Accounting and finance teams are investing in AI, machine learning, cloud ERP and stronger data capabilities, but adoption does not automatically mean measurable returns. The most important trend is therefore not a single tool: it is the shift toward connected, AI-enabled finance—and the work required to make that shift secure, useful and accountable.
What are the biggest technology trends in accounting and finance?
Survey findings point to six connected priorities: AI and automation, machine learning, cloud ERP, cybersecurity and privacy, data foundations, and workforce skills. They describe what surveyed leaders use or expect to invest in—not a universal technology roadmap or a guarantee of results.
In Gartner’s October 2024 survey of 383 finance leaders, cloud ERP, generative AI and machine learning were among the technologies respondents expected to invest in. Gartner published the results on March 19, 2025. Read Gartner’s survey release.
Other surveys provide different snapshots, not a single comparable adoption trendline. Deloitte’s October 2025 release on its Finance Trends 2026 survey found widespread reported AI deployment alongside much lower reported measurable ROI. Gartner separately reported that 59% of surveyed finance leaders used AI in their finance function in its 2025 survey, compared with 58% in its 2024 survey. The organizations, samples, dates and question definitions differ; these figures should not be combined. Deloitte’s survey release · Gartner’s AI survey release.
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How is AI changing accounting and finance?
AI is moving from experimentation toward active use, while finance leaders still face the harder question of whether it produces clear business value. Deloitte’s 2025 release on its Finance Trends 2026 survey reported that 63% of surveyed finance leaders had fully deployed and actively used AI solutions; 21% reported clear, measurable ROI. Those are separate measures: deployment does not establish that a tool has paid off.
Agentic AI—systems designed to act through sequences of tasks—appears to be at an earlier stage than broader AI use. In the same Deloitte survey release, 14% of surveyed finance leaders said they had fully integrated AI agents. That finding applies to the survey’s respondents, not to all finance teams.
For accounting and finance teams evaluating AI, start with a defined workflow or decision and a measure of success. Specify what result would count as improvement, who reviews system outputs, and how performance will be monitored. Avoid treating a general claim about AI capability as evidence that a particular tool will suit your processes.
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Adoption barriers depend on maturity
Deloitte reported that legacy technology and justifying ROI were barriers among early-stage AI adopters, while data privacy was a concern for advanced-stage adopters. The distinction matters: teams blocked by disconnected systems may need to address integration first, while teams using AI more extensively must pay close attention to how sensitive information is handled.
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Cloud ERP is among the investment areas identified in Gartner’s October 2024 finance-leader survey. Moving finance systems to the cloud can be part of modernization, but the survey does not show that migration is right for every organization. The practical question is whether a proposed system can support the processes and information flows the organization actually needs.
Before choosing or replacing a system, assess the workflow, integrations with existing applications, data quality, security requirements, implementation effort and the people responsible for ongoing operation. Legacy systems can also constrain AI adoption, as Deloitte’s survey findings on early-stage barriers indicate. Modernization should address a defined operational need rather than serve as a technology goal by itself.
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Data quality, cybersecurity and governance
AI-enabled finance depends on usable data and controls around access and outputs. ACCA and CA ANZ’s July 2026 report, based on a global survey of 1,600 finance professionals, emphasizes bridging data and skills gaps, defining business problems and ROI, and establishing governance. These foundations shape whether a technology initiative can be evaluated and managed responsibly. See the ACCA and CA ANZ report.
Privacy and cybersecurity are not separate from implementation. Deloitte identified data privacy as a concern for advanced-stage AI adopters. AICPA’s 2026 CPA Firm Top Issues Survey release also lists cybersecurity risk and data privacy among profession-level concerns. For any proposed system, teams should determine what financial or personal information it processes, who can access it, and how its use and outputs will be governed. Read AICPA’s survey release.
Skills and finance roles are changing
Technology change also changes what finance teams need to do well. Deloitte’s October 2025 release reported that 64% of surveyed finance leaders planned to prioritize AI, automation and data-analysis capabilities over traditional skillsets in the coming year. Separately, a summary of the AICPA & CIMA Technology, Productivity, and Skills Survey says 46% of finance leaders identified generative AI as their teams’ most significant skills gap for 2025. AICPA & CIMA reported 1,446 member responses collected in August–September 2025; the surfaced summary does not state a publication date. The two percentages come from different questions and respondent groups, so they are not directly comparable.
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Training alone may not resolve a skills gap if teams lack reliable data, clear ownership or an agreed process for using outputs. ACCA and CA ANZ’s report treats data readiness, business-problem definition, ROI and governance as linked requirements. That makes implementation an operating-model decision as well as a software decision.
Technology change is also a concern for accounting firms. AICPA’s 2026 CPA Firm Top Issues release identifies change management for technology and AI as a long-term issue facing firms, alongside cybersecurity risk and data privacy. This reinforces the need to plan for process changes and staff readiness, not just system installation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a finance technology investment
Use the same decision questions for AI tools, cloud ERP and related systems. They are practical evaluation criteria drawn from the barriers and priorities described in the cited surveys, not a vendor ranking.
Quick Recap
- Define the business problem. Name the workflow or decision that needs improvement and the people affected.
- Set a value measure. Decide how you will define and track ROI before deployment, rather than equating usage with success.
- Check systems and data. Confirm that integrations, legacy applications and data quality can support the intended use.
- Set privacy and governance controls. Identify information exposed to the system, access permissions, review responsibilities and how outputs will be overseen.
- Assess workforce readiness. Determine which skills, training and process changes are needed and who will own them.
- Scope implementation and ongoing operation. Make sure the organization has the capacity and ownership to deploy, maintain and evaluate the change.
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