As artificial intelligence and advanced digital tools reshape industries, dealerships find themselves at a crossroads.
On the one hand, the potential advantages of AI are undeniable – streamlining manual tasks, connecting siloed systems, and enhancing customer experiences across the vehicle lifecycle. On the opposite, implementation isn’t straightforward. Dealerships face complex operational environments, heavy sales pressures, and cultural resistance that may turn even essentially the most promising project into an uphill struggle.
So how can dealers approach AI implementation strategically, ensuring not only a smooth launch but long-term success?
Laying the foundations
Paul Humphreys, managing director, retail, at vehicle remarketer Cox Automotive Europe, stresses that preparation is all the pieces. “Effectively implementing AI, or any technology for that matter, requires several key steps. These include aligning the chosen solution with the dealer’s business goals, securing internal buy-in, ensuring data readiness, and conducting a comprehensive technology audit. All of those needs to be accomplished well prematurely of the rollout.”
For Humphreys, a comprehensive technology audit is especially vital in an industry where disjointed legacy systems remain common. “Starting the mixing with a transparent understanding of how AI will interact with existing systems sets the stage for fulfillment,” he says.
The last word goal needs to be to attach systems relatively than create recent siloes, reducing manual duplication and unlocking opportunities across the vehicle lifecycle.
Yet technology selection itself poses one other risk. Elliott Perks, CEO of AI-powered transportation solution Jigcar, warns against adopting tools designed for other industries. “Essentially the most critical risk with AI is buying something that may not designed to work for a dealer group. AI needs to be additive to a dealer’s way of operating – streamlining operations, not creating entire recent processes.”
Without industry-specific training data, he argues, models will simply underperform. “If a model is trying to unravel problems in the identical way for a dealer selling cars because it does for a fashion brand selling clothes, it’s destined to fail.”
James Leese, UK managing director at lifecycle management platform Impel, frames rollout not as a software installation but as a people-centred transformation.
At Impel, he explains, successful implementations follow a staged process: discovery and alignment with dealership goals, rigorous data preparation and integration, tailored training, controlled pilot projects, and at last a full rollout with ongoing support.
“Technology adoption rises or falls on human engagement,” Leese notes, adding that AI should be positioned as a tool to empower, not replace, staff.
Matthew Jones, chief technology and communications officer on the Greenhous dealership group, agrees on the importance of phasing. “The important thing to AI is to treat it as a staged rollout, not a straightforward switch you may flip.”
His roadmap starts with aligning technology to measurable business goals corresponding to lead conversion or stock optimisation. From there, accurate and connected data becomes the muse. Change management, pilot testing, and consistent communication complete the cycle.
“Communication mustn’t be treated as a single step,” he says, “it must run consistently throughout the method.”
The role of consultancy
With such complexity, it’s natural for dealerships to ask how much consultancy support they need to seek. The reply, in accordance with experts, depends as much on culture as on the technology itself,
Nevertheless, whether consultancy comes from internal champions, peer networks, or the seller itself, its value lies in relevance and accountability.
Jones argues that a few of one of the best consultancy already exists contained in the dealership. “The most effective consultancy can come from inside, as IT-savvy staff already understand workflows, data sources, and on a regular basis pain points.
“Bringing them into the method early helps shape practical solutions that truly work within the dealership environment.” He also sees value in peer learning, drawing insights from other groups which have already navigated AI rollouts.
Perks at Jigcar, nonetheless, believes consultancy mustn’t be a bolt-on cost. “Ideally, you simply need to partner with software providers that view the successful roll out of their AI solutions as a part of their core obligation as a vendor. Indeed, the necessity for consultancy often points to a misfit solution not tailored to automotive. “Having to pay consultancy fees to implement AI is a giant red flag,” he warns.
Impel’s Leese takes a middle ground, arguing that consultancy is important but needs to be embedded in a structured process.
At Impel, consultancy spans discovery, data integration, training, pilot optimisation, and ongoing support. “Rolling out AI technology in a dealership environment isn’t nearly installing software – it’s about orchestrating a smooth, people-centred transformation that delivers measurable results from day one.”
Gerard Thatcher, founder and CEO of Motortech.Ai, argues that the consultancy landscape in automotive retail is undergoing a serious shift from a conventional approach where consultants were relied upon for all the pieces from system setup and staff training to process redesign and long-term optimisation.
“Today, AI-driven platforms have replaced lots of these functions. Intelligent tools now provide automated guidance, real-time problem-solving, and user-friendly interfaces, which make external consultancy far less critical,” he says.
“Where a consultant might once have delivered staff training or monitored adoption, AI can now take over these tasks with greater efficiency. Built-in training modules, virtual assistants, and adaptive learning systems enable staff to develop proficiency on demand.”
Constructing scalable training
No rollout succeeds without people. Training is subsequently some of the strategic investments a dealer could make.
“Rolling out recent technology in a dealership is simply half the job – the true success comes when every team member can use it confidently and consistently,” says Leese who recommends starting with a skills gap evaluation to grasp the needs of various roles before developing role-specific learning paths.
Further, training formats needs to be blended – combining workshops, e-learning, and reference guides – and treated as continuous relatively than one-off. Departmental champions should help sustain adoption, while metrics and feedback will ensure ongoing improvement.
Greenhous’ Jones echoes the worth of champions. “Discover trusted staff in each department that may change into your data or AI champions. Train them first and allow them to share knowledge in a way that is sensible for his or her team. Individuals are much more more likely to embrace a brand new system when support comes from a colleague they know and trust relatively than a one-off session from a vendor.”
For Perks, cultural leadership is equally vital. He believes that by making AI adoption a part of leadership routines, dealerships normalise its use and forestall it from being dismissed as a “shiny recent thing.”
“Lead from the front through the use of AI at SLT level – where management are seen to be engaging with AI themselves each day… the culture trickles top down,” he says.
Thatcher at Motortech.Ai also cautions against rushing AI adoption with unrealistic timelines. “Overly ambitious implementation schedules can place undue pressure on staff and increase the likelihood of errors,” he says.
As an alternative, he advises setting achievable milestones that allow room for training, testing, and phased adoption. Staggered rollouts, he notes, give dealerships the chance to discover and resolve issues on a smaller scale before committing to a full deployment – reducing risk while constructing staff confidence along the best way.
Navigating the risks
Despite best intentions, technology rollouts are fraught with risks. Dealers face unique pressures – multiple sites, diverse workflows, and relentless sales targets – that may complicate adoption.
Humphreys at Cox Automotive acknowledges that a high-pressure environment, interruptions can quickly undermine sales performance. His advice: keep any AI solutions easy, prioritise seamless integration with Dealer Management Systems, and deliver training in accessible, time-efficient formats.
Perks highlights one other foundational issue: data. “Without solid data, AI cannot operate effectively. The successful output of AI relies on accurate data being inputted to analyse.” Expecting AI to perform miracles without reliable data, he warns, is a recipe for disappointment.
Leese sets out a more systematic view of common risks: low user adoption, poor data integration, operational disruption, scope creep, lack of ongoing support, and unintended customer experience gaps.
Each requires a proactive countermeasure, from piloting before scaling to mapping customer touchpoints prematurely. “By proactively addressing these risks, dealerships can turn potential pitfalls into opportunities for stronger adoption, smoother processes, and higher long-term returns.”
Sustaining success
If rollout is simply the start, then ongoing support is what ensures long-term success. All our experts agree that regular reviews, cultural reinforcement, and vendor accountability are non-negotiables.
Matthew Jones at Greenhous adds a note of caution around vendor accountability. “Dealers should be careful with third parties selling ‘AI magic’. “It’s vital to ask for working examples, check references, and have regular check-ins to carry suppliers accountable.”
“AI solutions require regular reviews to ascertain performance and clear ownership throughout the dealership to maintain it on course,” says Jones who recommends quarterly checks and the usage of departmental champions to watch staff confidence. By revisiting initial guarantees, dealerships can prevent drift and maintain alignment.
Dealers, Perks argues, must also not should worry about model updates themselves, pointing to the rapid evolution of AI models. “The underlying models that power AI are changing at an alarming rate. The landscape shifts every month and a few models are higher than others at particular tasks too,” he notes.
As an alternative, providers should abstract that complexity and handle seamless upgrades, while also assessing usage and impact against key KPIs.
Leese at Impel believes ongoing consultancy is important. He recommends scheduled performance reviews, continuous training for each recent and existing staff, departmental tech champions, open feedback loops, adoption tracking, and scalable optimisation as dealerships grow.
“The launch is just the beginning,” he insists, “True ROI comes from continual refinement, adaptation, and reinforcement.”
This Article First Appeared At www.am-online.com
