Finance teams are facing cost pressures and headcount constraints. Against this backdrop, they started adopting AI and automation. In 2025, many finance teams moved past experimentation and started running AI in real use cases, like invoice processing. In doing so, they learned that deploying AI is one thing, making it work inside a finance operating model is another.
Looking toward 2026, the focus is becoming more pragmatic. The priority is better AI execution with tighter governance and higher accuracy. The trends below reflect where those priorities are taking shape.
Cost pressure will continue to drive AI and automation
Cost optimisation continues to be a central theme for finance leaders as organisations plan for 2026. A recent Gartner survey of CFOs shows that more than half rank enterprise-wide cost optimisation among their top priorities. This reflects ongoing pressure to manage costs while supporting growth and investment decisions.
Most organisations already use ERP workflows, shared services, and RPA to handle routine work. What’s changing is how AI is being integrated with these existing capabilities to drive more insight and coordination across processes. AI can help surface patterns in spend and flag where costs are drifting.
With AI, finance teams gain better visibility into cost drivers across processes. They can act on that insight faster using automation. This will increase confidence that cost optimisation reflects business priorities.
AP and AR on the frontline of AI transformation
Accounts payable and accounts receivable continue to be areas where automation and AI deliver clear, measurable value for finance teams. These processes are high volume and inherently rules-based, making them well-suited for both traditional automation and AI-enhanced capabilities.
What makes AP and AR particularly useful for finance teams is the measurable impact on core performance metrics. Studies show that automation and AI together can increase touchless processing rates for invoices and improve match rates. This means more transactions flow through without human intervention. This shifts the team’s focus toward resolving meaningful issues rather than routine work, while keeping human judgment at the centre.
In AP, automation has long helped with tasks like invoice capture and three-way matching. Adding AI in finance improves these capabilities by handling variability in invoice formats, extracting data more accurately, and providing actionable insights. Accounts receivable is seeing a similar evolution. Analysts have highlighted that AI in finance tools help predict which customers are likely to pay late, prioritise follow-ups, and automate cash application. This reduces manual work and supports faster collections.
Gen and agentic AI are reshaping Finance workflows
Generative AI is moving into day-to-day finance work. McKinsey research shows that 44% of finance leaders are now using generative AI across five or more use cases, signalling a shift from pilots to practical adoption. In finance teams, this typically includes drafting variance commentary, summarising reports, and supporting management narratives, with humans retaining review and accountability.
Agentic AI in finance extends this further by connecting insight to action. BCG highlights that early adopters are using agent-based approaches to monitor accounts, prepare reconciliation proposals, and trigger predefined follow-ups under clear rules. This reduces manual coordination and speeds up response to exceptions without removing human oversight.
Shifting from periodic closures to continuous finance
Finance teams are steadily moving away from a heavily manual, month-end–driven close toward more continuous ways of working. This is a gradual redistribution of effort across the month.
Deloitte suggests that finance functions applying automation and analytics more frequently throughout the period experience shorter close cycles and fewer late adjustments. This is largely because issues are identified earlier rather than discovered at month end. Instead of batching reconciliations, variance checks, and controls testing, teams are running these activities more often and at lower cost.
You can leverage AI to perform daily or near-real-time checks on reconciliations, account movements, and exceptions. This reduces volatility at close and improves confidence in in-period numbers, especially for cash, working capital, and key balance sheet accounts.
Closing the AI ROI gap
AI adoption across finance functions has grown rapidly, but translating that into clear, measurable returns remains a challenge for many organisations. A McKinsey survey of CFOs shows that while AI is being used broadly across finance domains, a significant portion of teams still struggle to scale beyond isolated use cases and integrate AI into core processes so that value becomes measurable.
Part of the challenge lies in how ROI is tracked. Research from the Wharton School at the University of Pennsylvania found that around 72% of business leaders now use structured metrics to track returns on AI investments. This included productivity and throughput. However, many organisations still lack rigorous measurement frameworks that tie AI work directly to financial results.
A more disciplined approach helps bridge the gap between deployment and measurable results. Governance, measurement, and integration must be aligned with business outcomes. Only then AI’s impact on cost, quality, and speed becomes clearly visible.
How to get started with AI in finance?
Taken together, these AI in finance trends point to a clear direction for finance in 2026. AI is becoming part of how core finance work gets done. What stands out is that progress is less about replacing systems and more about working across them.
Finance leaders are looking for ways to introduce intelligence and automation without breaking existing processes. They want flexibility, but also clear boundaries around ownership, approvals, and compliance.
Our Digital Clerx platform is designed to let finance teams configure and run AI agents that operate across their existing stack, integrate with ERP and workflows already in place, and run within their own environment.
If you are looking for a headstart in your AI journey, we can help you. Talk to our experts to discover AI possibilities in your finance organization. We can show you how AI agents will transform your workflows through a tailored live demo.
