Variance reporting with evidence
Explain material plan-versus-actual variances from connected finance evidence.
What this workflow does
Explain material plan-versus-actual variances from connected finance evidence. It separates observed facts from inference, surfaces conflicting evidence, and delivers a review-ready finding set instead of an opaque answer.
What you gain
Reviewer-approved variance narrative
What the AI agent changes
How this worked before
Traditional automation could collect records, but a person still had to compare sources, resolve conflicts, and write the conclusion.
What the AI agent changes
The agent connects financial and operational evidence, weighs competing explanations, and produces a cited variance narrative for review.
Agent trigger
A reporting period closes or a material variance threshold is crossed.
Inputs
- Process context: actuals, plan, forecast, operational drivers, and commentary
- Approved policies, ownership, and exception rules
Agent flow
- 1
Collect source material from the approved systems and record its timestamp.
- 2
Cross-check conflicting signals and separate facts from inference.
- 3
Prepare a cited variance explanation and open questions with citations, unknowns, and confidence.
- 4
Deliver the approved report and preserve its evidence set.
Human decisions
After step 3
The accountable process owner approves a cited variance explanation and open questions.
Outcome
- Reviewer-approved variance narrative
- Evidence, exceptions, and audit trail
Guardrails
- Label inference separately from observed facts.
- Do not close the investigation while required sources are unavailable.
Risks and mitigations
Missing or stale evidence can produce a confident but incomplete finding.
Show source coverage, conflicts, timestamps, and unanswered questions.
Sources and evidence
Sources establish feasibility or impact. Not every metric comes from an identical implementation.
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