December 19, 2025

Automating evidence collection: A guide for audit and advisory firms

Deirdre Dolan

Sr. Director of Product Marketing

Increasing trust with AI for audit and advisory firms.

A manager handling five concurrent SOC 2 engagements may spend 8-10 hours weekly tracking which clients submitted which evidence versions, which control tests remain incomplete, and what's blocking final reporting. Partners lack real-time visibility into engagement health until managers manually compile status reports. Associates spend entire afternoons copying data from PDFs into Excel instead of developing professional judgment.

Automated evidence collection uses technology to gather, organize, and manage compliance documentation while preserving the professional judgment and evidentiary standards audit quality depends on. This approach centralizes evidence gathering within structured workflows that maintain chain of custody while reducing coordination overhead. The AICPA's SAS 142 now explicitly references automated tools and techniques for gathering evidence regardless of auditor location, marking a significant shift in professional standards toward technology-supported audit methods.

This article examines how audit and advisory firms implement automated evidence collection across compliance frameworks, what specific pain points it addresses, and how it transforms the audit lifecycle.

What is automated evidence collection?

The foundational principle remains unchanged: automated collection methods must still meet professional standards of sufficiency and appropriateness. What changes is the mechanism. Instead of emailing clients, downloading files individually, and tracking version control through spreadsheets, automated systems help centralize evidence gathering within structured workflows.

Modern platforms support evidence collection across major compliance frameworks including SOC 2, ISO 27001, HIPAA, PCI DSS, and HITRUST. Standardizing evidence collection methodology across frameworks prevents the context-switching overhead that traditionally consumed significant time resources.

Automated evidence collection is different from manual processes in three ways. Traditional approaches provide point-in-time evaluation during scheduled periods. Where systems are configured to support continuous monitoring, automated approaches assist with real-time assessment. Where manual approaches rely on sampling, automated approaches can support broader population-level analysis, when data quality and engagement context allow. Where manual coordination requires dedicated manager time, automated systems provide real-time visibility into outstanding evidence, responsibility, and aging.

Why manual evidence collection creates bottlenecks

Staff capacity constraints, inadequate technology, and regulatory complexity create interconnected challenges that manifest in measurable quality issues. These pain points compound when firms handle multiple concurrent engagements:

Staff capacity limitations create systematic errors: When practitioners handle excessive workloads across concurrent engagements, errors become systemic rather than exceptional. Gartner research shows 18% of accountants make financial errors at least daily, while 33% make several errors weekly.
Legacy technology forces outdated workflows: 80% of chief audit executives express dissatisfaction with their data analytics capabilities, reflecting real platform limitations. Legacy systems require manual download, check-out for editing, upload of revised versions, and email notifications for every change.
Point solutions create fragmentation: Firms combining specialized tools for requests, tasks, documents, and evidence extraction initially gain functionality but fracture visibility. Practitioners suffer "log-in fatigue" switching between disconnected systems, and data synchronization becomes another coordination burden rather than a solution.

Across multiple engagements, these challenges increase coordination demands and limit the time managers can devote to professional judgment and client advisory work.

Five ways automation changes audit execution

Automation transforms how audit and advisory firms execute work by helping auditors shift from sample-based approaches to comprehensive population analysis, freeing practitioners from administrative coordination to focus on professional judgment and higher-value advisory work.

1. Planning

During engagement planning, automated systems provide immediate access to prior period evidence, control test results, and exception patterns. Instead of reviewing last year's PDFs, managers query structured data showing exactly which evidence sources proved reliable, which required extensive follow-up, and which controls failed testing. Practitioners can identify client-specific challenges before fieldwork begins, allocate resources based on actual complexity, and set realistic timelines grounded in prior engagement data.

The shift from sample-based to population-based analysis, supported by AICPA guidance, represents the most significant planning change. When auditors can review broader data sets rather than relying solely on samples, risk assessment becomes more informed, with practitioners retaining responsibility for evaluating exceptions and determining audit response.

2. Evidence requests

Request management transforms from coordination burden to systematic workflow. Manual approaches consume disproportionate manager time: drafting PBC list emails, tracking which clients responded, following up on outstanding items, downloading submitted files individually, organizing folder structures, and compiling status updates for partners.

Automated approaches often provide clients with a portal showing exactly which documents are requested, who's responsible for providing them, and when they're due. When clients upload evidence, the system automatically notifies the appropriate team member, maintains version history, and updates engagement status dashboards in real time. The manager's role shifts from coordination to exception management, intervening only when requests age beyond thresholds or clients need clarification.

3. Testing procedures

When systems are configured with validated population data, automation assists auditors in reviewing user access controls comprehensively rather than through sampling, consistent with IAASB support materials on automated tools. Control testing traditionally required practitioners to select 25 items from populations of thousands. Once assessors map evidence to specific controls and configure testing parameters, automated approaches can help evaluate entire populations. This comprehensive coverage identifies patterns and exceptions that sampling approaches systematically miss.

The practitioner’s role shifts from manual sample selection toward evaluating comprehensive results, applying professional skepticism, and determining whether identified exceptions represent control deficiencies or acceptable variance. Professional judgment remains essential for evaluating materiality and determining control effectiveness, a core principle emphasized in PCAOB Auditing Standard 1105.

4. Review processes

For partners and managers, automation standardizes routine review procedures while preserving senior reviewer judgment for evaluating materiality, assessing risk, and exercising professional skepticism, as required by AICPA professional standards. Real-time engagement dashboards show portfolio visibility across concurrent engagements, highlighting which are progressing on schedule, which have outstanding client requests aging beyond thresholds, and which have completed testing awaiting final review.

This visibility shift eliminates the status meeting ritual where managers spend hours compiling updates to brief partners. Instead, partners access current engagement health across their portfolio on demand, intervening proactively when dashboards surface bottlenecks.

5. Reporting

Automated workflows, aligned with IAASB guidance on audit documentation when using automated tools, help practitioners maintain continuously updated draft reports rather than facing a last-minute compilation crunch.

Once assessors configure report parameters, they can incorporate testing results into standardized templates throughout the engagement instead of waiting until final delivery. The platform assists with organizing findings and formatting documents, but assessors drive all report content decisions and provide ongoing review and input.

This continuous approach enables partners to review evolving drafts and provide substantive feedback while testing remains in progress, rather than reviewing complete 60-page documents for the first time three days before client deadlines. Partners see how reports develop throughout the engagement, with assessors maintaining control over conclusions, risk assessments, and final recommendations at every stage.

Implementing automated evidence collection

Transitioning from manual to automated evidence collection requires comprehensive methodology transformation, not just technology adoption, a principle reflected in the AICPA's quality management standards. Firms that treat automation as simply digitizing existing workflows miss the opportunity to redesign processes for how modern audit teams actually work.

Establish governance foundation

Start by ensuring compliance with professional standards that now explicitly accommodate automated evidence collection. The AICPA's SAS 142 was deliberately developed as a principles-based standard rather than prescriptive rules, providing flexibility for automated evidence collection methods while maintaining quality standards. This governance foundation answers questions central to the AICPA's quality management framework: Which professional standards govern our automated evidence? How do we document that automated evidence meets sufficiency and appropriateness tests? Who has authority to approve methodology changes?

Define your measurement baseline

Establish a clear baseline for current evidence collection effort, including hours spent on request tracking, follow-ups, and documentation. How many hours does your average SOC 2 engagement require for evidence gathering? What percentage of total engagement time goes to request tracking? What's your current realization rate? These baselines let you measure actual improvement rather than relying on subjective assessments.

Redesign workflows for automation

Comprehensive workflow redesign connects evidence requests to client portals, links submitted documents to specific control requirements, integrates testing procedures with documentation repositories, and updates status dashboards automatically. The goal isn't automating existing workflows but redesigning them for how audit work actually happens: distributed teams, electronic evidence, concurrent engagements, and real-time client expectations.

This redesign phase determines whether automation delivers marginal improvement or transformational capacity expansion. Firms that merely digitize existing manual workflows typically see 15-20% efficiency gains. Firms that redesign workflows specifically for automated evidence collection often see materially higher efficiency gains, with some reporting improvements in the 30–50% range, depending on engagement complexity and starting point.

Build organizational capability

Successful digital implementations require long-term planning, organization-wide commitment, and clear accountability. This means securing partner-level commitment that automated evidence collection represents strategic direction and assigning clear accountability for methodology documentation, staff training, and client communication.

Run structured pilots

Select pilot engagements based on learning objectives, not convenience. Include at least one complex multi-framework engagement to test real-world coordination challenges. Document specific questions: Did automated request tracking reduce follow-up emails? Did real-time dashboards reduce manager coordination time? Successful pilots generate both quantitative metrics and practitioner stories that drive broader adoption.

Scale with quality controls

Review pilot metrics against baseline measurements. If automated evidence collection reduced evidence gathering time by 30-50% on pilot engagements, forecast what that means for annual engagement capacity. As automation scales, maintain systematic quality review. The AICPA's implementation guidance emphasizes that automated evidence must meet the same sufficiency and appropriateness tests as manual evidence.

Moving forward with engagement automation

The firms that build automated evidence collection capabilities now position themselves to handle the increasing compliance complexity and client expectations defining the next decade of audit and advisory work. The transition from manual coordination to systematic automation requires methodology transformation and new technology adoption.

Fieldguide's engagement automation platform streamlines evidence collection through centralized request tracking, client portals that surface requested items to clients, accept uploads, and can send automated reminders, and real-time engagement visibility for partners managing concurrent compliance engagements. According to verified case studies, firms using Fieldguide achieve 30-50% efficiency gains while expanding engagement capacity. Learn how Fieldguide streamlines evidence collection by requesting a demo.