Selecting business processes for AI optimization requires balancing business pain points with automation feasibility. A major challenge is the gap between business expertise and technical development teams, as developers often lack deep domain knowledge of specific operations.
Effective integration requires thorough analysis before implementation. The author suggests calculating labor costs by multiplying the number of operations by the average time per task. For example, 1,000 operations per month at 7 minutes each total 116 hours and 40 minutes of labor.
Technical teams should avoid tasks involving large contexts, such as documents exceeding 50 pages. Furthermore, processes with high volumes of events where AI offers only marginal time savings compared to human performance are often inefficient to automate.
Data maturity is a prerequisite for AI implementation. If a business lacks logs for process analytics, it should focus on improving data infrastructure or building AI-native platforms before attempting to delegate complex workflows to artificial intelligence.
