Here’s something that might surprise you: 80% of C-suite executives feel pressure to understand AI concepts that historically would never have crossed their desks. If you’re one of them, you’re not alone in wondering when exactly “transformer architecture” became required reading for running a business.

The pressure is real. Your board asks about your AI strategy. Your competitors announce AI initiatives. Your team looks to you for direction on which AI investments make sense. And somewhere between “machine learning” and “large language models,” you might feel like you’re back in school, cramming for an exam in a subject you never signed up for.

Here’s the thing though: you don’t need to become a tech expert to make smart AI decisions. What you need is a different kind of literacy. One that focuses on business outcomes rather than technical specifications.

The Questions That Actually Matter

When I work with leaders and business owners on AI strategy, we skip the technical jargon and focus on what moves the needle for their business. Instead of asking “How does this AI model work?” successful leaders ask questions that connect directly to business value.

Start with understanding what problems AI solves particularly well:

• Pattern recognition at scale.
• Prediction based on historical data.
• Automation of repetitive tasks.
• Natural language processing that can read and understand documents.

These capabilities translate into specific business applications: contract analysis that finds hidden savings, customer service that scales without adding headcount, or risk assessment that catches issues before they become problems.

The key is matching AI capabilities to your actual business challenges. If you’re drowning in contracts that need review, AI that can read and analyze documents at scale makes sense. If your challenge is forecasting demand, predictive AI models might be your answer. The technology should serve the business need, not the other way around.

Building Your AI BS Detector

With everyone claiming their solution uses AI (remember, AI washing is real), you need a reliable way to separate substance from hype. Here’s how to cut through the noise without getting lost in technical details.

First, ask vendors to explain their AI in terms of business outcomes, not technology features. If they can’t clearly articulate what specific problem their AI solves and how it creates measurable value, that’s a red flag. Good AI vendors can translate complex technology into clear business benefits.

Second, look for proof of concept opportunities. Any legitimate AI solution should be able to demonstrate value on a small scale before you commit to enterprise-wide implementation. It’s about seeing real results with your real data, because, in the end, that is all what matters to your business.

Third, pay attention to data requirements. AI is only as good as the data it learns from. If a vendor can’t clearly explain what data you’ll need, how much of it, and in what format, proceed with caution. The sexiest AI in the world won’t help if you can’t feed it the right information.

The Partnership Approach That Works

You know what successful AI adoption really looks like? It doesn’t have anything to do with becoming fluent in Python or understanding neural networks. It’s about building the right partnerships between business leaders who understand the problems and technical teams who understand the solutions.

Think of yourself as the translator between business needs and technical capabilities. You don’t need to design the AI system, it’s your role to clearly articulate what success looks like, set appropriate boundaries, and ask the right questions along the way.

This means getting comfortable with a new kind of conversation. When your technical team or AI vendor starts explaining their approach, your job is to keep bringing it back to business impact. “That’s interesting, but how does that translate to faster contract review?” or “Help me understand what that means for our customer response times.”

Making AI Decisions Without the PhD

Here’s a practical framework for evaluating AI opportunities when you’re not a technical expert. Think of it as your AI decision checklist that focuses on what you do know: your business.

Start by clarifying the specific problem you’re trying to solve. Not “we need AI” but “we need to reduce contract review time by 50%” or “we need to identify supply chain risks before they impact production.” Specific problems lead to specific solutions.

Next, evaluate the risk-reward ratio:

• What’s the worst-case scenario if this AI solution doesn’t work?
• Can you test it on a small scale first?
• What early indicators will tell you if it’s working?

You don’t need to understand the algorithm to understand the business risk.

Consider the integration requirements:

• How does this AI solution fit with your existing systems?
• What changes will your team need to make?
• Who needs to be involved?

These operational questions are squarely in your wheelhouse.

Finally, establish clear success metrics from day one. Not technical metrics like “model accuracy” but business metrics like “contracts reviewed per hour” or “revenue recovered from contract optimization.” If you can’t measure business impact, you can’t manage it.

The Human Side of AI Implementation

One thing the surveys don’t always capture is the human element of AI adoption. Your teams are likely feeling their own pressure: worry about job security, frustration with new systems, confusion about changing processes. Addressing these concerns doesn’t require technical knowledge. It requires leadership.

Be transparent about what AI will and won’t do in your organization. Share your vision for how AI enhances human work rather than replacing it. Create opportunities for teams to build their own AI literacy at a pace that makes sense for their roles.

Most importantly, model the behavior you want to see. Show that it’s okay to ask questions, to not understand everything immediately, to focus on practical application over theoretical knowledge. When leaders demonstrate that AI literacy is about business application rather than technical mastery, teams follow suit.

Your Next Move

The executives who succeed with AI aren’t the ones who can explain how a large language model works. They’re the ones who can clearly articulate their business challenges, ask smart questions about proposed solutions, and create environments where business and technical teams collaborate effectively.

You already have the most important qualification for making smart AI decisions: deep knowledge of your business. The gap isn’t as wide as it seems. It just requires a different approach to learning, one that starts with business needs and works backward to technology solutions.

Stop trying to become a technology expert. Start being the business expert who asks the right questions. That’s the AI literacy that actually matters.

Ready to build an AI strategy that makes sense for your business without getting lost in the technical weeds? Let’s connect and talk about practical approaches that work.