Finance chiefs brace for AI hyperautomation by 2026

Rossum AI chief executive and co-founder Tomas Gogar expects finance functions to move from experimental uses of artificial intelligence to widespread, outcome-focused deployment by 2026, as organisations tighten governance around return on investment and expand automation across end-to-end workflows.

Gogar set out four main trends for the sector. He pointed to mass adoption of AI that delivers measurable business results, the growth of hyperautomation, stricter demands for demonstrable ROI on AI projects, and a shift from reactive to predictive fraud prevention.

He argued that finance teams will shift from testing AI tools in isolation towards broader use in core processes and decision flows. He linked this change to a desire among senior leaders for clearer operational impact and better use of data.

AI beyond extraction

Gogar said many finance leaders had so far treated AI as a promising technology without fully realising its value. He expects this to change as systems evolve from basic data extraction toward reasoning and decision support inside workflows.

He predicted a more active role for AI in day-to-day finance operations. "AI agents will start to move proactively, summarising sizable reports into actionable insights, or assessing the urgency of a customer complaint and routing it to the correct team without human intervention," said Gogar, CEO and co-founder, Rossum AI.

He said this shift would increase the perceived value of AI as market excitement cools. He also expects human roles in finance departments to evolve toward strategy and oversight, while automated systems handle more routine processing.

Hyperautomation push

Gogar described current automation in finance as fragmented. He said some teams save time on individual tasks while others automate single steps in data handling. He said this approach limits the overall impact on performance.

Rossum AI research cited by Gogar found that more than half of finance leaders report that their processes are only partially automated. He said this shows a gap between current practice and full automation of processes from end to end.

He expects so-called hyperautomation to gain ground as companies look at processes in a holistic way. Hyperautomation connects steps across a workflow and adds human oversight on top of automated systems and AI agents.

Gogar said that in this model, teams that previously focused on manual document handling will supervise systems, handle exceptions and refine rules. He also expects managers to coordinate both human staff and AI-based agents within the same operational structure.

ROI as mandate

Gogar said organisations have grown cautious about AI initiatives that lack clear outcome measures. He linked this to concerns over data governance, audit trails and regulatory requirements, and said this caution is especially visible among chief financial officers.

He expects CFOs in 2026 to place stricter demands on teams proposing or running AI projects. "In 2026, CFOs will be hard on teams delivering measurable impact with AI, or risk losing AI funding altogether," said Gogar.

He said finance leaders will assess AI projects against operational key performance indicators rather than projections and narratives. He expects vendors and internal teams to face pressure to show quantifiable results from the outset of deployment.

Gogar linked this shift to a broader focus on data quality. He said sector-wide audits of data are becoming a priority and noted that 61.6% of finance leaders see improving the accuracy of financial data as their top priority for finance automation.

Predictive fraud strategies

Gogar forecast that fraud risk management in finance will move from reactive responses to predictive systems. He said recent years had seen strategies that responded after incidents, which he said caused significant disruption for businesses.

He expects growing use of automation that scans for early warning signs of fraud and cross-references supplier behaviour against industry benchmarks. He said this approach supports automated launch of investigative workflows that bring fraud teams in earlier in the process.

Gogar linked this trend with the financial impact of data breaches and payment fraud. "This will avoid the $3.9 million cost-risk that data breaches have on payment fraud," said Gogar.

He also warned that advances in defence would likely trigger a corresponding rise in the sophistication of fraud attacks. He expects finance leaders to emphasise continuous evolution of fraud prevention tools and processes rather than one-off upgrades.

"Next year, leaders must focus on constant fraud prevention evolution, to avoid reactive action," said Gogar.