Bringing People Along: The Missing Piece in Most AI Strategies
🧠 Bringing People Along: The Missing Piece in Most AI Strategies
AI strategy isn't just about models, platforms, or data pipelines. It's about people. 👥
We've seen this story play out more than once: An organization launches a promising AI initiative — technically sound, well-funded, aligned to a real business problem — and it still fails. 📉
Why? Because no one trusted the outputs. Because no one used the system. Because no one was ready to change.
🔄 That's not a tech problem. That's a change management problem.
🚀 AI Is More Disruptive Than It Looks
AI doesn't just optimize; it often reshapes workflows, reassigns judgment, and challenges expertise. Even small pilots can quietly destabilize long-held assumptions about how decisions get made.
And while the language around AI is technical, the reality is emotional. It introduces fear: 😨
"Will this replace me?"
"What if I don't understand the output?"
"Who's accountable if the model is wrong?"
Ignoring those reactions is a recipe for resistance. Anticipating them is a foundation for success. 🛡️
📊 The Real AI Adoption Curve (Not Just the Hype Cycle)
Here's what real AI change looks like inside organizations:
Excitement ✨: A compelling use case, vendor pitch, or leadership vision sparks interest.
Tension ⚡: Internal stakeholders push back — sometimes subtly, sometimes directly.
Disillusionment 😔: The system works technically but fails to get adoption.
Recovery 🔄: Adjustments are made, champions emerge, and trust is rebuilt.
Adoption 🎯: When people begin to trust and use the system consistently.
The difference between Step 3 and Step 5? Change leadership. 🧭
🛠️ Practical AI Change Management Principles
Here's what we recommend — and practice — to help organizations move through resistance into adoption:
1. Start with Collaborative Design 🤝
Include the people who will use (or be affected by) the system early, before requirements are finalized. Co-creation builds alignment and uncovers unseen risks.
2. Name the Impact, Honestly 🔍
Don't pretend AI will change nothing. Be transparent about what will be different, and give people a voice in how those changes are managed.
3. Build Confidence Before Trust 🏗️
Trust takes time. Start by showing that the system is accurate, understandable, and auditable. Let users test it. Challenge it. Watch it evolve.
4. Upskill the Influencers 📚
Train the domain experts, not just the data scientists. The people closest to the problem should be equipped to evaluate, translate, and advocate for the solution.
5. Make Feedback Loops Visible ↩️
Show how input is used to improve the system. That turns complaints into contributions, and skeptics into stakeholders.
🌱 Don't Just Deploy — Prepare to Evolve
Most failed AI projects aren't bad ideas. They're rushed ideas. They treat implementation like an endpoint instead of the beginning of a new way of working.
If you're serious about AI, you need to be serious about change; not just technical change, but cultural, structural, and emotional change. 🔄
That's the work.
AI doesn't succeed because the model is smart. It succeeds because the people using it are ready, supported, and involved.
Let's talk about how to make that happen — together. 🤝