How to Address Skepticism in Technology

After deploying over 200+ AI POCs across my entire career and across a variety of industries, I learned a hard way truth! The biggest threat to AI success has nothing to do with technology — and everything to do with the people. Years ago, we built the perfect AI system. Cutting-edge models (for that time). Impeccable accuracy. Seamless deployment. And then… only 7% of the anticipated user base used it. It sat there — untouched — while the business teams quietly returned to their old, familiar excel and “phone a friend” processes. The system worked. But the people didn’t trust it, didn’t understand it, and didn’t see how it fit into their day-to-day reality. This is how so many organizations get stuck in “Perpetual POC Purgatory” (copyright 2025 Sol Rashidi) — where brilliant proofs of concept never make it into real, scalable use. The Real Lesson: Scale Comes from Adoption, Not Pushing a model into Production After overseeing hundreds of AI initiatives, I developed the 3E Framework — a practical approach to break out of POC purgatory and build AI solutions that people actually use. This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 𝟭. 𝗘𝗻𝗴𝗮𝗴𝗲: Don't just announce AI—make stakeholders co-creators from day one. When marketing, operations, and finance help select use cases and metrics, they become invested gardeners rather than skeptical observers. 𝟮. 𝗘𝗱𝘂𝗰𝗮𝘁𝗲: Theory creates anxiety; hands-on experience builds confidence. This isn't about extensive technical training—it's about demystifying AI through guided exposure over months, not days. When done right, deployment day brings curiosity instead of resistance. 𝟯. 𝗘𝗺𝗯𝗲𝗱: The most successful implementations feel like natural extensions of how people already work. For example, integrate that new AI customer segmentation tool directly into the exact dashboards your teams already use daily. Scaling isn't about more sophisticated algorithms—it's about human adoption at every level. Think of AI systems like exotic trees in your organizational garden—you can select perfect specimens and use cutting-edge cultivation techniques, but if your local gardeners don't know how to nurture them, those trees will never flourish. The next time you face resistance to AI scaling, remember: technical hurdles are often the easiest to overcome. The real transformation happens when you nurture the human ecosystem around your AI. That is how you scale AI across the workforce.

AI Adoption is Stalling in Your Org—Here’s Why (And How to Fix It) AI isn’t the future. It’s now. And yet, in too many organizations, ambitious AI initiatives hit an invisible wall—cultural stall. A client of mine, a fast-moving, high-change-tolerance exec, recently found himself in this very situation. He saw AI as a catalyst for transformation. His company? More like a fortress of tradition. The result? A slow crawl instead of a sprint. So, why do even the smartest AI strategies grind to a halt? Three core reasons: 1. Fear: “Will AI Replace Me?” AI doesn’t just change workflows—it challenges identity. Employees fear obsolescence. Leaders fear looking uninformed. Unchecked, fear turns into passive resistance. 🔹 What smart leaders do: Flip the narrative. AI isn’t a job taker; it’s a value amplifier. Show—not tell—how AI makes work more strategic, not less human. Make AI upskilling a leadership priority, so people feel empowered, not endangered. 2. The Status Quo Stranglehold Big companies have institutional memory. “This is how we’ve always done it” isn’t just a mindset—it’s a roadblock. AI disrupts deeply ingrained habits, and people default to what’s familiar. 🔹 What smart leaders do: Instead of forcing AI as a hard pivot, position it as an acceleration of what already works. Connect AI adoption to existing business priorities, not as a standalone experiment. Find internal champions—people with credibility who can shift the narrative from the inside. 3. No Quick Wins = No Buy-In AI often feels abstract—too complex, too long-term, too risky. If employees can’t see immediate benefits, skepticism spreads. 🔹 What smart leaders do: Deploy fast, visible wins. Start with low-friction, high-value applications (automating reports, enhancing decision-making). Make results tangible and celebrated. Small victories create momentum—and momentum is everything. Bottom Line? AI Adoption Is a Mindset Shift, Not Just a Tech Shift. Your strategy isn’t enough. Your culture has to move at the same speed. The leaders who win with AI aren’t just tech adopters—they’re behavior shapers. So, if your AI initiative is stalling, ask yourself: Are you implementing AI, or are you leading AI adoption? The latter makes all the difference. 🔹 In my next post, I’ll share real-world success strategies from leaders who’ve cracked the code on AI adoption—so their teams aren’t just accepting AI, but accelerating with it. Stay tuned.

If your AI is technically flawless but socially tone-deaf, you’ve built a very expensive problem. AI isn’t just about perfecting the math. It’s about understanding people. Some of the biggest AI failures don’t come from bad code but from a lack of perspective. I once worked with a team that built an AI risk assessment tool. It was fast, efficient, and technically sound. But when tested in the real world, it disproportionately flagged certain demographics. The issue wasn’t the intent—it was the data. The team had worked in isolation, without input from legal, ethics, or the people the tool would impact. The fix? Not more code. More conversations. Once we brought in diverse perspectives, we didn’t just correct bias—we built a better, more trusted product. What this means for AI leaders: Bring legal, ethics, and diverse voices in early. If you’re not, you’re already behind. Turn compliance into an innovation edge. Ethical AI isn’t just safer—it’s more competitive. Reframe legal as a creator, not a blocker. The best lawyers don’t just say no; they help find the right yes. Design for transparency, not just accuracy. If an AI can’t explain itself, it won’t survive long-term. I break this down further in my latest newsletter—check it out! What’s the biggest challenge you’ve seen in AI governance? How can legal and engineering work better together? Let’s discuss. -------- 🚀 Olga V. Mack 🔹 Building trust in commerce, contracts & products 🔹 Sales acceleration advocate 🔹 Keynote Speaker | AI & Business Strategist 📩 Let’s connect & collaborate 📰 Subscribe to Notes to My (Legal) Self

Want to know what’s really hard about running a company right now? We’re selling in the age of distrust. People don’t trust AI. People don’t trust sales reps. People don’t trust success claims. And the worst part? We have a competitor that makes such wild, over-the-top claims that by the time prospects get to us, they don’t believe anything. That’s the reality of selling AI in 2025. Hype is everywhere. Everyone’s “the best.” Everyone’s “10X.” But the more noise there is, the harder it gets for real results to break through. So, how do you win in a market that doesn’t trust anyone? 1️⃣ Show, don’t tell. We run an open kitchen—real data, live demos, no fluff. See it for yourself. 2️⃣ Make credibility obvious. Instead of asking people to take our word for it, we let proof speak for itself—clear metrics, real customer stories, and tangible outcomes. 3️⃣ Start small. No magic bullets, no overnight transformations. Small, tangible wins build real trust over time. The companies that win now aren’t the loudest. They’re the ones that prove it—day in, day out. What am I missing? If you’re selling today, how are you tackling the trust gap?