Supplier Soundbytes: What it takes to build AI that automotive can trust
Artificial intelligence is moving quickly from experimentation into the everyday operation of fleets and vehicles. It is being used to improve driver safety, detect collisions, support predictive maintenance, reduce downtime and turn huge volumes of connected-vehicle data into decisions. But as AI becomes more deeply embedded in automotive, the question is shifting from what it can do to whether its results are reliable enough to trust.
That makes the gap between an impressive demonstration and a useful product increasingly important. In the real world, AI has to cope with unpredictable conditions, different vehicles, environments and driver behaviours. A false positive or delayed result can do more than reduce model performance—it can undermine customer confidence and prevent adoption. The challenge is therefore to build AI around meaningful business problems, with reliability treated as a product requirement rather than a technical detail.
Data is central to that challenge. Large datasets alone do not guarantee better AI. Models need sufficient breadth, depth and fidelity, while training data must reflect the different geographies, climates, vehicle types and operating conditions in which they will be used. Data quality also needs to evolve continuously, with real-world customer feedback helping teams identify weaknesses and improve models over time.
Bias is another consideration. Blind spots can emerge when certain customer groups, fleet types or operating environments are poorly represented in the data. Addressing this requires diverse perspectives during product development, explicit auditing and consideration of potential sources of bias before AI reaches customers. Privacy and responsible AI also need to be built into products from the outset.
The wider question is how businesses balance the speed of AI innovation with accuracy, transparency and consistency. Customer involvement, real-world testing and measures such as precision and recall can help ensure that AI delivers dependable results. The priority is not necessarily more AI features, but better ones that solve genuine problems and earn trust.
Looking ahead, opportunities include predictive maintenance, faster vehicle repair, autonomous vehicles and combining fleet intelligence with wider business data to improve decision-making. Human judgement will remain important, providing the context that AI alone cannot.
That is the backdrop to the latest Autology episode of Supplier Soundbytes from Mobility Global, featuring Sabina Martin, Vice President of Product Management at Geotab. Geotab is a global connected vehicle and telematics company, and Sabina discusses what it takes to build AI that delivers reliable results at scale—and why data quality, responsible development and customer trust are fundamental to making automotive AI work in the real world.
Sabina Martin
[Source: Geotab]
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