If there’s one thing I’ve learned from lots of of conversations with automotive leaders over the past yr, it’s that innovation is having a maturity moment, writes Jacqui Barker, Keyloop’s vice chairman of world engagement
Next month sees Innovation Week at AM so it’s a timely topic.
For much of the last decade, our industry has been fascinated by what may be possible. We’ve debated electrification, imagined autonomous vehicles and talked extensively about connected ecosystems, not to say artificial intelligence. Innovation often felt exciting, ambitious, and let’s be honest slightly ‘Tomorrow World’.
That’s changing.
This yr, as I travelled between industry events, recorded episodes of the Drivetime podcast, and spoke with experts from across the globe in automotive, mobility and technology, I noticed a subtle but necessary shift within the conversation. Essentially the most interesting discussions were now not centred on futuristic concepts or technology demonstrations. As a substitute, they’re focused on execution.
The questions have evolved from ‘Can we construct this?’, to ‘Can we scale it?’ It’s now not, ‘Will customers use it?’, but ‘How can we operationalise it?’ And it’s not, ‘Could this technology change the industry?’, it’s ‘How can we create measurable value from it?’
Innovation is growing up. And three themes have emerged repeatedly throughout the conversations I’ve had this yr, that I consider will shape automotive’s next chapter.
AI is moving from experimentation to practical application
Few technologies have attracted as much attention as artificial intelligence. Depending on who you speak to, AI is either the answer to each problem in automotive or the newest technology buzzword that can eventually settle into the background.
The truth, as all the time, might be somewhere in the center.
What strikes me most is how quickly the conversation has moved beyond fascination with the technology itself. Two years ago, most discussions revolved around what generative AI could do. Today, probably the most progressive organisations are focused on where it creates value.
At MOVE 2026, Juho Hyytiäinen, CEO of fleet intelligence platform Way, described AI as less about algorithms and more about context. Data alone, he argued, has limited value. The actual opportunity comes when organisations can apply context to data and use it to enhance decisions.
This has been a recurring theme all year long.
Peter Wilson of Volteras spoke concerning the industry’s shift from simply collecting vehicle data to helping businesses understand what that data actually means. Quite than presenting 1000’s of knowledge points, the subsequent generation of AI platforms will increasingly provide actionable recommendations and personalised insights.
At AM Live, Paul Hilton of JATO described a future where AI agents interact with structured automotive datasets autonomously, allowing machines to analyse information and surface insights far faster than human teams could manage manually.
Most significantly, not one person I spoke to viewed AI as a alternative for people. As a substitute, they viewed it as a approach to remove friction.
Predictive maintenance that forestalls downtime before it occurs, vehicle health monitoring that identifies issues before they turn into expensive repairs, smarter inventory management, higher marketing attribution, more intelligent fleet operations.
These use cases aren’t futuristic; they exist today.
What separates successful AI deployments from unsuccessful ones is commonly surprisingly easy: data quality.
Time and time again, conversations returned to the identical foundational challenge: Data.
Not glamorous or attention-grabbing, unfit of a keynote headline, but absolutely essential. Without connected, accurate and accessible data, AI becomes little greater than an expensive experiment.
The organisations creating probably the most value from AI today aren’t necessarily those with probably the most advanced models. They’re those which have invested time in creating trusted sources of data and connecting their data ecosystems effectively.
The age of AI experimentation is ending and the age of AI operationalisation is starting.
The software-defined vehicle is changing the foundations
The phrase “software-defined vehicle” is all over the place straight away. Unfortunately, many definitions make it sound much more complicated than it must be.
One of the crucial insightful explanations I heard got here from Christiane Soppa of Bosch. She compared the long run vehicle to a smartphone. Two people may own an identical devices, but they’ve entirely different user experiences depending on the software, services and applications they select to make use of. This same principle increasingly applies to vehicles.
That shift represents something greater than a technology upgrade; it represents a fundamental change in how value is created.
Historically, vehicle value was largely fixed at the purpose of sale. A customer bought a automobile with an outlined specification and that specification largely remained unchanged throughout ownership.
Software-defined vehicles challenge that assumption. For the primary time, vehicle capabilities can evolve constantly with each owner. Functions might be updated remotely, features might be added after purchase and performance can improve through software. Battery management can turn into smarter over time and latest services might be deployed throughout the vehicle lifecycle.
The result’s a vehicle that behaves less like a conventional automotive product and more like a digital platform. And that creates latest challenges and large opportunities. Because software-defined vehicles generate unprecedented amounts of knowledge, organisations must develop the flexibility to rework that data into useful insights.
Bosch, Way and Volteras all described variations of the identical challenge: creating meaningful intelligence from connected vehicle information. Whether predicting battery health, improving fleet efficiency or monitoring asset utilisation, the winners will likely be those able to turning data into decisions.
The shift to software-defined vehicles also requires a special approach to partnerships. No single organisation can construct every element of the software-defined ecosystem. The longer term belongs to organisations that may collaborate effectively across these ecosystems somewhat than attempting to manage every component themselves.
That’s the reason I increasingly consider the software-defined vehicle is less about software and more about collaboration. The software simply makes the collaboration possible.
Autonomous mobility is becoming an operational challenge, not a technology challenge
Autonomous vehicles have been a part of the automotive conversation for years and yet many discussions still feel trapped in the identical place.
We proceed to debate technological capability while overlooking a more necessary query: What happens once the technology works?
One of the crucial fascinating conversations I had this yr was with Carlo Lacovini, creator of The Human Side of Autonomous Mobility. Unlike many discussions around autonomy, his perspective was grounded in practical deployment experience. His story wasn’t about perfect technology. It was about what happens when modern technology collides with the actual world.
Every latest innovation should start with a pilot. Successful deployment requires consideration of customer expectations, operations, regulation, infrastructure, public acceptance, scaling challenges and all of the messy realities that emerge when innovation leaves the laboratory.
Carlo described the industry’s evolution beautifully: First got here technology, then ecosystems. Soon, he argued, success will likely be determined by operational excellence.
Carlo’s astute remark and lived experience aligns with what I’m seeing elsewhere. Essentially the most compelling discussions around autonomy now not focus exclusively on perception systems, sensors or vehicle intelligence. As a substitute, they concentrate on the realities required to run autonomous services at scale.
Sam Clarke from Gridserve highlighted the same challenge while discussing the UK’s growing electric freight infrastructure.
Technology alone doesn’t create transformation, supporting ecosystems do.
Vehicles require charging. Charging requires infrastructure. Infrastructure requires investment. Operations require optimisation. Customers require confidence.
Every innovation journey eventually arrives at the identical destination: execution, and execution demands ecosystems.
This may occasionally explain why so many mobility discussions now revolve around partnerships. No organisation can construct autonomous mobility alone. Success will rely on software providers, infrastructure partners, vehicle manufacturers, mobility operators and regulators working together effectively.
The technology is becoming increasingly capable, however the operational challenge is simply starting.
The common thread: from innovation to implementation
At first glance, AI, software-defined vehicles and autonomous mobility might seem like entirely separate trends, but I do not think they’re.
In reality, I consider they’re all manifestations of the identical underlying shift: The automotive industry is moving from invention to implementation.
For years, innovation success was measured by technical possibility, now it’s measured by operational reality.
The organisations that succeed over the subsequent decade won’t necessarily be those with probably the most exciting technology. They will likely be those that understand the best way to deploy technology effectively.
They may connect data as a substitute of making silos. They may construct ecosystems as a substitute of isolated solutions. They may concentrate on outcomes as a substitute of features. And maybe most significantly, they may do not forget that innovation is ultimately about solving human problems, not creating impressive technology demonstrations.
The conversations I’ve had this yr leave me optimistic. Not because automotive innovation is accelerating (even though it is). But since the industry’s mindset is changing.
We’re asking higher questions, specializing in outcomes, and starting to know that probably the most transformative innovations are rarely the technologies themselves. They’re the operational capabilities, partnerships and ecosystems that allow those technologies to create meaningful value.
Innovation is not slowing down, It’s growing up. And that stands out as the most significant innovation of all!
Creator: Jacqui Barker, vice-president of world engagement, Keyloop
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This Article First Appeared At www.am-online.com
