AI in finance: Separating hype from reality
Artificial intelligence in finance gets a lot of buzz these days, and for good reason. It’s a valuable tool for teams that want to move beyond basic automation and make faster, data-driven decisions. But before jumping in, it’s worth knowing what AI can actually do for your finance team versus what’s just marketing noise.
AI is changing how finance teams work, enhancing today’s tools with new capabilities like predictive insights, real-time analysis, and intelligent automation. Here’s how it adds value across the finance tech stack:
| Finance Function | Current Tech Stack | AI Capabilities |
| Data Management | ERP systems, Excel/Google Sheets, OCR scanning, AP automation | Intelligent data validation, automated categorization, vendor analysis, payment optimization |
| Reporting & Analysis | BI tools (Tableau, Power BI), SQL queries, static dashboards, variance analysis | Conversational analytics, natural language queries, automated insight generation, correlation discovery |
| Planning & Forecasting | Spreadsheet models, budgeting software (Adaptive Insights, Anaplan), statistical models, trend analysis | Predictive forecasting, scenario modeling, machine learning algorithms. |
| Risk & Compliance | GRC platforms, audit software, regulatory reporting, manual testing | Real-time risk monitoring, continuous control testing, regulatory change alerts, automated audit preparation |
| Cash & Treasury Management | Banking APIs, treasury systems, payment automation | Predictive cash flow modeling, liquidity optimization, intelligent payment timing recommendations |
So if AI can do all this, why isn’t every finance team using it yet? The truth is, many teams don’t know what problems they are trying to solve, which functions to prioritize, or if AI is worth the training and time investment to modernize old processes. In fact:
- 61% of finance teams have still not implemented AI in their workflows in 2025, according to a report by CFO.Connect.
- 30% of CFOs reported ‘more strategic use of data analytics and AI’ as their biggest challenge in 2025 (Lucanet research study).
- Adoption is relatively slow. As of 2024, 48% of businesses are still planning to test AI’s benefits for financial reporting and tax operations (KPMG global AI in finance report).
Yet teams shouldn’t underestimate the value of AI financial insights for the right use cases. If you’re wondering where to start, the next chapter offers plenty of inspiration.
Practical use cases of AI for finance analytics and operations
Here’s where AI for finance analytics is making the biggest difference in 2025.
Interactive financial analysis
According to a 2024 survey, finance professionals spend 35% of their time on high-value tasks like generating insights from reports. But AI-powered dashboards and conversational analytics are changing this dynamic.
Data exploration in AI tools
Instead of dedicating hours to manual analysis – scanning reports for key changes, finding opportunities and patterns in charts and graphs – you can simply ask questions and get AI-powered financial insights that support decision-making. This requires connecting your data to AI tools through no-code data integration platforms like Coupler.io.
For example, let’s say you want to analyze cash flow patterns for the past 6 months. Instead of diving into QuickBooks analytics and disconnected spreadsheets, you could integrate your cash flow reports with AI, for instance, using the Claude integration, to ask natural language questions like:
- What caused the sudden inventory spike in April?
- Which vendor payment changes could improve our cash flow position?
- Based on current trends, when will we hit our cash flow targets?
You’ll get a result that looks something like this:
Direct finance app-to-AI tool integrations via platforms like Coupler.io make it easy to work with large, up-to-date datasets and unlock deeper, more dynamic AI financial insights without the hassle of manually exporting or updating files.
AI Insights in dashboards
AI-powered financial dashboards also let you generate insights in a few clicks. For instance, Coupler.io offers dashboard templates for QuickBooks and Xero equipped with AI Insights. They operate like regular dashboards for tracking financial health, but include a button that generates key findings and trends in under 20 seconds.
Here’s an example from the Xero financial dashboard. The AI Insights highlighted the main trends ‘consistent profitability from November 2024…’ as well as 6 key findings to pinpoint growth opportunities and potential risks.
You can try the dashboards for yourself with a free Coupler.io account:
QuickBooks financial dashboard
Tracks earnings, monitor cash flow, and analyze balance sheet changes with a dedicated dashboard that provides a clear picture of your business finances.
Xero financial dashboard
Get comprehensive financial insights from your Xero account with key performance metrics, balance sheet analysis, and cash flow projections. Quickly assess your business health and make informed financial decisions.
QuickBooks revenue dashboard
Transform your QuickBooks data into actionable revenue intelligence with visualized sales trends, product performance metrics, and customer value analysis. This dashboard helps identify growth opportunities and optimize pricing strategies through easy-to-understand charts.
Revenue dashboard for Xero
Analyze revenue streams, customer spending patterns, and product performance with data automatically synced from Xero. Gain actionable insights into sales trends, top-performing products, and high-value customers to drive growth strategies.
Financial narrative generation
Rather than spending hours writing summaries and footnotes for financial reports, AI generates the narratives for you. It can ingest data from multiple sources – e.g., financial statements, budget reports, contextual notes about market conditions and operational changes – and convert them into clear explanations for stakeholders.
You can even provide the AI platform with specific instructions for different audiences. For instance:
- To prepare an MD&A for a quarterly SEC filing, you might tell it to use regulatory-compliant language and provide detailed explanations.
- If you need a standard profit and loss summary for management, you could ask for a business-focused overview with key takeaways.
To show how it works in practice, let’s take an annual P&L report from QuickBooks. Using a Coupler.io data integration between QuickBooks and Claude, we can ask the following:
“Generate a one-page executive summary of this P&L data for our board meeting. Focus on the three most significant revenue and expense changes compared to last year, explain what drove these changes, and highlight any trends that impact our 2026 strategy.”
The tool will generate a structured summary without you having to write or format anything yourself:
Create instant financial narratives
Get started for freeAI-driven risk assessment and management
What if you could review dozens of vendor contracts in the time it takes to analyze one? AI simplifies risk assessment by acting as a financial expert with all the context. Unlike basic rule-based systems, it can recognize nuance and understand how certain combinations of terms could create hidden risks. For instance:
- Advanced contract analysis: Purpose-built tools like LegalOn can scan vendor contracts, procurement documents, and credit or partnership agreements to spot inconsistencies or problematic terms that could harm your financial position. You can ask legal questions on the go and get redline summaries in seconds.
- Fraud detection: Platforms like MindBridge AI integrate with your existing finance or ERP software to monitor transactions and flag unusual patterns, suspicious entries, or duplicate payments that could indicate fraud. Over time, the system learns from past data to catch subtler risks.
- M&A due diligence: During acquisitions, AI can analyze proposals and documents for irregularities and compliance gaps that manual reviews or basic automation may overlook.
Advanced anomaly detection
Again, AI-driven anomaly detection goes beyond basic fraud alerts by learning your company’s normal transaction patterns over time. It can pick up on unusual activity that rigid, rules-based systems would miss entirely.
For example, AI might notice that your office supply orders have gradually increased by 30% over the past year, but your headcount only grew by 8%. Or it could find gaps in invoice collection just by analyzing the numbers in your everyday dashboards. Here’s how it works when you use a revenue dashboard with embedded, AI-generated insights:
As you can see, the AI doesn’t simply flag a problem – it explains the anomaly and describes its potential impact. This can save your team time on investigation and help prioritize which issues to address first.
AI-powered ESG reporting
Environmental, Social, and Governance (ESG) reporting is quickly becoming mandatory for SMEs across the US and EU. Currently, 66% of finance teams dedicate 3 or more days a month to the task – approximately 300 hours of manual work every year. But that’s where AI can help reduce the load.
- Scalable data collection: Frameworks like CSRD require collecting around 1,200 data points for reports. Purpose-built AI solutions can extract relevant metrics from unstructured data sources across departments, like spreadsheets, PDFs, receipts, emails, and financial reports.
- Auto-populated questionnaires: Investors, financial institutions, and potential clients often request ESG in non-standard forms. AI can fill these out automatically, saving you time and reducing errors.
- Compliance checking: Regulations and frameworks (CSRD, SEC climate rules, GRI, etc.) are constantly evolving. AI-powered software can compare your reports against multiple standards and identify gaps before they become issues.
For best results, look for AI-powered tools that specialize in ESG compliance. Some examples include Persefoni, Clarity AI, and Briink.
Next-level predictive modeling
Unlike traditional methods, AI-powered predictive modeling uses a combination of external and historical data to create adaptable forecasts. They can integrate factors like economic signals, market trends, and industry benchmarks to provide more accurate predictions.
Example: For annual budget forecasting, the AI takes into account historical spending alongside shifting factors like inflation rates and industry salary benchmarks. If inflation rises mid-year, the system would flag potential variances and suggest budget adjustments to reflect the new salary costs, operational expenses, etc.
Some enterprise-level tools that already offer this capability (to varying degrees) include AnaPlan PlanIQ and Workday Adaptive Planning.
Intelligent invoice processing
Basic OCR technology is more reliable than manual data entry for accounts payable, but it still has limitations. Because it relies on rules-based algorithms, OCR can misread invoices – in some cases, error rates can be as high as 50%. By comparison, AI-driven invoice processing is faster and more adaptable. Its key benefits include:
- Improved accuracy: AI-powered invoicing systems are better able to catch discrepancies in purchase orders and invoiced amounts. According to case studies by Docsumo, they can reduce errors by about 30% and increase overall accuracy by up to 120% compared to OCR-based tools.
- Self-learning capabilities: Crucially, AI algorithms learn from your historical invoice data, building knowledge that makes them increasingly accurate over time.
While QuickBooks and Xero offer basic AI automation, you’ll likely get better results with specialized document processing tools like Rossum, Tipalti, and Docsumo.
Strategies to optimize the ROI of AI-powered financial insights
AI ROI varies across finance teams. Some are seeing returns of 20% or more, while others are still struggling to quantify its impact. Let’s start with a basic formula and practical example to clarify what measurable AI ROI actually looks like.
AI ROI = (Total annual benefit – total AI cost) / Total AI cost x 100
For example: In 2024, A finance team implemented AI for accounts payable automation. They tracked:
- Time savings: $16,000 (invoice processing reduced from 3 days per month to 8 hours)
- Process improvements: $2,000 in early payment discounts captured.
- Error reductions: $3,500 from 40% reduction in duplicate payments.
- Total AI investment: $18,000 (software $9K, implementation support $4K, training $5K)
- Resulting ROI: 19%
Key strategies to maximise your AI ROI
➡️ Establish success metrics upfront. Define specific KPIs that align with your team and business objectives, such as hours saved on monthly reporting, increased forecasting accuracy, or reduction in manual errors. Make sure to document your current performance on these metrics (e.g., month-end close process = 20 hours) before AI implementation. This will give you a clear baseline to measure against.
➡️ Focus on quick wins. Start with high-impact use cases, such as risk management, predictive analytics, and data analysis, according to BCG’s 2025 CFO survey. That way, you’ll avoid spreading your resources too thin, and you can justify ROI much faster to higher-ups.
➡️ Opt for out-of-the-box vs in-house solutions. Some businesses choose to build custom AI tools for their specific software and finance needs. But providers (like Coupler.io) are usually cheaper long term, quicker to implement, and don’t come with maintenance costs. On average, 54% of finance teams buy AI software instead of building it themselves.
➡️ Provide proper training and support. For your team to get value from AI, they need clear guidance on how it works and where to apply it. Structured onboarding and clear usage guidelines (human quality checks, data privacy compliance, etc.) can make the adoption process much smoother. Ultimately, it prevents tool abandonment and sunk costs.
➡️ Allocate a dedicated budget to AI and track business impact against it. Separating AI investments from your general technology budget will make it easier to calculate returns and keep costs in check.
Risks of implementing AI in your finance function – and how to mitigate them
Using AI for financial insights comes with its own set of challenges. It’s important to be aware of the potential pitfalls – and how to dodge them – before getting started.
1. AI decisions can’t be explained to auditors or regulators
When you use AI to generate financial forecasts, report summaries, or business recommendations, you often can’t show auditors exactly how the system – and by extension, your team – reached those conclusions.
If external stakeholders ask: ‘What was your methodology here?’ or ‘Why did you choose this approach?’, you may struggle to justify your decisions. This can weaken your team’s credibility and damage stakeholder trust, even when all of your actions were above board.
How to mitigate it:
Be upfront about using AI tools. Realistically, you’re not going to keep detailed records of AI outputs and your team’s responses to them. But you should always be able to explain the business reasoning behind your decisions and how the outputs align with (or help resolve) current financial priorities.
2. Poor data quality leads to inaccurate insights
AI tools amplify whatever data quality issues already exist in your financial systems. For instance, if your accounting software is plagued with duplications or formatting inconsistencies, AI will process these errors in your reports and produce misleading insights. It becomes a major problem when you use those flawed insights to make strategic decisions about cash flow, investments, or financial planning.
How to mitigate it:
Start by cleaning up your financial data before implementing AI tools. Check your systems for duplicate entries, unreconciled accounts, incomplete or outdated customer data, and inconsistent naming. To prevent these issues from cropping up again, systematically review data quality regularly (e.g., monthly, quarterly).
3. Over-reliance on AI outputs and recommendations
Once you get comfortable using AI in your daily workflows, it’s easy to slip into autopilot mode. Your team can start trusting outputs without questioning whether the AI understands business context or recent regulatory changes. But even the most advanced, well-integrated AI systems can sometimes hallucinate and omit important details.
How to mitigate it:
You don’t need to triple-check every AI output, but make sure to regularly review recommendations and AI-driven decisions for your high-stakes contexts (e.g., budget approvals, regulatory filings, investor communications, etc.). Maintain a healthy degree of skepticism and validate the AI’s findings, especially when something feels off.
Getting started with AI financial insights
Getting started with AI for finance analytics is simpler than you might think. To take the first steps, all you need is a Coupler.io account and access to your preferred AI tool. Currently, Coupler.io offers Claude and ChatGPT integrations.
Coupler.io connects data from your existing business platforms – QuickBooks, Xero, Google Sheets, Salesforce, Stripe – directly to your generative AI tools. As we showed earlier, you can ask the tool about your data to understand performance drivers and create financial narratives in seconds. Setting up the integration takes less than 10 minutes!
As for dashboards that provide actionable insights inside Coupler.io with a click, you can choose from options for QuickBooks and Xero. Connecting your data to the templates is simple, and you can set up near-real-time updates to keep fresh data flowing.
In Coupler.io, you’ll also have access to a library of 160+ reporting templates for finance, e-commerce, sales tracking, and more. Try for free with a 7-day trial – no credit card required.