The conversation about AI in finance has moved past general-purpose tools. The next wave is autonomous agents – purpose-built for specific tasks like audit support, FP&A, fraud detection, and regulatory compliance – and adoption is accelerating faster than most finance teams have planned for.
PwC's May 2025 AI agent survey of 300 senior executives found that 79% had already integrated AI agents into their operations, with 66% reporting measurable gains in productivity and cost savings. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.
At MAVI, we're watching this happen in real time with our clients. Finance leaders aren't just hiring for capacity anymore – they're designing hybrid teams where skilled professionals use automated workflows to make faster, better-informed decisions. Controllers are using tools like Cursor to build niche agents that handle specific tasks: report generation, variance flagging, close tracking. These aren't off-the-shelf products repurposed for finance. They're tools built around specific business models, and that's what makes them useful rather than just impressive.
A few examples from companies already operating at this level:
These are operational systems running at scale, not pilots. They reflect what becomes possible when AI is configured for a specific function rather than applied generically across everything.
Despite the momentum at the enterprise level, broader adoption remains cautious. From conversations with finance leaders across our client base, MAVI has found that skepticism is still common. The technology isn't uniformly reliable, and concerns about cybersecurity, implementation cost, and internal adoption have slowed rollouts at most companies. Many teams are watching rather than building, which is understandable.
Custom agents require real expertise to configure correctly. A poorly built agent in a financial workflow can produce errors that are harder to catch than the manual process it replaced, which is a meaningful risk when the output feeds into board reporting or an audit.
The introduction of AI agents doesn't reduce the need for skilled finance professionals. It shifts what those professionals spend their time on. At MAVI, we're seeing accountants and analysts increasingly focused on interpretation, judgment, and decisions that require business context – while custom agents handle raw calculations at scale and surface data-driven analysis in the background.
Leaner finance teams working alongside intelligent systems are starting to outperform larger functional departments running entirely on manual processes. That gap will widen as the tools improve and more teams gain experience configuring them well.
If you're looking for professionals who can help design and build custom AI agents for your finance function, complete this form to access MAVI's network of finance talent with agent-building capability.
Purpose-built automated tools configured for specific finance tasks – report generation, fraud detection, contract review, variance analysis, FP&A modeling – without requiring human input at each step. Unlike general AI tools, custom agents are built around a company's specific workflows and data structures, which is what separates genuinely useful implementations from expensive experiments.
Quickly at the enterprise level, more slowly everywhere else. PwC's 2025 survey found 79% of senior executives had already adopted AI agents, with the majority reporting real productivity and cost benefits. Gartner expects agentic AI to appear in a third of enterprise software by 2028. Mid-market adoption is lagging, largely due to implementation complexity, cost concerns, and cybersecurity questions that most teams haven't yet worked through.
No, they change what finance professionals spend time on. Agents handle high-volume, repeatable calculation and data work. Accountants and analysts focus on interpretation, judgment, and the decisions that require understanding the business behind the numbers. The teams performing well right now tend to run both in combination rather than treating it as a choice.
Usually, by identifying one or two high-volume, repeatable processes where automation would save real time, and where the outputs can be reliably reviewed before acting on them. Building a useful custom agent requires someone with both finance domain knowledge and technical capability, which is the specific gap MAVI is helping companies fill now. Complete this form to learn more.