Froilan Mendoza is Founder & Chief Technology Officer of Fulcrum Solutions.
Every time I bring up AI in a team meeting or during a project discussion, the same question inevitably arises: Will I be replaced? It’s a valid concern. After all, AI can technically do many jobs faster, without rest, and at scale. I don’t dismiss that fear. Instead, I try to meet it head-on with transparency, empathy and a reframing of what AI really is.
To me, AI isn’t about replacing humans. It’s about co-intelligence. I intentionally use that term because the goal isn’t artificial substitution, it’s intelligent collaboration. In coding, for example, AI can take on the mundane or repetitive tasks like writing unit tests or automating peer review. That frees up engineers to focus on solving more complex business problems. The real value of AI is in how it enhances human capability, not erases it.
Reframing The Narrative: Show, Don’t Tell
Helping people shift their thoughts about AI starts with how I talk about it. I avoid painting it as a magic solution or all-knowing system. Instead, I position AI as just another tool. A powerful tool, yes, but a tool nonetheless that helps us get to the good stuff faster.
I’ve found leading with small, real examples is helpful. Show, don’t tell. When leaders across departments demonstrate where AI has saved time, improved accuracy or even boosted creativity, that helps ease concerns. People need to see that it’s working for others before they’re ready to adopt it. Because of this, I advocate for a gradual rollout with clear objectives and measurable results. Let people experiment, learn and adjust at a comfortable pace.
The Power Of Transparency
Transparency is one of the most powerful tools we have when navigating change. When introducing AI, I believe the initiative and its messaging should come from the top, but with full participation from the bottom. We can’t treat this like a black box. Everyone should know what’s being implemented, why it matters, how success is measured and most importantly, how it might impact their day-to-day.
In some industries, AI implementation comes with regulatory requirements. That gives us a natural on-ramp for creating documentation, updated policies and shared goals. But even when it’s not required, I suggest establishing visible metrics everyone can work toward. Targets like “reducing time-to-resolution for customer service requests by four hours” or “improving satisfaction scores on email support by 50%” give the entire team something concrete to aim for, and they can see their role in that success.
Those improvements might relate directly to revenue outcomes or bonuses in some organizations, creating a direct incentive for teams to embrace and optimize the tools, because they can see how AI adoption contributes to the collective bottom line.
A Real-World Example
One of the best implementations I’ve seen started with a single code repository. We were working with a client who wanted to integrate AI into their software development process, but understandably, the team was hesitant. So instead of rolling it out across the board, we piloted the tools in a less critical feature set. The dev team was encouraged but not overwhelmed to use AI to generate unit tests, suggest code completions and automate reviews.
What made this work wasn’t just the use of AI. It was the structure. The team tracked how the AI tools performed compared to previous workflows. They collected feedback, iterated on the setup and identified what improved and what didn’t. Adoption didn’t happen overnight. As expected, there was some resistance, especially from senior team members with long-standing habits. But as the results and the momentum became clear, attitudes shifted.
This isn’t a one-and-done process. It’s ongoing. The key was letting people experience the benefits firsthand, while giving them space to grow into the change.
Making It A Team Effort
If there’s one thing I’ve learned, it’s that ownership beats directives. AI integration shouldn’t be handed down as a fully-baked plan from leadership. Instead, you should involve your teams from the beginning during planning, target setting and tool selection. Ask what’s slowing them down. Review old retrospectives and pain points, and look for ways AI could help address them.
The people closest to the work usually have the clearest insight into where the friction lives. Looping them in early reduces resistance and builds their investment. They become partners in the process rather than passive recipients of change.
Start Small And Measure Well
One of the biggest drivers of confidence is seeing early wins. But those wins only matter if you’re measuring the right things. That’s why I emphasize instrumentation at every step. You need to prove (to yourself and your team) that things are improving and to know where they aren’t.
In software development, we’ve used metrics like code coverage improvements, time-to-deploy comparisons and defect rates post-release. We even look at how many times the code needs to be reworked or refactored due to missed requirements. Outside of engineering, in something like customer support, we’ve built in AI-powered prompts that ask customers directly if they got the help they needed. That feedback loop not only improves the model, it builds trust.
Every implementation should be tracked. Whether you’re measuring code quality, team velocity, customer satisfaction or AI accuracy, choosing metrics that align with both business goals and team priorities is essential. When teams see progress, they build momentum.
Final Thoughts
AI will continue to evolve, but so will we. As leaders, our job is to shape mindsets, not just to deploy tools. That means leading with empathy, being transparent about impact and designing adoption in partnership with the people who make our companies run.
I don’t expect everyone to love AI right away. Change is uncomfortable. But with a thoughtful, phased approach and a focus on co-intelligence, we can help teams move from fear to curiosity, and from resistance to results.
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