Audit technology is entering a more consequential phase. Across the accounting industry, cloud platforms, audit data analytics, and automation have already changed how CPA firms manage engagements. Now, AI in auditing is moving beyond individual tasks toward agentic capabilities that can help coordinate related activities across the audit workflow.
This evolution isn’t about removing auditors from the process. By helping teams organize information, surface exceptions, and manage handoffs, next-generation audit technology can give auditors more time to focus on risk, evidence, and professional judgment. As technology supports more of the workflow, human oversight, professional skepticism, and accountability become even more important.
Benefits of integrated, cloud-based audit software
Cloud-based audit software provides a flexible foundation for managing engagements, supporting collaboration, and adding new capabilities. Its value goes beyond remote access. An integrated audit platform can help information move more consistently between applications and across audit and tax workflows, reducing repeated data entry while giving managers better visibility into engagement status.
Scalability and operational flexibility
Cloud technology allows firms to adjust resources as engagement volumes and staffing needs change. It can also reduce some of the infrastructure, maintenance, and associated costs of physical servers and on-premise applications. For growing firms, a common technology environment can make it easier to add people, offices, services, or acquired practices.
Better collaboration
Cloud-based audit technology gives authorized team members access to current workpapers, data, and engagement information wherever the work is performed. When remote teams, specialists, reviewers, and client locations work from the same information, staff spend less time tracking files and status through email and spreadsheets. This can also reduce avoidable client follow-up and leave more time for substantive conversations.
Connected audit workflows
Integrated audit software and audit workflow automation can reduce duplicate entry and help information move across applications. CCH Axcess™ Audit solutions connect capabilities supporting engagement management, digital workpapers, methodology, confirmations, and analytics.
Fewer manual handoffs give reviewers a clearer path through the work, including where information originated, how it was used, and which matters require judgment.
Resilience and governance
Cloud audit technology can support continuity when firms cannot access a physical office or local server. Firms should also evaluate how a system uses encryption, multifactor authentication, permissions, and access controls to protect information and support oversight.
How AI and agentic capabilities are changing audit work
Audit data analytics, machine learning, and AI are becoming more deeply embedded in the audit workflow. Analytics can help teams examine larger populations and identify unusual activity, while generative AI can assist with defined activities such as summarizing documents and organizing information for review.
Agentic capabilities extend that progression. Instead of supporting one isolated task, an AI agent may assist with a sequence of related activities within a governed workflow, such as organizing incoming information, identifying missing items, surfacing exceptions, and routing work for review.
The value of agentic AI in audit is not autonomy for its own sake. The auditor remains responsible for evaluating evidence, questioning the output, documenting the rationale, and reaching the conclusion. Wolters Kluwer’s approach to Expert AI reflects that relationship: AI should augment professional judgment, not replace it.
Audit data analytics and risk assessment
Audit data analytics can help teams evaluate larger transaction populations and identify patterns or anomalies that warrant attention. Analytics may also support fraud-related procedures by surfacing unusual journal entries, transactions, or relationships for investigation.
Technology does not determine whether an item represents a misstatement, control deficiency, or fraud. It helps auditors decide where to look more closely.
Read more about the evolving role of agentic risk assessment in audit planning, including how embedded analytics can surface signals earlier in an engagement.
AI-assisted document analysis
Audit teams spend substantial time requesting, organizing, and reviewing documents. AI-assisted document analysis can help classify information, extract relevant details, identify gaps, and prepare items for review.
A more organized intake process can help teams recognize missing evidence earlier and reduce avoidable follow-up. Auditors still need to assess whether the information received is relevant and reliable.
Explore how document analysis in audit automation can support document intake and prepare information for auditor review.
Agentic AI, oversight, and traceability
Agentic AI changes the technology discussion from “Which task can be automated?” to “How should work move through the engagement?” Firms should evaluate how a system manages source information, permissions, exceptions, approvals, and escalation.
Reviewers also need to understand what information the technology used, what actions it took, and where professional judgment entered the process. Responsibility for reviewing agent-assisted work and documenting consequential decisions should be built into the workflow.
Firms can use a strategic framework for connected, agentic audit workflows to evaluate the integration, governance, and human review required to support these capabilities.
Where blockchain fits into audit technology
Blockchain remains relevant when auditors encounter digital assets or transactions recorded on distributed ledgers. Auditors may need to understand how information is recorded and whether blockchain data is relevant and reliable.
A blockchain record does not establish the transaction’s economic substance, the parties' authority, the asset’s value, or the appropriateness of its accounting treatment. For most firms, blockchain will remain a specialized competency rather than the technology organizing the broader audit workflow.
Training audit teams for AI-assisted work
Technology training cannot stop at teaching staff where to click. Audit professionals need to understand what the technology did, what information it used, where the output may be incomplete, and how their judgment should be documented.
Training should cover:
- Appropriate uses of approved AI and analytics capabilities.
- The relevance and reliability of underlying data.
- Review of AI-assisted output and the application of professional skepticism.
- Documentation, confidentiality, security, and escalation.
Staff need enough understanding to recognize when something does not look right, while managers need to know how technology-assisted work affects supervision and review. Firm leaders should address the related governance and risk considerations.
Emerging trends in audit technology
Audit leaders should focus on developments that materially affect how teams obtain evidence, manage work, and support review.
Agentic AI moves beyond isolated tasks
Agentic AI can support multistep work within defined parameters, coordinating information, tasks, exceptions, and review points. Its practical value will depend on whether firms can maintain clear authority, documentation, and human review.
Connected data creates capacity
Application programming interfaces and other integrations can reduce duplicate entry and unnecessary handoffs. They also provide a stronger foundation for analytics and AI, which cannot compensate for incomplete, inaccessible, or poorly governed data.
Analytics enters the everyday workflow
As audit data analytics becomes embedded within audit software, more engagement teams can apply data-driven procedures in the normal course of their work. Auditors must still define the purpose of the analysis, evaluate the data, interpret exceptions, and determine the appropriate response.
AI governance becomes part of audit quality management
Firms need clear policies for approved systems, permitted uses, client information, human review, documentation, and escalation.
Audit technology in practice
Advanced, connected audit technology can address specific points of friction across an engagement, from data preparation and review to confirmations and business continuity.
Reducing audit review time
For Drazen Henke & Associates, the review process had become an opportunity to reclaim time. After transitioning to CCH Axcess™ Engagement, reviewers could complete their work much faster, helping the firm move engagements through review more efficiently. According to Nick Nieting, the firm reported a 50% reduction in review process time.
Preparing trial balances more efficiently
At BNA CPA, preparing a trial balance for import used to take 30 minutes to an hour. Across 300 to 400 returns, that routine step consumed considerable staff time. After the firm adopted CCH Axcess Engagement for business tax, Jason Ackerman reported that the process took one-fifth the time it had before.
Supporting operational continuity
A hurricane put the cloud model to a real-world test for JC CPAs and Advisors. With CCH Axcess Engagement and CCH AxcessTM Knowledge Coach, the firm was able to continue working while local businesses that depended on on-premises systems faced disruptions. Melissa Knox emphasized the value of maintaining operations during a week when many others could not.
Streamlining audit confirmations
JamisonMoneyFarmer wanted to simplify a repetitive part of the confirmation process. With CCH Axcess™ Validate, the firm can enter a bank once and request balances for multiple clients rather than submitting each request separately. Andrea Armstrong said the change saves both time and resources compared with the firm’s previous system.
Expanding journal entry analysis
At Smith Marion & Co., journal entry testing once relied on a sampling process that took about 10 minutes. With AI-powered TeamMate® Analytics, the firm can complete that process in seconds while analyzing data for anomalies and trends. Pete Wesch described the shift as moving beyond sampling toward a risk-based analysis.
Adding TeamMate Document Linker, also powered by AI, can extend the workflow by matching supporting evidence with audit documentation.