From Resistance to Partnership: Leading your team through AI adoption
Kine Mette
You've identified where AI can help. You've built the business case. You've secured leadership buy-in.
Now comes the part most growth leaders underestimate: getting a team to actually use it—consistently, and without being pushed.
Here’s what typically happens. New AI tools are rolled out with enthusiasm. Week one, people experiment. Week two, usage starts to dip. By week four, the strongest performers are quietly working around the tools, defaulting to familiar workflows, while the investment starts to gather digital dust.
This usually isn’t a technology problem. The tools work. The challenge shows up elsewhere.
The leaders who succeed with AI adoption aren’t just implementing new systems. They’re changing how teams think about their work, their value, and their role in what comes next.
Understanding the Real Resistance
When teams resist AI adoption, it’s rarely because the technology doesn’t function. What shows up instead tends to be a set of quieter, more personal concerns:
- “This will replace me” — the existential fear that AI makes a role obsolete
- “This threatens my expertise” — the identity fear that years of skill-building no longer matter
- “This is more work, not less” — the practical fear that AI adds friction instead of removing it
- “This will expose my weaknesses” — the vulnerability fear that gaps in performance become visible
- “I don’t trust it” — the control fear that AI will make mistakes someone else gets blamed for
Most leaders respond with data and reassurance: AI won’t replace people, it will augment them.
That approach rarely lands. Fear doesn’t respond to logic alone, and reassurance often misses what’s actually at stake.
A different playbook is required.
The Partnership Playbook: Five Stages
Stage 1: Name the Future, Not Just the Tool
AI adoption stalls when it’s introduced as technology. It accelerates when it’s framed around relief.
Instead of:
“AI agents are being rolled out for lead qualification.”
Frame the change around the problem being removed:
“Sales teams currently spend most of their time on research instead of conversations. That balance is about to change.”
In one sales organization, adoption moved from roughly 40% to near-universal usage once the rollout focused on getting time back into real conversations—not on introducing new systems.
Stage 2: Create Safe Spaces to Experiment
People resist what they’re forced to use. They engage with what they discover works.
One growth team gave their content team access to AI tools with no requirements for three weeks. No metrics. No mandates. Just an invitation to explore and share observations.
The result wasn’t uniform adoption at first—and that turned out to be useful. The people who didn’t adopt surfaced friction points leadership hadn’t anticipated. Those insights shaped the next iteration. Within a month, daily usage spread organically, driven by firsthand value rather than pressure.
Stage 3: Celebrate the New Expertise, Not Just the Output
High performers often worry that AI diminishes the value of their experience. That narrative needs to shift.
One marketing organization began highlighting AI power users—not those who used it most, but those who applied it most creatively. A strategist who used AI to dissect competitor messaging and uncover white-space positioning. A sales rep who scaled personalization without losing voice.
Over time, being good at AI stopped feeling threatening. It started signaling adaptability and relevance.
Stage 4: Make Humans the Final Authority
Loss of control is one of the strongest blockers to adoption. It needs to be addressed directly.
In practice, this means AI surfaces insights, flags opportunities, and generates options—but doesn’t get the final word. Humans retain authority to adjust, override, or ignore recommendations.
One customer success team implemented a simple rule: AI could flag at-risk accounts, but CSMs decided whether and how to intervene. Adoption climbed rapidly. Just as importantly, override patterns revealed where human judgment consistently outperformed automation, improving the system over time.
Stage 5: Share the Wins, Own the Failures
Psychological safety determines whether AI becomes part of daily work or stays on the sidelines.
When AI performed well, success was credited to the people using it. When it failed, responsibility sat at the leadership level. That clarity mattered. Without fear of being blamed for mistakes, experimentation increased. Adoption followed.
The Cultural Shift That Matters Most
All of these stages point to a deeper shift: moving from protecting existing ways of working to expanding what’s possible.
Teams that succeed with AI focus on outcomes rather than activities. Impact matters more than hours worked. Creative problem-solving carries more weight than strict process compliance—even when it’s messier.
When a culture is oriented around how to achieve more, AI adoption feels natural.
When it’s oriented around how to preserve what exists, every rollout becomes a struggle.
The Timeline Isn’t What It Seems
In practice, meaningful adoption often happens within six to eight weeks once resistance is addressed. Cultural transformation takes longer—closer to six to twelve months.
The distinction matters. Using AI is a behavior shift. Thinking like an AI-native team—challenging assumptions, spotting opportunities, and pushing boundaries—is the real transformation.
That’s the work worth investing in.
Leading this kind of change requires more than rolling out tools.
At Nordic Growth Summit, conversations are grounded in real operating experience from leaders working at the intersection of growth, technology, and AI.
Speakers like Sara Maldon and Marcus Weiland bring perspectives shaped by hands-on work with teams navigating AI adoption in practice—not in theory.
The focus isn’t on packaged answers, but on understanding how change actually happens inside organizations when systems, incentives, and people collide.