By Steve Saah, Executive Director of Finance and Accounting Permanent Placement, Robert Half
The pace of AI adoption in finance and accounting has been steady but measured. Some organizations are still navigating questions about security, accuracy and oversight. Others are working through training and change management challenges. In many cases, leaders are simply moving carefully by design because they want to see AI tools implemented in their organizations in a thoughtful, consistent way that’s aligned with professional standards.
Taking a structured approach to where AI fits into workflows, how outputs are reviewed and what responsible AI use looks like can pay off, especially since many teams are still building the capabilities needed to work with AI effectively. Research for Robert Half’s latest Demand for Skilled Talent report found that technical, data and systems skills shortages are slowing progress across key initiatives for many finance and accounting teams, including AI and automation technologies implementation. The report also highlights the growing importance of soft skills like critical thinking and adaptability as AI becomes more embedded in everyday work.
Successful AI adoption in any organization also requires focused leadership. Without a clear plan, AI use can remain uneven across teams, with some professionals embracing it quickly, others holding back and managers not always prepared to set expectations or reinforce good habits.
Risk can also arise without good governance. That’s why finance leaders need to understand the benefits of AI for their organization, where progress on AI adoption can break down and which AI best practices can help their teams use AI tools responsibly.
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How finance and accounting teams are leveraging AI—and where progress can slow
Finance and accounting organizations are using AI to support a growing range of tasks across tax, audit, advisory, reporting, compliance and client service workflows. In most cases, AI isn’t replacing core work—it’s helping teams move faster to complete time-consuming tasks such as document intake, summarization, research preparation, analytics and exception spotting.
In many organizations, AI is quietly showing up in the platforms teams already use for research, reporting, planning and other daily tasks, rather than arriving through a single dramatic technology launch. Generative AI assistants for finance and accounting professionals are one example, helping teams work more efficiently within familiar systems and workflows.
Used thoughtfully, these efficiencies may also help reduce some of the pressure that contributes to employee burnout, especially during peak work periods such as tax season, the month-end close, audit deadlines or major reporting cycles. That can be especially true in document-heavy workflows. For example, when a client or internal stakeholder uploads a folder filled with mixed PDFs, statements and prior-year materials, AI can help generate a first-pass summary of what’s included, pull out key details for review and compare files against a prepared-by-client or internal request list so the team can more quickly see what is missing. AI can also help flag duplicate files, mismatches, patterns or outliers, enabling teams to identify and resolve issues earlier.
But seeing AI in workflows isn’t the same as being prepared to use it well. Many finance and accounting firms are still working to build the guidance, training and review standards needed to support consistent, responsible AI use. When expectations around approved tools, verification and oversight are unclear, it becomes harder to turn experimentation into repeatable value. That disconnect can limit the benefits of AI, even when the technology itself shows real promise.
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Successful AI adoption in finance and accounting requires clear guardrails, not just access
Finance and accounting leaders don’t need to solve every AI question or potential risk out of the gate. But they do need to create more structure around where AI can be applied, how it should be used and which AI best practices should guide adoption across the organization
A practical starting point is to define a short list of approved use cases. In many organizations, this work may include document intake, summarization, research and report preparation, forecasting support, variance analysis or identifying data patterns, outliers and items that need follow-up. These use cases also connect to the broader role of data and analytics in the modern finance function.
The next step is to set down clear guardrails for AI use. Teams should understand what tools are approved, what confidentiality rules apply, what information should never be entered into an AI system, and what verification steps are required before output becomes client-facing, management-facing or part of the file. This is also where AI governance in accounting begins to take hold in practical, day-to-day ways. The goal isn’t to create red tape, but to make good habits that are easy to adopt and apply consistently.
Training also needs to be practical. Professionals need to know how to ask better questions of AI, assess the quality of responses, and recognize when an answer is incomplete or needs further verification. Managers need support, too, because they often set the tone for whether AI use becomes disciplined and productive in the organization, or scattered and informal.
A game plan that can help turn AI experimentation into lasting value
As employees expand their use of artificial intelligence, human judgment becomes more important. AI can summarize information, surface patterns and draft language quickly, but finance and accounting professionals still need to decide whether the output is accurate, complete and appropriate to use.
AI literacy isn’t just about writing better prompts. It’s about using tools responsibly, checking the work carefully and bringing professional judgment to every AI-assisted task. That’s why team members working with AI should have clear guidance on how to:
Verify AI-assisted work before it’s more broadly circulated and delivered to stakeholdersProtect confidential or sensitive informationRecognize when outputs are incomplete, unsupported or off-baseEscalate concerns (and to whom) when something doesn’t look right
Finance and accounting leaders can also help strengthen AI literacy across their teams by making it part of day-to-day workflows and talent management strategies. That includes:
Incorporating AI guidance into employee onboarding and trainingClarifying where AI can support workflows and where more caution is neededHelping managers reinforce employees’ responsible use through regular coachingHiring and developing professionals with strong judgment, communication skills and adaptabilityRegularly assessing employees’ comfort with AI and their readiness to build new skills as part of their professional development
From AI in tax preparation and audit support, to AI in close processes, forecasting, reporting and financial analysis, finance and accounting teams have a growing range of opportunities to use AI tools to work more efficiently, improve productivity and deliver more value. Artificial intelligence can help reduce time spent on repetitive tasks, ease friction in document-heavy workflows and support better prioritization.
But those gains aren’t automatic. Realizing the benefits of AI use depends less on how quickly finance and accounting organizations can adopt these capabilities and more on how they intentionally put them to work with structure, purpose and oversight.
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