Your Marketing Team Is Using AI Right Now. Do You Know What They're Risking?

A few weeks ago, I was talking to a marketing director at a mid-sized B2B company. She told me, almost casually, that her team had been using ChatGPT to draft blog posts for months. "We just clean it up a bit before publishing," she said. "Saves us probably twenty hours a week."

Twenty hours. Per week. From one team.

I asked her one question: "Does anyone on your team fact-check the output before it goes live?"

She paused. "We skim it."

That pause told me everything. Generative AI has slipped into marketing departments everywhere, quietly, without much discussion. Nobody called a meeting about it. Nobody ran a risk assessment. Somebody on the team tried it, it worked well enough, and it just... became part of the workflow.

And the risks? Most people haven't thought them through. Not really.

Let's fix that.

What Exactly Is Generative AI?

You've heard the term. Maybe you've used the tools. But let's make sure we're talking about the same thing.

AI, broadly, means computer systems doing things that normally need human intelligence. Recognizing speech. Spotting patterns. Recommending products you might like. Your phone has been doing this for years.

Generative AI is a specific branch. It doesn't just analyze. It creates. Text, images, audio, video. You feed it a prompt, it gives you content that looks like a human made it.

How does it work? Most of these tools are trained on enormous piles of existing data, using techniques like generative adversarial networks. They learn the patterns and structures in that data, then produce new content by mimicking what they've seen.

Think of it as a student who has read every book in the library. He can now write something that sounds like it belongs on the shelf. The grammar is perfect. The tone is convincing. Whether it's actually correct?

That's a different question entirely.

The student sounds confident. The student is not necessarily right.

The Tool Explosion

ChatGPT launched in November 2022 and became a household name almost overnight. OpenAI built it on their GPT language model. Google responded with Bard, powered by their own LaMDA technology. Then came a wave of other tools. Jasper. Copy.ai. Many of them run on OpenAI's underlying engine. You think you're using different products, but under the hood, they're often powered by the same brain.

Salesforce announced Einstein GPT, baking generative AI directly into their CRM platform. This is where things are heading. Hundreds more tools are coming. Some standalone, some embedded in products you already use every day.

What caught my attention is the split.

Some companies have gone all-in. They see twenty hours of savings per team per week and they want more. Others have banned these tools outright. Locked them down. Told IT to block access.

That split tells you something important. The technology is powerful enough to excite people, and risky enough to scare them. Both reactions are justified.

Risk One: Your Secrets Might Leak

Intellectual property is where things get dangerous first. Because this one catches people off guard every single time.

Here's the mechanic. Generative AI tools learn continuously. When you type something into ChatGPT, that input can become part of what the system knows. It retains information to build its knowledge base.

Now imagine one of your employees pastes in a confidential financial report and asks for a summary. Or feeds it details about a product defect that hasn't been made public. Or, more innocently, asks it to help draft an internal memo containing trade secrets.

Where does that information go?

It might show up in someone else's output tomorrow. Your proprietary data, served to a stranger who happened to ask the right question. You just handed your company's secrets to a system that shares them with the world.

Not on purpose. Not maliciously. But the effect is the same.

I want you to sit with that for a second. A mid-level employee, trying to be efficient, pastes a confidential pricing strategy document into a chat box. Two weeks later, a competitor mentions a suspiciously similar strategy in their pitch deck. Coincidence? Maybe. Can you prove anything? Not easily.

There's a flip side too. The content these tools generate might contain someone else's intellectual property. The tool was trained on data that includes copyrighted works. If it regurgitates someone's protected material and you publish it, the legal headaches land on your desk. Not the tool's desk. Yours.

Risk Two: Nobody Knows Who Owns AI Content

The legal situation around AI-generated content is genuinely fascinating, and by fascinating I mean chaotic.

The US Copyright Office has a clear position: they only register works created by humans. If an AI writes your blog post, you may not be able to claim copyright on it.

Let me translate what that means in practice.

You published it. A competitor copies it word for word. You want to sue. The court asks: who wrote this? You say: a machine. The court says: then you don't own it. You have no recourse.

Your content, which you thought was an asset, is legally orphaned.

There are lawsuits working through federal courts right now that could reshape this landscape completely. The Congressional Research Service has weighed in, noting that the Copyright Act protects "original works of authorship." The emphasis is on authorship. And authorship, traditionally, requires a human.

We're watching the law try to catch up with technology in real time. It's messy.

Then there's the infringement question. Works on the internet are not automatically public domain. A lot of people think they are. They're wrong, and that misconception could cost them.

OpenAI has acknowledged that their training data includes copyrighted works. They've said so openly. If an AI tool had access to copyrighted material and produces something substantially similar to the original, the owner might have a case against you. US case law supports this reasoning.

You download an image from Google for a presentation. That's probably infringement unless you have permission or it's genuinely in the public domain. You know this. Most people know this.

Now scale that up to an AI trained on millions of copyrighted works. Every piece of content it generates is potentially built on someone else's protected material.

See the problem?

Risk Three: AI Makes Things Up

This is the risk that should make every editor lose sleep.

OpenAI says it plainly: "ChatGPT will occasionally make up facts or hallucinate outputs." Google says something similar about Bard: it "may give inaccurate or inappropriate information."

That's corporate-speak for "our tools lie sometimes, and we can't always tell when."

I want to be very specific about what "lie" means here. The AI is not trying to deceive you. It doesn't have intentions. It's a pattern-matching engine that sometimes connects dots that don't exist, producing confident, fluent, completely fabricated information.

It gets worse.

When you ask ChatGPT for citations, it will sometimes fabricate sources entirely. Fake authors. Fake paper titles. Fake publication dates. Fake URLs that look real but lead nowhere. The citations look perfect. They format correctly. They follow academic style. And they are one hundred percent invented.

A busy editor who doesn't dig deeper would never know the difference.

Remember CNET? The tech publication quietly published 78 AI-generated articles over two months. Seventy-eight. They were riddled with errors. When it came to light, CNET had to pause their AI content operation entirely and issue corrections.

Their credibility took a hit that no amount of cost savings can buy back.

78 articles. Two months. One credibility crisis.

And here's what really keeps me up at night: when inaccurate content gets picked up and republished, the errors spread. They compound. Content A has a fabricated citation. Content B cites Content A as a source. Content C cites Content B. Nobody goes back to check the original.

Eventually the line between fact and fiction gets blurry enough that nobody trusts anything. That's toxic for any brand built on authority. And what brand isn't?

Risk Four: Google Is Watching

A lot of marketing teams use generative AI specifically for SEO content. Blog posts, landing pages, product descriptions. The goal is simple: rank higher on Google and get more organic traffic.

But Google is not naive about this.

In February 2023, they released a clear policy on AI-generated content. Their ranking system rewards content that demonstrates E-E-A-T: expertise, experience, authoritativeness, and trustworthiness. And they explicitly state that using AI to manipulate search rankings violates their spam policies.

Let me say that again, because marketing teams keep missing it: using AI primarily to manipulate search rankings is a violation of Google's spam policies.

Google can detect AI-written content. The detectors aren't perfect, and they sometimes flag human writing by mistake. But they're getting better. Fast.

What makes AI content easy to spot? It follows patterns. It lacks randomness and variance. It has no real personality. It repeats the same ideas, just worded differently. The style is straightforward but the detail is thin. No lived experience behind the words.

Since generative AI follows patterns and lacks originality, blog posts it writes for your company may be nearly identical to those it writes for a competitor, if given a similar prompt. Same skeleton, different skin.

Put yourself in Google's shoes for a moment. If your blog post reads like five hundred other blog posts generated from the same prompt with the same tool, why should Google rank yours above anyone else's?

They won't. They'll probably demote all of them.

So Should You Use It?

Here's my honest take.

Generative AI offers real benefits. For small companies that can't afford a full content team, it opens doors that were previously locked shut. The cost savings alone can be transformative, especially when you're operating on a thin marketing budget.

But the risks are not theoretical. I keep coming back to this because it's the point people keep missing.

Confidential data can leak. Copyright ownership is murky at best, nonexistent at worst. The tools fabricate information with a completely straight face. And Google is actively working to identify and penalize low-quality AI content.

The answer depends entirely on how much risk your company can stomach, and how thoughtfully you put guardrails in place. What's your tolerance for a fabricated citation showing up in a client-facing report? What's your plan if a competitor copies your AI-generated blog post and you can't do anything about it because you don't technically own it?

If you decide to use it, educate yourself first. Understand exactly where the dangers are. Build a process where every piece of AI-generated content gets reviewed by a human who knows what to look for. Not a skim. A real review, with source verification.

Treat the AI as a drafting tool, not a publisher.

Because right now, somewhere in your company, someone is probably pasting a confidential document into a chat box and thinking they're being efficient.

They might be.

They also might be handing your competitive advantage to a machine that doesn't know the difference.