AI for Business: The Questions Every Leader Should Be Asking Right Now

I’ve sat through enough AI strategy meetings to know the pattern. Thirty minutes in, someone asks “Should we be doing AI?” Someone else says “We need an AI strategy.” Then the room spins in circles for an hour. The problem isn’t a lack of ideas. It’s a lack of the right questions.

The Ten Questions That Cut Through the Fog

Most leadership teams start with the wrong question. They ask “What can AI do?” when they should ask “What problem are we solving?” I’ve watched this play out across industries. The teams that move fastest don’t begin with technology. They begin with clarity.

Here are the ten questions I recommend every leadership team answer before committing budget, talent, or time to an AI initiative. These aren’t theoretical. They’re designed to make your next strategy meeting productive instead of circular.

First: What specific business outcome are we trying to improve? Not “explore AI.” Not “stay competitive.” A measurable outcome. Revenue per customer. Time to resolution. Cost per transaction. If you can’t name it, you’re not ready.

Second: Where is human time currently wasted on repetitive work? AI’s early wins come from automating tasks humans hate doing anyway. Look for bottlenecks in workflows, not moonshots.

Third: What data do we actually have access to, and is it clean enough to use? Most AI projects stall here. You can’t train a model on data you don’t own or can’t structure.

The Questions About Risk and Readiness

Fourth: What happens if this AI system gets it wrong? In financial services, a wrong answer can cost millions or trigger compliance violations. In customer service, it might just annoy someone. Know the stakes before you deploy.

Fifth: Who owns the outcome? AI projects fail when no one is accountable. Assign a leader. Give them authority. Make success or failure visible.

Sixth: Do we have the talent to maintain this, or are we renting it? Build vs. buy is an old question, but it matters more with AI. If you’re licensing a solution, you’re dependent on a vendor’s roadmap. If you’re building in-house, you need engineers who understand machine learning. Most organizations need a hybrid approach.

Seventh: How will we measure whether this is working? Define success metrics before you start. Not “we’re using AI.” Not “it feels faster.” Numbers. Benchmarks. A timeline.

The Questions About Culture and Change

Eighth: What will we stop doing when AI takes over this task? This is the question most teams skip. If AI handles tier-one support tickets, what do those support reps do next? Redeploy them or lose them. There’s no middle ground.

Ninth: How will we explain this to the people affected by it? Employees. Customers. Stakeholders. Transparency builds trust. Silence breeds fear. I’ve seen AI transformation efforts collapse because leadership assumed everyone would just “get it.” They won’t.

Tenth: What’s our plan if this works better than expected? Success creates its own problems. If your AI tool saves 40% of a team’s time in month one, you need a plan for month two. Scale it. Expand it. Replicate it. Or watch the momentum die.

What This Means for Your Organization

These ten questions won’t give you an AI strategy. They’ll give you the foundation to build one. The organizations moving fastest on AI aren’t the ones with the biggest budgets or the most data scientists. They’re the ones with the clearest answers.

If you’re preparing your leadership team for an AI for Business keynote or strategy session, start here. Print these questions. Send them in advance. Make people come prepared with answers, not just opinions.

The gap between hype and execution is wide right now. The way across isn’t more technology. It’s better questions.

Joel Comm is a New York Times bestselling author, internet pioneer, and AI keynote speaker. He has spoken for Microsoft, IBM, Cisco, the United Nations, and Harvard Business School. Learn more at joelcomm.com.