AI for data and analysis, across 4 workflow patterns.

Lets non-analysts ask data questions in plain English and get answers from the firm's data warehouse or reporting database. Translates the question into a query, runs it, and presents the result with the underlying numbers and the SQL behind them. Different from generic text-to-SQL because the pattern works against the firm's actual data model with documented semantics — it knows what 'active customer' means in your business, not what 'active customer' might generically mean. Refuses to answer questions it can't ground in the schema, surfacing uncertainty rather than guessing. The pattern's value is collapsing the 'I'll ask the data team and hear back next week' loop into something that happens in the meeting where the question came up.

Continuously analyzes the firm's metrics for changes worth noticing — unusual movements, emerging trends, anomalies, segments performing differently than expected — and pushes a digest of what matters to the people who should care. Different from a dashboard because the pattern decides what's worth surfacing rather than letting humans scan everything; different from generic anomaly detection because the pattern speaks in business language ('your premium segment churn is rising while standard is stable') rather than statistical jargon. The pattern's value is replacing the 'I look at the dashboard every Monday and hope something jumps out' habit with proactive flagging of what's actually moving.

When a leader is weighing a decision — should we open a new region, raise prices on this tier, hire ahead of plan, kill this product line — the pattern builds a structured model of the decision and lets the leader explore scenarios: what assumptions would have to be true for this to work, what does sensitivity look like, what's the expected vs. downside-case impact. Doesn't make the decision; structures the thinking. Different from generic forecasting because the model is constructed per decision rather than from a fixed template, and integrates with the firm's actual data so scenarios start from reality. The pattern's value is replacing the slow consultant-style analysis cycle with something a leader can iterate on in an afternoon.

Takes a request — formal ('weekly board report'), informal ('I need to see how this region is doing') — and produces a tailored report or dashboard: the right metrics, the right time periods, the right comparisons, the right visualizations, in the format the audience prefers. Different from H33 (one-off questions) and H34 (pushed insights) in that this pattern is for recurring or substantial information products: things that someone would otherwise spend hours constructing in a BI tool. The pattern produces drafts the analyst or report owner refines, then schedules and delivers them on cadence. The value is the difference between everyone reusing the same five generic dashboards and everyone getting reports actually tuned to what they need to see.