As a product development executive, leader in hardware innovation, and consultant for startups and scale-ups, and currently Vice President of Product Development at Polaroid, I spend my days thinking about how artificial intelligence is reshaping the way we design, build, and think about hardware.

The AI gold rush isn’t hypothetical. It’s here. And like many leaders in hardware and connected product design, I’m experiencing a tension: excitement over a transformative new tool and concern about its rapid integration into workflows, often without structure, oversight, or even visibility.

Across our global teams, engineers are quietly experimenting with AI in their daily tasks. They're not hiding it, they’re just doing what good engineers do: testing the tools at their disposal. But silent adoption creates challenges. When teams work independently, the organization loses the ability to learn together, missing chances to develop coherent strategies for integrating and governing AI tools effectively.

Because in the end, successful integration hinges on trust.

This isn't the first wave of technological change in hardware design. We've previously experienced major shifts with the introduction of sophisticated computer-aided engineering tools, especially advanced multiphysics simulations like thermal analysis, fluid dynamics, and structural simulations. Initially, these simulation tools weren't immediately trusted or widely adopted. Engineers approached them cautiously, validating outputs carefully, and learning the boundaries of their reliability while improving their accuracy.

AI differs not just in scale but in speed and accessibility. Previous tools were introduced methodically and top-down, carefully tested by R&D before wider implementation. AI, however, emerged bottom-up, rapidly adopted by anyone with internet access and a laptop. This democratization is powerful, but it comes with risks.

I’ve spent my entire career designing hardware—not because I find it easy, but because I love it. There’s something deeply satisfying about solving physical problems, prototyping, testing on real bodies, and refining through hands-on experimentation. That process—filled with ergonomic tests, sketches, and mockups—is still, in my view, irreplaceable.

I’ve tried tools like MidJourney and Vizcom. They’re fun, even entertaining. But they don’t get to the heart of what makes great hardware design meaningful. When it comes to creating products that fit the human form, perform reliably, and stand the test of time, we need more than rendered imagination. We need craft, insight, and iteration. AI can’t replicate that—not yet, and maybe not ever.

It can help offload the tedious parts. I run a design studio, and part of that involves writing emails, marketing blurbs, and proposals. AI helps me get through those tasks faster so I can spend more time where I belong: sketching, prototyping, and solving the hard problems. That’s been the biggest benefit—not automation for its own sake, but creative rebalancing. AI gives me back time to focus on what I love most.

That mindset extends to my team. They’re younger, have more room to experiment, and they do play with AI tools. But while they’re fun and occasionally surprising, most of what’s generated still doesn’t meaningfully advance our design process. Our work requires precision, insight, and physical iteration—things AI doesn’t yet offer in a reliable or integrated way.

What excites us, though, is how AI is showing up in the hardware our clients are building. The real opportunity isn’t how AI helps us design—it’s how it’s enabling new tools in the world. We’re working on products that assist doctors with remote therapy, enhance diagnostic tools, and support patient care in new ways. That’s the kind of application that gets us out of bed in the morning. 

With every new technology platform or toolset, and with the general maturation of manufacturing alongside design and engineering, we’ve seen something subtle but powerful happen over the past few decades: deeper integration. Teams that once worked in parallel now converge. Those who thrive are those who can bring together supply chain thinking, manufacturing expertise, material innovation, and advanced engineering.

Consumer electronics brought out the best in this convergence, especially for electrical engineers. But I’d argue it was the mechanical designers who defined the interface, who built the shell between emerging technologies and the real world.

On the design side, we’ve always chased that moment when an innovation doesn’t just give us better tools—it gives us more freedom. More expressive range. More opportunity to shape something meaningful.

Whether it was early CAD, SolidWorks, or modern simulation platforms, the deal was always the same: learn the tools deeply and you’ll unlock their potential. Fluency was the price of freedom.

Now something else is happening. The tools are changing—and so is the nature of fluency.

What we’re seeing with AI is a different kind of liberty. You don’t necessarily need to master a tool in the traditional sense. Instead, you need to engage with it intelligently. If you can articulate your intent—if you can curate a process—AI can help you realize ideas that once required years of technical training. That’s a profound shift.

Decades of physical product development now intersect with cloud connectivity, AI, and advanced computing, marking a shift in the way we design hardware. In the past, devices like Sony Handycams or Casio organizers stood on their own, disconnected and limited in their scope. Today, a fusion of intelligence and context powered by IoT, powerful chipsets, and machine learning models are accelerating innovation at scale.

This moment is about speed, not just of computing, but of experimentation and iteration. AI allows us to compress what once took six months into six days. At Fluke, during the discovery phase, we’re using tools like Enzzo to quickly mine customer insights, build personas, model value propositions, and simulate bill-of-materials, at pace. That acceleration allows us to play faster, test ideas more rapidly, and refine more precisely.

When we move into the delivery phase, AI helps bridge the historically conflicting models of hardware’s waterfall approach and software’s agile processes. From prototyping to firmware to rendering, AI-fueled tools enable faster iteration, even across physical and digital boundaries. Then comes the sustain phase, where products are in-market and generating data. AI is changing how we ask questions about usage, performance, and customer behavior. No longer limited by dashboards or SQL skills, teams can now pose natural language queries and get answers in seconds. That’s a revolution in operational intelligence, and a lever for continuous improvement.

However, what makes this generation of technology different from past shifts is not just power or capability, it’s usability. With natural language interfaces, you no longer need to write code to be productive; you just need curiosity. That radically lowers the barrier to adoption, speeding up learning curves across entire organizations.