BOTTOM LINE UPFRONT
Agentic AI is no longer optional; it’s the CFO’s new lever for value creation. To unlock its impact, finance leaders must build on clean, structured ERP data, enable real-time forecasting, train agents like team members, and optimize models for both cost and accuracy. Ask yourself these five questions to move from AI hype to measurable business outcomes.
The AI era is upon us (and it’s not new news). We don’t blame CFOs for having reservations; the tech complexity and job displacement fears can’t be reduced to “just noise”. But at the end of the day, AI shouldn’t be treated as the enemy. It should be treated, rather, as an efficiency enabler, one that replaces static planning and manual processes with adaptive, data-driven intelligence that drives value.
Agentic AI, especially.
Agentic AI, (autonomous, task-driven systems that can reason, plan, and execute) represents one of the most transformative developments in artificial intelligence. Agentic AI goes far beyond automation, learning and evolving with your business to drive better forecasting, reporting, and decision-making. CFOs can make the most out of agentic AI in their ERP…if they know how to use it. Ask these five questions to get started:
1. How can you structure your ERP data for intelligence?
ERPs like NetSuite hold some of the most valuable, system-of-record financial and operational data in the business. But even good quality ERP data needs the right definitions, governance, and context to be consistently usable – especially when you’re trying to power AI-driven analysis and automation. In other words: the goal is to make sure that the data you already trust is standardized and organized in ways that enable reliable insight at scale.
As a CFO, part of your role is partnering across the business to strengthen that foundation – aligning Finance, IT, and other stakeholders on common data definitions, ownership, and control. But you can’t go at it alone; ownership of the company’s data strategy should be cross-functional. Finance and IT, along with other relevant functions, need to align on creating a leverageable single source of truth.
But clean, trusted data is only part of the equation. It also needs to be structured in a way that lets AI act on it…and that’s exactly what NetSuite’s new AI Connector Services do. It transforms ERP records into a structured and contextualized format that any LLM can interpret, bridging the gap between raw transactions and business-ready insights. All to say: if you provide the clean data foundation, NetSuite makes it intelligent.
2. How can you look at forecasting as a touchless, dynamic capability?
Today, forecasting that only happens bi-annually, or even quarterly, is out of date. CFOs need to be forecasting consistently, and agentic AI is the key to making it happen. Agents can check in on forecasts weekly, adjusting to changes in real time and eliminating the latency of manual spreadsheet updates.
So what exactly does a touchless, real-time forecast mean? NetSuite’s Director of Technology, Saran Sankar, breaks it down as:
- Rapid multi-scenario modeling, which can be done using predictive forecast explanation in NetSuite Planning and Budgeting – helping finance teams analyze multiple forecast drivers quickly.
- Auto-generated narratives explaining forecast changes, powered by GenAI. NetSuite’s Intelligent Performance Management (IPM) insights produce AI-generated commentary that helps teams quickly understand problems. This supports quick querying, real-time adjustments, and informed decision-making… without the need for deep technical input.
And as Brian Chess, NetSuite’s SVP of Technology & AI, explains in a recent podcast: The goal isn’t just faster forecasting: it’s smarter forecasting. Natural-language querying and AI-driven commentary help finance teams analyze trends, explain results, and make adjustments as conditions shift… all within the systems they already use.
3. How can you enhance your finance team with AI agents?
The (too-often-overlooked) truth about AI is that it’s not about cutting jobs – it’s about elevating efficiency. Still, there’s a not-too-far away world in which the most effective finance teams are those who oversee both humans and AI contributors (to reiterate: these teams are by no means fully automated). Just like you’d staff a team with specialists, your AI agents would also have specialties (for example, a forecasting agent, compliance agent, close process agent, etc.). Each frees your team to spend less time gathering data and more time interpreting it.
But while AI is moving quickly, we’re not quite there yet – and AI should never operate unchecked. As Chess says, keeping a human in the loop is non-negotiable. The most effective systems maintain human oversight to ensure accountability and context-driven judgment.
Still, agents can learn to specialize. Today, agents in NetSuite draft reports, tag anomalies, structure raw data, summarize long-form documents, and more, saving countless people-hours that can instead be allocated to tackle more strategic, high-impact tasks.
4. How can you effectively train your agents?
All that said: AI agents don’t come pre-trained for your business. They must learn how to best be your partner, and it’s your responsibility to teach them. You need to give them context, rules, and reinforcement to help them not only understand what to do, but also how your business thinks. In other words: train your agents like you train your analysts. Do it by:
- Giving agents business-specific frameworks to follow (i.e., forecasting logic, coding standards, and accounting policies).
- Defining boundaries to reduce hallucinations and variance.
- Continuously refine outputs through feedback loops.
- Formatting them to ask questions vs. make assumptions about which direction they should take.
It’s important to underscore that one of the major roadblocks in the agentic AI space is the learning gap between the tech and the industry/business expertise. To get the most out of this type of AI, you need an experienced implementation partner with in-depth expertise in your business’s specific industry to ensure that you’re implementing the right AI for the right use cases.
5. How can you optimize model use for cost efficiency?
Yes, AI should cut costs for your business… but it isn’t free. Poorly trained agents or inefficient models can rack up expenses without adding value, which means you need to make sure that you:
- Choose the right model for the right task (text, data, code, vision, etc.). Your implementation partner can help you navigate that decision.
- Prioritize deterministic behavior in high-stakes tasks like forecasting, close, or compliance.
- Monitor performance and re-train agents to improve efficiency. As Sankar points out: NetSuite’s AI-driven digital assistant features, for example, help manage these interactions through user-friendly interfaces, but must be combined with structured training and governance to be effective.
It’s one of Chess’s foundational points: The most efficient AI isn’t something bolted on top of your ERP; it’s intelligence built directly into it. When AI is embedded in your workflows, it draws from existing data, avoids duplication, and delivers insight without added integration cost.
At the end of the day, cost efficiency comes from having a deployment partner that does more than simply “turn on AI.” You need one that can design operating models where cost, accuracy, and usability work in concert to create measurable value.
The CFOs that will come out in front of the AI race are those that know how to get the most juice from their agentic AI squeeze – and who build systems where humans AI collaborate seamlessly. Asking these five questions helps you get there.