388 tools your AI can actually use

Forecast when work actually lands, and check whether what was promised matches what was scoped.

Size teams, route work, and find where the queue — not the people — is the constraint.

Attrition, onboarding, contribution and knowledge concentration, with cohort privacy built in.

Team boundaries, coordination cost, decision hygiene, and the incentives a metric quietly creates.

What the bad case costs, how correlated your failures are, and which policies survive being wrong.

Incident dynamics, CI assurance, reliability growth, and where observability spend earns its keep.

Inference spend, model routing, workflow economics, and whether AI-assisted work is actually paying.

Data rights, output IP, privacy budgets, evaluation integrity, and agent action control.

Establish that a change caused an outcome, rather than that the two moved together.

Drift, reliability, missingness and calibration — whether the number is fit to decide on at all.

Whether an analysis is independent, current, transportable, and still worth trusting here.

Margin decomposition, cost to serve, working capital, and the delivery-to-cash chain.

Fund, defer, expand or abandon — across roadmaps, platform bets and financing decisions.

Churn economics, feature uplift, recurring-revenue mechanics and negotiation ranges.

Control coverage, identity chains, data sovereignty and software supply chain exposure.

Lifecycle NPV across build, buy and partner — and whether an exit from a vendor is executable.

Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.

Separate what a change caused from what merely moved alongside it.

Turn uncertainty, cost and risk appetite into a defensible choice, with the reasoning left inspectable.

Trace how load, failure and knowledge propagate through a graph of teams, services or components.

Choose a policy over evolving states when today's action changes tomorrow's options.

Run the system forward many times to see what the bad tail actually looks like.