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The Fortexx Group

Where AI Belongs in a Marketing Team

(And Where It Doesn’t)

AI is now the coworker nobody remembers hiring.

It’s in brainstorming. It’s in copywriting. It’s in decks, reports, emails, and “quick ideas” that somehow look the same across half the industry.

The problem isn’t that AI showed up.
The problem is how many teams are letting it drive instead of assist.

Used right, LLMs and other AI tools can make a marketing team sharper, faster, and more consistent.
Used wrong, they quietly flatten your voice, dull your strategy, and turn your brand into one more generic thing on the internet.

So the real question isn’t:

“Are you using AI?”

It’s:

“Are you using AI in the right places—and avoiding it in the wrong ones?”

Let’s draw the line.

First distinction: thinking vs. typing

Most of the fear and confusion around AI in marketing comes from mixing up two jobs:

  1. Thinking work – judgment, strategy, positioning, tradeoffs, ethics
  2. Typing work – drafting, summarizing, polishing, formatting, versioning

AI is extremely good at #2.
It is inconsistent and risky at #1.

If you let AI help you type faster, you win.
If you let it decide what to say, who to be, and what to promise, you’re handing the wheel to something that has no stake in the outcome.

Keep that distinction in mind as we go.

Where AI absolutely does belong in a marketing team

1. Research and synthesis (not conclusions)

Most marketing “research” used to mean:

  • 27 open tabs
  • Screenshots in a random folder
  • A messy doc full of half-notes

LLMs are great at turning mess into signal—as long as you stay in control of the conclusions.

Good uses:

  • Summarizing long articles, reports, or transcripts
  • Pulling out key themes from interviews or survey responses
  • Comparing how competitors talk about the same benefit
  • Extracting Category Entry Points from qualitative data (situations, triggers, contexts customers mention)

What AI should do:

  • “Give me the 5 most common reasons people say they’re interested in this product.”
  • “Summarize how these competitors position themselves in 3–5 bullets each.”
  • “Cluster these 100 open-ended responses into 5–7 themes.”

What you still have to do:

  • Decide which patterns actually matter
  • Decide what they mean for your brand
  • Decide what tradeoffs you’re willing to make based on them

AI can surface patterns.
It cannot own the stakes.

2. First drafts and versions (not final voice)

AI is a machine that never gets tired of saying, “Here’s another way to phrase that.”

Use it.

Good uses:

  • Generating rough outlines for blog posts, landing pages, and emails
  • Proposing multiple headline options from a clear brief
  • Turning one piece of content into variations for different formats (email, social, SMS, internal memo)
  • Helping non-writers get a decent first pass they can then edit

Example workflow:

  1. You define the idea, audience, and goal.
  2. You tell AI: “Give me a first draft that hits A, B, C and avoids X.”
  3. It spits out something 60–70% there.
  4. You or a writer then:
    • Cut clichés
    • Fix tone
    • Add real examples and specific details
    • Align with brand voice

What you don’t outsource:

  • The core idea
  • The stance
  • The promise you’re making
  • The final sign-off

AI should be a typing accelerator, not your VP of Brand.

3. Repurposing and structuring content

You create a lot more than you realize: calls, internal docs, decks, voice notes, transcripts.

AI is great at:

  • Turning long-form into short-form
  • Turning spoken into written
  • Turning chaotic into structured

Examples:

  • Turn a webinar or podcast transcript into:
    • A blog recap
    • 5 social posts
    • An internal “summary + key takeaways” doc
  • Turn a messy meeting transcript into:
    • Action items
    • Decisions made
    • Open questions
  • Turn an internal strategy doc into:
    • A client-facing one-pager
    • Training material for the field team

The thinking still has to be evaluated. But the formatting, structuring, repackaging? That’s prime AI territory.

4. QA support and consistency checks

Humans get tired. AI doesn’t.

Use it to catch:

  • Tone mismatches across channels
  • Inconsistent terminology (product names, features, pricing language)
  • Obvious contradictions between pages or campaigns
  • Overpromising / under-disclosing

Examples:

  • “Compare these three landing pages for consistency of claims and tone.”
  • “Highlight any parts of this copy that might sound like an absolute guarantee or medical/financial claim.”
  • “Check this text against our brand guidelines and flag anything that feels off.”

AI won’t catch everything. But it will catch enough to save time and prevent dumb mistakes.

You still need human review. But AI is a solid second set of eyes.

5. Internal enablement and training material

Your team needs:

  • Playbooks
  • FAQs
  • “How we do things here” docs
  • Training snippets

Most of that can start with AI as a collaborator.

Good uses:

  • Turning scattered notes into a draft playbook
  • Turning complex product docs into field-friendly explanations
  • Creating practice scenarios and roleplay prompts for sales/field teams
  • Summarizing past campaigns into “lessons learned” docs

Again: you own accuracy and nuance.

AI just helps you get past the blank page.

Where AI does not belong (or needs heavy guardrails)

Now for the dangerous side.

1. Strategy and positioning decisions

If you ask an LLM:

  • “What’s the best positioning for this product?”
  • “What should our brand stand for?”
  • “Who’s our ideal customer?”

…it will happily answer with something that sounds intelligent.

But AI is pattern-matching across generic data. It’s not:

  • In your P&L
  • Dealing with your constraints
  • Living with the consequences

Use AI to explore options, not decide direction.

Wrong way:

  • “AI said we should be the affordable, premium, disruptive market leader focused on innovation and customer-centricity.”

Right way:

  • “Give me 10 ways to position a mid-priced, reliability-focused brand in a saturated telecom market.”
  • Then you decide which few are strategically sound and operationally real.

If a strategic decision could:

  • Commit you to a risky promise
  • Lock you into a segment
  • Change how resources get allocated

…it belongs to humans, informed by data, not to a prediction machine trained on everyone else’s copy.

2. Pricing, promises, and risk-heavy claims

Any place where the words:

  • “Guarantee”
  • “Risk-free”
  • “Results”
  • “Safe”
  • “Approved”

start to show up? AI output should be treated as raw material, not gospel.

LLMs don’t:

  • Understand regulations in your jurisdiction
  • Know your legal risk tolerance
  • Know exactly what your operations can deliver
  • Understand what counts as deceptive or misleading in your context

They will cheerfully:

  • Suggest guarantees you can’t legally offer
  • Describe outcomes you can’t consistently deliver
  • Borrow language from industries you are not in

You can prompt around this (“avoid guarantees,” etc.), but that doesn’t replace human legal and ethical review.

Rule of thumb:
If legal, compliance, or your conscience would care, AI is not the final author.

3. Sensitive 1:1 communications without review

LLMs can draft:

  • Outreach templates
  • Follow-up frameworks
  • Alternative phrasings

Good.

But dropping unreviewed AI replies straight into:

  • Customer complaint threads
  • High-stakes sales conversations
  • HR/people issues
    is asking for trouble.

It’s not just about tone. It’s about:

  • Accountability
  • Specific context
  • Emotion and nuance

Use AI to:

  • Suggest reply options
  • Help with tone softening or tightening
  • Structure your response

Then:

  • You decide what to actually send
  • You own the message

If the relationship would be damaged by a tone-deaf sentence, AI should never be the sole author.

4. Replacing human insight with “AI said so”

The laziest version of AI adoption is this:
“We don’t have to talk to customers anymore. AI can analyze personas for us.”

No.

AI can:

  • Analyze what people have already said
  • Analyze your existing data
  • Help you see patterns faster

It cannot:

  • Sit across from a customer and feel the hesitation behind their words
  • Notice body language and emotional subtext in a field conversation
  • Ask a spontaneous follow-up that changes the direction of an interview

If your “customer understanding” is 100% desk work, AI will accelerate your distance from reality, not your intelligence about it.

The right pattern is:

  • Humans in the field, in calls, in research
  • AI helping process, summarize, and distribute what those humans discover

5. Brand voice and creative direction “on autopilot”

AI is trained on the average of the internet.

If you let it, it will:

  • Drag you toward generic wording
  • Water down strong, specific opinions
  • Push you into the same phrases everyone else is using

You lose:

  • Edge
  • Distinctiveness
  • Memory structures in your category

AI can absolutely help brainstorm and polish. But:

  • Your voice should be defined by humans
  • Your creative direction should be driven by a clear, human-chosen strategy

Ask AI:

  • “Give me 10 variations on this headline, keeping this tone and phrase.”
    Not:
  • “Write me a brand voice and creative direction for this company.”

You can’t outsource soul.

A simple framework: Green, Yellow, Red

To keep this practical, treat AI usage like a traffic light.

🟢 GREEN – Go nuts (AI-native zones)

  • Summaries and syntheses of existing text/audio
  • First drafts for content from clear briefs
  • Repurposing / atomizing existing content
  • Structuring docs, agendas, playbooks
  • QA passes for consistency and clarity
  • Brainstorming lists, angles, and options

These are low-risk, high-leverage.

Use AI aggressively here and free your humans for higher-value thinking.

🟡 YELLOW – Use with guardrails

  • Customer-facing copy (final pass must be human)
  • Campaign concepts (must be validated against strategy and reality)
  • Outreach templates (must be customized and reviewed)
  • Internal docs that express policy or standards (must be checked for accuracy and tone)

Here, AI is a helper, not a decider.

You need:

  • Clear prompts
  • Clear review
  • Clear accountability

🔴 RED – Keep human in charge

  • Positioning, promises, and high-level strategy
  • Pricing/offer structure and guarantees
  • Legal, compliance, and regulatory-sensitive language
  • High-stakes 1:1 communication
  • Brand voice definition and core creative direction
  • Decisions that materially affect people (jobs, compensation, promotion, serious feedback)

AI can support the thinking around these, but it should never own the final word.

Building an AI policy that doesn’t suck

If you don’t define how AI is used in your team, people will:

  • Hide it
  • Overuse it
  • Underuse it
  • Argue about it

You don’t need a 25-page document. You do need something like:

  1. Principles
    • AI assists, humans decide.
    • We never use AI to make promises we haven’t vetted.
    • We respect privacy and confidentiality in what we feed it.
  2. Allowed uses (green)
    • List out the obvious “yes” areas for your team.
  3. Guardrailed uses (yellow)
    • Allowed, but must be reviewed and attributed internally as AI-assisted.
  4. Prohibited uses (red)
    • Clear list: “We don’t use AI to X, Y, Z.”
  5. Attribution and transparency
    • Internally: people should be comfortable saying “AI helped draft this.”
    • Externally: you decide when/if you disclose use, but you never lie about what’s human.

Make it real by:

  • Walking through examples
  • Showing what “good AI use” looks like
  • Updating the policy as tools and your comfort evolve

The teams that win with AI

The winning teams are not the ones shouting “We use AI!” the loudest.

They’re the ones who quietly:

  • Use AI to get rid of low-leverage typing and formatting
  • Invest more human energy into insight, strategy, and creative judgment
  • Protect their brand voice and promises like an asset, not a playground
  • Train their people to think clearly about when and why they’re using these tools

In other words:
They put AI where it belongs—and keep it out of places it doesn’t.

If you can do that, you don’t have to fear AI or worship it.

You just treat it like what it is:
A powerful tool that makes a good team better, and a careless team dangerous.