Why AI Adoption in the Aftermarket Feels Stuck

Let’s get one thing straight up front: the automotive aftermarket is not “behind” on AI.

In fact, most of the companies I talk to are already using it. Copilot is on. ChatGPT is open in a browser tab. Someone is experimenting with summaries, emails, maybe even forecasting.

And yet, when you ask a simple follow-up question like, “Is this actually helping you do your job better?” I tend to get an awkward pause. It was, but its plateaued.

That disconnect is not a technology problem. It is a fit problem. And a “I’m not really sure if my data is safe” problem.

The aftermarket is a complex, relationship-driven, margin-sensitive business. No LLM was discussing our industry in their board room with their engineers. The reality is these tools were built to be impressive to users, not role-tailored.

If AI feels shallow or risky today, it is because it is being layered onto the aftermarket instead of designed for it.

Let’s talk about what actually matters if you want AI to work here.

Not All AI Models Are the Same. And Security Is the First Filter.

One of the biggest misconceptions I see is the idea that “AI is ChatGPT.” But if you’ve been dabbling, you realize its much more complicated than that  

Different large language models (LLMs) are optimized for different jobs. Some are strong at reasoning, through with trade-offs. Others are better at summarizing messy information. Some are fast and cheap. Others are slower but more precise.

If you are running an aftermarket business, that distinction alone matters, a lot. But what matters more is what happens to your data when you use them.

Here are the questions I rarely hear asked out loud, but everyone should be thinking about:

• Where does this data go?
• Is it retained?
• Is it being used to train something else?
• Who inside my organization is pasting what into public tools?
  

The aftermarket runs on trust. Customer pricing, sales activity, margin data, vendor conversations. This is not information you can afford to be casual with.

Banning AI does will work. People will still use it.

The only viable path forward is giving teams a secure environment where they can use AI intentionally, with guardrails.

That means:

• Clear data boundaries
• No training on customer data
• Controlled access
• The ability to use different models for different jobs, safely
  

Security is not a feature. It is the prerequisite for adoption.

The Model Is Not the Product. Context Is.

This is the part almost every AI pitch skips.

Between the user and the language model sits the most important piece of the system: the context layer.

If you have ever felt like an AI tool “almost” gets it but never quite does, this is why. The reality is results are probability based so if it doesn’t understand what a “good response” looks like, it will guess. And often do so incorrectly.

This is why Generic AI tools rely heavily on what you put into the prompt. That means users are responsible for explaining their business, their terminology, their structure, and their intent every single time.

That works for a while. Then it breaks.

People notice that the more they use a tool like ChatGPT, the better it seems to perform. That is not because it understands your business. It is because the current conversation still contains enough context.

Once that context window fills up, older information drops off. The AI forgets. Quality degrades. Users start re-explaining everything again.

In the aftermarket, this is a non-starter.

A real AI system for this industry needs a persistent context layer. One that understands the business outside of a single chat session.

That context layer defines:

• What entities exist
• How they relate to each other
• What data is relevant
• How questions should be interpreted based on role
  

Without this, AI remains a clever assistant. With it, AI becomes operational.

Entity + Context Awareness Is the Difference

The aftermarket is not abstract. It is built on concrete entities.

Accounts. Reps. Branches. Vendors. SKUs. Programs. Buying groups.

If an AI cannot reliably understand and reference these entities, its answers are at best incomplete and at worst misleading.

This is why entity-aware systems produce better outcomes:

• They reduce ambiguity
• They anchor responses in real business objects
• They allow AI to reason across relationships, not just text
• They create consistency across teams
  

No amount of clever prompting can fix a system that does not understand the underlying structure of the business.

This is also why “just train your people to write better prompts” is bad advice. It shifts responsibility to the user instead of fixing the system.

Patchwork AI Is a Dead End

This is what I often see when talking to aftermarket organizations:

Someone copies CRM data into ChatGPT or Co-Pilot. Someone else pastes sales notes from email. Another person uploads a spreadsheet somewhere else.

Each interaction is isolated. Nothing compounds. Nothing is shared.

This patchwork approach creates three predictable outcomes:

• Different teams get different answers to the same question
• Work is duplicated instead of reused
• No institutional knowledge is built
  

That is not AI transformation. That is productivity theater.

A Shared Knowledge Base Is Where Real Leverage Comes From

The aftermarket does not need more individual AI power users. It needs shared intelligence.

A connected knowledge base allows insights to move across the organization instead of living in private chat histories.

When done right, it enables:

• Sales activity to inform management decisions
• Customer history to persist across reps
• Pricing learnings to compound over time
• Leaders to see patterns instead of anecdotes
  

The best practices here are not complicated:

• Centralize structured data instead of copying it between tools
• Allow AI to reference shared knowledge, not just user input
• Maintain clear permissions and security controls
• Design for reuse, not one-off answers

This is how AI becomes an organizational asset instead of a personal shortcut.

What We Are Building at Tromml

At Tromml, we are building SecureAI specifically for the automotive aftermarket.

Not as a generic chatbot with industry branding. As a secure, context-aware system designed around how this industry actually works.

That means:

• Multiple AI models, selected intentionally by task
• A persistent context layer trained on aftermarket entities
• Ready-made prompts built for real aftermarket roles
• A shared knowledge foundation that compounds value
• Enterprise-grade security that teams can trust
  

The goal is not to get people to “use AI more.” The goal is to help them make better decisions with less friction and less risk.

The Aftermarket Is Ready. The Tools Need to Catch Up.

I spend a lot of time in conversations with aftermarket leaders. What I hear is not skepticism. It is impatience.

People want AI that helps them:

• Sell smarter
• Protect margin
• Prepare for real conversations
• Understand what changed and why
• Act with confidence instead of guesswork
  

The technology is here. The intent is here.

What has been missing is a system that respects the complexity of this industry and the trust it runs on.

That is the gap we are closing.