Many professionals view AI adoption as a technology challenge. But in practice, it’s a people challenge. Empowering teams to confidently embrace new ways of working is essential.
As generative AI becomes the norm, leaders face a widening capability-adoption gap. Most organizations struggle to keep pace with the rate technology is advancing. Often, adoption is slow, implementation is uneven, and organizational culture, skills, and workflows lag behind what AI now makes possible.
This gap raises an important question: How can leaders empower their teams to bridge the distance between AI’s potential and what their organizations are currently achieving?
This guide outlines four strategies to help your workforce view AI as a collaborative partner—one that builds confidence, enhances capabilities, and unlocks meaningful transformation.
4 Key Strategies To Build Team Confidence with AI Tools
1. Earn Trust Before You Expect Adoption
According to Harvard Business School Professor Iavor Bojinov in the online course AI for Leaders, which he co-teaches with HBS Professor Karim Lakhani, “Technology is only 20 percent of the challenge; the other 80 percent is cultural.” Adoption doesn’t come from rolling out a new tool—it comes from earning trust.
When employees feel supported, informed, and assured, real adoption follows. Your priority should be building confidence, not compliance. Show how AI fits naturally into daily workflows, strengthens decision-making, removes bottlenecks, and elevates the work your team already does well.
“You can build the most elegant, optimized models in the world. But if people don’t adopt them, they don’t create value,” Bojinov explains in AI for Leaders. “And that’s where many AI efforts stall—not because the technology failed, but because trust hasn’t been earned.”
AI for Leaders outlines three pillars of trust that support successful adoption:
- Trust the algorithm: Your team must understand how the model works and why it makes specific recommendations. Transparency around assumptions, data sources, and outputs demystifies AI and builds credibility.
- Trust the developer: Even the strongest models fall short when users doubt the intentions of the people who built them. As Bojinov notes in AI for Leaders, “If users feel excluded from the process—or uncertain about its intent—skepticism grows.” Involving employees early fosters ownership and reduces resistance.
- Trust the process: Clear governance builds psychological safety. When employees know what guardrails exist, how errors are handled, and where to turn when issues arise, confidence increases naturally.
2. Meet Your Team Where They Are
Teams don’t become AI-empowered overnight. To support them, leaders must understand what each person needs to feel motivated and capable of using AI.
The “Hearts and Minds” framework, developed by HBS Professor Tsedal Neeley and featured in AI for Leaders, centers on two questions:
- Do I believe this change is worth it? That’s the heart, or emotional buy-in.
- Do I believe I can do this successfully? That’s the mind, or sense of capability.
Using these dimensions, employees typically fall into four groups:
- Low buy-in, low capability: These individuals may feel overwhelmed or excluded in an AI-driven workplace. They’re not resisting learning AI, but they’re feeling stuck. They need encouragement, hands-on support, and positive early experiences.
- High buy-in, low capability: Motivated about becoming AI-enabled but unsure where to start, this group benefits most from corporate upskilling, such as accessible online courses and professional development opportunities.
- Low buy-in, high capability: They understand AI but question its relevance. To engage them, clearly communicate AI’s strategic value and long-term impact.
- High buy-in, high capability: These are your AI champions. Highlight their wins, involve them in pilots, and encourage them to coach others. As Bojinov emphasizes in AI for Leaders, “Change doesn’t move through mandates—it moves through people.”
3. Build the AI Infrastructure That Empowers Everyone
A real-world example comes from Moderna, highlighted in AI for Leaders. CEO Stéphane Bancel observed that AI adoption varied widely across teams, noting, “What’s very interesting about the adoption within Moderna is that it was not really even, which was expected. You have pockets that really drove adoption very quickly because I believe the managers talked to the employees about GPTs and what they can do.”
Moderna’s experience reinforces a key insight: effective AI adoption happens when leaders build AI capacity tailored to their teams’ needs.
Early AI empowerment often focuses on custom, one-off solutions. While these can create quick wins, they rarely scale or benefit teams broadly.
“Organizations focus on one-off use cases—small, bespoke models built by highly specialized teams,” Lakhani explains in AI for Leaders. “These use cases prove valuable, but they’re often isolated.”
To truly empower your workforce, businesses must move beyond isolated experimentation and build an AI factory: a standardized system that repeatedly turns raw data into actionable insights. Centralizing capabilities accelerates innovation, enabling teams to build on each other’s work rather than starting from scratch.
As demand grows, leaders should ask:
- Are more employees requesting AI tools than the current system can support?
- Does the organization have the technical infrastructure and talent to support everyday AI use?
If the answer to either is yes, it may be time to expand your AI capacity.
Centralized systems also unlock network effects. When teams build on shared tools and patterns, every improvement becomes a resource for others.
Moderna invested early in a centralized AI infrastructure. As Bancel shares in AI for Leaders, “As soon as ChatGPT was available, we wanted our own version to put confidential information about our science, contracts, and business into the system. We didn’t want the information we were providing to be helping competitors.”
Moderna launched mChat—a secure, internal version running on Amazon Web Services—before transitioning to generative AI tools like ChatGPT and internally developed custom GPTs built with GPT Enterprise, which allows teams to create specialized AI agents tailored to their needs. This enabled the company to:
- Protect sensitive data
- Enable safe experimentation
- Build more than 2,000 custom tools
- Scale AI across research, clinical, manufacturing, and human resources
As Lakhani emphasizes in AI for Leaders, “The key is balancing quick wins with a long-term plan to lower development costs and scale value over time.” Regularly assess whether demand is surpassing supply to ensure your AI-driven business model can keep up with your team’s growing needs.
4. Decide When to Go Deeper Or Scale Wider
After successfully piloting an AI tool, leaders must determine whether to deepen adoption within existing teams or expand to new ones.
In AI for Leaders, Bojinov describes this decision using the “Explore Versus Exploit” framework:
- Exploring means going deeper: Strengthen adoption, refine workflows, and build capability within current teams
- Exploiting means going wider: Scale proven tools to new groups using defined playbooks and repeatable practices
According to Bojinov, five factors determine whether it’s time to deepen or expand:
- Adoption rate and stability: Are teams using AI tools consistently? If not, focus on strengthening confidence before scaling.
- Demonstrated return on investment (ROI): Is the value clear and measurable? If impact remains uncertain, reinforce adoption with existing teams first.
- AI readiness of new teams or markets: Do new areas have the infrastructure, data, and digital maturity needed for success?
- Resource availability: Do you have enough talent, training capacity, and support to expand without undermining current progress?
- Leadership bandwidth: Scaling requires ongoing attention and guidance. If teams are already stretched supporting early adoption, expanding too soon can slow everyone down.
Most organizations benefit from deepening adoption before expanding. By strengthening early teams’ skills, you refine your approach and increase their confidence and reliance on AI tools.
Moderna’s journey shared in AI for Leaders illustrates this principle well. Once teams became more comfortable with AI, the company shifted to what Bancel called “AI 2.0″—scaling their highest-impact use cases across the organization.
Close the Gap Between AI’s Potential and Your Team’s Performance
Organizations that win with AI won’t be those with the most advanced models—but those that unlock their people’s full potential.
When teams trust the tools, understand their role in the transformation, and have the infrastructure and support to use AI confidently, true AI empowerment becomes possible.
If you’re ready to transform AI from a mandate into a catalyst for meaningful change, now is the time to equip yourself with the frameworks and strategies that will guide your workforce toward measurable impact.
Explore AI for Leaders to gain the toolkit to build your team’s confidence with AI—and download our free course flowchart to identify the best digital transformation and AI course for your organization’s goals.
