AI Tools for Finance Professionals: ChatGPT, Copilot, and What Your Team Needs to Know

AI Tools for Finance Professionals: ChatGPT, Copilot, and What Your Team Needs to Know

Excerpt: A practical overview of the AI tools finance professionals are using in 2026 — and what training finance teams need to use them safely, effectively, and compliantly.

Introduction: AI Tools Are Already in Your Finance Function

The question for most finance teams in 2026 is not whether to use AI tools — it is whether staff are using them well, safely, and consistently. AI features are embedded in Excel, in accounting platforms like Xero and QuickBooks, in Microsoft Teams, in email clients, and in the ERP systems most finance functions depend on. Alongside these embedded tools, a growing number of finance professionals are using general-purpose AI assistants like ChatGPT and Claude for drafting, analysis, and research.

This article provides a practical overview of the AI tools most relevant to finance teams, what each one can and cannot do well, the compliance and data privacy risks teams need to manage, and what training should cover for each tool category.

ChatGPT and Claude: General-Purpose AI for Finance Tasks

Large language model (LLM) assistants like ChatGPT (OpenAI) and Claude (Anthropic) are increasingly used by finance professionals for a wide range of tasks. Understanding both their capabilities and limitations is essential for finance teams.

What Finance Professionals Use ChatGPT and Claude For

Common finance use cases for general-purpose LLMs include: drafting management commentary and board reports; summarising lengthy regulatory documents, annual reports, or contracts; preparing Q&A content for investor presentations; generating first drafts of financial analysis based on data provided by the user; explaining complex accounting standards or tax rules in plain language; and creating templates for financial models, budget frameworks, or reporting structures.

What These Tools Do Poorly

General-purpose LLMs are not connected to live financial data (unless explicitly integrated). They cannot access your accounting system, pull current exchange rates, or look up the latest regulatory guidance in real time without tool integrations. They are also subject to hallucination — generating plausible-sounding but factually incorrect information. In financial contexts, hallucinated figures, misquoted accounting standards, or incorrect regulatory references can have serious consequences. Every material output from an LLM requires human verification.

Data Privacy Risks

This is the most significant compliance risk for finance teams using public AI tools. Entering client financial data, commercially sensitive projections, personal taxpayer information, or confidential merger and acquisition details into a public ChatGPT or Claude account creates material data privacy risk. Many organisations explicitly prohibit entering certain data categories into public AI tools — finance teams need clear guidance on what is and is not permissible. Training on this point is not optional.

Microsoft Copilot: AI Embedded in the Finance Toolset

Microsoft Copilot is integrated across Microsoft 365 — including Excel, Word, PowerPoint, Teams, and Outlook. For finance teams already working within the Microsoft ecosystem, Copilot represents the most immediately accessible AI capability, with the significant advantage that it operates within your organisation's Microsoft tenant rather than sending data to a public AI system.

Copilot in Excel

Copilot in Excel can generate formulas from natural language descriptions, analyse data and surface insights, create charts, identify trends and anomalies, and help with data cleaning. For finance professionals who spend significant time on spreadsheet work, Copilot can meaningfully reduce the time spent on technical formula construction and routine data analysis. Training should cover how to prompt Copilot effectively for finance-specific tasks and how to verify Copilot-generated formulas before relying on them.

Copilot in Teams and Outlook

Copilot can summarise Teams meeting transcripts, draft email responses, and extract action items from conversations. For finance managers running multiple reporting and planning meetings, the ability to get an AI-generated meeting summary with action items is a meaningful productivity gain. Training here is relatively straightforward — the primary risk is over-reliance on AI summaries rather than ensuring key decisions are properly communicated.

Copilot in Word and PowerPoint

Finance teams creating board packs, investor presentations, and management reports can use Copilot to draft sections, create slides from documents, and refine financial narrative. The quality of Copilot's output in Word and PowerPoint depends heavily on the quality of the source documents and prompts provided — training on effective prompting for financial document creation is valuable.

Data Governance With Copilot

Because Copilot operates within your Microsoft 365 tenant, it benefits from your organisation's existing data classification and access controls. Copilot can only access files and data that the user already has permission to access — it does not break through existing permissions. However, it can surface information from across a user's accessible content in ways that feel unexpected. Finance teams should ensure their Microsoft 365 data classification and access policies are in good order before deploying Copilot widely.

AI in Accounting Software: Xero, QuickBooks, and Sage

The major cloud accounting platforms have integrated AI features that finance professionals and accountants are increasingly using — often without fully understanding how they work.

Xero

Xero uses machine learning for automated bank reconciliation (suggesting transaction matches and categorisations based on historical patterns), cash flow forecasting, and smart invoice recognition. Xero's AI learns from user corrections — when a user overrides an AI categorisation, the system updates its model. Finance professionals using Xero benefit from understanding how this learning loop works and the importance of correcting incorrect categorisations consistently.

QuickBooks

QuickBooks Online incorporates AI for transaction categorisation, receipt capture and matching, cash flow projections, and anomaly detection. QuickBooks' Intuit Assist feature provides conversational AI for financial queries and reporting. Training for QuickBooks users should cover how to interpret AI-generated insights and the limitations of cash flow projections based on AI pattern analysis.

Sage

Sage Intacct and Sage 50 incorporate AI for automated data entry, duplicate detection, and reporting. Sage's AI features are particularly relevant for finance teams managing high transaction volumes where manual processing would be error-prone. Training should cover how to audit AI-automated entries and maintain appropriate oversight of AI-driven data processing.

AI for Financial Modelling and FP&A

For FP&A teams, AI is beginning to transform the core work of financial planning and analysis.

Platforms With AI-Enhanced FP&A Capabilities

Anaplan, Pigment, and Workiva have all integrated AI capabilities into their platforms. Features include natural language querying of financial data, AI-assisted scenario modelling, automated variance commentary, and predictive forecasting. These capabilities can significantly reduce the time FP&A teams spend on routine reporting tasks, freeing capacity for higher-value analysis.

AI in Financial Modelling: Opportunities and Risks

The key risk in AI-assisted financial modelling is model integrity. When AI generates assumptions, fills forecast cells, or drafts model commentary, the outputs must be verified against the underlying financial logic of the model. AI tools do not understand your business — they identify patterns. Finance professionals need the modelling expertise to know when an AI-generated output makes financial sense and when it does not. Training on this judgement skill is as important as training on how to use the AI features.

What to Include in an AI Tools Policy for Finance Teams

Every finance team using AI tools should have a documented AI use policy. Key elements include:

• Approved tools: which AI tools are approved for use, and for what categories of task
• Data classification: which data categories can be used with which tools (distinguishing, for example, between internal operational data and confidential client or M&A data)
• Output verification requirements: the expectation that all material AI-generated outputs are reviewed and verified by a human before use
• Prohibited uses: explicit prohibition on entering certain categories of sensitive data into public AI tools
• Incident reporting: how to report a suspected AI-related data privacy incident
• Training requirements: the training finance staff must complete before using approved AI tools

Training on the AI tools policy should be part of onboarding for new finance staff and refreshed annually for existing team members.

Frequently Asked Questions

Is it safe to use ChatGPT for financial analysis?

It depends on what data you are using. Using ChatGPT with publicly available financial data or hypothetical scenarios carries limited risk. Using it with confidential client data, commercially sensitive projections, or personal taxpayer information carries significant data privacy risk. Always check your organisation's AI use policy before entering financial data into any public AI tool. Many firms operate a tiered approach — public ChatGPT for non-sensitive drafting tasks, enterprise AI tools (like Microsoft Copilot within your M365 tenant) for work involving confidential data.

What is the difference between Microsoft Copilot and ChatGPT for finance use?

The main practical differences are integration and data governance. Microsoft Copilot is integrated into the tools finance professionals already use (Excel, Word, Teams) and operates within your Microsoft 365 tenant under your organisation's data governance policies. ChatGPT is a standalone tool that does not integrate natively with your finance systems and sends data to OpenAI's servers. For most enterprise finance use cases, Copilot offers stronger data governance and better workflow integration. ChatGPT (particularly enterprise versions) may offer more flexible and powerful language capabilities for specific tasks.

How accurate is AI-generated financial commentary?

AI-generated financial commentary is often stylistically fluent but requires careful verification of specific figures, ratios, and factual claims. LLMs generate text based on patterns rather than by calculating from your actual financial data — unless they have been given the data directly. Any AI-generated commentary that includes specific financial figures or comparative references must be verified against source data before use in any formal document.

Do accounting software AI features comply with GDPR?

Major accounting platforms (Xero, QuickBooks, Sage) are designed to comply with GDPR and publish data processing agreements. However, your organisation remains a data controller and has obligations around how client and employee data is processed. Review the data processing terms of your accounting platform and ensure your AI feature use is consistent with your privacy notices and client agreements. If in doubt, consult your data protection officer or legal team.

What prompt engineering skills do finance professionals need?

Finance professionals benefit from understanding how to structure effective prompts for finance-specific tasks: how to provide sufficient context for accurate outputs, how to specify the format and level of detail required, how to ask AI tools to verify or challenge their own outputs, and how to use prompt frameworks like chain-of-thought for complex financial analysis. A half-day prompt engineering workshop for finance teams is one of the most consistently high-value AI training investments.

Are there AI tools specifically designed for finance rather than general use?

Yes — and this category is growing rapidly. Finance-specific AI tools include Bloomberg Terminal's AI features, Refinitiv's AI analytics, Kensho for financial research, and a growing range of specialised tools for audit, tax, risk management, and compliance. For most finance teams, however, the highest-priority training focus is on the general-purpose and productivity AI tools already in use — Copilot, ChatGPT, and AI features in existing accounting and planning software.

Train Your Finance Team to Use AI Tools Safely and Effectively

Learnsignal provides AI tools training designed specifically for finance professionals — covering the tools your team is actually using, the governance and compliance considerations relevant to financial services, and the practical skills needed to get value from AI while managing the risks.

For group training on AI tools for finance teams, visit our corporate training page to discuss a programme tailored to your organisation's tools, policies, and regulatory context.