The work behind this perspective
As an FP&A Consultant at Galloway, I developed reusable AI workflows for quality-of-earnings (QoE) rollforwards, backlog analysis, and Monthly Reporting Package commentary. I also built a Power BI dashboard using AI to support business development resource allocation. The sections that follow set out my recommended validation approach for these finance tasks.
Choose checks that match the task
Specify the inputs, requested output, and intended audience. Establish what a reviewer must verify and what should be flagged as unresolved. The three types of work above suggest different checks; using one general instruction to review everything leaves the standard unclear.
| Task | Review focus |
|---|---|
| QoE rollforward | Reconcile the starting position, movements, and ending position; identify adjustment sources and unresolved differences. |
| Backlog analysis | Confirm the snapshot date, project population, and backlog definition; trace material figures to the source. |
| Monthly reporting commentary | Check quantified statements against the reporting package; distinguish supported causes from proposed explanations. |
Resolve definitions before interpretation
Make the period, business population, units, sign conventions, and adjustment treatments explicit. Backlog should mean the same thing to the workflow and the reviewer. An organic-growth explanation should use the intended entity comparison. When source reports use different definitions, resolve the difference or show it as an open question.
Keep important figures traceable to the relevant source file, table, or record. Missing information should remain visible. If an assumption is necessary, label it and distinguish it from a sourced fact. This gives the reviewer a route back to the evidence instead of asking them to trust the final prose.
Test the explanation, not just the total
Reconciliation checks arithmetic and completeness within the defined task. It does not prove the proposed cause of a movement. An increase associated with a change in the reporting population should not become an unsupported claim about stronger demand.
Review unusual movements, changed mappings, new entities, and incomplete inputs, while also inspecting ordinary items. Ask whether the explanation matches the comparison basis and whether alternative interpretations remain plausible. Where the evidence supports only an observation, keep the language at that level.
If the same error recurs, address the definition, mapping, or instruction that produces it. Repeatedly correcting the final paragraph leaves the underlying workflow unchanged.
Give a person responsibility for its use
Identify who approves the output and what that approval covers. Preparing analysis, distributing management reporting, and making an operating decision are distinct responsibilities. Define the review needed for the intended audience and how unresolved issues will be communicated.
Retain enough context to explain which inputs, definitions, and material corrections produced the result. Reconsider the checks when the source data or task changes. A familiar output format should not conceal a changed analytical problem.
A practical review before use
Before using the output, I recommend that the responsible reviewer answer these questions. An unresolved answer should become a specific follow-up or a clearly stated limit on how the output can be used.
- Can material figures be traced and reconciled to the defined inputs?
- Do the definitions and comparison periods match the question being asked?
- Are causal explanations supported, with assumptions and uncertainty visible?
- Does the accountable reviewer understand the intended audience and use?