This module applies the automation decision framework to a practical quarterly tax provision workflow. It examines common provision steps, including trial balance import, account mapping, Schedule M-1 rollups, provision adjustments, financial statement reconciliation, memo drafting, journal entry posting, and workpaper documentation. Through workflow mapping activities, the module identifies which steps are strong automation candidates, which require partial automation and human review, and which fall inside firm risk boundaries. The module shows how tax teams can save time by automating repeatable mechanical work while keeping professional judgment and formal controls in place.

This module focuses on spreadsheet-based review controls that make recurring tax workpapers easier to verify and maintain. It explores common self-checking formula patterns, including tie-to-trial-balance checks, year-over-year variance flags, required-field checks, tax-category coverage checks, SUMIFS sanity checks, carryover ties, and round-trip rounding controls. The module emphasizes that formula-driven workpapers should not only calculate results, but also flag issues such as missing inputs, unmapped accounts, unusual movements, tie-out breaks, and rounding drift. It also covers how to select the right control for specific workpaper risks and place those controls where reviewers can use them effectively.

This module introduces safe, reusable AI prompt patterns for low-risk, repeatable tax support tasks. It examines prompt templates for weekly status updates, reviewer-note checklists, tax research framing, regulation summaries, and variance commentary. The module emphasizes that AI should support drafting and organization, not tax judgment, sign-off, or decision-making. It also covers practical guardrails such as factual tone, source limits, bracketed placeholders, no unsupported inference, and required human verification. The module closes by reinforcing key risk controls: use synthetic data only, document every automation aid, verify outputs before use, and respect firm automation boundaries.