CFOs need better business intelligence

In a volatile business environment, agentic AI-enabled decision-making is essential to provide the agility, innovation, and compliance that financial departments require.

In my conversations with chief financial officers and their team members, it’s clear organizations across all sectors are under pressure to make smarter decisions. The current business climate is unpredictable, and improving key performance metrics is now more important than ever.

New solutions, powered by agentic AI, can deliver that much-needed improvement – provided organizations are ready to take advantage of them. Being properly prepared requires creating the right roadmap and engaging the right strategic technology partner.

The common conundrum

Every company is different, so each CFO has unique objectives and opportunities. But the key challenges are almost universal.

CFOs are typically tasked with reducing capital and operating expenditures while preventing revenue leakage. They must also ensure the effectiveness of internal controls, and the accuracy of financial statements. And they’re responsible for protecting the enterprise from exposure by ensuring 100 percent compliance with data protection regulations, improving risk identification and mitigation rates, and eliminating fraud incidents.

A company’s own data is an important source of the information required to help CFOs achieve these goals.

Traditional decision-making methods don’t deliver results

Unfortunately, in a highly volatile business environment, legacy business intelligence systems are no longer up to the task. There are several reasons for this shortfall:

• Analytics systems often fail to support strategic foresight and transformative innovation – instead providing business users with yet another dashboard.
• The results are often, at best, a topic for discussion at the next team meeting – not sufficient for a decision-maker to act upon immediately and with confidence.
• Systems typically fail to personalize their output to provide insights contextualized for the person viewing them – instead offering a one-size-fits-nobody result.
• Systems often aggregate data within silos, which means their output still requires additional interpretation to be valuable.

In short, many legacy systems miss the big picture, miss actionable meaning, miss the persona – and miss the point.

Based on my experience, I recommend an organization address this through multi-AI agent systems.

With the introduction of Gen AI Strategic Intelligence System by Capgemini, this could be the very system that bridges the gap between the old way, and a value-driven future. This system converts the vast amounts of data generated by each client, across their enterprise, into actionable insights. It is agentic: it operates continuously and is capable of independent decision-making, planning, and execution without human supervision. This agentic AI solution examines its own work to identify ways to improve it rather than simply responding to prompts. It’s also able to collaborate with multiple AI agents with specialized roles, to engage in more complex problem-solving and deliver better results.

How would organizations potentially go about doing this?

Create a plan for agentic AI-enabled business intelligence

First, organizations must develop a well-defined roadmap to align business objectives with technology, to take full advantage of AI-enabled decision-making.

This starts by identifying the end goals – in this case, the finance team’s core business objectives and associated KPIs. These are the foundation on which the team creates value for the organization, and strengthening them is always a savvy business move. What’s more, it’s not necessary to achieve massive impact on these critical components. Even small improvements – in the range of one to two percent – can deliver enormous benefits.

The roadmap should take advantage of pre-existing AI models to generate predictive insights. It should also ensure scalability, reliability, and manageability of all AI agents – not just within the realm of finance, but across the enterprise. And it should leverage domain-centric data products from disparate enterprise resource planning and IT systems.

Finally, the roadmap must identify initiatives to ensure the quality and reliability of the organization’s data by pursuing best-in-class data strategies. These include:

• Deploying the right platform to build secure, reliable, and scalable solutions
• Implementing an enterprise-wide governance framework
• Establishing the guardrails that protect data privacy, define how generative AI can be used, and shield brand reputation.

An experienced, innovative technology partner

Second, the organization must engage the right strategic technology partner – one that can provide business transformation expertise, industry-specific knowledge, and innovative generative AI solutions.

Capgemini leverages its technology expertise, its partnerships with all major agentic AI platform providers, and its experience across multiple industrial sectors to design, deliver, and support agentic strategies and solutions that are secure, reliable, and tailored to the unique needs of its clients.

This solution draws upon the client’s data ecosystem to perform root cause analysis of KPI changes, and then generates prescriptive recommendations and next-best actions – tailored to each persona within the CFO’s unit. The result is goal-oriented insights aligned with business objectives, ready to empower the organization through actionable roadmaps for sustainable growth and competitive advantage.

Applying agentic AI to generate revenue insights

Here’s a use case that demonstrates the potential of an agentic AI solution.

A finance department requires a 360-degree view of the revenue cycle. Using AI and machine learning, the department hopes to improve sales forecasting and generate automated insights to power revenue growth. This requires a comprehensive view of the sales pipeline, orders, and revenue – with the ability to break these down by customer segment, product segment, sales channel, and geography.

An analytics solution powered by agentic AI can help identify customer behavior – including product preference and churn factors – and provide a comprehensive view of the forecast versus actual performance. It can then provide insights into product and price mix, revenue leakages, and opportunities to prioritize top performing customers.

*The impact can be a five to 10 percent boost to sales forecasting, a 10 to 20 percent improvement in reporting timelines and accuracy, and a five to 10 percent reduction in variance between forecasts and actual results.

The Gen AI Strategic Intelligence System by Capgemini works across all industrial sectors, and integrates seamlessly with various corporate domains. Download our PoV here to learn more or contact our below expert if you would like to discuss this further.