AI’s role in Bridging the Physical to Digital for Hardware Manufacturing.
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Vineet Thuvara
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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.
"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."
But speed isn’t without risk. We take that trade-off seriously at Fluke, where safety and precision are paramount. There’s a line we won’t cross with generative AI. While we’re happy to use it for product recommendations or visual renderings, we never deploy it where safety or compliance is on the line. There is an ethical responsibility tied to innovation, one that pushes barriers but never jeopardizes the intention of designing in the first place. The desire to keep our world up and running. With this at the forefront, our cycle of innovation prioritizes trust and human capacity, keeping a check on AI, its applications, and results.
This is where democratization comes in. For innovation to grow within any organization, there must be increased access to design tools, software, and training across departments. We’ve seen repeatedly that passion and curiosity can trump credentials. The ability to experiment, test, and iterate is innately human. AI enables more people to try, fail, learn, and eventually build meaningful things. That’s how innovation should work.
Hardware has always been difficult; long lead times, complex integrations, and in some cases, expensive mistakes. But AI can help flatten that curve, giving us faster feedback loops and more dynamic product-market fit testing. At the same time, it rebalances the power dynamic between software and hardware. Hardware has been commoditized by software for years. Now, as AI commodifies software, the spotlight turns back to hardware as the irreplaceable interface to human experience.
"But speed isn’t without risk. We take that trade-off seriously at Fluke, where safety and precision are paramount. There’s a line we won’t cross with generative AI. While we’re happy to use it for product recommendations or visual renderings, we never deploy it where safety or compliance is on the line."
It's this, that also led me to return to hardware after years in cloud computing. Every interaction, whether with a device, a tool, or a service, ends in the physical world. You can’t feel, touch, or experience software without hardware. The magic happens at that intersection.
Still, trust doesn’t happen overnight. It’s our job to educate and show the art of the possible with AI, so we host AI demo days, lunch-and-learns, and summit events to expose our teams to different use cases. But more importantly, we explore what’s possible when you can use AI responsibly in an environment where you are free to try and test, before scaling.
Ultimately, this is about responsible acceleration. By giving people the tools and guidance, we can create environments where people are free to explore without setting them up to fail.
AI is still in its infancy. But its learning curve is exponential. And that’s where our responsibility must lie: guiding it carefully, while it’s still building capacity to walk, as one day, we’ll need to trust it to run.
Vineet Thuvara
Chief Product Officer at Fluke Corporation
