Rob Garf and Dr. Janet Sherlock
Cordial
Chief marketing officers rarely agree on a topic like AI.
Yet 87% of senior marketing and digital executives surveyed in new research from Cordial and organizational strategist Dr. Janet Sherlock agree that their companies face a major skills gap in adopting artificial intelligence.
That level of consensus should get the attention of CEOs and boards. It also reveals something important about the current AI conversation: The greatest obstacle may not be access to technology. It is whether organizations have the skills, operating models and trust required to use it effectively.
“The tech is one piece of the equation, but there’s a whole people aspect,” said Rob Garf, chief strategy officer at Cordial. “Organizations are struggling with figuring out how to embrace, how to adopt and how to enable.”
The AI readiness gap need to be addressed, in part, by creating actions which lead to measurable outcomes.
AnswerMyQ
The AI readiness gap is therefore not simply a technical problem. It is an organizational challenge—and one that cannot be solved by purchasing another platform.
AI May Change Marketing Teams, Not Eliminate Them
Much of the public conversation about AI has centered on jobs being eliminated. The research, however, suggests a more nuanced outcome.
“The whole concept of organizations shrinking because of AI was not evident in the results that we found,” said Sherlock, founder of org.works and former chief digital and technology officer at Ralph Lauren. “Not only were we not seeing reductions in marketing teams, we’re actually seeing growth.”
That does not mean every position will remain unchanged. Repetitive tasks will increasingly be automated, and some roles will be redesigned. But marketing organizations may need more people with different capabilities, rather than simply fewer people.
Companies that cut too quickly also risk eliminating the very institutional knowledge required to make automation work. Employees understand the informal processes, customer nuances and workflow exceptions that are rarely captured in an organizational chart.
Garf warns that reducing junior and middle-management ranks can mean “losing a lot of the intelligence, the intellectual capital and the workflow knowledge” needed to automate successfully. It can also weaken the pipeline of future leaders.
The more useful question is not, “How many jobs can AI replace?” It is, “How should jobs change so people and AI can create better outcomes together?”
The Skills Gap Is Broader Than Prompting
When executives hear “AI skills,” many initially think about prompting, data science or model development. Those capabilities matter, but they are only part of the readiness equation.
Garf said senior executives discussing the research frequently focused on distinctly human qualities: curiosity, collaboration and higher-order thinking.
“As they’re thinking about hiring, they’re finding people who are curious [and] people who are collaborative, because it’s going to have to span mission and outcome, and not just role,” he said. “Some of the mundane tasks will go away, so it’s going to require us all to up-level our thinking and come in with a new mindset.”
That shifts the talent discussion. AI readiness cannot reside solely in an innovation team or technical center of excellence. Marketing, technology, data, commerce, creative and customer-service teams will need to work across traditional organizational boundaries.
According to Phil Alexander, Founder and CEO, AnswerMyQ, “The 87% are right that there’s a gap, but it isn’t a prompting gap. It’s a governance and execution gap. AI can generate an answer in seconds. Someone still has to verify it, apply policy, and turn it into action. Readiness isn’t measured by how much AI you deploy. It’s measured by how reliably that output becomes a trusted outcome.”
There is also a risk that companies will overreact to their perceived skills deficit by replacing internal talent with outside expertise. Sherlock found it notable that marketing leaders expect continued reliance on agencies, consultants and other third parties even as internal teams grow.
External specialists can accelerate learning and provide capabilities a company does not need to own permanently. But outsourcing too much can prevent organizations from developing their own institutional knowledge.
“I believe you need a really good balance,” Sherlock said. “It would go better for retail companies if they have a better balance between third parties, internal [talent] and hopefully keeping that bench strength of their junior teams to build the capabilities that they need.”
Personalization Finally Gets Context And Memory
For years, retailers have promised personalized customer experiences. Too often, personalization has meant inserting a first name into an email or recommending a product based on a single transaction.
AI has the potential to move retailers closer to understanding intent. Garf describes two increasingly important concepts: context and memory.
Consumers may engage with a brand through approximately nine touchpoints during a shopping journey. Those interactions can include search, social media, a retailer’s website, email, text, an app, a marketplace and a physical store. Most organizations still treat those touchpoints as partially disconnected events.
AI agents can help connect them, building a collective memory and predicting what a customer is trying to accomplish. That creates the possibility of solving a customer’s problem in real time instead of simply pushing the next offer.
But the technology will only be as effective as the workflows surrounding it.
“Retailers aren’t thinking about the accountability [or] the changes in processes that are required to set that experience up for success,” Garf said.
If no one owns the outcome, if data remains fragmented or if every AI-generated action requires layers of approval, real-time personalization will remain an aspiration.
Trust Must Be Earned Inside And Outside The Organization
Sherlock sees two distinct trust gaps.
The first is internal. Employees wonder whether their jobs are secure and whether AI will change their roles beyond recognition. Leaders who avoid these questions may unintentionally increase resistance.
The second is consumer trust. Shoppers increasingly want to know whether product images, recommendations and other AI-generated content are authentic and accurate.
“Am I buying what I’m seeing?” Sherlock asked. “Is the model that I’m looking at real? Should I trust how this product is going to look on me or how it’s going to wear?”
This makes brand strength and content governance more—not less—important. AI can dramatically increase the volume of content produced across channels, countries and customer segments. Yet greater volume also raises the risk of inconsistency, inaccuracy and brand erosion.
“The question is, how do you put the proper guardrails where you’re protecting, but not micromanaging, your brand?” Garf said.
AI Readiness Is Organizational Readiness
The 87% finding is significant because it acknowledges a widespread problem. But identifying a skills gap is easier than closing it.
Companies will need to train current employees, preserve junior talent, use outside partners selectively and redesign workflows around customer outcomes. They will also need to give employees room to experiment while establishing clear accountability and brand safeguards.
As Sherlock put it, “It’s not about the technology. It is about the people, and how they’re structured, and how they work together.”
The next phase of AI will not be won by the companies with the most tools. It will be won by the organizations most prepared to change how their people learn, collaborate and make decisions.