Knowledge work automation
9 min read
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Jul 17, 2025
Beyond simple automation, agentic AI is redefining audit. Explore the platforms, frameworks, and future of an AI-powered practice.
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The numbers are staggering: 72% of companies are already piloting or using AI in financial reporting processes, with projections showing this will reach 99% by 2027. Yet despite this explosive adoption, many auditors remain trapped in the same time-consuming manual processes that have plagued the profession for decades. The average audit professional spends countless hours on client information gathering, data ingestion, transaction analysis, and documentation—tasks that intelligent systems could handle with remarkable efficiency.
This disconnect between AI's potential and audit reality stems from a fundamental misunderstanding of what modern AI can accomplish. While most professionals think of AI as sophisticated automation—chatbots that answer questions or tools that extract data—the future belongs to something far more powerful: agentic AI systems that can reason, plan, and act autonomously to complete complex audit cases from start to finish. According to IBM, this shift moves us beyond simple generative outputs to goal-driven actions.
The transformation isn't just about efficiency gains, though those are substantial. Advanced AI users already save 71% more time than beginners, unlocking seven additional weeks of capacity per employee annually. The real revolution lies in moving from task-based automation to case-based delegation, where AI agents become trusted partners capable of handling entire audit workstreams while maintaining the professional judgment and oversight that defines quality auditing. This is the core of the agentic AI proposition.
The AI Audit Workflow: From Tedious Tasks to Delegated Cases
Traditional audit workflows remain stubbornly manual despite decades of technological advancement. Consider the typical engagement process: auditors spend hours gathering client information through email chains and phone calls, manually ingesting data from disparate systems, painstakingly analyzing transactions through sampling methods, and documenting findings in standardized but time-intensive formats. Each step requires human intervention, creates bottlenecks, and introduces opportunities for error or oversight.
These pain points aren't merely inconveniences—they represent fundamental limitations in audit coverage and quality. According to the Journal of Accountancy, traditional sampling methods create "needle in a haystack" scenarios where significant risks may go undetected simply because they weren't included in the sample. Manual data processing cannot handle the scale of modern financial datasets, leading to incomplete analysis and potential blind spots. Time-intensive documentation requirements, as TechRepublic points out, force professionals to spend disproportionate effort on administrative tasks rather than analytical thinking and client advisory services.
The solution isn't better automation of these existing processes but rather a fundamental reimagining of how audit work gets accomplished. Instead of programming systems to follow predetermined workflows, agentic AI introduces autonomous agents that can reason through complex scenarios, adapt to changing circumstances, and complete entire audit cases with minimal human intervention while maintaining full transparency and professional oversight, a vision shared by platforms like V7 Go or UiPath.
This paradigm shift moves beyond the traditional question of "How can we automate this task?" to ask "How can we delegate this entire case to an intelligent agent?" The distinction, as Automation Anywhere clarifies, is crucial: automation handles repetitive steps within existing workflows, while delegation empowers AI agents to own complete processes, make contextual decisions, and deliver finished work products that meet professional standards. The implications extend far beyond efficiency gains. When AI agents handle routine case work, human professionals can focus on higher-level strategic analysis, complex judgment calls, and value-added advisory services. Research published by INFORMS supports this, indicating that as AI handles black-box tasks, managerial attention shifts to strategic interpretation. This represents a fundamental evolution in professional roles, from transaction processors to strategic advisors, from task managers to case supervisors.
The 2025 AI Audit Software Landscape: A Functional Breakdown
The enterprise audit software market has consolidated around several dominant platforms, with a new generation of specialized and agentic AI tools emerging to address specific workflow challenges. Here's a breakdown of the key players and categories shaping the 2025 landscape.
Agentic AI & Document Intelligence: The New Frontier
This emerging category moves beyond traditional automation to create truly autonomous audit agents.
V7 Go
V7 Go represents the cutting edge of agentic AI technology, launched in 2024. The platform's Index Knowledge technology breaks down complex documents into analyzable components, while its Chain of Thought reasoning enables AI agents to work through multi-step audit processes independently. As detailed in the Harvard Business Review, this agentic architecture allows for sophisticated conditional logic and workflow routing, with the ability to escalate high-sensitivity findings to human review automatically. The platform's multimodal capabilities handle text, tables, charts, images, and videos simultaneously, making it particularly effective for complex financial document analysis. V7 Go's accuracy rates of 95-99.9% on complex tasks, combined with its ability to provide transparent AI citations linking back to source materials, address traditional concerns about AI reliability in audit contexts.
Reality Check
Early adopters report remarkable results, with one financial services client achieving 21x faster processing of financial statements while improving accuracy by 54%. The platform's ability to deploy complex AI workflows at scale particularly appeals to firms handling large volumes of documents with varying formats and complexity levels.
All-in-One GRC & Audit Management Platforms
This category is dominated by comprehensive platforms offering end-to-end audit, risk, and compliance (GRC) management, increasingly enhanced with sophisticated AI features.
AuditBoard
AuditBoard commands the premium market with over 50% of Fortune 500 companies as clients. The platform's purpose-built AuditBoard AI represents an advanced implementation of artificial intelligence in GRC, featuring automated content generation for risk and control descriptions, intelligent mapping between risks and requirements, and cross-audit summaries for executive reporting. The system's automated vendor assessments use historical data to streamline third-party risk management, while intelligent staffing recommendations optimize team assignments based on skills and IIA standards. AuditBoard's connected risk approach unifies audit, risk, ESG, and compliance processes, creating a comprehensive ecosystem for enterprise governance.
Workiva
Workiva focuses on integrated reporting excellence, serving mid-market to Fortune 500 companies with particular strength in financial services and regulated industries. The platform's Workiva AI leverages generative capabilities for content creation and refinement, combined with extended file intelligence for sophisticated document analysis. With over 100 pre-built connectors to systems like SAP, Oracle, NetSuite, and Workday, Workiva excels at data transformation and unified reporting across financial and sustainability metrics. The platform's strength lies in its ability to connect financial close processes to final reporting, with automated data workflows that eliminate manual reconciliation.
Diligent HighBond
Diligent HighBond offers a transparent pricing structure in the enterprise segment, with plans starting at $5,000 annually while maintaining enterprise-grade capabilities. Recently rebranded as part of the Diligent One Platform, the system integrates board management with comprehensive GRC capabilities, making it particularly attractive for organizations seeking unified governance solutions. The platform's AI-powered applications provide automated risk identification, continuous monitoring, and anomaly detection across 100+ third-party data providers.
Reality Check
User sentiment across professional forums reveals interesting patterns. AuditBoard users consistently praise the platform's user experience and customization capabilities, with one implementation specialist noting, "The team at AB make things very easy." However, users also report challenges with complex reporting and long control naming conventions. Workiva generates strong advocacy among users who appreciate its comprehensive data connectivity, though some note performance issues during peak usage periods. Diligent HighBond receives praise for its intuitive interface and analytics capabilities, though users sometimes find the reporting features slower than competitors.
Intelligent Data & Anomaly Detection Specialists
This specialized segment focuses on AI-powered examination of financial data, offering capabilities that go far beyond traditional audit sampling methods.
MindBridge AI
MindBridge AI pioneered the application of ensemble artificial intelligence to financial auditing, combining statistical models, machine learning algorithms, and business rules to analyze 100% of transactions rather than samples. According to Allenvisioninc, the platform's unsupervised learning approach identifies patterns without predefined parameters, while its explainable AI capabilities provide clear justification for every flagged transaction. User reviews often highlight this transparency. MindBridge's strategic alliance with KPMG demonstrates its credibility in high-stakes audit environments. The system processes hundreds of millions of data rows while maintaining transparent audit trails that satisfy regulatory requirements.
Thomson Reuters Audit Intelligence
Thomson Reuters Audit Intelligence represents the newest entrant in this space, launched in September 2024 with ambitious plans for expansion. The platform combines generative AI with decades of Thomson Reuters' audit expertise, offering intelligent risk segmentation that can reduce sample sizes by up to 50% while maintaining audit quality. The system's ability to ingest both general ledger and subledger data provides comprehensive analysis capabilities, while automated documentation generation streamlines the creation of audit work papers. As reported by InsideAI News, Thomson Reuters' planned integration with CoCounsel, their professional-grade generative AI assistant, promises to deliver full agentic capabilities by 2025.
Reality Check
Users of MindBridge consistently highlight its effectiveness at identifying high-risk transactions that traditional methods might miss. As one practitioner noted, "Since using this product, we have obtained clients because we use AI. Not because we lowered our price, but because we are using AI to target the risk at the transaction level." However, users also report interface inflexibility and limitations in data search capabilities that require workarounds.
Embedded Workflow Accelerators
This category represents tools that integrate directly into existing audit workflows, enhancing productivity without requiring wholesale platform changes.
DataSnipper
DataSnipper has achieved remarkable market penetration with over 500,000 users across 125 countries, including adoption by all Big Four firms. The platform's Excel-native approach leverages auditors' existing skills while adding AI-powered document processing capabilities. Its DocuMine feature, launched in 2024, uses large language models to analyze documents and extract relevant information, while the Advanced Extraction Suite processes bulk documents with up to 70% efficiency improvements. The platform's "no hallucination" approach ensures that all AI outputs link directly to source documents, maintaining audit quality while dramatically improving speed.
XBert
XBert targets the SME audit market with continuous monitoring capabilities powered by over 80 machine learning algorithms. The platform's strength lies in its ability to provide 24/7 scanning of accounting files, automatically identifying errors and inconsistencies in real-time. According to Appadvisoryplus, XBert's practice management integration makes it particularly valuable for smaller firms seeking to combine audit automation with workflow optimization. The system's auto-resolve functionality enables one-click resolution of identified issues, significantly reducing manual review time.
Reality Check
DataSnipper users consistently praise its seamless Excel integration and time-saving capabilities. One user reported, "The tediousness of capturing is definitely solved by using DataSnipper and the amount of time-saving is fantastic." However, some users note performance issues with image and text processing that can slow overall audit workflows. XBert receives strong feedback for its ability to identify issues quickly, with one user noting it "removed the term 'rework' from the firm's vocabulary."
Overcoming the Trust Deficit: A Framework for Evaluating AI Audit Tools
The adoption of AI in audit contexts faces a fundamental challenge: the profession's well-founded skepticism about automated systems making critical decisions. Professional auditors are trained to maintain professional skepticism, verify information independently, and take personal responsibility for audit opinions. The introduction of AI systems that can operate autonomously creates tension between efficiency gains and professional accountability.
This trust deficit manifests in several ways. Auditors worry about AI "hallucinations"—instances where systems generate plausible but incorrect information. Recent research highlights the risks of algorithmic audits. They question whether AI can truly understand the nuanced contexts that inform professional judgment. They fear regulatory scrutiny of AI-driven audit procedures and wonder whether they can adequately defend AI-generated conclusions to clients and oversight bodies. The solution isn't to dismiss these concerns but to establish rigorous evaluation frameworks that separate genuinely trustworthy AI systems from those that merely promise efficiency gains. Professional auditors need tools that enhance rather than replace human judgment, systems that provide transparency rather than black-box recommendations, and platforms that strengthen rather than weaken audit quality.
Verifiability
Verifiability forms the foundation of trustworthy AI audit systems. Every AI-generated insight, recommendation, or conclusion must link directly to source materials that human auditors can review and verify. This requirement eliminates systems that rely on opaque algorithms or training data that cannot be audited. DataSnipper's approach of ensuring all AI outputs connect to source documents exemplifies this principle, while V7 Go's AI citations provide transparent links between conclusions and supporting evidence.
Accuracy
Accuracy must be measurable and consistent across different types of audit tasks. Systems should provide specific accuracy metrics for different use cases, with clear documentation of testing methodologies and performance benchmarks. MindBridge's transparent reporting of risk detection rates and Thomson Reuters' disclosure of sample size reduction percentages demonstrate approaches to accuracy transparency.
Data Security
Data security in AI audit tools requires more than standard cybersecurity measures. These systems process the most sensitive financial information of public and private companies, requiring enterprise-grade security that meets or exceeds regulatory requirements. SOC 2 Type II certification provides baseline assurance for US-based audit services, while ISO 27001 certification demonstrates comprehensive information security management for international operations. As Imperva explains, these certifications are critical for data security. However, AI-specific security concerns require additional attention to model security, training data protection, and algorithm transparency.
Customization and Integration
Customization capabilities determine whether AI tools can adapt to specific audit methodologies, client requirements, and regulatory environments. Generic AI systems often fail in professional contexts where nuanced understanding of industry-specific requirements drives decision-making. The most effective AI audit tools provide configurable business rules, customizable risk parameters, and flexible reporting formats that align with firm-specific audit approaches. Integration with existing audit management systems and client environments often determines practical success or failure. AI tools that require wholesale replacement of existing systems face significant adoption barriers, while those that enhance existing workflows achieve faster implementation and user acceptance. Excel-native and spreadsheet-based solutions like V7 Go succeed partly because they leverage existing user skills and established workflows.
The Future-Ready Firm: Building Your AI-Powered Practice
The integration of AI into audit practice represents more than a technological upgrade—it requires a fundamental reimagining of professional roles, client relationships, and service delivery models. Firms that successfully navigate this change will gain significant competitive advantages, while those that resist risk obsolescence in an increasingly AI-powered marketplace.
Evolving Auditor Roles: This reflects the shift from transaction processing to strategic advisory services. As AI agents handle routine data analysis, document review, and compliance checking, human auditors increasingly focus on interpreting results, making complex judgments, and providing strategic guidance to clients. Academic research shows this evolution requires new skills in AI supervision, data interpretation, and technology-enabled client advisory services. The concept of AI as a "Chief of Staff" for auditors illustrates this transformation. As described by PYMNTS.com, AI agents orchestrate multiple specialized functions—managing data collection, coordinating analysis across different risk areas, maintaining audit documentation, and providing real-time insights that inform human decision-making. This partnership model amplifies human expertise rather than substituting for it.
Changing Client Expectations: As AI capabilities become more widely understood, clients increasingly expect auditors to provide predictive analysis, real-time risk monitoring, and strategic insights that go beyond traditional compliance reporting. A striking 52% of organizations now want auditors to prioritize predictive analysis, while AI-adopting firms report that 50% offer more innovative services to clients.
Technology Infrastructure: Requirements extend beyond individual AI tools to encompass comprehensive platforms that can support agentic AI operations. This includes robust data management systems, secure API integrations, and scalable computing resources, a topic covered by firms like Aisera and McKinsey. Firms must also invest in governance frameworks that ensure AI systems operate within professional and regulatory requirements. AuditBoard's analysis highlights that the most successful firms will adopt a portfolio approach, combining specialized tools with comprehensive platforms.
Talent Development & Regulatory Adaptation: Firms need professionals who can effectively supervise AI agents and translate AI insights into actionable client recommendations. Wolters Kluwer emphasizes this need for new skills. The transformation also creates new career paths in AI governance and technology-enabled assurance services, a trend noted by industry observers. Regulatory adaptation represents both a challenge and an opportunity. Emerging frameworks like the EU AI Act and NIST AI Risk Management Framework will shape how AI can be used in audit practice. Firms that proactively engage with these developments will be better positioned to leverage AI capabilities while maintaining regulatory compliance.
