The messy middle of AI adoption: How to lead when your team is split

You’ve invested in licenses. 
Hosted training sessions. 
Rolled out internal playbooks. 

And yet, some team members still won’t touch AI. 
Others are using it off the radar in unofficial workflows. 
A few early adopters are quietly outpacing everyone, delivering campaigns 30–40% faster than peers. 

Welcome to the unpredictable reality of AI adoption.

Teams adopt AI at different speeds, and that’s normal

Most CMOs assume that once AI tools are available, usage will follow. But tools don’t drive adoption. Behavior does. 

Some of your team jumped in eagerly. Others are still hesitating. A few are actively avoiding the shift. And it’s not because they don’t understand the tech, it’s because they’re processing a change in expectations, identity, and workflow. 

AI adoption is not a one-size-fits-all journey. It’s a spectrum of engagement, and your job isn’t to standardize it overnight. It’s to guide people toward adoption in a way that protects output quality, ROI, and team morale. 

You’ll meet these four AI personas

Understanding where people fall on the adoption curve helps you lead with empathy, not mandates: 

• The Enthusiasts: Already building AI into workflows and testing edge cases. These are your 10x producers; their output per FTE can be 2–3x higher when workflows are optimized. 
• The Cautious Optimists: Curious, but unsure where AI fits in their day-to-day role. 
• The Skeptics: Doubtful it creates value or worried it could lower quality. 
• The Resistors: Avoiding adoption (sometimes silently) out of fear, overwhelm, or uncertainty. 

The mistake most leaders make? Treating this as a tooling issue. It’s not. It’s not a procurement problem, it’s a change management challenge that, if solved, can reduce campaign cycle times by 50%. 

You can’t force adoption, but you can accelerate it

Telling your team to “use AI” isn’t a strategy. It’s a hope. And hope isn’t scalable. 

Instead, shift from compliance to enablement. Pair expectations with enablement, so adoption becomes the natural next step, not a forced initiative. 

Here’s how. 

1. Make success visible 

Mandates rarely inspire. Stories do. 

Don’t just launch tools, spotlight the wins. Show how a marketer used AI to cut campaign build time by 40%, boost ad performance by 63%, or increase content throughput by 30%. Show the before and after. Ask them to share prompts, workflows, or pitfalls. Normalize progress in team meetings, Slack threads, and retrospectives. 

When AI success becomes visible, it becomes contagious. 

2. Customize by function 

Not everyone needs ChatGPT. Not every use case is cross-functional. 

AI’s value looks different in Sales Enablement than in Content Ops or RevOps. Map high-impact, role-specific use cases, like AI-driven ABM personalization for marketers or predictive lead scoring for RevOps; to show exactly how AI accelerates outcomes in each function. 

Adoption increases when AI feels personal and practical, not theoretical. 

3. Coach, don’t correct 

If someone isn’t using AI, don’t assume it’s laziness. Ask why. 

Are they afraid it’ll make them obsolete? Worried they’ll get lower quality output? Confused about what’s expected? 

You can’t solve invisible resistance. Create psychological safety for people to share concerns so you can address them with training, governance, and clear expectations. 

4. Empower role models 

Your best AI advocates are already on the team. 

Instead of pushing from the top down, pull from the inside out. Formalize “AI champions” in each function or region who lead workshops, share templates, and track adoption wins. Peer-led enablement is more trusted (and more effective) than executive mandates.

Fix the system before blaming the person

Most resistors aren’t resisting AI itself. They’re resisting: 

• Poorly designed workflows 
• Vague output expectations 
• Fear of being penalized if AI-generated work fails 

If your system is broken, AI will only amplify the pain. 

Adoption improves when: 

• AI is embedded in clear, repeatable workflows 
• Expectations are aligned to new capabilities 
• Leaders make it safe to test, fail, and learn 

Don’t just plug in AI. Redesign processes so AI accelerates outcomes instead of introducing friction. 

You don’t need 100% adoption, you need 100% enablement

Not everyone needs to become a prompt engineer. 

But everyone does need to feel supported in learning, applying, and evolving with AI. When adoption enablement is done well, even 70% active use can deliver measurable gains in revenue, capacity, and speed. 

AI adoption isn’t a light switch. It’s a curve. 

And the best leaders don’t shove people over it. They orchestrate the journey. 

Executive takeaway 

Leading through adoption, not around it

Teams don’t resist AI because they’re unwilling. They resist because they’re uncertain, unsupported, or unclear on the why. 

Building AI-capable teams starts with empathy, structure, and a system that rewards progress, not perfection. 

2X accelerates AI adoption with tailored enablement, embedded champions, and scalable workflows, reducing cycle times by 50% without compromising quality or ROI.

Author

Lisa Cole

Lisa Cole serves as the Chief Marketing, Product and AI Officer at 2X, where she helps marketing leaders deliver greater impact with fewer resources. Former CMO for Huron, FARO Technologies, and Cellebrite, and author of Brand Gravity and The Revenue RAMP, Lisa has a proven track record of transforming marketing organizations into high-performing, scalable growth engines. She specializes in leveraging AI, strategic outsourcing and growth marketing strategies to scale marketing, driving operational excellence, and accelerating revenue growth.