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Key Takeaways
- More than two-thirds of organizations use AI across multiple business functions, signaling a move toward integrated AI workflows.
- The key to unlocking AI’s potential lies in shifting from treating it as a search tool to interacting with it as a dynamic partner and teammate.
- To advance AI readiness, teams must cultivate AI intuition through practice, starting with low-risk tasks and working toward complex collaborations.
Most teams use AI the same way they’ve used search engines for the past two decades: type a short question, see the first answer, and move on. That mentality holds us back. And for leaders looking to make AI useful within their organizations, that difference can make or break adoption.
According to McKinsey’s latest global researchMore than two-thirds of organizations now say they use AI in more than one business function, and half say they use AI in three or more functions.
That shows that the shift is not just in the tools; it is 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 isn’t about finding better clues or learning new platforms – it’s about rewiring the way 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 Increase Their Output Tenfold with AI
Change the mindset: treat AI like a smart colleague
Every day I see teams approaching AI with transactional, one-off questions and expecting polished, perfect answers. But that’s not how AI works. It is not an automaton that provides insights on command. It’s more like a junior analyst: smart, fast, but not clairvoyant.
When I had to compare the ad performance of three systems (each with different data formats and structures), I didn’t spend an hour manually aligning spreadsheets. I described the goal to the AI, corrected a mistake I made, and within minutes had a repeatable solution that would otherwise have eaten my afternoon. I treated the AI like a capable teammate, not a search box, and it got results.
Once teams start working with AI as a thinking partner instead of a search bar, collaboration improves, brainstorming sessions become faster, and briefings become sharper. Overall, knowledge sharing becomes more dynamic and accessible.
Master the conversation: Learn to talk to AI like a teammate
The biggest barrier is not technical. It’s psychological. Most people don’t know what to ask of AI. And when they try, they fall back on search habits: short entries, little context, hoping for a silver bullet.
But working with generative AI isn’t about coming up with the perfect prompt; it’s about having a productive conversation. You clarify, rephrase, ask to repeat. 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 an employee.
Like any employee, AI makes mistakes. We have become accustomed to trusting the best 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 crafting customer-facing content—you need to verify, push back, and ask for explanations. I’ve asked AI to explain its logic or fix its own mistakes, and often it can correct itself. But only if I catch it.
It’s like taking on board a brilliant intern: they’re enthusiastic, 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 trust
You don’t need a company-wide AI strategy to build fluency. Start with low-risk, repeatable use cases and let your team practice and build comfort in a secure sandbox with tasks like:
- A summary of internal documents
- Improvement of minutes
- Preparing first-pass reports
Encourage experimentation, but set boundaries. Clarify what is safe to enter into public tools like ChatGPT, and where you need enterprise-grade platforms with the right compliance. Make sure your team understands data rules around ownership, privacy, and non-disclosure agreements before copying and pasting anything confidential.
Leaders must go first. When managers share how they personally experiment, it gives permission for others to follow suit. Modeling experimentation and curiosity indicates that AI is not a threat to jobs, but rather an opportunity to work smarter.
And most importantly, give people permission to start small. Adopting AI is less like flipping a switch and more like exercising a new muscle.
Develop 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 will be the ones building AI intuition – a working sense of how, when and why to interact with AI.
Remember how clunky the early days of internet adoption felt? We didn’t know what to do with it. Now it’s infrastructure. AI follows the same trajectory, but faster.
In two years we’ll be laughing about the way we use 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.
Get started now: don’t wait for the perfect strategy
The AI shift is not just technological, but also behavioral. It’s about changing the way we work, not just what we work with.
Like any skill, AI combines intuition with practice. Every conversation, every correction, every experiment creates a more confident and adaptive team. That mindset will become muscle memory, and that’s when real transformation happens.
If you wait until your organization has rolled out the perfect AI strategy, you’re already falling behind. The future of AI is not in the hands of fast engineers, but in the hands of curious, adaptable teams that treat AI as a partner and not a product.
The sooner your team learns to think with AI and not just with it, the sooner you stop playing catch-up and start leading the change. Start small, experiment often and keep learning. That’s how AI proficiency and future-proof leadership are built.