What is conversational reporting software and how should CFOs use it?
Conversational reporting software lets a CFO or treasurer type a question such as “What was our cash position across all entities last Friday?” and receive an answer built from the company’s own finance data. The reply can be a figure, a table, a chart or a file. Finance teams are ready for it. In June 2024, 58% of the 121 finance leaders surveyed by Gartner said their function used AI, and 28% applied it to analytics. UK businesses have followed the same path. The Office for National Statistics reported in July 2026 that around 35% of UK firms with 10 or more employees used at least one AI technology, up from around 12% in autumn 2023.
This guide defines the category, compares it with dashboards, lists the questions finance leaders ask it, sets out its limits and gives you selection criteria for a vendor review.
What is conversational reporting software?
Conversational reporting software is a tool that connects a large language model to an organisation’s financial data, so that users can request reports, analyses and charts in natural language. The user writes a question, the software retrieves the relevant figures from connected systems, and it returns an answer that the user can refine with follow-up questions. Each follow-up builds on the previous answer.
A general chatbot works from public text and whatever the user pastes into it. Conversational reporting software reads governed company data such as bank balances, invoices and forecasts, and it applies the access rights each user already holds in the source system. A treasurer who cannot see a subsidiary’s accounts in the treasury platform should therefore not see them in the chat either.
Vendors sell the same idea under several labels, including conversational analytics, natural language reporting, “talk to your data” and AI financial assistant. What separates one product from another is the data it can reach and the controls around it.
How does conversational reporting software work?
Most tools stack four layers between the question and the answer. The table shows what each layer does and what a finance leader should check before trusting the output.
Layer | What it does | What the CFO should check |
|---|---|---|
Data connection | Pulls bank, ERP, receivables, payables and forecast data through APIs or integrations | How often each source refreshes and which entities are covered |
Access control | Applies the user’s existing rights to every query | Whether rights are inherited from the finance system or rebuilt separately |
Language model | Interprets the question, selects the data and drafts the answer | Which model provider is used and its data retention terms |
Output | Returns a number, table, chart or file (Excel, PowerPoint, PDF) | Whether each figure shows its period, entity and source |
In a well-designed tool, the model calls functions that query the finance system and writes its answer around the values those functions return, so the figures come from the system of record. The Model Context Protocol (MCP) is one open standard for this kind of connection, and it lets an AI client such as Claude query a finance application with the user’s own login and permissions.
Conversational reporting vs dashboards and BI: which fits which case?
A natural language interface sits next to dashboards and spreadsheets. Each format answers a different kind of question, as the comparison shows.
Criterion | Spreadsheet reporting pack | Dashboard or BI tool | Conversational reporting software |
|---|---|---|---|
Best for | Month-end packs with manual commentary | Indicators reviewed on a fixed cadence | Ad hoc questions between two reporting cycles |
Time to answer a new question | Hours, depending on extraction | Days if a new view must be built | Minutes, if the data is connected |
Skills needed | Excel modelling | BI configuration | Clear questions and finance judgement |
Main risk | Formula and copy-paste errors | Views that age as the business changes | Misread questions and unchecked answers |
Keep a dashboard for the indicators your executive committee reviews every week, because a fixed view keeps periods comparable. Use conversational reporting for the questions that arrive between two board meetings, such as a lender query on covenant headroom or a CEO request on one customer’s payment history. Spreadsheets remain useful for one-off models that need manual assumptions.
What can CFOs and treasurers ask an AI reporting assistant?
Useful prompts name an entity, a period and a measure. The four areas below cover most of the requests a finance team receives.
Cash position and liquidity
Treasurers use it to check balances across banks and currencies without opening each portal. A typical request reads “Show me today’s cash balance by entity, converted into sterling, and flag accounts below their minimum threshold.” A follow-up such as “Which of these entities had the largest outflow last week?” then narrows the analysis.
Actuals vs forecast and variance analysis
Variance requests often take a controller half a day in spreadsheets. You can ask for actuals against budget by category for the last quarter, then ask which categories missed by more than 10%. The same approach supports cash flow variance analysis on a 13-week cash flow forecast, comparing an earlier snapshot of the forecast with what happened week by week.
Working capital and DSO
Credit control questions often come from the board at short notice. “What is our DSO month by month this year, split by entity?” or “Which ten clients hold the largest overdue balance?” return answers in one exchange instead of an aged debtors export and a pivot table.
Board and lender reporting
Some tools turn the conversation into a deliverable. After three or four questions, a CFO can ask for a five-slide summary of Q3 cash performance or an Excel file for the bank. Each file still needs a reviewer before it leaves the finance team.
Where does AI-driven finance reporting fall short?
Answers are only as reliable as the data underneath. In the PwC 2025 Global Treasury Survey of 350 treasurers, 76% cited poor data quality as a forecasting challenge. A chat interface exposes uncategorised transactions or a stale ERP feed faster than a monthly pack does.
Language models can also misread a question. “Last month” may be read as a calendar month or a rolling 30 days, and “cash” may or may not include savings accounts. Ask the tool to state its assumptions and show intermediate calculations, then check one figure against the source system before you reuse the answer.
Governance is the third limit. AI maturity remains low in treasury, since only 26% of the treasurers PwC surveyed in 2025 rated their AI capabilities as moderately or very mature. Before rollout, decide who may use the tool, which outputs need a second reviewer, and how prompts and answers are logged for audit.
How to choose a conversational reporting tool
A short demo on sample data says little about fit. Ask each vendor to run your own questions on your own data, and score the answers against the criteria below.
Criterion | Question to ask the vendor | Warning sign |
|---|---|---|
Data coverage | Which bank, ERP, receivables and forecast data can the assistant query, and for which entities? | Only data exported into the tool by hand |
Permissions | Does each user see only the entities and accounts they can already see in the finance system? | A separate access model to maintain |
Traceability | Can every figure be traced to a period, entity, category and source? | Answers without a stated period or perimeter |
Calculation method | Are totals computed by the system or estimated by the model? | No way to see intermediate steps |
Outputs | Can it produce Excel, PowerPoint or PDF files and save views to a dashboard? | Answers that must be retyped into a report |
Deployment and pricing | Does it run inside the platform or through a separate AI client, and how is usage billed? | A second AI subscription per user |
A mid-market group with several entities should weight permissions and data coverage most heavily. A single-entity SME will usually gain more from output formats and ease of set-up.
Worked example: the cost of answering ad hoc questions by hand
The figures below are assumptions for illustration. Replace them with your own before building a business case.
Assumption | Manual process | Conversational reporting |
|---|---|---|
Ad hoc questions per month | 20 | 20 |
Time per question | 1.5 hours | 20 minutes, including a check against the source |
Hours per month | 30 | About 6.7 |
Hours per year | 360 | 80 |
Cost per year at £60 per hour | £21,600 | £4,800 |
Hours saved = 360 − 80 = 280 per year, which equals £16,800 at a fully loaded cost of £60 per hour. The calculation leaves out licence costs and set-up time, so subtract them before you compare tools. The larger gain is often speed, because a lender or board question answered the same day changes the conversation.
How Agicap brings conversational reporting to treasury data
Agicap is a cash management and treasury platform for SMEs and mid-market companies. Its Smart Assistant, currently in private beta, lets users ask questions in natural language directly inside Agicap. Powered by Claude (Anthropic) and enriched with Agicap’s product knowledge, it requires no installation and no separate AI subscription.
The assistant reads data from the Agicap modules a company uses, which covers bank balances across entities, actuals vs budget by category, weekly variances in short-term forecasts, DSO by month and customised dashboards. It can turn a conversation into a PowerPoint, PDF or Excel file, and each user only sees the data their Agicap permissions allow. Administrators can also grant or remove access to the assistant user by user.
Teams that already work in Claude can connect it to Agicap through the Agicap MCP, which uses the same authentication and permissions as the platform. Inside the platform, smart reports add AI-generated charts, tables and counters to customised dashboards, which keeps the fixed view and the conversational layer in one place.
Test any tool with last quarter’s board questions
Collect the ten ad hoc questions your board, CEO and lenders sent last quarter, and note how long each answer took your team. Run the same ten questions in each shortlisted tool on your own data, then compare time to answer, traceability and the number of corrections needed. A tool that answers eight of ten correctly with a visible source deserves a pilot. To run that test on your treasury data, Ask your own questions live:
Frequently Asked Questions (FAQs) about Conversational Reporting Software
Are AI-generated answers accurate enough for board reporting?
They can be, provided the underlying data is reconciled and a finance team member reviews the output. Accuracy depends on the data feed and on whether totals are computed by the finance system. Ask the tool to show its calculation steps and check one figure per answer against the source before the file reaches the board.
Does conversational reporting replace a BI tool?
For most finance teams, the two work side by side. Dashboards keep recurring indicators comparable over time, while conversational reporting handles the questions that no dashboard anticipated. Some platforms let you save a conversational answer as a dashboard item, which closes the gap between both formats.
Is financial data safe in an AI reporting tool?
Safety depends on how the tool connects to your data. Prefer software that inherits user permissions from the finance system, logs every request and states its model provider’s data retention terms. Under UK GDPR, client and employee data in invoices or payroll also needs a lawful basis for processing, so involve your data protection lead early.
