One of the earliest lessons we learned about computers was “garbage in, garbage out”: the quality of the data determines the quality of the output. That lesson taught business leaders to keep looking for more—and better—data.
When I worked in a Fortune 100 company, getting information meant filling out a request for the data team that specified the necessary data. That team had the expertise and access to create custom reporting that could then be analyzed. However, this capability was limited to large companies who had the staff and access to complete that kind of analysis.
For most of business history, leaders have made important decisions with incomplete information. Not because they wanted to. Because gathering and analyzing every piece of relevant data simply wasn’t practical.
- A business owner had a question.
- The accounting department ran reports.
- Operations gathered numbers.
- Sales looked at trends.
- Marketing pulled campaign results.
A week later, everyone met to discuss what they had found. By the time one question had been answered, three new questions had emerged.
AI has significantly changed what information is available and how quickly it can be accessed.
Imagine connecting your CRM, accounting software, and production software to an AI that can be queried in real time using normal human language. Suddenly, you can ask the kinds of questions that uncover opportunities for significant improvement—and move from insight to action much faster.
When AI has access to the operational data inside your business, something remarkable becomes possible. You can start having a conversation with your business. Not because the AI has opinions. Not because it has intuition. But because it can analyze enormous amounts of information in seconds and help you understand what the data is actually saying.
Imagine asking questions like:
- “Which customers generate the highest lifetime value?”
- “What changed during the months our profit margin declined?”
- “Which service line consistently produces the highest profit after accounting for labor?”
- “What happens to our cash flow if we hire two additional technicians?”
- “Which managers consistently have the lowest employee turnover?”
- “What operational bottlenecks are costing us the most time?”
A few years ago, answering questions like these often meant asking several people to gather information, waiting days for reports, and hoping no one overlooked an important detail.
Today, an AI can often assemble that information in minutes. That doesn’t mean every answer is automatically correct. It doesn’t eliminate the need for experienced leadership. It doesn’t replace judgment. It simply allows leaders to move from question to understanding dramatically faster.
And that’s a profound shift. The AI isn’t deciding what your business should do. It’s helping you understand what’s actually happening inside your business. That distinction matters.
I’ve spent years coaching business owners, and one lesson has remained remarkably consistent. The quality of the answer almost always depends on the quality of the question.
Ask, “Why are sales down?”
You’ll probably get a list of symptoms.
Ask, “What changed in the buying behavior of our most profitable customers over the last six months?”
Now you’re much closer to discovering something you can actually act on.
The same principle applies when working with AI. If your questions are vague, you’ll receive broad answers. If your questions are thoughtful, specific, and driven by curiosity, AI can help uncover patterns that would have been difficult—or impossible—to find manually.
That means one of the most valuable leadership skills of the next decade may not be learning how to use AI. It may be learning how to think well enough to ask questions worth exploring.
The organizations that gain the greatest advantage won’t necessarily be the ones with the most sophisticated technology. They’ll be the ones whose leaders have learned to combine human judgment with machine-assisted analysis.
Technology can process information. Technology can recognize patterns. Technology can summarize years of operational data in moments. But technology doesn’t decide which questions matter.
Leaders do. And that’s where the greatest opportunity lies.
The future won’t belong to leaders who have all the answers. It will belong to leaders who consistently ask better questions.
In the final article, we’ll look beyond efficiency, productivity, and automation.
Because once AI creates capacity, changes the work, improves visibility, and accelerates understanding, one question remains.
What kind of organization will you build with the opportunity it creates?
