Meet the Author: Ellery Connell brings 30 years of experience in 3D tech and design leadership, driving innovation in footwear, apparel, and consumer products. He specialises in 3D design, DPC, AR/VR, and generative AI, helping companies boost ROI and efficiency.
Over the course of my career, I’ve had the opportunity to work with some remarkably talented people across design, technology, product creation, engineering, and business. One thing I’ve come to appreciate is that most organizations aren't short on expertise or ideas. The harder challenge is creating an environment where those ideas can move, connect with the right people, be explored, and ultimately become something useful.
As organizations grow, the processes and systems created to help people work together can gradually become barriers of their own. Handoffs multiply, responsibilities become more specialized, tools become more complex, and before long some of the people best equipped to solve a problem can find themselves separated from the information or capability they need to act on it.
This isn't a new problem, and technology certainly hasn't created it.
I've watched versions of the same cycle play out through decades of work in 3D, digital product creation, PLM, realtime visualization, immersive technology, automation, and now AI. Each new wave arrives with tremendous potential, and our natural tendency is to focus on what the technology can do.
But innovation has never really been about having the newest tool or even generating the most ideas. It happens when an organization can recognize a meaningful opportunity, connect the right expertise around it, experiment, learn, and turn what it discovers into action. The technology keeps changing. The more interesting question, at least to me, is whether the systems surrounding it are helping that happen or quietly getting in the way.
The Lesson from Colorway
Years ago, while I was at Foundry, I had an experience that changed the way I thought about technology adoption. We were working with footwear designers and experimenting with Colorway, a tool that allowed people to explore variations of a 3D design without requiring them to become 3D artists themselves. At the time, much of the traditional workflow depended on someone with specialized 3D knowledge sitting between the designer and the digital asset.
I knew the technology well. I could make the changes they asked for quickly. But that still meant the designers had to explain what they wanted, wait for me to make the change, look at the result, and then decide what they wanted to try next. So instead, I put the capability directly in their hands.
Within about half an hour, the designers were exploring ideas and combinations I never would have created myself. It was not because they suddenly understood 3D better than I did. They didn't. They did, however, understand footwear better than I did.
The technology hadn't suddenly become more powerful. The people hadn't suddenly become more talented. What changed was the system between the person with the idea and their ability to explore it. We had removed a barrier, and once that barrier disappeared, the technology stopped being the center of the conversation. It became the conduit for people who already had deep expertise to think more freely.
I think we sometimes misunderstand democratization of technology as making everyone an expert in the technology. That's not really the goal. The footwear designers didn't need to become 3D experts. They needed enough access to the capability that their own expertise could interact with it.
That's a distinction I've carried with me ever since, though I didn't yet appreciate how much bigger the idea could become applied across an organization.
The Democratization of 3D
In a recent conversation, a digital product leader described his goal not as figuring out every possible use for the digital assets his team was creating, but simply making those assets accessible to more people across the organization. I have spoken many times about how one of the great challenges of the current landscape of Digital Product Creation is the underutilization of the assets that are created. Failing to leverage the hard work that goes into choosing tools, training users, and creating digital assets means that an enormous amount of ROI is left on the table.
His thinking was essentially this: if a hundred more people can access this today than yesterday, those hundred people can begin discovering applications we haven't thought of yet. I love that idea. Emergent use cases for new technology are a key missing factor of many innovation initiatives. A good innovation system doesn't have to predict every future use. Sometimes it simply needs to create the conditions in which someone else can discover it.
But putting powerful capabilities into more hands exposes another challenge: how do we connect the increasingly specialized expertise that exists across an organization?
Conditions for Innovation
Designers get better at design. Engineers get better at engineering. Technology teams develop deeper technical expertise. Product, marketing, development, operations, and leadership each bring their own perspective and increasingly capable tools. That specialization is valuable, but it can also create a missing layer between them.
The designer may understand the intent but not the technical constraint. The engineer may understand what is possible but not why it matters to the customer. Leadership may understand the business objective without seeing the friction created several layers below. Somewhere between those perspectives, context gets translated, simplified, handed off, and sometimes lost. The challenge isn't necessarily that any one part of the organization is failing. It's that no one piece owns the space between them.
Looking back, what made the Colorway experiment work wasn't really Colorway. The technology did matter, but so did a set of conditions around it. The designers understood the problem. They had direct access to a capability that allowed them to explore it. The technical barrier between intent and experimentation had been reduced. And because the feedback was immediate, one idea could quickly lead to another.
Since then I have had a number of other experiences that have made me realize that there are a handful of conditions that repeatedly show up when innovation is able to move rather than stall:
Clarity: Do we understand the problem we're actually trying to solve?
Connection: Can the people who understand the problem work directly enough with the people capable of solving it?
Capacity: Do they have the time and freedom to explore rather than simply keep up with today's work?
Translation: Can intent move between creative, technical, operational, and business teams without losing its meaning?
Learning: Does what happens after implementation make its way back into the system so the next decision is better than the last?
None of these ideas is particularly revolutionary on its own. What matters is what happens when they exist together. These conditions, when healthy, create a framework where innovation begins to look less like a special initiative and more like a natural consequence of how the organization works. An observation can find the right expertise. An idea can become an experiment. An experiment can become an implementation. Implementation creates feedback, and that feedback informs the next opportunity.
The organization isn't simply getting better at executing ideas. It is getting better at learning and adapting. And, increasingly, I think that ability to adapt may be one of the most important competitive advantages an organization can build.
But what happens when a new technology doesn't simply change what people can do, but begins changing the role people play in the system itself?
The Elephant in the Room
Of course, it's difficult to have a conversation about innovation today without talking about AI. The potential is enormous, and I'm personally excited about many of the ways it can help us work, explore, and solve problems differently. But I think we risk repeating a familiar mistake if our first question is simply, "Where do we plug in AI?" A better question might be: "Where is our current system preventing people from creating value, and can AI help remove that constraint?" Used well, AI can create capacity, improve access to information, accelerate exploration, and help expertise travel farther through an organization.
But AI also complicates some of what I've argued here.
If creating capacity means removing repetitive or lower-level work, what happens when some of that work is also where people learn? Early in my career, I spent countless hours researching, experimenting, making mistakes, and doing things that today could probably be accomplished with AI in a fraction of the time. It wasn't always efficient, but it was part of how I developed the judgment that now allows me to recognize patterns quickly. So if AI begins doing more of the work traditionally given to people early in their careers, where do the experts of ten or twenty years from now come from?
I don't think the answer is to preserve inefficient work simply because that's how we've always learned, and I certainly don't think it's to avoid AI. Maybe the opportunity is exactly the opposite: use AI to accelerate the foundational work while deliberately creating better opportunities for mentorship, experimentation, and higher-level problem solving. I think that's a much larger conversation, and one I intend to return to separately. But it adds an important condition to everything I've said so far: we shouldn't judge an innovation system only by how efficiently it produces today's work. We should also ask whether it's building the capabilities we'll need tomorrow.
Returning to DPC
Which brings me back to DPC, because it gives us a very practical place to test these ideas. DPC has spent years promising faster decisions, fewer physical samples, better collaboration, greater reuse, and more connected product experiences. Yet too often we still measure progress by the number of 3D assets created, the percentage of a line digitized, or the tools we've deployed. Those things can matter, but they aren't the outcome.
How we can truly test our conditions to see if they are working is by asking the right question about how they impact our outcomes:
Did decisions happen sooner? And were they better informed?
Did we reduce unnecessary samples or rework?
Did designers gain more time to actually design?
Could people outside the 3D team really use what was created?
Did information survive the handoffs?
Did we learn something that changed what happened next?
Those are measures of a system becoming more capable, not simply more digital. And that distinction matters because the goal was never really to create more digital content. The goal was to create a better way of creating products. And if that's the outcome we're really after, someone has to take responsibility for creating the environment in which it can happen.
A Question for Leadership
That ultimately makes this a leadership question. When an organization isn't moving as quickly as we would like, our instinct is often to ask how we can get more from our people. How do we make them more productive? How do we automate more of their work? How do we give them better tools?
But increasingly, I think the more useful question is much simpler: What are we doing that's getting in their way?
Sometimes the answer is a broken workflow or an unnecessary handoff. Sometimes it's access to assets or information, a lack of time to think deeply, or a technology that requires people to work around it rather than with it. The job of leadership isn't to have every answer. It's to create an environment where the expertise already present inside the organization has the opportunity to find them.
That also changes the responsibility leaders have as AI becomes part of the organization. If technology creates new capacity, leadership gets to decide what happens to that capacity. We can simply use it to produce more with fewer people, or we can use some of it to expose developing professionals to harder problems sooner, create stronger mentorship, and build capabilities the organization doesn't have today. Efficiency and development don't have to be opposing goals, but making them complementary requires intention.
Most organizations I've worked with have never lacked talented people, good ideas, or ambition. The challenge is building systems that allow those things to connect, grow, and become meaningful outcomes. If we can create clarity, build connections, add capacity, assist translation, and foster learning, innovation ceases to be something we periodically ask people to do and starts becoming part of how the organization operates.
Maybe that's the real opportunity in front of us: not finding new ways to demand more from exceptional people, but creating the conditions that allow them, and those who will follow them, to become exceptional in the first place.
