizations that thrive will be those that don't just generate ideas, but create the capacity to bring those ideas to life. The question isn't whether you can afford to create this capacity - it's whether you can afford not to.

Are you ready to bridge the innovation realization gap?

If you're looking for a structured way to build your AI skills and create innovation capacity in your organization, I invite you to join my AI learning sequence. This program will guide you through practical, bite-sized lessons that you can immediately apply in your work. You'll learn how to craft effective AI prompts, develop strategies for integrating AI into your workflow, and create a culture of experimentation in your team. By the end of the sequence, you'll have the tools and confidence to lead AI-driven innovation in your organization, turning ideas into tangible results. Sign up here: AI Learning Sequence

Related: Time for Crazy Experiments
Related: Embrace the Useless
Related: Declare an AI Recess
Related: Make Space to Fail
Related: Broaden Your Experimental Portfolio
Related: Beat the Odds
Related: Scrape Your Knees
Related: Court Serendipity
Related: ​Beyond the Prompt: Jenny Nicholson
Related: Start Using AI for Yourself

Join over 24,147 creators & leaders who read Paint & Pipette each week