IFRS 8 requires disclosure of segment information on the basis of the “management approach” — the segments an entity uses internally for decision-making, as reviewed by the chief operating decision maker (CODM). In theory this should make segment reporting simple: report what management already sees. In practice, it is one of the most judgment-heavy and heavily scrutinized areas of financial reporting. The CODM must be identified, the aggregation criteria assessed, segment assets and liabilities allocated, and reconciliations built between segment totals and the primary financial statements — all under the watchful eye of auditors and regulators who know that segment disclosures are where inconsistencies and earnings-management signals tend to hide.
The friction is rarely conceptual. It is operational. Segment reporting sits at the intersection of management accounts, group consolidation, and disclosure drafting, and the same numbers must be consistent across the segment note, the entity-wide disclosures, and the narrative in the MD&A. Changes in internal management structure mid-year, newly acquired businesses, or a reorganization of cost allocations can cascade through the entire note. Controllers spend days building reconciliation tables and second-guessing whether the aggregation of two operating segments is genuinely justified under the IFRS 8 criteria.
ChatGPT helps by acting as a structured drafting and review partner. It can turn your management reporting pack into a first-draft segment note, test your aggregation rationale against the IFRS 8 criteria, generate reconciliation tables, and pressure-test your disclosures for the questions an auditor or regulator would ask. It does not replace judgment — but it dramatically reduces the time spent on structure, consistency checks, and drafting, and it forces the judgment calls into the open where they belong.
Where the AI adds the most value
The highest-value use cases are: (1) converting internal management reporting into a compliant segment note structure, (2) documenting the aggregation assessment and CODM identification in a defensible memo, and (3) drafting the entity-wide disclosures (products and services, geography, major customers) and checking them for internal consistency with the segment note. Each of these is a task where the underlying logic is well-defined by the standard, but the execution is repetitive and error-prone.
The key discipline is to give ChatGPT your actual source material — the management reporting pack, the prior-year note, the group accounting policy — rather than asking it to write generically. The prompts below are built around that principle.
First, read these files completely before responding:
[management_pack.xlsx exported as markdown] — monthly internal segment P&L reviewed by the CODM, including revenue, EBITDA, segment assets and liabilities
[prior_year_segment_note.md] — last year’s published segment note for structure and wording continuity
[group_accounting_policies.md] — our policy on segment identification, allocation of central costs, and inter-segment pricing
Here is a reference for what I want to achieve:
[Upload a peer company’s segment note as markdown, or describe the structure you want: segment table, reconciliation to group revenue and profit, entity-wide disclosures]
Here’s what makes this reference work:
– Segment table shows revenue, inter-segment revenue, profit measure, assets, liabilities, and capex per segment
– Every segment total reconciles explicitly to the primary statements with a labelled reconciling item
– Narrative explains the CODM, the measure used, and any aggregation
– Entity-wide disclosures are separate and cross-referenced
Here’s what I need for my version / SUCCESS BRIEF:
Type of output + length: full segment note draft, 900-1,200 words plus tables
Recipient’s reaction: the auditor should find no unexplained gaps and the CFO should recognize the numbers
Does NOT sound like: boilerplate copied from last year without reflecting this year’s structure
Success means: every figure in the note traces to the management pack or a stated reconciliation
My context file contains my standards, constraints, audience. Read it fully before starting.
DO NOT start executing yet. Ask clarifying questions first.
Give me your execution plan (5 steps max) before you begin.
Notice the emphasis on reconciliation. IFRS 8.28 requires a reconciliation of segment revenue, profit or loss, assets and liabilities to the entity’s primary statements, and this is where most review comments land. Asking ChatGPT to build the note around explicit reconciling items — rather than treating the reconciliation as an afterthought — changes the quality of the output substantially.
Documenting the judgment calls
The second high-value prompt addresses the part that auditors probe hardest: the aggregation assessment. IFRS 8.12 permits aggregation of operating segments only where they have similar economic characteristics and meet all the criteria in the standard. That assessment needs a written rationale, and it needs to be consistent year over year. ChatGPT is well suited to drafting that memo — provided you give it the facts and ask it to challenge your conclusion rather than simply agree with it.
First, read these files completely before responding:
[segment_facts.md] — description of each operating segment, products, customers, margins, growth profiles, and the managers responsible
[prior_year_aggregation_memo.md] — last year’s assessment and conclusions
[org_chart_and_codm_notes.md] — how the CODM reviews results and what is reported internally
Here is a reference for what I want to achieve:
[Upload an audit-ready technical memo as markdown, or describe the structure: conclusion first, criteria tested one by one, evidence per criterion, residual risk]
Here’s what makes this reference work:
– Opens with the conclusion and the segments aggregated
– Tests each IFRS 8.12 criterion explicitly with evidence, not assertion
– Flags where the evidence is thin and what would strengthen it
– Notes any change from prior year and explains it
Here’s what I need for my version / SUCCESS BRIEF:
Type of output + length: technical memo, 700-1,000 words
Recipient’s reaction: the auditor accepts the conclusion and the file is self-standing
Does NOT sound like: a generic summary of the standard with our names inserted
Success means: every criterion is addressed with a specific fact from our files, and weak points are identified
My context file contains my standards, constraints, audience. Read it fully before starting.
DO NOT start executing yet. Ask clarifying questions first.
Give me your execution plan (5 steps max) before you begin.
A practical tip: after ChatGPT produces the memo, ask it a follow-up — “act as a skeptical audit manager and list the five questions you would raise on this memo.” The answers are usually a useful preview of the review comments you will receive, and addressing them before the audit starts saves a full review cycle. The same technique works on the segment note itself.
What to try next: run the segment note prompt against last year’s published note and this year’s management pack, then diff the output against the prior-year disclosure. Any change in segment structure, allocation methodology, or reconciling items will surface immediately — which is exactly the list of items your audit committee will want explained.
Published on 21 October 2026 on growwithgpt.com