The Reason Only 9% of CMOs Have Cracked AI Isn't a Mystery. It’s Operations.

An ADWEEK and NewtonX survey of 500 marketers recently uncovered a startling reality. While 69% of companies mandate or strongly encourage AI use, 43% of marketers have still received no formal training, and only 9% have integrated AI deeply into their workflows. This despite a plethora of press, pronouncements, and earnings reports signaling that AI is going to change the way we do business!

The disconnect here isn’t a simple skills or responsibilities gap. Instead, it’s an under-resourced, un-managed mandate meant to satiate investors, not solve real marketing problems. Today’s challenge has less to do with the technology’s availability or accuracy, and everything to do with putting more foundational operational rigor around AI within marketing organizations.

CMOs Have an Ops Problem, not an AI Problem

The No. 1 issue we see at Consiglieri is that most marketing organizations lack the fundamental operational discipline to manage today’s escalating content and channel workloads, before AI is even introduced. Companies drop AI into a broken system and expect efficiency, but end up exposing systemic gaps instead. As a marketing leader told me earlier this year, it’s like strapping a rocket engine to a brick. 

You cannot automate a process you never bothered to design in the first place. Instead, marketers need to start with the basics. Set an 18-month vision, strategy, and measurement plan tied to business objectives. Write down the holistic marketing plan with key initiatives and priorities. Establish financial rigor and prioritize investments. Establish a centralized, consistent workflow process with decision points and responsibilities. Not sexy work, but work that will net efficiency gains beyond anything AI can promise right now. And then when AI is layered on top of these basics, you can fly a jet instead of a brick.

Building and Testing the Model

Even for brands that have their operations in working order, the implementation of AI into the organization has been scattershot at best. The Adweek x NewtonX research found nearly 25% of marketers deploy AI tools without testing them first, and 4% publish AI-generated content with no human review. That’s not surprising, given a PEX report finds 43% of marketers say their company’s AI policy is unclear or non-existent. Even when individuals find efficiencies – like a social management workflow that saves 10 hours a week, or a content creation model that cuts two weeks out of concepting – there is rarely a system to scale those wins organization-wide. Instead, it becomes every marketer for themselves.

There’s nothing wrong with individual AI experimentation. But marketing leaders must establish a disciplined evaluation model with pilot frameworks to produce credible insights and team accountability. Just like with operations, rigor and consistency are crucial with AI. Without those basic tenets, it’s nearly impossible to share learnings and scale successes into the transformation that executives are expecting.

Operational Change Requires Cultural Adoption

Operational rigor can create the structure for AI adoption, but it only works if teams trust and culturally adapt to the systems being put in place. Change management is just as important as workflow design, governance and measurement.

In the Adweek article, Doug Zarkin, former CMO of Take 5 Oil Change, notes that using AI for AI's sake without clear value is a mistake driven by FOMO. Doug’s spot on. That fear is manifesting as anxiety, driven by a lack of organizational alignment and expectations. Gartner found 40% of AI pilots are hindered by staff anxiety, because teams are being held accountable to a standard nobody has defined. In a world where layoffs are ever-present, I’d be anxious, too!

AI isn’t going away, which means leaders need to build adoption into the operating model from the start. McKinsey found that the 21% of companies who make it to scaled AI production have a few things in common. Here are the steps marketers should be taking:

• Starting with key friction points: What’s actually slowing down your work, not what’s the latest-and-greatest tool good at doing it. AI drives the greatest impact not by improving outputs alone, but by transforming the systems behind those outputs.
• Addressing cultural resistance head-on: Open communication, clear expectations, and the removal of use-it-or-else mandates creates a system where employees want to experiment for their own benefit.
• Investing in training: If you want staff to invest in these tools, you’re going to have to show them how. Your staff has neither the space nor the motivation to invest their limited free time to become AI experts, nor should they be expected to.
• Building a test-and-learn plan: It seems obvious, but having alignment on what you’re aiming to accomplish with these implementations and a framework for how to test your way to scale are non-negotiables at the start.

Marketing leaders willing to invest the time and effort to establish foundational operations and a measurable AI implementation framework have an opportunity to drive the real efficiency the C-suite is demanding. And they might even increase staff engagement and job satisfaction along the way.