The Dummy’s Guide to Marketing in the AI World

Or: How to Sell Something Complicated Without Turning It Into Porridge

There is a peculiar disease spreading through the AI market.

It starts when someone sees a genuinely complex technology, panics, and decides the only way to sell it is to remove every useful detail until all that remains is a sentence like:

“Our agentic AI agent empowers teams to unlock autonomous productivity.”

Which is not marketing. It is a hostage note assembled from LinkedIn posts.

This is now everywhere. Every product is intelligent. Every workflow is autonomous. Every chatbot is an agent. Every agent is agentic. Every agentic agent is apparently one demo away from saving the world, reducing churn, transforming customer experience and giving the CFO a warm sense of strategic alignment.

Marvellous.

Unfortunately, customers are not as stupid as the decks suggest. They may not know the technical implementation in detail, but they know when they are being served reheated fog.

And right now, AI marketing is serving a lot of reheated fog.

The problem is not simplification

Let us be clear. Simplifying a complex idea is good. It is necessary. Nobody wants to sit through a 90-minute architectural funeral where every component is introduced by its internal code name.

Good marketing makes complexity understandable.

Bad marketing removes the complexity completely, then acts surprised when the customer asks a practical question.

The problem is not that vendors simplify. The problem is that they dumb down.

Simplifying says:

“This is a governed AI execution layer that can interpret work, retrieve approved context, call controlled tools and produce auditable outcomes.”

Dumbing down says:

“The agent knows what to do.”

No, it does not. The agent does not “know”. The model predicts. The platform retrieves. The policy layer constrains. The tool layer executes. The validation layer checks. The audit layer records. The human approves when the consequence is high.

That is the product.

If your marketing removes all of that because it sounds too technical, you have not clarified the offer. You have amputated it.

Welcome to the age of the agent agent

My current favourite phrase is agentic agent.

An agent, by definition, is supposed to have some agency. That is why it is called an agent. It should be able to interpret intent, work toward a goal, use tools, maintain state and make bounded decisions.

So when someone says they have an “agentic agent”, what they are really saying is:

“We have an agent agent.”

It sits proudly alongside “ATM machine”, “PIN number”, “AI intelligence”, and “our strategic strategy for transformation transformation”.

There is a legitimate phrase hiding somewhere under the rug. You can say a system has agentic behaviour if it can plan, act, observe, adapt and continue toward an objective. That means something.

But “agentic agent” usually means someone in marketing discovered a word that tested well with investors and applied it generously, like cheap aftershave.

The next time someone says they have an agentic agent, the correct response is not applause. The correct response is:

“Lovely. Does it actually plan, use tools, enforce policy, validate outputs and leave an audit trail, or does it just draft an email while wearing a Salesforce badge?”

The email demo has done enough damage

The standard AI agent demo is now so predictable it should come with heritage protection.

A customer sends an email. The agent reads it. The agent writes a reply. Everyone nods as if a goat has just solved trigonometry.

This is apparently the future of enterprise software.

After decades of CRM, workflow, integration, identity, compliance, audit, data platforms and security models, the miracle is that the computer can write:

“Thanks for reaching out.”

This is useful in the same way a kettle is useful. But I do not want to attend a strategy workshop where someone calls the kettle a “thermal beverage transformation platform”.

Writing is not the hard bit. The hard bit is knowing whether the email should be answered, whether the user has authority, whether the customer is entitled to the request, whether the answer creates a legal commitment, whether personal data is exposed, whether the source is current, whether approval is required, and whether the whole thing is being logged in a way that will survive first contact with an auditor.

That is where real AI products live.

That is also the point where many demos mysteriously develop a cough.

Customers do not need childish simplicity

There is a difference between making something understandable and treating the buyer like a golden retriever with a procurement budget.

Enterprise customers do not need to understand every internal model detail. They do not need a lecture on transformers, embeddings, retrieval pipelines, context windows, tool schemas and orchestration loops before breakfast.

But they do need to understand the operating model.

They need to know where the data goes. They need to know where the model sits. They need to know what gets logged. They need to know whether their data is used for training. They need to know how permissions are enforced. They need to know how GDPR, privacy, retention and deletion are handled. They need to know how the system behaves when it is wrong.

Those are not “technical edge cases”. Those are the actual buying questions.

If your marketing cannot answer them, then your marketing is not customer-friendly. It is customer-hostile with pastel colours.

The real marketing job is translation, not sedation

Good AI marketing should translate complexity into business meaning.

It should not sedate the buyer until they stop asking sensible questions.

A serious AI proposition should explain the system in plain English without hiding the machinery. For example:

“The agent does not get unrestricted access to your data. It only retrieves approved context through controlled connectors. Sensitive fields can be redacted before model exposure. Tool calls are mediated by policy. High-risk actions require approval. Every step is logged for audit.”

That is not too technical. That is reassuring.

It tells the customer you know what the grown-up problems are.

Compare that with:

“Our agent understands your business and takes action autonomously.”

That sentence should be taken outside and composted.

What does “understands” mean? What action? Under whose authority? With what data? Against which policy? Logged where? Approved by whom? Reversed how?

If you cannot answer those questions, stop saying “autonomous” and start saying “unattended risk generation”.

Do not hide the pipes

There is an old instinct in marketing to hide the plumbing and only sell the bathroom.

Normally, fair enough. Nobody buying software wants every pipe, valve and washer explained.

But AI is different because the plumbing is where the trust lives.

In AI, the customer needs to see enough of the pipes to believe the water is not coming through the light fittings.

When a vendor says “our agent accesses your enterprise knowledge”, the customer should immediately ask: how?

When a vendor says “our agent acts on your behalf”, the customer should ask: under what authority?

When a vendor says “our agent learns”, the customer should ask: what does it store, where, for how long, and can I delete it?

When a vendor says “our agent integrates with your systems”, the customer should ask: read-only, write-capable, scoped, audited, tested, isolated?

This is not pedantry. This is basic hygiene.

The fact that many vendors cannot answer these questions without reaching for the phrase “enterprise-grade” tells you quite a lot.

The dummy’s guide to AI marketing

So here is the actual dummy’s guide.

Do not start with “AI”. Start with the problem.

Do not start with “agentic”. Start with the work the system performs.

Do not say “the agent knows”. Explain what data it retrieves, what policy it applies and what it is allowed to do.

Do not say “autonomous” unless you can also explain the boundaries.

Do not say “secure” unless you can explain identity, permissions, isolation, logging and testing.

Do not say “compliant” unless you can explain data classification, retention, deletion, consent, audit and approval.

Do not show another harmless email reply unless you can also show the system refusing a dangerous one.

Do not describe MCP as magic. Explain which tools are exposed, how calls are controlled and what gets logged.

Do not call it an “agentic agent” unless you also enjoy saying “round circle” and “wet water”.

Do not remove the complexity. Make the complexity understandable.

That is the job.

A better way to explain AI agents

Here is how I would explain a serious enterprise agent to a normal executive who has things to do and no interest in being buried alive under terminology:

“An AI agent is not just a chatbot. It is a controlled execution system. The model helps interpret the request, reason through the task and generate outputs. The platform controls what data it can access, what tools it can use, what actions require approval, what gets logged, and how the result is validated. The value is not that it can write a response. The value is that it can perform work safely inside your business rules.”

That is clear. It is not childish. It does not pretend the hard parts do not exist.

It also gives the buyer confidence that you are not merely throwing a model at their data and hoping the insurance policy is up to date.

The AppGenie version

At AppGenie, this is the line we hold.

We do not sell magic. We do not sell a chatbot with a promotion. We do not sell “agentic agent” nonsense to people who have already lost valuable hours of their lives to webinars with titles like Unlocking the Future of Autonomous Enterprise Intelligence.

We sell controlled intelligence.

We build AI systems that operate inside real business constraints. That means the model does not wander around the organisation like an overconfident consultant with a visitor pass. It works through defined data access, governed tools, explicit policy, validation, audit and escalation.

The LLM handles interpretation, reasoning, drafting, summarisation, planning and adaptation.

The platform handles identity, access, policy, data boundaries, tool execution, validation, audit, testing and escalation.

That is the difference.

We do not pretend the hard parts do not exist. We design for them.

We know customers are going to get tired of being told that everything is autonomous, agentic, intelligent and transformative. They are going to ask better questions. Where does the model sit? Where does the data go? How are permissions enforced? What gets logged? What happens when the system is wrong? Where does the agent stop?

When they ask those questions, most vendors blink.

We do not.

Because the future of AI in business is not a chatbot that can write a charming email. It is governed execution, with intelligence where it helps and controls where they matter.

That is what we build.

Stop selling fog

AI marketing does not need to become more complicated. It needs to become less stupid.

The goal is not to bury buyers in architecture diagrams. The goal is to stop insulting them with empty language.

Simplify the message, yes.

Remove the substance, no.

A good AI story should make the customer think:

“I understand what this does, why it matters, how it is controlled, and where it fits in my business.”

A bad AI story makes them think:

“I have just watched a chatbot reply to an email and somehow there are four new line items on the quote.”

The winners in AI will not be the ones who shout “agentic” the loudest. They will be the ones who can explain the real system clearly, prove the controls, show the evidence and still make the buyer smile.

Because in a market full of agent agents, autonomous autonomy and intelligent intelligence, plain English may turn out to be the most advanced technology of all.

• Back to Blog