A look at the AI landscape in finance

AI in finance isn’t on the sidelines anymore, it’s showing up in reports, forecasts, and workflows. From automating routine work to supporting strategic planning, AI is moving full steam ahead. But today’s tools are just the start. In this blog, we’ll walk through where AI is today, where it’s headed, and how finance teams can keep up.

Finance AI landscape today versus the future

From simple automation tools to adaptive systems that support real-time decision-making, AI in finance is shifting fast. We’re seeing this shift across finance teams where, for years, teams have leaned on rule-based automation to accelerate tasks like closing the books, tracking performance and forecasting revenue. These systems followed predictable logic and required structured inputs to function.

Generative AI has expanded what’s possible for finance teams. Unlike earlier automation, which required structured input, generative AI can analyze unstructured data to generate written content or summaries and highlight patterns or that might otherwise have been missed in the past. Many finance teams are using these tools to speed up financial reporting, simplify variance analysis or draft budget narratives. But the conversation is already moving beyond productivity gains. As AI capabilities mature, the focus is shifting toward systems that don’t just generate but can also decide, act and adapt in real time.

A growing number of companies are beginning to explore a new generation of AI: Agentic AI. You might’ve seen it mentioned in vendor roadmaps or heard your own team wondering how it fits into the future. Unlike generative AI, which works within a defined set of tasks, agentic AI is designed to act, adapt, and handle decision-making. It’s not just creating data but acting instinctively with it.

As finance leaders look ahead to the next 3–5 years, AI won’t be something on the side. It’ll be built into how finance teams budget, plan and respond to change.

According to McKinsey, by 2030 nearly 70% of companies will have adopted some form of AI to improve decision-making at scale. This major shift means finance teams will need the right tools, infrastructure and mindset to keep pace.

Future of Generative AI

Generative AI, or gen AI, refers to systems that can create content based on large data sets. In finance, gen AI is being used to automate repetitive work like writing budget narratives, summarizing performance metrics, and assembling reports.

It’s especially useful for tasks that are consistent and well-defined. For example, in the finance AI landscape, an analyst might use gen AI to pull together a monthly board report using predefined parameters. It reduces the time spent compiling data and allows more focus on what the data means.

In the future, generative tools will move from standalone assistants to embedded copilots within platforms that finance teams already use. For example, instead of pulling a budget summary manually, users will be able to ask, “What changed in OPEX last quarter?” and get an AI-generated answer within their planning tool. Prophix is already delivering this with Prophix Copilot, which helps users generate insights and draft content directly within their planning workflows.

Gen AI will continue to help finance teams move faster. But as complexity increases, teams will need more than content creation; they’ll need tools that can adapt and act.

Future of Agentic AI

Agentic AI is designed to go beyond automation. Instead of producing an output and stopping there, it acts, adjusts based on results, and continues learning along the way. Agentic AI represents the next leap in finance teams, not just interpreting information but acting on it. It’s how CFOs move from reacting to market shifts to actively shaping them by driving enterprise value.

Think of it this way: gen AI might help build a forecast. Agentic AI takes that forecast, tests it against new market conditions, updates it and pushes a workflow forward without being prompted.

This is exactly the kind of intelligence we’re building into Prophix One Intelligence. The ability to detect workflow bottlenecks, reroute approvals, and recommend plan changes, all without extra input. It can handle routine decisions, keep processes moving, and catch risks before they escalate. Whether it’s reassigning tasks in a delayed workflow or adjusting a cash flow plan based on updated inputs, agentic AI can help finance teams lead with speed and precision.

As Prophix CEO Alok Ajmera shared at Prophix Live! 2025, CFOs are evolving from a financial manager to the chief enterprise value officer. Technology is transforming our role from number-crunching to strategic value creation.

Generative AI vs agentic AI: how they work together

Generative and agentic AI are not competing technologies, but complementary tools.
Gen AI helps create things: reports, projections, and summaries. Agentic AI helps make them work by applying those outputs, monitoring results and adapting accordingly.
Together, they offer a more complete AI model for finance. This is the approach behind Prophix One Intelligence, which combines gen AI and agentic AI to help finance teams plan, decide and act without friction. From drafting board-ready content to rerouting approvals automatically, it’s designed to support both insight and execution at scale.

Learn more on how both generative and agentic AI are evolving together inside Prophix One Intelligence.

This direction is a key part of the Prophix roadmap: a smarter, more adaptive platform built to help finance teams operate at their full potential.

Future of generative AI and agentic AI for finance teams

The next five years will bring rapid changes to the future of AI and how finance works. Leaders who explore AI now will have a clear advantage of quicker decisions, more accurate forecasts and the ability to contribute strategically across the business. Those who wait may find themselves playing catch-up.

If you’re not sure where to start, begin by looking at your team’s most time-intensive processes. Are there reporting tasks that take hours to complete? Is your variance analysis delayed by slow data consolidation? These are strong entry points for AI adoption.

The AI landscape in finance is still taking shape. But the direction is clear, and it starts with finance leaders making practical decisions today.