Automate Finance Commentary with ChatGPT – From Manual to Meaningful
Finance teams lose days every cycle drafting variance explanations and management commentary by hand. This guide shows how to use ChatGPT to turn structured financial data into clear, reliable narratives, while keeping analysts in control. You’ll learn strategic considerations, concrete workflows, and prompts to cut reporting time without diluting quality.
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The Challenge: Manual Narrative Commentary
Every reporting cycle, finance teams are forced into the same grind: pulling numbers from ERP and spreadsheets, chasing last-minute adjustments, and then spending days drafting variance explanations and management commentary manually. Analysts copy old text, tweak a few sentences, and search through workbooks to understand what really drove the numbers. The result is long evenings, rushed reviews, and commentary that often feels generic.
Traditional approaches to commentary rely on individual heroics and tribal knowledge. Analysts keep their own templates, reuse previous board decks, or maintain Word files full of boilerplate. None of this is connected to real-time data in your ERP, consolidation system, or BI tools. When conditions change mid-close, teams must rewrite large sections, and there is no systematic way to ensure consistency of tone, logic, and level of detail across countries, business units, or functions.
The business impact is significant. Reporting cycles stretch from days to weeks, and leadership gets commentary that often restates numbers instead of explaining the drivers behind performance. This delays decisions, undermines trust in finance, and consumes capacity that could be spent on scenario modelling or strategic analysis. In competitive markets, slow and shallow insight is a real disadvantage: while others iterate their plans weekly, you are still polishing last month’s commentary.
This challenge is real, but it is absolutely solvable. With the latest generative AI capabilities, especially tools like ChatGPT, finance teams can turn structured data into accurate, consistent narratives in minutes—while retaining a strong human review step. At Reruption, we’ve helped organisations move from manual reporting work to AI-assisted workflows and know how to bridge the gap between theory and a working solution. In the sections below, you’ll find practical guidance on how to do this in your own finance organisation.
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Our Assessment
A strategic assessment of the challenge and high-level tips how to tackle it.
From Reruption’s perspective, using ChatGPT for financial narrative commentary is not about copying text into a chatbot; it’s about designing a robust workflow that connects your data, your governance rules, and your finance team’s expertise. Based on our hands-on experience delivering AI automations and internal tools, we’ve seen that finance gains the most when ChatGPT is treated as a controlled component in the reporting process, not a side experiment.
Anchor AI Commentary in a Clear Finance Governance Framework
Before you let ChatGPT generate a single sentence of commentary, define how narrative reporting fits into your existing finance governance. Which sections can be automated (e.g. standard variance explanations, cash flow drivers), and which must remain fully manual (e.g. sensitive M&A topics, regulatory issues)? Who is the ultimate owner of the narrative—the system, or a named analyst?
Reruption’s experience shows that adoption succeeds when there is a documented policy for AI-generated financial commentary: what sources are allowed, what review is mandatory, and how fact-checking is performed. This avoids the extremes of “let’s automate everything” and “we can’t use AI because of risk,” and makes compliance and audit teams part of the design, not a late-stage obstacle.
Treat ChatGPT as an Analyst Co-Writer, Not a Black Box
Strategically, ChatGPT for finance reporting works best when positioned as a co-writer that drafts, structures, and refines commentary—while analysts stay accountable for the substance. That mindset change is critical. The goal is not to replace financial judgement, but to remove the repetitive writing work that consumes it.
This means designing workflows where analysts provide structured inputs (key drivers, management messages, risk notes) and ChatGPT turns them into cohesive narratives that match your reporting style guidelines. Strategically, you want analysts spending time on what matters: deciding what to say, not wrestling with how to phrase the third bullet in the EBIT section.
Start with One Reporting Area and Prove Value Fast
Instead of attempting to automate the entire board pack at once, choose one high-impact, lower-risk area such as monthly variance commentary for OPEX or regional revenue commentary. This gives you a contained environment to test data flows, prompts, and review processes without overwhelming the team.
In our PoC-style projects, we see the best results when finance leaders define a clear success metric for a first use case—e.g. “reduce commentary drafting time by 50% without increasing review corrections.” A focused scope lets you demonstrate tangible value to stakeholders quickly and build the internal confidence and sponsorship needed to expand to more complex reporting areas.
Prepare Your Team for New Roles and Skill Sets
Automating commentary with ChatGPT subtly changes what finance analysts do. They shift from primary authors to prompt designers, reviewers, and curators of AI-generated insight. Strategically, you should invest in building these skills: how to structure inputs, how to critique AI outputs, and how to translate management intent into templates and guidance the model understands.
We recommend nominating a few “AI champions” in finance who work closely with IT and Reruption-type partners to shape prompts, templates, and quality criteria. This creates internal expertise and reduces reliance on external vendors for every iteration. Over time, the finance team becomes comfortable adjusting the AI to new KPIs, reorganisations, or reporting standards.
Design for Risk Mitigation and Auditability from Day One
For finance, risk is not an afterthought. When deploying ChatGPT for financial reporting, you need clear controls: no direct model access to raw ERP data, robust logging of prompts and outputs, and a documented human approval step before anything enters official reports. Thinking through this architecture early will save painful rework later.
Strategically, combine secure technical setup (e.g. approved enterprise ChatGPT environments, anonymised inputs where needed) with process controls such as checklists for reviewers and periodic back-testing of AI narratives against underlying data. This approach aligns with internal audit expectations and gives CFOs confidence that automation improves quality rather than introducing hidden risk.
Used thoughtfully, ChatGPT can turn financial narrative commentary from a manual time sink into a fast, consistent, and insight-rich part of your reporting cycle—without losing human oversight. The key is to combine governance, focused pilots, and new analyst skills, not just drop a chatbot on top of your spreadsheets. Reruption’s blend of AI engineering and finance-focused implementation means we can help you design, prototype, and harden these workflows in your own environment; if you’re exploring this, a conversation around a concrete PoC is often the fastest way to see what’s possible.
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Best Practices
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Standardise Your Commentary Templates Before You Automate
Before connecting anything to ChatGPT, align on the structure and tone of your financial commentary. For example, define a consistent layout for revenue, margin, OPEX, cash flow, and outlook sections. Decide how many bullet points per variance, how you describe drivers (volume, price, mix, FX), and what level of quantification is expected.
Convert these decisions into a “master template” that will guide the prompts you use with ChatGPT. Include example paragraphs of strong commentary, preferred phrasing (e.g. "EBIT improved mainly due to" vs. "The main reason was"), and terms to avoid. This gives the model a clear pattern to emulate and dramatically reduces back-and-forth edits.
Example system prompt for standardised commentary:
You are a senior finance analyst drafting monthly management commentary.
Follow this structure for each KPI section: Context, Key drivers, Risks & opportunities.
Use concise, professional language and quantify drivers where possible.
Avoid speculative statements and keep each section under 120 words.Feed Structured Variance Drivers, Not Just Raw Numbers
ChatGPT performs best when you provide structured, pre-aggregated inputs instead of expecting it to infer everything from raw tables. Before calling the model, transform ERP and spreadsheet data into a compact summary of variance drivers by account, region, or business unit.
This can be as simple as a small script or Excel macro that creates a JSON or text block like “Revenue +8% vs budget: Volume +5pp, Price +3pp, FX 0pp; Top 3 countries: DE +12%, FR +9%, IT +4%.” Feed this into ChatGPT with clear instructions on how to turn it into commentary.
Example user prompt with structured inputs:
Data for Q2 revenue vs budget:
- Total variance: +8% (+€4.2m)
- Key drivers: Volume +5pp, Price +3pp, FX 0pp
- Top countries: DE +12% (+€1.5m), FR +9% (+€0.9m), IT +4% (+€0.3m)
Write a 3–4 sentence commentary explaining the variance and its drivers.Build a Repeatable Close-Process Workflow Around ChatGPT
Integrate ChatGPT into your monthly and quarterly close as a defined step, not an ad-hoc tool. For example, set up a workflow like: (1) close data in ERP, (2) run your variance calculation scripts, (3) export structured driver summaries, (4) call ChatGPT with predefined prompts per section, (5) analyst review and edit, (6) final sign-off.
This workflow can start manually (copy-paste from spreadsheets into a secure ChatGPT environment) and later be automated via APIs or internal tools built together with IT. The important part is that every cycle runs the same process, so you can measure time saved and error rates, and improve prompts incrementally.
Example internal checklist:
- [ ] ERP close completed and validated
- [ ] Variance driver file generated (revenue, OPEX, EBIT, cash)
- [ ] ChatGPT prompts executed for each section
- [ ] Analyst review completed and changes logged
- [ ] Controller/CFO sign-offCreate Reusable Prompt Libraries for Different Audiences
Board, executive management, and operational leaders need different levels of detail. Configure separate prompt templates for each target audience and store them in a central library or internal wiki. This lets analysts generate commentary tailored to the reader without reinventing instructions every time.
For example, a board-style prompt might emphasise strategic implications and risks, while an operational prompt focuses on actionable levers for managers. Over time, you can refine these prompt libraries based on feedback from each audience.
Example prompt for board-level commentary:
Audience: Supervisory Board
Style: High-level, focused on strategic impact and risks.
Instruction: Summarise the revenue and EBIT variances in max 150 words.
Highlight 2–3 key strategic messages and any material risks.
Avoid operational detail and internal jargon.Implement a Structured Review and Feedback Loop
To maintain quality and build trust, make review systematic. Require reviewers to tag each generated section with a simple status ("accepted", "edited", "rejected") and, where edited heavily, capture a short note (e.g. “tone too positive given risk profile”). This qualitative feedback helps you adjust prompts and templates over time.
A simple spreadsheet or shared form can collect this data. After a few cycles, patterns will emerge: maybe cash flow sections need more conservative language, or OPEX explanations consistently miss one cost driver. Use this evidence to fine-tune your prompts and, if needed, add more structured input data.
Example feedback capture:
Section: Q3 OPEX commentary
Status: Edited
Reason: AI did not mention hiring freeze as driver.
Action: Update prompt to explicitly include major one-off or policy changes.Automate Safely with a Technical PoC Before Scaling
Once manual workflows with ChatGPT are stable, consider a technical PoC to integrate with your existing tools. Reruption’s AI PoC format is well-suited here: we define the inputs (variance tables, drivers), outputs (draft commentary per section), constraints (no direct database access, audit logs, role-based access), and metrics (time saved, edit rate, error rate).
Engineering tasks typically include building a small internal web app or Excel add-in, connecting to a secure ChatGPT API, and encoding your validated prompt templates. Running this PoC across 1–2 reporting cycles gives hard data on performance and cost per run, and provides a clear roadmap for scaling to the full reporting stack.
Expected outcome for mature setups: 40–70% reduction in drafting time for standard commentary sections, significantly more consistent tone across reports, and analysts reallocated from repetitive writing to value-adding analysis, while maintaining or improving quality through stronger structure and governance.
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Frequently Asked Questions
ChatGPT can produce highly consistent and clear variance explanations if you feed it structured, validated data and clear instructions. It does not access your ERP directly or calculate numbers itself; instead, it transforms the inputs you provide (e.g. variance drivers, KPIs, country performance) into narrative.
In practice, we recommend treating ChatGPT as a drafting assistant with a mandatory human review step. Finance analysts remain responsible for verifying that the text matches the numbers and context. With good prompts and structured inputs, review effort typically drops to light editing instead of writing from scratch.
Your team does not need to become data scientists to benefit from ChatGPT in finance reporting. The key skills are: structuring data and drivers clearly, formulating good prompts (instructions), and critically reviewing AI-generated text.
We usually train finance analysts to think in terms of: (1) What are the 3–5 key facts the model must include? (2) Who is the audience? (3) What tone is appropriate? Once they master this, they can work with predefined templates and only adjust them for new use cases. Technical integration (APIs, security, automation) can be handled by IT and engineering partners like Reruption.
A basic, manual workflow using ChatGPT (copying structured data from spreadsheets into a secure ChatGPT interface with standard prompts) can usually be piloted within one or two reporting cycles. This is enough to validate whether AI-generated financial commentary saves time and meets quality expectations.
Building a more integrated solution—e.g. a small internal web app or Excel add-in that connects to ChatGPT via API—typically takes several weeks for a focused PoC, depending on your IT landscape and security requirements. From there, scaling to additional reports and entities is incremental, as you reuse prompts and components.
Direct usage costs for ChatGPT are usually modest for financial commentary, because the text volumes per report are relatively small. The main investment is in configuring workflows, designing prompts, and integrating the solution into your close process.
In our experience, finance teams can realistically aim for a 40–70% reduction in time spent on standard commentary sections (e.g. recurring P&L and cash flow explanations), plus higher consistency and fewer last-minute edits from senior stakeholders. For mid-sized teams, this often frees several analyst days per reporting cycle, which can be reallocated to deeper analysis and scenario work—driving clear ROI in both cost and decision quality.
Reruption supports companies end-to-end, from identifying high-impact finance reporting use cases to delivering working AI solutions. With our AI PoC offering (9.900€), we can quickly test a concrete use case such as automating monthly variance commentary: defining inputs and outputs, selecting the right model setup, prototyping a workflow or tool, and measuring time savings and quality.
Beyond the PoC, our Co-Preneur approach means we embed with your finance and IT teams to turn the prototype into a robust internal capability: designing governance, integrating with your existing tools, and training analysts to work effectively with ChatGPT. We operate in your P&L, not in slide decks, so the outcome is a live, usable solution, not just a concept.
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