Part of the AI in finance series.
AI can draft variance commentary. It cannot explain a number it has not been told about. In a group reporting context, that distinction is the whole ballgame: the quality of AI-generated commentary is a direct measure of the close process underneath it, and the place to start is traceability, not the drafting tool.
What are finance teams already doing with AI commentary?
Finance teams are already experimenting, and the r/FPandA community is candid about what works. One practitioner opened a thread asking directly: “Is board deck commentary something you can realistically automate?” The thread answer was nuanced - yes for structure and language, no for the underlying judgement.
A second practitioner described a practical workaround for sourcing the narrative: “Ask the business to explain in a long detailed form if possible and dump their word soup into AI to make it finance commentary appropriate.” That is a legitimate use of AI as a prose editor, not as an analyst.
A third described feeding the model historical packs: “Feed it a PDF of management reporting package and tell it to generate commentary, using these examples over the past year as a template.” Style transfer works. Causal explanation does not follow automatically.
Taken together, these practitioners are using AI to edit, reformat and match house style - tasks where it performs well. The harder question is what happens when a number moves and nobody has told the model why.
What does AI actually draft well?
AI drafts the structural and linguistic shell of variance commentary reliably. Given a data file with actuals, budget and prior year, a well-prompted model will describe the direction and scale of every movement, order the variances by materiality, maintain a consistent tone, and reproduce last year’s house style if you supply examples. The first pass arrives quickly and is often grammatically superior to an analyst writing under deadline pressure.
That is genuinely useful. The commentary that says “revenue was £2.4m above budget, driven by volume in the UK segment” can be drafted by AI if the volume split and the segment label are already in the data. Structure, ordering, tone and a clean first pass are all within scope.
What can AI not know?
AI cannot know why a number moved unless someone has explicitly told it. This is the hard limit, and one practitioner put it plainly in a thread about AI rollouts in finance teams: “AI doesn’t work without context.”
The context that explains a movement is almost never in the reporting pack itself. It sits in a conversation between a business partner and a commercial manager, in a purchasing decision made late in the month, in a price renegotiation that closed after the budget was set. If that context has not been captured - in a commentary field, a variance note, a structured driver input - then AI is pattern-matching against numbers it cannot interpret.
The practical consequence: a model given a pack with a £600k overhead overspend will produce a sentence. That sentence will describe the overspend. It will not tell you it was a one-off dilapidations provision triggered by a lease exit, because nobody wrote that down in a field the model could read.
Why is a group environment harder?
In a group, several technical adjustments alter the numbers between the entity ledger and the consolidated pack, and each one is silent unless it is labelled. Intercompany eliminations reduce revenue and cost simultaneously; if the elimination is partial or disputed between entities, the residual reads as a performance variance. FX translation moves the reported value of a foreign subsidiary without any underlying operational change. Top-side consolidation journals - goodwill amortisation, fair-value adjustments, acquisition accounting entries - sit above the entity and below the group number, invisible unless the group reporting layer maps them explicitly.
An AI model given the consolidated output and asked to explain movements will treat all of these as performance. It has no way to distinguish a genuine commercial variance from an elimination timing difference or a translation effect unless the consolidation process has tagged each line. Groups that run consolidation as a manual, end-of-month reconciliation exercise are handing AI a number with no provenance and asking it to explain the journey.
This connects directly to the wider challenge of AI in FP&A forecasting: the same data-quality dependency that limits forecast automation limits commentary automation. Clean, tagged, traceable actuals are the prerequisite in both cases.
How does traceability connect commentary to the ledger?
Every sentence in a board pack commentary should trace back to a number, and every number should trace back to a source in the ledger or consolidation layer. Finance AI governance and the audit trail sets out the full framework; the commentary implication is specific.
When a variance note says “cost of sales was £1.1m above budget,” the model producing that sentence needs to know: which entities contributed to that £1.1m, whether any part is an elimination or a top-side, and what the entity FP&A lead identified as the operational driver. If your close process does not capture those three things systematically - in structured fields that the reporting layer can read - then the AI commentary is unsourced inference.
Traceability is not primarily an AI governance requirement. It is a management accounts quality requirement that AI makes visible. Teams that have always produced commentary by memory and judgement can absorb the gaps; a model cannot. Introducing AI commentary generation exposes every place where the numbers have no attached explanation.
Who signs the board pack commentary?
The finance team signs it. The board pack is a professional work product, and accountability for its content rests with the reporting lead and ultimately the CFO. AI is a drafting assistant; it is not a preparer in the regulatory or professional sense, and it carries no accountability for a number that is wrong or an explanation that is misleading.
This matters practically. If a commentary sentence attributes a margin decline to mix when the real cause was a consolidation error, and that sentence goes to the board unsigned and unchecked, the error belongs to the team. Reviewing AI-drafted commentary requires the same judgement as reviewing analyst-drafted commentary - arguably more, because the prose quality can obscure a weak underlying explanation. The shift toward agentic AI in FP&A will increase the volume of AI-generated output across the close cycle; the human review requirement does not diminish with volume.
Where should a group FP&A team start?
Start with the close, not the commentary tool. The board pack commentary is only as good as the close process that produces the numbers it describes. If entity variance explanations are collected informally, if consolidation adjustments are unlabelled, and if the group-to-entity bridge is rebuilt manually each month, then AI commentary will be fluent and unreliable.
The practical sequence is: structured driver capture at entity level, labelled consolidation adjustments at the group layer, a traceable bridge from entity actuals to consolidated output - and then AI commentary as the drafting layer on top of that foundation. Why AI financial close pilots stall covers the close-layer dependencies in detail. Once the foundation is in place, AI commentary moves from a risk to a genuine efficiency - and the Finance Value Score gives group finance teams a structured read of where they stand across the whole close-to-report cycle, and what the gap to a fully traceable, AI-embedded process is worth in pounds.
Common questions
Can AI write board pack variance commentary?
AI can draft the structure, language and directional description of variance commentary when the underlying numbers and drivers are supplied to it. It cannot independently identify why a number moved - that explanation must come from the business partner or entity FP&A lead and be captured in a form the model can read. The drafting is reliable; the causal reasoning is not autonomous.
Why is AI variance commentary harder in a group consolidation?
In a group, FX translation, intercompany eliminations and top-side consolidation journals all alter the reported number without any operational cause. Unless the consolidation process labels these adjustments explicitly, an AI model will treat them as performance variances and produce commentary that attributes operational meaning to accounting mechanics. Clean, tagged consolidation output is a prerequisite.
What does 'traceability from commentary to ledger' mean in practice?
Every sentence in a board pack commentary should reference a specific variance, that variance should map to a line or group of lines in the consolidation layer, and each line should carry a source entity and an adjustment type. Traceability means an auditor or senior reviewer can follow the chain from the prose back to the ledger entry without reconstructing it from memory.
Who is accountable for AI-generated board pack commentary?
The finance team remains accountable. AI is a drafting tool and carries no professional or regulatory accountability for the content of a board pack. The reporting lead and CFO are responsible for the accuracy and completeness of the commentary regardless of how the first draft was produced. Reviewing AI-generated prose requires active judgement, not passive sign-off.
What should a group FP&A team fix before using AI for commentary?
Fix the close process first: structured variance driver capture at entity level, explicitly labelled consolidation adjustments, and a traceable bridge from entity actuals to consolidated output. AI commentary built on top of an unlabelled, manually reconciled consolidation will be fluent but unreliable. The quality of AI commentary is a direct indicator of the quality of the close underneath it.