Data and Analytics: A CFO’s Best Friend

Data and analytics. Not quite Laurel and Hardy, but almost; the pair are seldom seen without each other.

Data is currently serving as the star of debates in CFO circles about predictive vs reflective reporting, automation talks and, of course, the centrepiece of any C-suite strategy plan.

The numbers don’t lie; however, it takes a master controller to turn a few ones and zeros into a clear, meaningful strategy. 

“As the stewards of corporate strategy, CFOs are thinking about AI in two ways: how it can help finance teams become more effective, and what risks need to be managed before AI has a material impact on the business,” says Ed Hardy, US Finance Services Leader at Deloitte, exclusively to Finance Chief.

AI is becoming a central boardroom feature, precisely because it can automate easily.

Talks about gen AI and agentic AI may focus on customer journey and procurement, while plain AI started out by automating simple tasks using vast amounts of valuable data. 

“Deloitte’s latest CFO Signals Spotlight report showed 59% of CFOs said balancing business pressures to deploy AI quickly while adequately managing risks is the greatest challenge in developing effective AI governance frameworks. That suggests the issue may not be slow adoption, rather acknowledgement that guardrails need to keep pace with the potential to scale AI,” says Ed.

As business models adapt to shifting market dynamics, CFOs and finance leaders are increasingly relying on sophisticated predictive modeling and real-time reporting to navigate uncertainty.

Moving beyond traditional historical reporting, advanced analytics now empower organisations to identify hidden operational efficiencies, optimise working capital and forecast risk with unprecedented precision.

However, unlocking the full potential of these digital assets requires more than simply deploying the latest technology. 

Analysis of the numbers is something that can be automated too.

For instance, Oracle uses AI agents not only to collate data, but to understand it. Users can interact with agents to ask questions, which require an understanding of the material, as well as instruct it to perform other tasks with the data.

Which begs the question: who is doing it the best? 

Success lies in fostering a robust data-driven culture, standardising governance frameworks and ensuring seamless integration across legacy systems.

When high-quality financial data is combined with intelligent visual analytics, decision-makers gain a clearer, holistic view of their commercial trajectory.

Ultimately, those who harness analytics effectively will not only protect their bottom line, but also uncover sustainable new avenues for growth in an increasingly competitive marketplace.

Agentic AI, as the name suggests, is one of the newer waves of AI – it operates with agency.

Much like humans, however, the agency is driven by an overarching goal. It breaks down complex objectives into sequential steps, evaluates real-time options, uses external software tools and adjusts its approach as conditions evolve.

If something doesn't go quite as planned, it reflects on the result, adjusts its strategy and tries a better path without needing constant intervention.

It acts as a reliable partner – quietly taking care of the tedious mechanics and connecting messy pieces of data to turn everyday workflows into seamless, stress-free experiences.

Agentic

Oracle’s latest major advancement in data and analytics is the deployment of Oracle Analytics AI Agents within Oracle Analytics Cloud (OAC), natively operating across Oracle Fusion Cloud EPM data models.

Moving beyond basic conversational interfaces, these domain-specific AI agents combine large language models (LLMs) with Retrieval-Augmented Generation (RAG) and semantic metadata. 

By incorporating organisational knowledge documents – such as internal policies, governance rules and complex financial definitions – AI agents enable business users to query complex enterprise data using natural language with high accuracy and context awareness. 

Features like Descriptor ID mapping ensure that aggregations, filtering and entity resolution remain mathematically precise even when dealing with non-unique data attributes across multinational divisions.

Furthermore, administrators can provide custom supplemental instructions, sample starter prompts and strict access controls to align AI outputs with corporate reporting standards. 

By unifying conversational AI directly with enterprise resource planning datasets, Oracle equips the Office of the CFO with continuous, real-time variance analysis, faster closing timelines and fully auditable, decision-ready financial intelligence.

Business Logic 

Anaplan’s latest innovations centre on CoModeler, Custom Analyst and Agent Studio, alongside a new set of out-of-the-box applications.

Together, these additions bring conversational, generative, predictive and agentic AI more directly into enterprise planning workflows, with a particular emphasis on helping finance and other business teams make faster, better-informed decisions.

CoModeler is designed to help model builders create, extend, troubleshoot, and optimise planning models through natural-language conversations, while also supporting structured inputs such as CSV specifications.

It can help with model building, performance optimisation, documentation and model health, and it is activated through Agent Studio.

“The mandate to adopt AI has created a new critical challenge for business decision-making: AI must do more than retrieve answers – it must compute them with precision and confidence,” says Adam Thier, Chief Product and Technology Officer at Anaplan.

Custom Analyst gives business users a conversational way to explore custom planning scenarios and get trusted, governed, permission-aware insights from their models. It can return context-rich answers, generate charts and summaries, and show data lineage so users can validate results with confidence.

Agent Studio serves as the centralised environment for building, testing and managing these AI capabilities across planning models.

For finance teams especially, the broader platform story is about combining Anaplan’s deterministic planning engine with conversational AI to support more transparent, auditable and scalable decision-making

Governance

BlackLine’s latest AI direction centres on Verity AI, its embedded intelligence layer for agentic financial operations.

Designed for finance and accounting teams, Verity AI brings specialised agents into record-to-report and invoice-to-cash workflows, with a strong emphasis on governance, transparency and control.

The suite includes agents such as Verity Accruals, Verity Prepare and Verity Match.

Verity Accruals automates the accruals process and can reduce manual work by up to 80% while helping teams close up to three days faster. 

Verity Prepare supports account reconciliations by identifying higher-risk items and retrieving supporting documentation, while Verity Match helps improve match rates and reduce manual intervention in high-volume transactions.

“Finance organisations aren’t looking for AI that simply automates more tasks – they’re looking for AI they can trust with their most critical financial processes” notes Owen Ryan, CEO of BlackLine.

BlackLine also states that Verity AI is built on an auditable, traceable framework with human oversight, and that ISO 42001 certification underpins its AI management approach.

Overall, the platform positions AI as a way to make finance operations more continuous, efficient and decision-ready without losing control.