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For many CMOs, especially those at private-equity backed companies, the conversation around AI isn’t being driven by curiosity — it’s being driven by pressure. Boards and investors are pushing for faster turnarounds, leaner teams, and tighter budgets. In response, AI is often held up as the answer: a way to automate, accelerate, and optimize.
But in conversations we’ve had with CMOs, one thing is clear: the guidance on how to implement AI is often missing. They’re being told to “get smarter” with their budgets, teams, and output — but not given a clear path to get there.
So they hesitate. Not because they don’t see the value of AI, but because no one’s connecting the dots between big promises and practical actions.
This blog aims to do just that. We’ll walk through where AI can truly support marketing teams (and where it can’t), and outline specific steps CMOs can take to build AI into their operations responsibly, creatively, and with a focus on long-term efficiency and brand integrity.
Adopting AI doesn’t mean replacing your team or rewriting your playbook overnight. It means knowing where to plug it in, how to maintain oversight, and how to keep your strategy (and your standards) firmly in place.
Let’s start with a reality check: AI isn’t magic. It’s powerful, but it’s also imperfect, and it’s only as good as the guidance and context it’s given. Marketing leaders who approach AI as a plug-and-play solution will be disappointed. But those who use it as a support system — to speed up thinking, not replace it — can gain a serious edge.
AI’s biggest strengths lie in its ability to process large amounts of information, spot patterns, and produce variations quickly. That makes it ideal for tasks like:
Synthesizing research
Use custom GPTs or summarization tools to condense market reports, customer interviews, or competitor analysis into digestible insights, fast.
Iterating content ideas
Need five variations of a headline, or want to test different tones for a campaign? AI can spin up options in seconds, giving your team a strong starting point.
Optimizing media spend and targeting
AI tools can rapidly analyze campaign performance, recommend shifts in spend, and test targeting approaches with speed and precision.
Custom GPT Agent
While AI can accelerate execution, it’s not built for big-picture thinking or emotional intelligence, both critical to effective marketing. It struggles with:
Generating original ideas
AI pulls from what already exists. It can remix, but it can’t invent. Net-new creative concepts still need human minds.
Managing authenticity or brand tone
Without oversight, AI-generated content can sound off-brand, overly generic, or even misaligned with your company’s voice and values.
Reading emotional and cultural nuance
Marketing is often about timing, tone, and cultural relevance. AI lacks the context to navigate those subtle dynamics on its own.
As one of the CMOs we spoke to put it: “Use AI to help you do things faster. There are so many other things people can do to help drive business forward.”
The key takeaway: treat AI as a co-pilot, not the driver. Let it handle the heavy lifting where possible, but keep the strategic thinking, quality control, and brand storytelling in human hands.
Let’s move past the “just start experimenting” advice. CMOs need specific, strategic actions to integrate AI in ways that actually move the needle. The good news? You don’t need to overhaul your entire martech stack or become an AI expert to begin. Start by making small, intentional moves in areas with clear upside.
Start by looking at where your team is spending the most time on manual, repeatable
tasks — content formatting, brief creation, and research summaries. These are ripe for AI support.
Example Action Step
Create a custom GPT agent trained on your brand’s voice and workflows. Upload past campaign briefs, tone-of-voice guidelines, and content examples into the agent.
This becomes a private, brand-aware tool your team can use to:
• Draft first-pass social posts from blog content
• Auto-format briefs using internal templates
• Summarize market reports or sales notes into insights for marketing
You aren’t replacing human judgment, just accelerating it.
One of the best ways to drive adoption is by creating grassroots momentum. Business change management strategies often recommend starting with early adopters in less senior roles who are closest to the work and most eager to test new tools.
Example Action Step
Nominate 2–3 internal AI champions from different roles (e.g., marketing ops, project management, IT). Give them a sandbox to test tools and report back with findings.
Encourage them to:
• Share quick wins weekly in Slack or during stand-ups
• Document use cases that worked (or didn’t) in a shared “AI Playbook”
• Host monthly AI show-and-tells to demo real work in progress
This cross-functional approach can help AI adoption feel less intimidating and more like a team sport.
AI is most effective when used to support creativity, not generate polished output on its own. Think of it as your brainstorming partner, not your copywriter.
Example Action Step
In your next campaign kickoff, prompt AI for 5 possible campaign angles based on customer data, personas, or past performance.
Then — and this is key — bring those ideas to the team to workshop and refine. AI gets you to version 1. Human creativity gets you to version 10.
You can also use AI for:
• Fast A/B testing variants
• Unlocking new ideas when campaigns stall
• Drafting creative rationale for internal alignment
A/B testing, as imagined by AI — image generated with ChatGPT 4.0.
Scaling content with AI introduces real brand risk if not managed carefully. Misaligned tone, inaccurate messaging, or vague positioning can all creep in if you’re not careful.
Example Action Step
Build a simple QA checklist for any AI-assisted content before it goes live.
Include prompts like:
• Does this reflect our brand voice and tone?
• Has it been cross-checked with our messaging strategy?
• Would we be comfortable putting a name on this?
AI can move things faster, but it doesn’t remove the need for editorial standards or thoughtful storytelling. Keep human oversight as a non-negotiable.
Before introducing any AI tools into your marketing organization, pause and evaluate the risks, especially around data privacy and brand integrity.
Check data security protocols
Avoid tools that retain or train on your inputs without consent. Look for enterprise-ready tools with workspace-specific settings or private APIs.
Set internal use guidelines
• What kind of data is allowed?
• When is it appropriate to use AI?
• Who reviews the output before it’s shared externally?
Establish content approval workflows
Don’t assume AI-generated content is ready to ship. Ensure human oversight is baked in from the start.
Governance doesn’t have to slow you down, but it does protect your brand while giving teams freedom to explore responsibly.
Even well-intentioned AI efforts can go sideways if you’re not careful. Here’s what to steer clear of:
“Set it and forget it” thinking
AI tools require active guidance and regular refinement — they’re not plug-and-play solutions.
Over-delegating brand voice
AI can help draft, but it can’t intuit tone, nuance, or values. Keep ownership of messaging with your humans.
Waiting too long to engage
You don’t need to be an expert to begin. Start small and learn by doing — delaying only puts your team further behind.
The goal is progress with purpose, not perfection.
Think of AI integration as a capability, not just a tool. Building fluency means equipping your team with the skills and mindset to evolve with it.
Invest in learning
Encourage your team to use platforms like LinkedIn Learning, Grow with Google AI, Microsoft Learn, IBM SkillsBuild, AWS AI & ML Training, Harvard & OpenAI/Microsoft collaborations, webinars, and AI prompt libraries.
Offer prompt training
Writing good prompts is a new kind of literacy. Teach your team to ask better questions and critique output effectively.
Bake AI into onboarding
Include your AI tools, best practices, and governance policies in new hire documentation and training flows.
A note on prompt reuse:
One of the most common frustrations is the time it takes to craft the “perfect prompt,” only to find it doesn’t translate well to different types of content.
This is a normal part of the learning curve. To improve efficiency over time:
• Save effective prompts in a shared library for future adaptation.
• Use custom GPTs or agents trained on your brand tone and content types.
• Accept that not every prompt will be one-size-fits-all. But patterns do emerge the more your team practices and documents learnings.
Long-term fluency isn’t a one-time rollout — it’s a cultural shift toward more efficient, empowered, and data-informed marketing.
You don’t need a fully baked AI strategy to begin, but you do need to begin with intention. The pressure to drive efficiency is real, especially in PE-backed environments. But that pressure doesn’t have to lead to panic or premature automation.
Start where you are. Identify repetitive tasks. Invite experimentation. Keep humans in the loop. Focus your early efforts on three things: efficiency, creative acceleration, and brand authenticity. Always prioritize context, quality, and oversight.
Another gold statement from one of our CMOs: “AI won’t replace marketers — but marketers who use AI well will replace those who don’t.”
We’re helping marketing teams explore emerging AI tools, create brand-aligned agents to improve workflow efficiency, and recommend solutions that support real business needs, not just shiny trends. Whether you’re looking to enhance campaign speed, increase personalization, or reduce repetitive tasks, we’re here to help you navigate AI with clarity and purpose. If you’re exploring what that could look like inside your team, get in touch — we’d love to hear where you’re at and help you build a path forward.
