Analytics teams spend significant time on work that is necessary to analysis but does not require an analyst to perform every step manually. Finding data, moving between systems, writing queries, comparing sources, reconciling differences, investigating issues, and documenting findings all consume analytical capacity.

I help analytics leaders redesign how analytical work gets done through AI-assisted analytical workflows that save time and increase analytical capacity. I look across the workflow—from the business question through data retrieval, analysis, validation, and delivery—to determine where AI can perform routine work, assist the analyst, and reduce unnecessary manual effort.

The opportunity extends beyond generating SQL or summarizing documents. AI can increasingly coordinate work across databases, spreadsheets, code, documentation, and analytical environments, allowing analysts to direct and validate more of the work rather than manually execute every step.

The result is greater analytical capacity: less time spent on repetitive execution and more capacity for investigation, interpretation, validation, and decision support.

Analytical judgment remains essential. Faster analysis does not automatically produce better conclusions. Analysts still need to determine whether the analysis is appropriate, what the evidence supports concluding, and what that evidence means for the decision at hand.

My focus is on improving both the efficiency and quality of analytical work—using AI to reduce manual effort while keeping people responsible for the judgment required to produce trustworthy analytical outcomes.