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Marketing teams build AI acumen when experimentation becomes visible, practical, and connected to business outcomes. Jakki Geiger, Betsy Daitch, and Grant Johnson describe three complementary approaches: Model the behavior, turn pilots into shared learning, and create functional ownership. Together, these practices help scattered tool use become a repeatable team capability grounded in sound human judgment.
Some marketers are already building workflows, digital twins, and small applications. Others are still determining where AI fits into their work.
That variation does not necessarily indicate resistance. Different functions have different workflows, inputs, risks, and opportunities.
In Leading Teams Through the AI Learning Curve, CMOs Jakki Geiger of Arango, Betsy Daitch of Canoe Intelligence, and Grant Johnson of Chief Outsiders discussed how their teams are developing AI capability.
Their methods differ, but they share a pattern: Make experimentation visible, connect it to a real problem, establish ownership, and create ways for learning to move across the team.
Jakki joined Arango with an ambitious 60-day agenda that included a product launch, website, positioning, messaging, and brand identity.
AI became part of how her small team approached that workload.
Jakki built digital twins for herself, the CEO, the chief product officer, and the company. She also explored AI-assisted sales development, a sales-enablement knowledge base, content workflows, and AI capabilities already available in the technology stack.
“I’ve built digital twins for myself, my CEO, my CPO, and for the company. I’m trying to lead by example, model the way, so my team feels more comfortable using this type of technology.”
Modeling turns AI from an abstract executive initiative into observable work. The team can see where the output helps, where it falls short, and where human judgment remains necessary.
Recorded sales conversations provide one example. AI can surface customer language, pain points, objections, and questions. Sales gains continuity while marketing receives stronger inputs for positioning, messaging, and content.
At Canoe Intelligence, Betsy’s team maintains an “Upleveling Marketing Efficiency” tracker connecting business problems, possible tools, and pilots.
That structure keeps the focus on work that needs improvement rather than a stream of disconnected product demonstrations.
Meeting transcripts offer a simple example. Betsy uses Gemini to extract decisions, action items, owners, and due dates. The workflow reduces administrative effort and helps projects keep moving.
The team also incorporated AI experimentation into its quarterly business review. Each marketing function would pitch one AI project for the following quarter.
“It really is, it’s on the job. It’s experiential. It’s learning from each other.”
Product marketing, growth, operations, and corporate marketing can each begin with a workflow relevant to their work. The projects then create opportunities for colleagues to learn from one another.
Grant identified two common AI-readiness gaps: Unclear proficiency expectations and little protected time for learning.
He described workshops organized around a specific business problem, with several marketing functions using AI together. A shared project allows participants to see how research, content, creative, targeting, and operations connect.
A functional champion can also monitor developments, test tools, and help colleagues understand what is relevant. Not everyone needs the same depth of expertise.
Early effort may exceed immediate time savings while the team develops competence. The return appears after the workflow becomes reliable enough to remove work from the team’s plate.
AI experimentation can become a collection of impressive demonstrations with limited operational value.
A focused use case begins with a recurring problem or desired outcome. The previous and new workflows can then be compared across:
Not every pilot needs a direct revenue figure. It does need a reason to exist and evidence that the workflow became more useful.
Individual experimentation becomes a team asset when someone records the inputs, process, decision points, limitations, and result.
Without documentation, valuable work remains trapped in private chat histories. The team may repeat the same discovery process or become dependent on one advanced user.
The artifact does not need to be a lengthy manual. A concise workflow card can be enough when another qualified teammate can understand and repeat the process.
Documentation also supports governance by making data access, review, and ownership more visible.
All three leaders described AI as an extension of team capability, not a replacement for strategic judgment.
Grant summarized that relationship:
“It’s the human plus AI.”
A model can generate options, summarize information, or automate part of a process. People still distinguish strong strategy from weak strategy and determine which outcome matters.
Team acumen includes recognizing where AI helps, where it introduces risk, and where the answer requires context the model does not possess.
Jakki made her own AI use visible. Betsy created shared experiments across functions. Grant described focused workshops and functional ownership.
Together, their examples show how AI acumen develops through use, reflection, documentation, and exchange.
One experiment becomes more valuable when the rest of the team can learn from it.
It is the ability to select useful applications, work effectively with AI, evaluate outputs, manage risk, and repeat successful workflows.
No. Proficiency can vary by function, while shared standards and internal champions help learning travel.
Focused projects, visible leadership examples, peer demonstrations, and documented workflows connect learning to everyday work.
Time, quality, reuse, correction, business impact, and risk provide a useful view of progress.
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