AI Foundations Will Decide the Winners. The Real Challenge? How to AI at Scale.
By Sakis Tassoudis | CEO onedealer international
March 2026
AI is becoming a defining capability in automotive retail and increasingly the line between leaders and slow movers. We see this shift every day across our customers. From smarter customer engagement to improved forecasting and operational decision-making, AI is already delivering real, measurable value.
But there is a growing gap between AI ambition and AI readiness. According to Gartner, the automotive industry is currently experiencing a phase of “AI euphoria,” where companies pursue disruptive value from AI before building the necessary technological and organizational foundations. Nowhere is this more visible than in automotive retail.
Our sector is undergoing one of the most profound transformations the industry has experienced in decades. Consumer expectations are evolving rapidly, digital and physical journeys are converging, and traditional sales models are being redefined. What the automotive industry needs to do is deliver!
For OEMs, this shift creates a new strategic challenge. Delivering a premium and consistent customer experience across markets while maintaining visibility and control over network performance is becoming increasingly complex. At the same time, organizations must leverage AI to improve decision-making and operational efficiency, enable truly seamless omnichannel customer journeys, and respond faster to changing market dynamics.
In Europe, this challenge is amplified by the structure of the retail ecosystem itself.
Legacy Dealer Management Systems still dominate dealership operations and were never designed to support modern digital sales journeys. National regulations, market standards, and language differences make the deployment of common applications across countries difficult. And OEM platforms often need to coexist with or compete against dealer systems. It can be chaotic!
At the same time, importers play a crucial role in translating OEM strategy into local market execution, adding another layer of operational complexity across the network.
This fragmentation makes one thing clear: AI cannot succeed on fragmented foundations.
What the industry increasingly requires is not another standalone application, but a strategic solution. One that connects dealership processes, data, and intelligent capabilities across OEMs, importers, and dealer networks.
Trusted enterprise platforms – supported by a unified AI data foundation, can enable automotive businesses to move beyond isolated experiments and begin empowering true retail orchestration from customer engagement and sales execution to network performance and market visibility.
Equally important, a trusted technology platform is essential to secure long-term investment in AI. It provides the stability, scalability, and governance required to ensure that AI initiatives evolve into sustainable capabilities rather than isolated experiments. Gartner predicts that by 2027 more than half of enterprise generative AI models will rely on proprietary or industry-specific data, highlighting the growing importance of trusted platforms capable of managing and activating these datasets.
In practice, this means the success of AI will depend less on algorithms alone and more on the strength of the underlying data and platform architecture on which it operates.
The implications are significant: greater transparency across the retail ecosystem, faster decision-making, improved market efficiency, and ultimately stronger sales performance and brand value.
Working with automotive clients around the world, OneDealer consistently sees two realities emerging:
- The question for automotive retail is no longer whether AI will play a role, but how deeply it will be embedded in the way the industry operates.
- The real shift is not that AI is arriving in automotive retail it is that retail operations are beginning to reorganize around it.
The winners are the ones that focus on creating the right foundations that allow AI to operate at scale.
