CFOs aren’t looking for another dashboard, chatbot, or analytics tool. The modern finance leader is expected to improve profitability, manage risk, support growth, and deliver answers faster than ever before — often with leaner teams and increasing complexity across entities, business units, and reporting requirements. This is why AI has become such an important topic in finance. But there’s a problem: much of the conversation around AI focuses on technology rather than outcomes. For finance leaders, confidence comes down to trust. An AI-generated insight is only valuable if the underlying data is accurate and the reasoning can be trusted. Many organizations know their ERP data contains inconsistencies, duplicate records, or incomplete classifications — feeding unstructured data into a generic AI model often produces equally unreliable outputs. The reality is simple: AI is only as good as the data it receives. The most effective financial AI solutions are designed to work with trusted financial information and financial-specific structures that reduce the risk of errors and hallucinations. After working closely with finance leaders across industries, we’ve identified eight things CFOs actually want from AI — and where most solutions still fall short. — PRIORITY ONE —

01 · REAL-TIME VISIBILITY

Instant Visibility Into What’s Actually Happening

CFOs hate lag. By the time many management reports are distributed, the information is already outdated. The organization may have missed opportunities, overlooked risks, or continued operating on assumptions that are no longer true. What finance leaders want isn’t simply faster reporting. They want systems that actively monitor performance and surface meaningful changes automatically. Think about how your credit card company handles fraud. It doesn’t wait until month-end to tell you something unusual happened. It alerts you immediately because timing matters. Finance leaders increasingly expect the same level of responsiveness from financial systems. WHAT CFOS NEED AI TO DO HERE:

1. Analyze financial performance as soon as data becomes available
2. Flag anomalies automatically — unexpected spend, margin erosion, revenue dips
3. Explain why results changed, not just what changed
4. Surface trends across entities, departments, and regions
5. Generate executive-ready summaries quickly

The shift is from periodic reporting to continuous financial intelligence. AI should be doing the monitoring so your analysts are doing the thinking. — PRIORITY TWO —

02 · FORECASTING

Forecasting That’s Actually Reliable

Most financial forecasts are still built around spreadsheet assumptions that become outdated quickly. AI changes the economics of forecasting because it makes scenario modeling significantly faster. Imagine a manufacturer facing rising transportation costs. Traditionally, finance teams might spend days updating models to understand the impact on profitability. With AI, a CFO should be able to ask: “What happens to our margin if shipping costs increase another 10% while sales soften in the Northeast region?” And receive an answer in seconds. WHAT CFOS NEED AI TO DO HERE:

1. Model multiple scenarios quickly — best case, worst case, pricing changes, staffing shifts
2. Identify the biggest drivers behind forecast variance
3. Explain forecast changes in plain language for non-finance stakeholders
4. Support rapid “what-if” analysis without rebuilding models from scratch

— PRIORITY THREE —

03 · CLOSE PROCESS

The Real Opportunity Starts After the Close

Many discussions about AI focus on helping finance teams close the books faster. But the real opportunity often begins after the close. Once the books are closed, organizations have clean, trusted financial data available for analysis. Unfortunately, most finance teams immediately move on to preparing for the next reporting cycle — there isn’t enough time to perform meaningful analysis. As a result, many organizations never fully extract the value hidden within their financial results. This is where AI creates leverage. Imagine completing month-end close on Friday afternoon. AI can compress much of the interpretation work into minutes — so instead of spending days preparing analysis, finance leaders can spend their time understanding what the results mean and what actions should be taken next. WHAT CFOS NEED AI TO DO HERE:

1. Detect unusual variances immediately after data sync
2. Automate KPI analysis and reduce manual review cycles
3. Standardize analysis workflows across teams and business units
4. Generate narrative commentary automatically
5. Deliver actionable insights immediately after close — not days later

— PRIORITY FOUR —

04 · COST CONTROL

Continuous Cost Control and Spend Optimization

Cost control remains one of the highest-leverage responsibilities of the CFO. But most organizations still identify spending issues through periodic reviews — and by the time a cost issue appears in a report, it may have been growing for weeks or months. AI changes this by continuously monitoring financial performance. More importantly, AI can identify relationships that humans often miss. Rising transportation costs may appear to be a procurement issue — but deeper analysis might reveal that delayed fulfillment also increased customer returns, replacement shipments, overtime labor, and margin pressure simultaneously. Without AI, these drivers often exist in separate systems. AI can connect multiple operational and financial drivers at once. WHAT CFOS NEED AI TO DO HERE:

1. Analyze spending patterns continuously, not periodically
2. Surface spend anomalies and unusual cost increases automatically
3. Identify the most material changes affecting profitability
4. Correlate operational and financial trends to surface root causes
5. Enable conversational investigation of margin compression, expense growth, or budget overruns

The shift is from “finding problems in a report” to “being told about problems before they become material.” That’s the standard AI should be held to. — PRIORITY FIVE —

05 · RISK & COMPLIANCE

Risk Monitoring and Audit Readiness

CFOs are accountable to boards, auditors, regulators, and investors. That makes explainability non-negotiable. An AI-generated insight that cannot be explained is not an asset — it’s a liability. Every financial conclusion should be traceable back to source data and supported by transparent reasoning. Trust and accuracy are equally important. Generic AI models are designed to answer almost any question about almost any topic. That flexibility creates risk in finance — generic models often make assumptions when context is incomplete, which can lead to misinterpretation of financial information. For a CFO, being confidently wrong is often more dangerous than having no answer at all. WHAT CFOS NEED AI TO DO HERE:

1. Detect fraud indicators and unusual transactions automatically
2. Monitor financial risks continuously, not in quarterly reviews
3. Provide transparent, explainable insights tied directly to source data
4. Support audit readiness with traceable reasoning — every insight should show the formula, the data, and the logic
5. Reduce the risk of AI hallucinations through financial-specific model design

— PRIORITY SIX —

06 · DECISION SUPPORT

Better Decision Support — Not Just Better Reporting

Traditional financial reports tell leadership what happened. The CFO’s real job is helping the organization decide what to do next — and most reporting infrastructure doesn’t support that. The difference between a reporting tool and a decision-support tool is the ability to answer forward-looking questions: What should we do? What decisions carry the highest risk? What operational levers matter most? WHAT CFOS NEED AI TO DO HERE:

1. Help simulate business decisions — hiring, pricing, expansion, budget reallocation
2. Allow strategic follow-up questions in a conversational flow
3. Generate AI-assisted recommendations tied directly to financial data
4. Build executive-ready summaries for leadership discussions and board meetings

The goal is for CFOs to spend less time gathering information and more time driving strategic outcomes. AI should be compressing the gap between “what does the data say?” and “what should we do about it?” — PRIORITY SEVEN —

07 · NATURAL LANGUAGE

Natural Language Access to Financial Data

Not every executive speaks “finance spreadsheet.” A CEO asking why margins declined shouldn’t need to wait days for a custom report. They should be able to ask a question and receive a clear answer immediately. This applies to the CFO as well. The ability to have a genuine conversation with financial data — asking follow-up questions, drilling into specifics, exploring hypotheticals — changes the speed and quality of financial decision-making. WHAT CFOS NEED AI TO DO HERE:

1. Answer questions in plain English with contextual, data-backed responses
2. Maintain context across a conversation — follow-up questions should build on previous ones
3. Generate board-ready narratives and presentation content from conversational inputs
4. Reduce dependence on technical reporting skills across the organization

— PRIORITY EIGHT —

08 · TEAM PRODUCTIVITY

Productivity for the Entire Finance Team

CFOs think about their team’s capacity as much as their own. The most talented financial analysts in an organization are often spending the majority of their time on work that AI can automate: formatting reports, writing commentary, aggregating data from multiple systems, and rebuilding the same analyses in slightly different formats for different audiences. WHAT CFOS NEED AI TO DO HERE:

1. Generate executive summaries automatically after close
2. Export analyses in the format the audience needs — PowerPoint for boards, Word for management packs, Excel for detail reviewers
3. Automate repetitive variance commentary so analysts write it once, not twelve times
4. Allow finance teams to scale insight generation without proportionally increasing headcount

What CFOs Don’t Want: The Non-Negotiables

Understanding what CFOs need from AI is only half the picture. The other half is recognizing the failure modes — the ways AI tools create the illusion of value while introducing new problems. The best AI financial tools are designed specifically for enterprise finance environments: explainable, integrated, secure, and built around the actual workflows finance leaders use every day — not general-purpose AI applied to a finance use case.

A CFO’s Evaluation Framework for AI Financial Tools

Before committing to any AI financial tool, the following questions will quickly separate tools that deliver real value from those that look good in a demo.

AI That Works the Way Finance Leaders Actually Work

The CFOs getting the most from AI aren’t using general-purpose tools applied to finance. They’re using purpose-built systems that understand the structure of financial data, integrate with the systems their teams already use, and produce outputs that can be shown to auditors, boards, and investors without a second thought. Rather than relying on raw transactional ERP data, the most effective tools analyze finalized financial statements — the cleanest and most trusted version of financial information available. This approach allows organizations to begin benefiting from AI without waiting for lengthy data-cleanup projects, while giving finance leaders greater confidence in the quality of the data being analyzed. Telli was built with exactly these requirements in mind — starting with the conviction that AI has no place in enterprise finance unless it’s explainable, private, and genuinely integrated with the financial data that drives decisions.