Can we trust AI in automotive dealerships?

Every day artificial intelligence (AI) plays a greater role in our lives and in business. Even if you never open ChatGPT, you are interacting with AI in some form. It powers search engines and social media feeds; it optimises Uber routes and Waze directions; it filters spam from inboxes and recommends what we watch next. 

Yet the question persists: can we trust it?

The concern comes from a genuine place. AI does hallucinate. Large language models (LLMs) can generate answers that sound fluent and convincing, yet are factually wrong. They can struggle when asked open-ended or speculative questions. General purpose AI trained on broad internet data is not automatically suitable for regulated or technical uses.

To understand why, it helps to look at how these models work. LLMs are optimised to predict the next token in a sequence of words and to generate helpful, coherent responses. In doing so, they select the most likely explanation based on patterns in their training data; plausibility, however, is not the same as truth. Training data can contain noise, meaning models learn accidental correlations. They can contain out-dated information, presenting an old fact as current truth, and they can average conflicting data and sources.

One point is worth making from the outset. AI is not inherently trustworthy or untrustworthy. It is a system. Trust depends on design, data inputs, scope and oversight.

General purpose AI versus domain-specific AI agents

When most people talk about AI hallucinations, they are often referencing experiences with generative AI and LLMs. They ask a tool like ChatGPT to write an email, summarise a document or explain a complex topic; sometimes the result is impressive, sometimes it drifts. These tools are trained on broad internet data and are designed to answer almost anything. They search for plausible responses across vast data inputs.

That is very different from a domain-specific AI agent.

A domain-specific AI agent, such as AIME, reduces risk by focusing solely on specific tasks, constraining scope and inputs. They integrate structured data to be referenced, so the answer stems from validated, structured data, not the whole internet. Finally, they apply task-specific workflows and guardrails to prevent responses going off track. Instead of attempting to answer any conceivable question, the agent focuses on clearly defined jobs: responding to service enquiries, qualifying sales leads, booking appointments, providing accurate information about stock and finance options.

A general chatbot might attempt to answer a question about vehicle availability based on patterns it has seen online. A purpose-built automotive communication agent will check live stock records. AIME is designed to answer based on fact. If she can’t answer a question, she will bring in a human to help.

This is the fundamental shift from generative to agentic AI. One is broad and exploratory; the other is structured and task-driven.

How to reduce hallucination in practice

How do we move from "unpredictable chatbot" to "reliable digital employee"? It comes down to two pillars:

1. The first pillar is process, rules and guardrails.

Unlike a standard LLM that tries to "figure it out" on the fly, an AI agent like AIME follows internal workflows. You can program it with hard boundaries: what it can say, what it cannot say, and exactly when it needs to hand the keys back to a human.

If AIME, for example, encounters a query outside its authorised knowledge or confidence threshold, it does not improvise, it escalates to your team. This "human-in-the-loop" fail-safe ensures that the AI never goes rogue. That safeguard matters. In practice, with AIME, those escalations are rare because the scope and data are carefully defined; the mechanism remains in place as a layer of assurance.

This is how trust is engineered. Clear boundaries. Clear rules. Clear accountability.

1. The second principle is high-quality, detailed and up-to-date input data.

If you do not want an agent answering based on the broader internet, do not allow it to. Provide it with correct, structured input data. For AIME, that means feeding detailed information about your business: locations, opening hours, services offered, aftersales policies. It means connecting directly to stock records, finance APIs and other relevant systems.

When a customer asks about the availability of a specific model at a particular site, the answer comes from your live data, not from a pattern learned elsewhere. When they ask about finance options, the response is grounded in your actual products coupled with live estimates.

Even if you are simply looking to get better results from generative AI tools such as ChatGPT, the same principles apply.

1. Persona: Assign a specific role, for example “You are an accountant advising on VAT compliance”.
2. Task: Write clear, structured prompts, specifying exactly what you want with clear parameters
3. Context: Provide grounding context from trusted documents and, where possible, instruct the model to reference only those sources.
4. Format: Define how you want the output structured

In order to validate the output, you can also tell the LLM to admit uncertainty; for instance, ask it to respond with a clarifying question if confidence is low or evidence is insufficient. Finally as a sanity check, request sources for factual claims so you can verify legitimacy.

These practices do not eliminate risk entirely, yet they significantly reduce it. They shift AI from free-form improvisation towards controlled execution.

The bottom line

AI should not be trusted blindly, but nor should "hallucinations" scare you away from the single biggest productivity gain of the decade. AI excels at listening, interpreting, routing and responding. When built with the right processes, inputs and safeguards, an AI communication agent is more than just a tool. It’s a scalable, consistent member of your team that can do much more than a human would ever be capable of doing. Leaving your staff to focus on where they add real value.

Sohib Ghafouri, Founder of Infinity Motors, who has taken the journey and tested multiple AI tools in his Swindon dealership, says: “The quality and human-like nature of conversations is impressive. I trust her to speak to my customers”.

Trust, in the end, is not about believing that a machine is intelligent. It is about ensuring the system around it is engineered to do the specified task reliably and with the appropriate safeguards in place.

If you’re considering implementing AI into your customer service and sales teams, feel free to reach out.