Finance leaders weighing the promise and pitfalls of artificial intelligence have new guidance from Gartner, which this month published its 2025 Hype Cycle for AI in Finance. The framework highlights technologies closest to mainstream adoption and identifies three near-term focus areas for CFOs: generative AI, composite AI, and responsible AI.
“The pace and potential of AI developments in finance can be overwhelming,” said Alex Levine, Director Analyst in the Gartner Finance practice. “The AI in Finance Hype Cycle aims to help finance leaders cut through the noise and focus on technologies likely to have the most impact in the near-term.”
Gartner’s Hype Cycle methodology charts the maturity and adoption of technologies, showing how innovations move from early promise, through periods of over-inflated expectation and disillusionment, before stabilising into productive use. The finance-specific cycle released this month captures more than a dozen AI applications, each positioned by expected time to adoption.
Near-term priorities —
Among the cluster of technologies forecast to reach mainstream adoption within two years are generative AI in finance, responsible AI, intelligent applications in finance, hyperautomation, explainable AI, and embedded AI in ERP systems. Gartner notes that many of these are currently descending from the “peak of inflated expectations” into the “trough of disillusionment,” a phase where the reality of implementation challenges can outstrip initial hype.
Generative AI is singled out as the most immediate driver of change. The report notes that 80% of independent software vendors are expected to embed generative AI capabilities into enterprise applications by 2026, compared with less than 5% in 2024. “Finance leaders are looking for technologies that help them to collect, review and assess the growing amount of data in the increasingly complex world of finance operations,” Levine said. “Top finance technology vendors know this and see GenAI as a top competitive area, differentiating their products on enterprise readiness, pricing, infrastructure, safety and indemnification.”
Composite AI, also highlighted as a near-term focus, integrates multiple AI techniques to improve reasoning and adaptability. By combining machine learning, rule-based methods and optimisation, the approach allows companies to tackle problems where limited data but strong domain expertise exist. “The business impact of composite AI is significant, as it enables organisations with limited historical data but strong domain expertise to leverage AI for more complex reasoning tasks,” Levine said. The model, however, requires skills in combining and managing multiple systems, with challenges around trust, security and ModelOps complexity.
Responsible AI is the third pillar. Gartner defines this as a framework ensuring ethical, transparent, fair and accountable deployment. “RAI currently flies under the radar for many finance leaders, but it is vital to understand and get right for long-term AI success,” Levine said. With regulatory frameworks such as the EU Artificial Intelligence Act advancing, corporate finance teams must address not only value and efficiency but also auditability, privacy and fairness in AI use.
Mid-term and longer-term outlook —
Beyond the near-term cluster, Gartner places technologies such as decision intelligence, causal AI, and citizen data science on a two-to-five-year horizon. Data storytelling in finance is moving toward broad adoption, while digital twins in finance and data literacy are expected to take between five and ten years to mature. Agentic AI, artificial general intelligence, and quantum AI are considered a decade or more from practical use.
For CFOs, the message is clear: balancing investments in today’s near-ready AI tools with preparation for longer-term innovations will be essential. Gartner’s cycle offers a map of expectation and adoption, but success will depend on execution. As Levine put it, “RAI has grown as AI becomes more deeply integrated into business and society. RAI practices are increasingly formalised through governance structures and industry regulations, requiring organisations to address both organisational and societal responsibilities.”
