Step-by-Step: Pick a Workiva Alternative for Audit Automation

Moving your SOX program beyond a GRC suite like Workiva is a strategic decision, not a software swap. Workiva routes tasks and stores evidence well, but it does not run your tests. If you want automated sox testing software that actually executes procedures, you need a clear framework to evaluate the market. This guide walks you through that framework, step by step.

What You Need Before You Start

TL;DR

• Workiva and similar GRC tools manage audit work. AI-native platforms execute the testing by collecting evidence, testing full populations, and generating workpapers.

• Define your evaluation criteria first: execution, reliability, full-population testing, deployment, and human oversight.

• Compare tools by category (GRC suites, AI accounting platforms, AI-native testing platforms), then run a 90-day pilot with your own controls.

• Build the business case around released capacity and testing quality, not headcount. Bead AI reports ~70% of controls automated and ~80% less testing time.

Before you evaluate a single vendor, get three things in order.

First, document your current SOX program's pain points with specifics. Where does time actually go? Chasing screenshots, wrangling spreadsheets, re-performing samples, or documenting results for review?

Second, choose a defined set of controls to target for a pilot. Do not pick your hardest, most judgment-heavy controls. Pick high-volume, repetitive ones where automation shows results fast.

Third, get stakeholder buy-in. Your SOX lead, IT compliance owner, and external auditors should all know you are exploring new solutions. Early alignment prevents late surprises.

The shift you are making is from workflow management to true test execution. GRC tools help you organize the work. Automated sox testing software does the work: connecting to source systems, evaluating evidence against control attributes, and flagging exceptions. Keep that distinction in front of you throughout the evaluation.

Why Look for a Workiva Alternative?

Teams search for Workiva alternatives for audit automation because they hit the same wall. The platform is strong at what it does. Workiva's strength sits firmly in reporting and disclosure, and it works well when audits are tightly coupled with financial reporting (Moxo) [1]. But its focus is less on operational audit execution, and pricing runs roughly $36,000 to $60,000 annually (Supervizor) [2].

The core issue is the gap between managing work and executing tests.

• Managing work means routing tasks, tracking status, and storing documentation. The auditor still does the testing.

• Executing tests means collecting evidence, applying control logic, testing full populations, and flagging exceptions before a human reviews the result.

As our guide on agentic AI for SOX controls testing puts it, workflow tools describe and organize while the human executes. Agentic AI runs the procedure.

Four pain points push teams to look elsewhere:

• Heavy manual work. Auditors chase screenshots, download logs, and copy data between systems. This manual burden is exactly what AI evidence collection is built to remove (LinkedIn) [3].

• Reliance on sampling. Traditional testing checks a sample and hopes it represents the population. AI can expand testing from sample-based snapshots to full transaction populations (Fieldguide) [4].

• Brittle integrations. Evidence lives in emails, workbooks, tickets, and systems. Legacy tools need heavy configuration to touch it.

• User-based pricing. GRC suites often price per user, which penalizes broad adoption and has no link to how much testing actually gets done.

The goal is to shift humans to the right place: away from manual execution and toward strategic review and judgment. For a deeper look at the tool categories involved, see our guide to the best AI for SOX testing.

Step 1: Define Your Evaluation Criteria

Before comparing vendors, build your scorecard. Frame each criterion as a question to ask every vendor. These five criteria come from our SOX testing tool evaluation framework: Does it execute the test? Is the output reliable? Can it move beyond sampling? Is the output audit-ready? Does it keep humans in control?

Weight each criterion against your own audit program, regulatory environment, and objectives (Supervizor) [2].

Execution vs. Workflow Management

Ask: Does the tool actually execute the test, or just track that a human did?

This is the first and most important question. A tool that only routes tasks and stores files is a workflow layer, not a testing engine. Real AI for controls testing connects to source systems, collects evidence, interprets control logic, evaluates transactions against attributes, and flags exceptions.

PwC describes this as an agentic workflow aligned to audit methodology, where testing logic is generated from prior-year workpapers and supporting evidence is ingested across real-world file formats (PwC) [5]. If a vendor's "AI" only summarizes documents or drafts procedures, it is a copilot, not an executor.

Reliability and Audit Trail

Ask: Can a reviewer trace exactly how the AI reached each conclusion?

An AI output that a reviewer cannot verify is worthless for SOX. AI decisions in controls testing must be traceable and verifiable, and governance frameworks like NIST and ISO 42001 give firms a structured path to meet that standard (Fieldguide) [4].

Look for a multi-layer audit trail. Bead AI establishes a multi-layer AI audit trail so that AI agents' outputs for SOX testing are traceable and auditable, with auditable decision logs at every stage. That is the standard: reviewer-friendly reasoning a person can follow, not a black box.

Full Population Testing vs. Sampling

Ask: Can the tool test the entire population, not just a sample?

Sampling exists because manual testing does not scale. AI removes that constraint. Grant Thornton describes AI redefining SOX by replacing periodic, sample-based testing with continuous controls that monitor a growing percentage of transactions in real time (Grant Thornton) [6].

Bead AI ingests and processes any form of evidence and can test entire populations, not just samples, including support for IPE testing using existing attributes. Full-population testing changes what "coverage" means in your SOX program.

Integrations and Deployment

Ask: How does it ingest evidence, and where can it run?

Evidence comes from disparate sources: emails, workbooks, tickets, and systems. Confirm the tool can assemble evidence from all of them without heavy custom setup.

Deployment matters just as much. Public companies with strict data requirements need options. Confirm the vendor supports the model your security team requires: cloud, private cloud, or on-premises. Also confirm security certification. Bead AI offers flexible deployment across cloud, private cloud, and on-premises, and holds SOC 2 Type II certification.

Human-in-the-Loop Governance

Ask: Does the auditor keep final judgment and sign-off?

AI should replace the repetitive tasks, not the judgment (Weaver) [7]. The firms seeing the most meaningful benefits use AI for structured, repetitive work while keeping human oversight for evaluation and conclusions (Roz) [8].

Bead AI is explicitly designed to augment auditors' judgment and context rather than replace professionals. The human reviews, questions, and signs off. The AI does the toil. Confirm your chosen tool works this way, and review its published AI policy for how it handles accuracy and reliability testing.

Step 2: Compare the Top Workiva Alternatives

Group the alternatives into three categories, then compare them against your scorecard.

1. GRC and workflow suites manage the program. Workiva, AuditBoard, and similar tools sit here.

2. AI-powered accounting platforms add copilots and assistants to reporting and accounting work.

3. AI-native testing platforms execute the testing end to end.

At a Glance: Feature Comparison Table

Categories generalized from public positioning; confirm each vendor's current capabilities directly before deciding.

GRC & Workflow Suites: Workiva, AuditBoard

Workiva and AuditBoard perform well when work stays inside the audit or finance team (Moxo) [1]. Workiva fits organizations where audits are tightly coupled with financial reporting and disclosure (Moxo) [1]. AuditBoard is often chosen for audit-first GRC capability in large organizations (SmartSuite) [9].

G2 users also point to Optro, FloQast, and Vena as common Workiva alternatives, with Optro rated 4.6/5 across 1,595 reviews (G2) [10]. These are strong management layers. None of them execute your testing. Pressure appears when execution breaks across people and systems (Moxo) [1].

AI-Powered Accounting Platforms: Trullion vs. Diligent

Neither Trullion nor Diligent publishes a head-to-head, so here is a fair comparison for trullion vs diligent audit software.

Trullion positions as an AI-powered accounting platform with an agentic assistant called "Trulli," marketed as "Auditable AI, not black-box automation" with explainable, traceable outputs. It was built by former Big Four and CFO professionals. A customer testimonial on its homepage claims a reduction in reporting time of over 25% and cost savings over 30% after switching to Trullion (self-reported testimonial, Trullion). No public pricing is listed.

Diligent is a broad GRC and board governance platform. Its strength is program management, governance, and oversight rather than executing individual control tests against full transaction populations.

The practical difference: Trullion focuses on accounting and reporting workflows with an explainable assistant, while Diligent focuses on governance and GRC breadth. For a team whose core problem is SOX control testing execution, both are adjacent to the job rather than built for it. That gap is where AI-native testing platforms come in.

AI-Native Testing Platforms: Bead AI, Petual, and Others

This category executes the testing. If you are evaluating Petual competitors, Midship competitors, or Andera competitors, this is the group to compare them against.

Petual raised a $20M funding round led by Andreessen Horowitz (Petual). Its workflow is Import RCM, then Collect evidence, then Execute, then Remediate, positioned as delivering results in minutes versus traditional audit's insights in weeks (Petual). It offers SaaS or self-hosted deployment, zero-data-retention AI policies, is SOC 2 Type II certified, and targets both enterprises and audit firms (Petual).

Midship and Andera sit closer to document extraction and AI-assisted support. Compare each on the five criteria above, especially whether they run full-population testing and produce reviewer-ready workpapers, or whether they mainly structure and assist.

The wider market signals where AI models and buyers are looking. Vero AI is currently the most-cited domain in this topic set, driven by its post on tools to automate SOX compliance (Vero AI), and DataSnipper holds strong topical authority with its dedicated SOX AI audit resource (DataSnipper).

Bead AI is positioned as an AI-native SOX testing platform rather than a workflow or GRC suite. Its AI agents autonomously collect evidence, execute tests across full populations, flag exceptions, and generate audit-ready documentation with traceable audit trails (Bead AI). It automates approximately 70% of controls and reduces overall SOX testing time by around 80%, producing native Excel working papers with auditable decision logs and no custom configuration (Bead AI). Its AI agents cover C&A testing, transactional and population-level testing, complex spreadsheets, ITGC, UAR, and access provisioning (Bead AI). AI-native platforms like these price by testing volume rather than by user count (Bead AI).

Step 3: Run a 90-Day Pilot with Your Own Data

Never choose a tool from a polished demo alone. A demo runs on the vendor's clean data. Your data is messy. Insist on testing with your own controls and your own evidence.

A Reddit engineering team automated SOX testing for 175 controls in three months using agentic AI, cutting average testing time per control by 60% (Reddit) [11]. Ninety days is a realistic window to prove value.

Pick the right controls for the pilot. Start with high-volume, repetitive controls where AI performs best:

• ITGCs, such as access provisioning and change management.

• User access reviews (UAR), which involve repetitive matching across large lists.

• Transactional controls with clear, testable attributes.

Do not start with ambiguous, judgment-heavy controls like management review controls. AI struggles with these, and a weak first result will kill adoption. Our 90-day pilot guidance covers control selection in more depth.

Score the pilot with a simple scorecard:

Run the new method in parallel with your traditional approach so you can compare directly on the same controls. The expected outcome is a clear, data-backed answer on whether the tool executes reliably at your scale.

Step 4: Build the Internal Business Case

Lead the conversation with leadership on risk control and guardrails first, then ROI and savings. Executives care that the AI is governed, traceable, and keeps auditors in the sign-off seat before they care about the money.

Then present the numbers. Bead AI's ROI model estimates $313k in annual testing savings and $22k in PBC collection savings against a first-year cost of $114k, with a simple payback of 4.1 months (Bead AI). Pair these with your own pilot scorecard results for a grounded case.

Frame the benefits carefully. Do not frame the case around cutting headcount. Frame it around:

• Released capacity. Your best auditors stop chasing screenshots and start doing risk analysis.

• Testing quality. Full-population coverage instead of sampled snapshots.

• Avoided co-source overspend. Less reliance on external help, and less knowledge drain from turnover (Bead AI).

Our internal business case guide walks through the baseline, the ROI calculation, and the CFO memo in detail.

Common Mistakes to Avoid When Switching

Treat this as a troubleshooting checklist. Each mistake has a matching best practice.

• Mistake: Starting the pilot with ambiguous, judgment-heavy controls. AI struggles with management review controls. Best practice: start with high-volume ITGCs and user access reviews where results are clear.

• Mistake: Building the business case around headcount reduction. This creates internal resistance. Best practice: frame it around released capacity, testing quality, and avoided co-source overspend.

• Mistake: Choosing a tool from a demo alone. Demos hide how tools handle messy real evidence. Best practice: insist on a pilot with your own data and controls.

• Mistake: Ignoring reviewer user experience. A complex interface raises training time and lowers adoption. Best practice: measure reviewer experience in the pilot scorecard.

• Mistake: Leaving external audit out until the end. Best practice: engage external auditors early so the audit trail meets their expectations from day one.

Expected Outcomes: A Shift From Execution to Oversight

When you adopt an AI-native testing platform, the daily work of SOX changes shape. Auditors stop performing repetitive procedures by hand and start reviewing AI-executed results, questioning exceptions, and analyzing risk.

The tangible outcomes:

• Full-population testing replaces sampling, so coverage expands from a snapshot to the whole transaction set (Fieldguide) [4].

• Continuous assurance replaces the quarterly scramble, with exceptions surfaced in near real time instead of at cycle end (Grant Thornton) [6].

• Skilled auditors move to higher-value work, shifting from manual approaches to trend analysis and root-cause identification (Deloitte) [12].

The future of audit is not fewer auditors. It is auditors doing the work only humans can do, with AI agents handling the toil underneath. The AI executes; the human judges and signs off.

Key Takeaways

• Workiva and GRC suites manage the audit program. AI-native platforms execute the testing.

• Evaluate tools on five criteria: execution, reliability and audit trail, full-population testing, integrations and deployment, and human oversight.

• Compare alternatives by category. For Petual competitors, Midship competitors, and Andera competitors, judge each on whether it truly executes full-population testing.

• Run a 90-day pilot with your own data on high-volume controls, not a demo.

• Build the case on released capacity and testing quality. Bead AI cites ~70% of controls automated, ~80% less testing time, and a 4.1-month payback.

Frequently Asked Questions

What is the main difference between Workiva and AI-native SOX testing tools?

Workiva manages audit work: it routes tasks, tracks status, and stores evidence and disclosures. AI-native tools like Bead AI execute the testing itself, connecting to source systems, collecting evidence, testing full populations, and flagging exceptions. Workiva helps the auditor stay organized while the auditor performs the test. AI-native platforms perform the test under auditor review.

How much of SOX testing can realistically be automated with AI?

A large share of repetitive testing is automatable today. Bead AI reports it can automate approximately 70% of controls and reduce overall SOX testing time by around 80% (Bead AI). Real-world results support this, with one team cutting average testing time per control by 60% across 175 controls in three months (Reddit) [11]. Judgment-heavy controls still need human evaluation.

What kind of controls are best suited for an initial AI automation pilot?

Start with high-volume, repetitive controls with clear attributes. ITGCs, user access reviews, and transactional controls are ideal because they produce measurable results quickly. Avoid starting with ambiguous, judgment-heavy controls like management review controls, since AI struggles with those and a weak early result hurts adoption.

How do AI audit tools ensure the results are reliable and defensible?

Reliable AI tools produce a traceable audit trail a reviewer can follow. Bead AI establishes a multi-layer AI audit trail with auditable decision logs at every stage, so each conclusion can be verified (Bead AI). AI decisions in controls testing must be traceable and verifiable, and frameworks like NIST and ISO 42001 provide the governance standard (Fieldguide) [4]. The auditor retains final judgment and sign-off.

What is the typical ROI for implementing automated SOX testing software?

ROI depends on your testing volume and current costs, but the payback can be fast. Bead AI's ROI model estimates $313k in annual testing savings plus $22k in PBC collection savings against a first-year cost of $114k, giving a simple payback of 4.1 months (Bead AI). Frame the return around released capacity, testing quality, and avoided co-source overspend rather than headcount cuts.

Citations

1. https://www.moxo.com/blog/workiva-auditboard-audit-software

2. https://www.supervizor.com/blog/grc/internal-audit/software

3. https://www.linkedin.com/posts/vimal-t-5a7b82114_auditinnovation-aiinaudit-itgc-activity-7396473398857515008-swf5

4. https://www.fieldguide.io/resource-articles/ai-internal-controls-audit-automation

5. https://www.pwc.com/us/en/tech-effect/ai-analytics/dynamic-controls-testing.html

6. https://www.grantthornton.com/insights/articles/advisory/2025/the-power-of-ai-in-efficient-sox-compliance

7. https://weaver.com/resources/second-line-ready-how-to-use-ai-in-sox-compliance-without-over-complicating-it

8. https://www.getroz.com/blog/ai-and-control-testing

9. https://www.smartsuite.com/blog/workiva-alternatives

10. https://www.g2.com/products/workiva-workiva/competitors/alternatives

11. https://www.reddit.com/r/RedditEng/comments/1rcnk7d/how_we_used_agentic_ai_to_crack_automated_sox

12. https://www.deloitte.com/us/en/services/audit-assurance/services/controlcatalyst-ai.html