CMOs Need To Think Like Data Architects To Win With AI

As CMOs contend with flatlining budgets and rising performance expectations, a new operational challenge is also emerging: Integrating AI not just for productivity, but for strategic resilience. Gartner’s latest CMO Spend Survey reveals that marketing leaders are making investments towards AI to maximize marketing performance and enhance efficiency—ultimately, to do more with less.

However, driving ROI at scale via AI requires more than deploying plug-and-play tools; CMOs need to fundamentally change how they lead. Yet only 21% of CMOs feel they have the AI literacy to achieve their goals in the next couple of years, according to a recent IBM study. One wrong cue or misaligned algorithm can be catastrophic for a marketing team using AI to analyze consumer data and forecast trends to guide their strategies.

Traditionally, CMOs have focused on the human elements of marketing: Team coaching, overall strategy, outreach, and KPIs. Technology has been widely viewed as a tool on the sidelines that the IT department can step in to help out with when failures arise. Now, this view doesn’t stand, as AI becomes increasingly integrated into marketing.

The most resilient marketing leaders won’t just be strong strategists, but also adept at managing automated systems and data flows. They’ll also be custodians of these tools, ensuring AI performs to brand standards and achieves real business outcomes.

The bottom line is that CMOs should no longer only be thinking about adopting AI, but learning how to manage it, too. Here are the three intrinsic skills tomorrow’s marketing leaders need to be building now to do so.

Prompt engineering

A Salesforce survey shows that nearly three-quarters of marketers are using generative AI (genAI) for content creation, but nearly half of them don’t know how to get the most value out of it. That’s dangerous for teams using genAI to help with creating email campaigns; weak prompts lead to vague and unhelpful responses that can damage brand trust or misalign with audience expectations, resulting in email campaigns that fail to engage the target audience and low conversion rates.

The remedy to this is for CMOs to establish prompt governance frameworks, similar to brand guidelines. That includes defining stylistic elements, like tone and formatting. Prompt templates should also be developed for specific workflows, whether that’s creating emails as opposed to product information. Marketing leaders need to ensure their teams are well-versed in these guidelines for all use cases and best practices for prompt design and engineering. Hands-on training and nurturing critical thinking about genAI outputs are just some of the ways to do that.

Model customization

CMOs need to know how to mold AI models according to the shifting needs of their brand, audience, and wider market. AI tools shouldn’t be stagnant, but need to be retrained and tested amid strategy changes or market shifts.

Training models like LLMs and ML algorithms isn’t a ‘one and done’ job—it requires iterations so marketing teams can rely on AI with reassurance and accuracy. If a company introduces a new product that needs to be marketed, the marketing team needs to update its AI stack accordingly. Again, guidelines around brand, tone, voice, and other marketing parameters should be used in this case. CMOs can collaborate with data teams to test outputs and ensure alignment with marketing parameters.

Data literacy

Excellent data literacy sets apart the strongest marketers using AI from the rest of the crowd. The reason is simple: AI is only as effective as the data it’s trained with, which means that solid data management skills are non-negotiable.  \

AI-savvy CMOs have a firm grasp on the data types that strengthen AI rollout in marketing strategies. To fine-tune an LLM-based tool, for instance, that could involve supplying high-performing copy or ensuring CRMs are accurate to avoid hallucinations or bias in outputs.

Security breaches, hallucinations, and biased outputs are just some of the consequences of poor data management. The reality is, however, that automation tools aren’t accountable for these failures, which is why the people behind the machines need to at least understand the basics of managing data.

Marketers need to be familiar with data privacy laws and regulations, like the GDPR. These regulations don’t just ensure people’s information is being used safely and ethically for marketing purposes, but also help teams practice good housekeeping when it comes to storing data, whether that’s collecting the data that’s strictly necessary, making sure it’s up to date, and not keeping it longer than needed.

A CMO who understands data can also spot earlier when a dataset is likely to cause biases or hallucinations further down the line. For instance, duplicate leads might cause bloated statistics about the size of a certain demographic in a customer base, like claiming that women comprise the majority of their audience. As a result, a marketing team is misled by error-riddled insights and misaligns messaging to target that majority base, realizing too late that the CRM data was false.

Solutions include training around identifying errors and weak data that can cause hallucinations and bias. Keeping up to speed with regulations is also vital, as the legal ramifications of breaching privacy guidelines are massive. Auditing AI processes and ensuring transparency are crucial to protecting any company from legal issues and data handling headaches.

Let go of old habits and embrace new practices with AI

Mastering these skills also includes embracing the fact that AI has changed our definition of productivity. The top marketers know it’s about working smart, not just hard. As they refine these skills, they’ll also become more aware of where AI is best placed for stronger productivity and ROI.

This frees up valuable time for marketing leaders to focus on high-level strategic and creative elements of their job, like refining strategy blueprints based on KPIs and performance, crafting stories and compelling narratives, building case studies, and establishing value propositions that help guide both team members and AI tools.

Ironically, with AI in the picture, marketers have more time to focus on the human aspects of their role. Those who make the most of that opportunity will be shaping the marketing landscape of tomorrow.

By Ibrahim H, Founder of Myuser

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