Over the past year, some of my so-called "crazy" ideas have come to life in various contexts. I'm not usually one to brag, and I'm definitely not taking all the credit. Execution is what matters.
But watching those ideas take shape made me pause and ask myself:
What really counts as innovation? And how do you create the right conditions for it to thrive in any organization?
I recently listened to Adam Grant’s podcast that reinforced something very powerful:
Ideas are free. Execution is what matters.
It's so simple, but damn if it isn't true. I've observed too many organizations chase the next shiny idea instead of focusing on how to actually bring ideas to life and create real value.
I did what any curious person does and Googled "innovation." The definition that came up was pretty generic:
"Making changes in something established, especially by introducing new methods, ideas, or products."
That's ridiculously broad. And honestly, that's part of the problem. Everyone throws around "innovation" like it's some magic buzzword that'll solve everything.
Here's what I've learned from various experiences: Real innovation is deliberate, tied to solving actual problems, and designed to deliver value whether that's a better product, a faster process, smarter decisions, or a completely new way of working.
In today's AI-enabled world, the gap between idea and execution feels smaller than ever. But here's the catch: that's only true if you've built the right environment for it. AI is just a tool. My experiences have taught me to start small, move quickly, collaborate often, and for the love of all that's holy, avoid getting stuck in endless POCs that never see the light of day. Trust me, it's a trap I've seen too many teams fall into across different industries.
Why Innovation Matters for Data Teams (And Every Team, Really)
Innovation in data isn't about chasing the latest shiny object. It's about getting real business results.
When I think about successful projects I've observed or been part of in past roles, they generally fall into these buckets:
Efficiency Automating manual processes that were eating up valuable time so teams could focus on work that actually matters
Decision Advantage Turning piles of raw data into insights that help people make faster, better choices
New Capabilities Using AI or analytics to do things that were literally impossible before
Problem Reframing Sometimes the biggest breakthrough is just asking a better question
Here's what bugs me: too many people think "data" just means BI dashboards or reports. But honestly? Every project is a data project. Some create new data, some analyze it, others use it as the foundation for completely new capabilities.
The magic happens when you stop thinking of data as just a reporting tool and start seeing it as raw material for transformation.
What Actually Drives Innovation
Psychological Safety (This One's Huge)
I cannot stress this enough: fear kills innovation faster than anything else. I've been in environments where people were terrified to fail, where ideas got shot down before they even had a chance to breathe. Nothing good comes from that.
The teams where I've seen real breakthroughs? They're the ones where people feel safe to experiment, to mess up, to try something that might not work. Where recognition focuses on curiosity and problem-solving, not just end results.
Actually Carving Out Space
This is where a lot of leaders talk a big game but don't follow through. Innovation gets crushed by "business as usual" unless you literally create protected time for deep work, exploration, and cross-functional collaboration.
I'm not talking about some feel-good perk. This is an investment in future capability. Even dedicating 10-20% of time can be a game-changer based on what I've seen work in various contexts.
Getting Outside Your Own Head
Some of the best ideas I've encountered have come from the weirdest places. A conversation with someone in a completely different field. A process observed in manufacturing. A pattern followed in medical setting. A completely unrelated article someone stumbled across.
Breakthrough ideas happen when you connect dots that don't seem related. I make it a point to learn from other industries, other functions, other ways of thinking. Then I bring those fresh perspectives to whatever I'm working on. You'd be amazed what you can discover when you stop living in your own bubble.
Solving Real Problems (Not Imaginary Ones)
Here's something I learned through various experiences: innovation for innovation's sake is useless. Every meaningful project I've observed or been a part of started with a real business pain point. Where was time being wasted? Money being lost? Opportunities being missed?
That focus keeps ideas relevant and actually adoptable. Nobody cares how clever your solution is if it doesn't solve a problem they actually have.
Sharing What You Learn (Even the Failures)
I'm a big believer in creating way and opportunities to share experiments and lessons learned. Slack channels, demo sessions, internal wikis, hackathons, whatever works for your culture.
The key is sharing early and often, including the stuff that didn't work. Some of the biggest wins I've seen came from building on someone else's "failed" experiment. Sharing breaks down silos and speeds up everyone's learning.
Leadership That Actually Models Innovation
This one's on the leaders. You can't just tell people to innovate. When leaders experiment publicly, admit their mistakes, and challenge their own assumptions, it gives everyone else permission to do the same.
Getting From "Cool Prototype" to "Actually Useful Thing"
Here's a hard truth: A proof of concept isn't innovation. It's just a test.
Don't get me wrong. POCs are important. But they're only the starting point. I've watched too many promising ideas die in the prototype phase because nobody thought about what comes next.
Real innovation means thinking beyond "Can we build this?" to "How do we make this work at scale in the real world?"
The questions that should be asked early:
What does production readiness actually require?
How will this integrate with existing systems and processes?
What risks need planning for: technical, operational, user adoption?
Who's going to own this thing post-launch, and how will it be supported?
Without this forward thinking, your POC stays a slide deck forever. We’ve all seen this happen in way too many organizations.¹ The gap between innovation teams and operations teams can be deadly if you don't bridge it early.
It's not enough to prove an idea works in isolation. It has to work reliably, securely, and sustainably when real people use it for real work.
When I think about scaling innovation, I focus on building for resilience, not just functionality. The projects that keep delivering value long after launch? They include monitoring, governance, and room to iterate. You're not just shipping a feature; you're creating something that can evolve.
My Bottom Line
Innovation isn't a one-time thing you check off a list. It's a habit. A way of approaching problems with curiosity, focus, and clear business purpose.
The people and teams that consistently win aren't the ones with the most ideas. They're the ones who turn ideas into real impact, over and over again. That's what I find exciting about this whole space.
This post reflects my personal observations and experiences from various roles and contexts. It doesn't represent the opinions of any current or former employer or organization I'm affiliated with.
References
Gartner Inc. "Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept by End of 2025." Gartner Press Release, July 29, 2024. https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025
TechSee. "Why 85% of AI Proofs of Concept Fail and How to Avoid Common Pitfalls." TechSee Blog, 2024. https://techsee.com/blog/ai-proof-of-concept-fails/
More Than Digital. "Artificial Intelligence: Successfully Scaling Proof of Concepts." More Than Digital, 2024. https://morethandigital.info/en/artificial-intelligence-successfully-scaling-proof-of-concepts/