Marketing’s AI Moment Isn’t Over. It Hasn’t Really Started Yet.
As part of a big project, we’ve been talking to marketing leaders for several months about AI adoption, and one pattern keeps emerging: organizations don’t know how to implement AI. Many aren’t sure why they personally need it, and so they’re stuck debating the “what” to do with AItools, platforms, apps, etc.. Despite the enormous hype cycle, very few marketing organizations have made systemic changes. At best, progress is incremental. At worst, we’re standing still in a moment ripe for disruption. Most marketing leaders have acceptance, in fact they’re leaning in. But we have heard many genuinely don’t know how to move forward.
We’re in Danger of Treating AI Like Just Another Technology Transition
A core part of the problem is that too many organizations are deploying AI using the same enterprise technology playbook they’ve used for two decades. But AI is fundamentally different. It isn’t like migrating to the cloud or implementing a new CRM, it’s actually about human augmentation and cultural response. This makes it deeply personal and highly individualized. How each person works, thinks, and creates matters. Yet we’re trying to deploy it with the same approach we used for the last major IT rollout.
Compounding this, there’s still a lot of genuine confusion about what AI even is. Automation or intelligence? Generative or agentic? Embedded feature or standalone application? Replacement or augmentation? These aren’t semantic questions because they fundamentally shape how we think about deployment, adoption, and value.
Three Deployment Patterns Are Emerging
From our conversations, AI is currently being deployed in two primary ways, with a third just beginning to surface. First, there are thin horizontal applications embedded in existing tools (think Copilot licenses rolled out across an entire organization), delivering wide reach but shallow integration. Second, there are vertical deep solutions that fundamentally change specific workloads, with agents transforming how a marketing team operates. That approach is narrow in scope but can be profound in impact. The third, still emerging, is a personal OS approach, which may be the most promising. Since people adopt at different rates and have different needs, a personalized model could actually support the varied adoption curves we’re seeing across teams.
The Fear Factor No One’s Addressing
What’s really paralyzing progress is fear, and it's showing up in a few different ways from people afraid they will lose their jobs, to deep questions about how to design brands and customer experiences with AI in the mix. Much of this is not really being adequately addressed at the C-suite level - yet. Organizations are building zero-trust security frameworks while simultaneously being told to embrace AI. Major platforms are only beginning to take safety and governance seriously, and there are precious few industry or governing actors stepping up to publicly underpin the trust infrastructure these systems require. It’s difficult to move fast when the ground feels this unstable.
Are We Building on a Cracked Foundation?
Perhaps most critically, unfinished digital transformation is a major barrier to AI adoption. AI isn’t arriving in a vacuum, it’s inheriting yesterday’s problems: platforms that still aren’t fully integrated, poor data quality, wildly uneven tech adoption across teams, and “human glue” everywhere serving as the patch holding things together. We’re trying to build the future on a digital foundation we never finished constructing.
And here’s the question most marketing leaders must address: have we catalogued our customer journeys and associated processes well enough to even understand where AI might actually help? For most, at this moment, the answer is no. We’re excited about AI’s potential and anxious about being left behind, but we haven’t done the foundational work to know where it fits, how it should work, or why specific people should be using it in specific ways.
Until we address the how, the why, and the what, AI in marketing will remain more promise than practice. And that may be the most dangerous place to be: not moving forward, not standing still. Just stuck.
What CMOs Could Do About This: Five No-Regret Moves
The good news is that none of these moves require betting the farm on a single platform or waiting for perfect conditions. They’re actions you can take now that will pay dividends regardless of how AI evolves.
Map your customer journeys before you map your AI use cases. Most organizations are shopping for AI solutions without understanding their own processes deeply enough. Before evaluating tools, catalogue your customer-facing workflows end-to-end. Identify where “human glue” is masking broken processes. AI layered onto dysfunction just scales the dysfunction.
Address the fear directly. Stop pretending your team isn’t scared. Create space for honest conversation about what AI means for roles, careers, and daily work. Fear that goes unspoken becomes passive resistance. Acknowledge the legitimate concerns around trust, safety, and governance and then channel that energy into building internal frameworks rather than letting it stall progress entirely.
Fix your foundation first (or at least in parallel). Audit your data quality, platform integration, and tech adoption gaps. You don’t need everything perfect, but you need to know where the cracks are. Prioritize the integrations and data hygiene efforts that will unblock AI’s effectiveness rather than treating AI and digital transformation as separate initiatives.
Experiment with the personal OS model. Rather than another top-down rollout, give individuals the latitude to find their own AI workflows. Provide access, training, and psychological safety to experiment. Then you can then harvest the best practices that emerge organically. We know that people adopt technology when they discover personal value, not when it’s mandated from above.
Pick one vertical use case and go deep. Instead of spreading thin horizontal AI across every team, choose one specific workload where AI could fundamentally change outcomes. It might be content production, media optimization, customer insights, for example. Invest in making it transformative. One genuine success story will do more to drive adoption than a hundred LLM licenses gathering dust.
The Bottom Line
Until CMOs do the foundational work such as mapping customer journeys, fixing data and platform gaps, and creating space for personalized adoption, AI will remain more promise than practice. The way out starts with five no-regret moves that any marketing leader can act on now.