Market Data 
Investing.com — Oppenheimer’s recent survey shows trends in the adoption and priorities around machine learning and generative artificial intelligence within the enterprise financial software market.
Conducted among 134 enterprise financial software buyers, the survey provides insights into organizational investment focus, key pain points, and anticipated structural changes within the financial sector.
The findings suggest that while ML and Gen AI adoption is lagging in financial departments compared to front-office functions, these technologies are emerging as essential tools for improving operational efficiency, strategic forecasting, and compliance within the financial ecosystem.
The survey indicates that one of the largest obstacles within the finance departments, particularly in the office of the CFO, is “data gravity,” which refers to the difficulty of managing and integrating fragmented data across systems.
This fragmentation hampers efficient decision-making and the effective deployment of AI technologies. Addressing this challenge by unifying data systems is seen as critical for financial teams aiming to harness AI capabilities for enhanced analytics and forecasting.
The analysts flag that ML and Gen AI hold the potential to simplify complex data environments, improve productivity, and support initiatives, yet require cohesive data infrastructures to be fully effective.
In terms of budget priorities, enterprise financial buyers are increasingly directing resources towards analytics, business intelligence, and continuous planning tools, which are anticipated to benefit from integrated AI functionalities.
The survey reveals that 51% of respondents identified business process automation as a top investment area, while 42% prioritized strategic solutions such as analytics and reporting, planning, and ML-driven corporate performance management. These trends suggest a sustained demand for tools that offer immediate, strategic insights, particularly in today’s volatile economic environment.
Interestingly, organizations are willing to allocate additional funds for Gen AI and ML functionalities. On average, financial software buyers are prepared to pay nearly 6% more for subscription services that incorporate these technologies, signaling an acknowledgement of their added value.
However, generative AI and ML are expected to take longer to become mainstream in the financial sector than in other enterprise functions due to the complex integration and compliance needs of financial systems.
This slower adoption rate underscores a growing recognition of the medium-term potential of AI technologies within finance, with nearly half of surveyed organizations planning implementation within the next year.
