Is Leadership Focused on the Right Kind of AI?

While Generative AI dominates the headlines, the data tells a different story. According to research from Andrew Ng and McKinsey, a staggering 80% of successful AI projects in business come from supervised learning.

Why? Because it's practical, powerful, and has a clear path to ROI.

Instead of asking "How should we adopt GenAI?", the more immediate, value-driven question is: "What critical business question, if answered, would transform our bottom line?"

Supervised learning uses your own historical data to build a crystal ball for your business, answering questions like:
-Which customers are most likely to churn?
-Which sales leads are most likely to convert?
-How can we minimize inventory shrink and waste?
-What product should we recommend to this specific customer?

Solving these challenges directly translates to reduced costs, increased revenue, and higher customer lifetime value.

Getting started is more accessible than you think:
-No PhDs Required: Identify an intellectually curious employee who understands your business context and upskill them. Their domain knowledge is your greatest asset.
-Use Existing Tools: Many companies start by leveraging probabilistic features in software they already own (like Hubspot AI or Salesforce Einstein) or by building pilots in Excel or Google Sheets.

AI is a tool, and value is derived by the humans who use it. Start with a tangible business problem, secure a few small wins with clear financial impact, and build momentum. These successes will fund your long-term AI roadmap and foster a culture of data-driven innovation.

Your Call to Action for Next Week:

Don't just contemplate AI. Put it to work.
1. Identify: Ask your team: "What single insight, if we had it, would be the most valuable for increasing sales or enhancing profitability?"
2. Quantify: Estimate the dollar value of gaining that insight.
3. Act: You've just defined the business case for your first high-impact AI project.

This is how you build a practical, profitable AI strategy. It's not about chasing the latest trend; it's about solving core business problems. The opportunity to create significant value is waiting in the data you already own.