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Key Takeaways

• More than two-thirds of organizations use AI across multiple business functions, signaling a move towards integrated AI workflows.
• The key to unlocking AI’s potential lies in shifting from treating it as a search tool to engaging with it as a dynamic partner and teammate.
• To foster AI readiness, teams must cultivate AI intuition through practice, starting with low-risk tasks and building towards complex collaborations.

Most teams are using AI the same way they have used search engines for the last 20 years: type in a quick question, skim the first response, move on. That mindset is holding us back. And for leaders trying to make AI useful inside their organizations, that difference can make or break adoption.

According to McKinsey’s latest global survey, more than two‑thirds of organizations now say they are using AI in more than one business function, and half report that they are using AI in three or more functions.

That shows the shift isn’t just in tools; it’s already spreading across workflows. What many leaders still struggle with is not the adoption of platforms, but the adoption of a mindset.

The real opportunity with generative AI is not about finding better prompts or learning new platforms — it’s about rewiring how we think. If we want to build teams that are truly AI-ready, we don’t just need training on tools. We need to retrain our mental models.

Related: This Ex-Amazon AI Leader Reveals How Entrepreneurs Can 10x Their Output With AI

Shift the mindset: Treat AI like a smart colleague

Every day, I see teams approach AI with transactional, one-shot questions and expect polished, perfect answers. But AI doesn’t work like that. It’s not a vending machine that dispenses insights on command. It’s more like a junior analyst: smart, fast, but not clairvoyant.

When I needed to compare ad performance across three systems — each using different data formats and structures — I didn’t spend an hour manually aligning spreadsheets. I described the goal to AI, adjusted one error it made and within minutes had a repeatable solution that would’ve otherwise eaten up my afternoon. I treated the AI like a capable teammate, not a search box, and it delivered.

Once teams begin operating with AI as a thinking partner rather than a search bar, collaboration improves, brainstorm sessions get faster and briefers sharper. Overall knowledge sharing becomes more dynamic and accessible.

Master the conversation: Learn to talk to AI like a teammate

The biggest barrier isn’t technical. It’s psychological. Most people don’t know what they can ask AI to do. And when they try, they fall back on search habits: brief inputs, little context, hoping for a silver bullet.

But working with generative AI isn’t about crafting the perfect prompt; it’s about having a productive conversation. You clarify, rephrase, ask it to iterate. You coach it like you would a human team member. When you do that, something clicks. You stop treating the AI like a tool and start treating it like a collaborator.

Like any collaborator, AI makes mistakes. We’ve gotten used to trusting the top result in a search engine. That doesn’t work with AI. Especially when you’re dealing with complex or high-stakes tasks — analyzing data or drafting client-facing content — you have to verify, push back and ask for explanations. I’ve asked AI to explain its logic or fix its own misfires, and often, it can self-correct. But only if I catch it.

It’s like onboarding a brilliant intern: they’re eager, capable and creative, but they still need feedback, guidance and guardrails.

Related: How to Train AI to Actually Understand Your Business

Implement: Start small to build confidence

You don’t need an enterprise-wide AI strategy to start building fluency. Begin with low-risk, repeatable use cases and let your team practice and build comfort in a safe sandbox with tasks like:

• Summarizing internal docs
• Improving meeting notes
• Drafting first-pass reports

Encourage experimentation, but set boundaries. Clarify what’s safe to input into public tools like ChatGPT, and where you need enterprise-grade platforms with proper compliance. Make sure your team understands the data rules around ownership, privacy and NDAs before they copy-paste anything confidential.

Leaders have to go first. When managers share how they’re personally experimenting, it gives permission for others to follow. Modeling experimentation and curiosity signals that AI isn’t a threat to jobs, but rather an opportunity to work smarter.

And most importantly, give people permission to start small. AI adoption is less like flipping a switch and more like training a new muscle.

Build AI intuition, not just skills

The teams that win in this next wave of transformation won’t be the ones with the flashiest tools. They’ll be the ones who build AI intuition — a working sense of how, when and why to collaborate with AI.

Remember how clumsy the early days of internet adoption felt? We didn’t know what to do with it. Now, it’s infrastructure. AI is following the same trajectory, but faster.

In two years, we’ll laugh at how we’re using it today. But those who start now, those who experiment, iterate and learn will be miles ahead.

Related: AI Isn’t Plug-and-Play — You Need a Strategy. Here’s Your Guide to Building One.

Move now: Don’t wait for the perfect strategy

The AI shift isn’t just technological, it’s behavioral. It’s about changing how we work, not just what we work with.

Just like any skill, AI intuition compounds with practice. Every conversation, every correction, every experiment builds a more confident and adaptive team. That mindset will become muscle memory, and that’s when real transformation happens.

If you wait for your organization to roll out the perfect AI strategy, you’ll already be behind. The future of AI isn’t in the hands of prompt engineers, but in the hands of curious, adaptable teams who treat AI as a partner, not a product.

The sooner your team learns to think with AI and not just at it, the sooner you’ll stop playing catch-up and start leading the change. Start small, experiment often and keep learning. That’s how AI fluency and future-ready leadership are built.