AI for Business Leaders

Modern AI excels at: language understanding, pattern recognition, content generation, and autonomous decision-making within defined parameters.

AI is less suitable for: truly novel situations, tasks requiring physical world interaction, or decisions with significant ethical implications.

The key insight: AI amplifies human capability rather than replacing it. The best implementations keep humans in the loop.

Start with problems, not technology. What's costing you money? What's frustrating customers? Where are your bottlenecks?

High-value AI use cases typically share characteristics: repetitive tasks, high volume, clear success criteria, and available data.

Examples: customer service automation, document processing, lead qualification, code development assistance, and predictive analytics.

Quantify the current cost of the problem you're solving. Include direct costs, opportunity costs, and time costs.

Estimate implementation costs realistically: technology, integration, training, and ongoing maintenance.

Calculate expected ROI with conservative assumptions. Plan for a pilot phase before full deployment.

Buy when: the solution exists, time-to-value is critical, and you lack specialized expertise.

Build when: your needs are unique, you need full control, or the technology is core to your competitive advantage.

Hybrid approaches often work best: use platforms for infrastructure, customize for your specific needs.

AI projects are iterative by nature. Expect to refine and improve continuously—this isn't a weakness, it's how AI works.

Set clear success metrics before starting. How will you know if the project succeeded?

Plan for change management: AI changes how people work. Invest in training and communication.