It is difficult to get through a day without hearing about artificial intelligence, or AI. Its impact on business will be revolutionary, and I do not believe we have seen anything comparable since the arrival of the internet.

What makes AI different is its speed of change. It is the fastest-moving technology I have experienced in my career, and meaningful updates to these Large Language Models (LLMs) seem to arrive weekly, and sometimes daily.

Opportunity and Obligation

For business leaders, that dynamic creates both an opportunity and an obligation.

There is a great deal to learn, and the pace makes it difficult to keep up. In a typical week, I spend six to eight hours— much of in the evenings and on weekends—building my AI skills and expanding my understanding of where the technology is headed.

Jensen Huang, the founder, president, and CEO of Nvidia, the world's most valuable company, made a profound statement at the 2025 Milken Institute Global Conference that has stuck with me: “You’re not going to lose your job to an AI, but you’re going to lose your job to someone who uses AI.”

As leaders, we have a responsibility to help our teams prepare for anything that will fundamentally change the way business is done and the way work gets accomplished. AI can make all of us more effective and efficient, but there is a real learning curve. Unless your company has a dedicated training budget, much of that education will fall to the leaders closest to the work.

That is one reason I invest so much personal time in learning AI, and why I also lead training sessions with my team every Monday and every Friday. I do so because I am committed to the team’s success and convinced that AI will reshape how they perform their jobs. I don’t want any of them, or me, left behind. 

Lessons Learned

Along the way, I have learned several lessons about leading an AI initiative that may be useful to others beginning the same journey.

1. First, start by thinking big. Really big. Brainstorm everything AI could potentially do, not only within your department or organization, but across your industry and beyond.
2. Then, consider how the technology may change the competitive landscape and how your company needs to adopt it to create an advantage or at the very least maintain their current competitive position.
3. Next, create a vision for where you want to go. Once that vision is clear, choose a starting point. Make it small, visible, and highly achievable.
4. Early success matters, so start with a sure thing. Quick wins build confidence, create momentum, and make it easier to move toward larger long-term goals. From there, move as quickly as possible by stacking one practical win on top of another.

Where to Begin?

If you are wondering where to begin, two practical places to start are knowledge agents and workflows.

Knowledge agents are relatively easy to build once you understand the process, and they can deliver immediate value.

Every business decision is only as good as the information available when the decision is made, and that's where knowledge agents become powerful. They can combine internal information and broader knowledge available on the web and deliver it directly into the hands of users at the moment they need it.

With a single prompt, an agent can perform research that might otherwise take a team member hours to complete. Used well, knowledge agents can help teams work faster, see more clearly, and make better-informed decisions.

Knowledge agents also can help …

• Identify trends and patterns and do predictive analysis like how to improve cycle time;
• Minimize warranty expenses;
• Conduct vendor payment research;
• Track root cause of EPO’s and budget variances;
• Identify non-selling or slow selling options;
• Conduct margin analysis to identify best and worst performing floor plans; and
• Analyze indirect spend and make sourcing recommendations to minimize costs, as well as trade performance, customer satisfaction correlation, audits, inventory optimization, real-time competitive market analysis with incentive recommendations, financial analysis, should-cost modeling, commodity driver tracking, forecasting, vendor performance, buyer behavior trends, estimating, proformas, design studio option recommendations for spec homes, change order analysis, failed inspection analysis, insurance compliance analysis, and on and on.

Does any of that (or maybe all of that) resonate with you and your team? The use cases are almost endless (and as such. Can be overwhelming), but as I suggested earlier, start small, with a sure thing, and build out from there.

Workflows automate redundant tasks that consume time and hinder your full potential—the must-do's that don't add a lot of value.

Advances in AI have greatly expanded the work it can do. Agentic, multistep task execution is not only possible, but also available on a user’s laptop. Team members can create their own workflows.

Like knowledge agents, the workflow use cases are almost endless but could include:

• Invoice matching;
• Auto-routing approvals;
• Task follow up;
• Meetings not taken;
• Bid analysis;
•  Warranty request processing;
• Tracking budget to actual;
• Updating dashboards;
• Confirming trade availability; and
• Notifications such as weather delays, change order processing, inspection requests, scheduling service requests, homeowner notifications, updating CRM records … and on and on.

AI is no longer a future consideration. It is already changing how work gets done, how decisions are made, and how companies compete.

The good news is that, despite its speed of technology, it’s not too late to catch up. But the longer leaders wait, the more effort it will take to close the gap.

We owe it to ourselves, our teams, and our organizations to lean into AI now, build practical skills, and turn early use cases into measurable business value.

AI is not going away. The real risk is not that AI replaces people, but that people and companies who fail to use it will fall behind those who do.