By late 2026 the question is no longer whether an audit or GRC platform has an AI feature. All twelve platforms reviewed on this site do, and so do at least five purpose-built tools most audit functions did not have on a shortlist two years ago. The harder, more useful question is what each of those tools is actually good at today: which ones draft, which ones summarize, which ones pull a number off a PDF and link it back to the page it came from, which ones score a transaction as anomalous, and which ones are, underneath a new name, a chat window with an audit-shaped skin. Gartner’s word for that last category, agent-washing, is the subject of a companion guide on this site; this page answers the question that comes before it: which tool does which job.
This guide is a catalog, not an evaluation. It covers what AI feature each of the twelve internal audit and GRC platforms reviewed on this site has shipped and when; what five standalone tools bought alongside a platform, DataSnipper, MindBridge, Fieldguide, Trullion and Blast Audit, actually do; where Microsoft 365 Copilot and Copilot in Power BI fit for a team that already owns them; a table that sorts every capability by task rather than by vendor name; the data-use questions worth asking before a pilot; and a one-page worksheet for running that pilot. The site’s guide to types of internal audit software covers the platforms themselves as products, and the twelve-question checklist and demo script for testing any one vendor’s claim live in a companion guide. This page sits between the two.
How to read this guide
What this guide is for. A catalog, not a ranking: what AI feature each of the twelve internal audit and GRC platforms covered on this site has shipped and when, what five standalone tools bought alongside a platform actually do, where Microsoft’s general-purpose Copilot products fit, a table that sorts every capability by task rather than by vendor, the data-use questions worth asking before a pilot, and a one-page pilot worksheet. It does not repeat the evaluation method built for testing any one vendor’s claim; that lives in a companion guide, linked throughout.
Evidence. Research-based: vendor documentation, release notes and pricing pages, including Fieldguide, Trullion and Blast Audit’s own product and pricing pages and Microsoft’s Power BI documentation, fetched directly for this guide; the research dossiers and product reviews this program has already built for DataSnipper and MindBridge; and the analyst reports and surveys cited below. We have not used any of these products hands-on for this guide.
Last verified. 27 September 2026.
In this guide
- What audit teams are actually using in 2026
- AI inside the audit and GRC platforms
- The standalone tools, and the generalists
- What AI is actually good at today, task by task
- The data-use questions to ask before you pilot anything
- How to pilot AI in your audit function
- Worked example: Lakeshore Bancorp tests two evidence tools
- Questions about AI tools for internal audit
- Sources and verification
- Related guides
What audit teams are actually using in 2026
Two different numbers get called “AI adoption in internal audit,” and they measure different things. The IIA’s 2025 North American Pulse of Internal Audit, 405 audit leaders, found that 41 percent of functions already use generative AI in their work. Wolters Kluwer’s own 2025 survey of 4,214 internal auditors found a close current figure, 39 percent, with another 41 percent planning to adopt within the next twelve months. Both numbers describe use in the loosest sense: an auditor pasting a question into a chat window counts the same as a platform running a multi-step agent. The IIA’s separate Vision 2035 survey, 6,506 respondents, asks the harder question and gets a smaller answer. Audit management software itself is implemented at a high level by 40 percent of functions, but AI is used at an advanced level by only 7 percent. Gartner’s own 16 September 2026 figure, that 84 percent of audit functions have adopted audit management software, is a platform number, not an AI number; most functions have a system of record, and a much smaller share is doing anything sophisticated with AI inside or alongside it.
That gap matters for how to read everything below. A platform or tool that shipped an AI feature in the past twelve months, which describes most of the sixteen products in this guide, has not necessarily been used by more than a handful of functions long enough to have a settled opinion about it. Where a review or a rating exists for a specific AI feature rather than the product generally, this guide says so. Where it does not, that absence is itself useful information, and a team still running audits from Excel and SharePoint is not behind by skipping tools this immature.
AI inside the audit and GRC platforms
The table below covers the twelve internal audit and GRC platforms reviewed elsewhere on this site. Each row states the named AI feature and when it shipped, from the vendor’s own documentation, and what job it actually does, using the task categories this guide uses throughout: drafting, summarizing, evidence extraction, anomaly scoring, test generation, or something specific enough to name on its own. This is not robotic process automation, which follows a fixed script step by step rather than judging the content it touches; the site’s guide to RPA in internal audit covers that older, narrower category, and several of the platforms below still run both side by side. This table does not restate the model names or data-use statements the companion guide to evaluating AI in audit software already tracks feature by feature; see that guide for the evidence behind any one row before a vendor call.
| Platform | Named AI feature(s) and launch date | What it actually does today |
|---|---|---|
| Optro (formerly AuditBoard) | Accelerate: Audit Agent and Document Intelligence (22 Oct 2025); Midship, a SOX-automation AI company, acquired 6 May 2026 | Drafting and evidence extraction inside the audit workflow; Midship targets automated SOX test execution |
| TeamMate | TeamMate+ AI Editor (2 Jun 2025); Expert AI | Drafting assistance inside workpaper narrative; Expert AI answers methodology questions. TeamMate Analytics’ Document Linker (Azure AI Vision, US only) extracts data from linked evidence |
| Diligent One Platform | ACL AI Studio (general availability 25 Sep 2025; agentic mode 2 Feb 2026; runs the Claude 4.6 model as of 17 Sep 2026); AuditAI (10 Mar 2026); orchestration, risk and internal audit agents (15 Sep 2026) | Anomaly scoring across ACL Analytics data sets; AuditAI drafts; the newer agents aim at orchestrating steps across the audit lifecycle |
| Workiva | Flowchart visualizer, evidence analyzer and control creator (Winter 2026); report and remediation agents; Tie-Out, Benchmarking and Sustainability agents plus an MCP gateway (29 Jul 2026, advanced tiers) | Evidence extraction and reconciliation (Tie-Out, evidence analyzer); drafting (report and remediation agents); process mapping (flowchart visualizer) |
| Onspring | Onspring AI (14 Oct 2025); Agentic GRC (27 Jul 2026, Silver service tier or above), both on Anthropic’s Claude models | Workflow automation across GRC processes generally, not one audit-specific task; the newest capability is gated above the base tier |
| LogicGate Risk Cloud | AI Skills (included by default, opt-in); Spark AI Reporting Insights; Automated Evidence Testing; Config Newton; GRC Agents (general availability 1 Sep 2026, named OpenAI models) | Automated Evidence Testing is the closest thing in this table to anomaly scoring by name; Reporting Insights drafts and summarizes; Config Newton assists configuration, not audit work itself |
| IBM OpenPages | OpenPages MCP Server and watsonx Orchestrate chat (9.2, 23 Mar 2026); eight AI model configurations including audit-plan drafting (9.2.1, 23 Jul 2026) | Drafting an audit plan; conversational query through watsonx Orchestrate |
| MetricStream | Model Gateway and an AI Governance and Trust Framework (7 Apr 2026); MetricStream Assistant and Policy Assistant (May 2026) | A conversational assistant and a policy-question assistant; the Model Gateway is plumbing for multiple models, not a task in itself |
| Archer | Evolv AI Compliance (15 Sep 2026, Amazon Bedrock Guardrails inside the customer’s own AWS account); Evolv Foundation and Evolv Workplace (14 Sep 2026); earlier Archer Assurance AI and AI Governance (2024) | Compliance-specific automation; Archer’s own pages disagree on how many purpose-built models exist, 492 against 526, and a 30 Jun 2026 release’s accuracy and speed claims carry no published methodology |
| ServiceNow IRM | Now Assist for IRM, being renamed Otto for Integrated Risk Management (both names live as of Sep 2026); On-Demand Rationalization (general availability); Proactive Clustering (early access) | Rationalization and clustering are a genuinely distinct task: finding and merging duplicate controls across frameworks, not drafting or summarizing |
| SAI360 | AI embedded across modules in GRC Elevate 6.0 (21 May 2026) | A conversational assistant bundled into most editions; the Essentials edition excludes the AI Chat Assistant entirely |
| Protecht | Cognita AI assistant (announced 8 Sep 2025, full release Nov 2025, single-tenant AWS) | A conversational assistant scoped to Protecht’s own risk and audit data, with no cross-customer sharing stated |
Group the twelve rows by task rather than by vendor and three clusters show up. Drafting and summarizing is the most common capability: nine of the twelve platforms ship some version of it, from the Audit Agent of Optro (formerly AuditBoard) to IBM’s audit-plan drafting to SAI360’s chat assistant. Evidence extraction sits inside the platforms already built around document-heavy work: Optro’s Document Intelligence, Workiva’s Tie-Out and evidence analyzer, and TeamMate Analytics’ Document Linker. Only two platforms in this table do something close to true anomaly scoring across a full population: Diligent One’s ACL AI Studio, because it sits on top of ACL Analytics’ data-testing engine, and LogicGate’s Automated Evidence Testing. Full-population scoring like this is the automation layer underneath what the site’s guide to continuous monitoring describes as a program, not a replacement for building that program. ServiceNow’s rationalization and clustering features are the outlier in this table: a genuinely different job, merging duplicate controls across frameworks, that nothing else here claims to do.
The standalone tools, and the generalists
Five more tools get bought alongside a platform rather than instead of one, each doing a narrower job well rather than running the whole audit lifecycle.
| Tool | What it is | AI feature and launch date | Good at today |
|---|---|---|---|
| DataSnipper | An Amsterdam-based Excel add-in that links workpaper figures to source documents, called Snips | Alwin, an agentic automation platform (22 Sep 2026, gated to the Elevate tier); Excel and Disclosure Agents (1 Oct 2025, run on Azure OpenAI); four prebuilt SOX agents | Evidence extraction and linking at volume, with agents processing hundreds of documents in parallel per the vendor; four named SOX procedures: segregation-of-duties review, user-access-change review, journal-entry control review and management-variance review |
| MindBridge | An Ottawa-based full-population transaction risk-scoring platform, not a workpaper or case-management tool | Agentic Risk Assessment, an Analysis Designer Agent and an MCP server, all launched 22 Sep 2026 | Anomaly scoring across 100 percent of a transaction population, general ledger, spend or close, rather than a sample; the Analysis Designer Agent turns a plain-language objective into a scoring configuration |
| Fieldguide | An AI-native workflow platform built mainly for CPA and advisory firms, with a corporate internal audit product covering SOX, ITGC and operational audits | Field Agents, organized by role: Planner, Auditor, Reviewer, Financials and Orchestrator | Drafting and evidence review across a full engagement, by the vendor’s own description, which claims more than half of the top 100 audit and advisory firms as partners; how much of that agent depth carries into the corporate internal audit product specifically was not something this guide could confirm without a demo |
| Trullion (Trulli) | A lease accounting (ASC 842 and IFRS 16) and revenue recognition (ASC 606) compliance platform with an audit module | Trulli, an AI agent announced 7 May 2025, available across Trullion’s Lease, Revenue and Audit modules | Research, querying accounting standards and policies the customer has uploaded, and summarizing long documents with a citation back to the file it read; no underlying model or data-use policy is named on the pages this guide reviewed |
| Blast Audit | A newer Excel add-in positioned explicitly as a lower-cost DataSnipper alternative, down to a built-in “DataSnipper Migration” import tool | An AI Agent for automating Excel tasks and an AI Extraction feature for natural-language data pulls with reusable templates, alongside a conversational Probe for querying documents | Evidence extraction and OCR, stated at about 10,000 pages a month per user; no request-tracking module comparable to DataSnipper’s Collect and UpLink appeared on its feature list when this guide checked |
Price evidence is thin outside Blast Audit, which is the only one of the five that publishes a number: EUR45 a user a month billed monthly, or EUR450 a year billed annually, two months free, one tier, every feature included, no seat minimum (Blast Audit’s own pricing page, accessed 27 September 2026). Everyone else gates the number behind a sales conversation. Sacra, a research firm, estimates DataSnipper’s mid-tier at roughly $175 a seat a month with a five-seat minimum, plus a roughly $62-a-user add-on for its Cloud Collaboration Suite; DataSnipper itself confirms none of this, so treat it as a rough guide, not a quote, and a low-confidence one. Vendr’s own marketplace listing for DataSnipper carries no median at all. MindBridge fares slightly better: Vendr’s median across its listed deals is $42,092 a year, ranging $21,890 to $49,046, though Vendr gives no purchase count behind that figure. Neither Fieldguide nor Trullion publishes anything this guide could find. The site’s pricing guide has the base-platform numbers this section does not repeat.
Two more tools belong in this catalog because most audit functions already own them, whether or not internal audit chose them: Microsoft 365 Copilot and Copilot in Power BI. Neither is audit-specific, and neither should be mistaken for one of the five tools above. The site’s DataSnipper vs TeamMate Analytics vs Copilot comparison goes deeper on Copilot specifically against the two Excel-native tools it gets compared to most.
| Tool | Price | What it does for an audit team | The catch |
|---|---|---|---|
| Microsoft 365 Copilot | $30 a user a month for the enterprise add-on, on top of a qualifying Microsoft 365 base license (E3 or E5) that most large audit functions already carry. A smaller function on a Business plan (up to 300 users) instead sees the Microsoft 365 Copilot Business add-on at $21 a user a month list, $18 on promotion paid yearly through 31 December 2026, or the bundled Business Standard with Copilot ($23.50) and Business Premium with Copilot ($32) plans | Drafting emails, memos and a first-pass summary of a long document or meeting inside Word, Outlook and Teams | Nothing it drafts is grounded in your engagement’s own workpapers, risk assessment or prior-year file; treat its output the way you would a junior auditor’s unchecked first draft, with no source-linking back to evidence |
| Copilot in Power BI | A separate purchase from the $30-a-month Copilot license. Microsoft’s own documentation requires a workspace on Premium Power BI capacity (P1 or above) or paid Fabric capacity (F2 or above); trial capacity does not qualify | Natural-language questions against a data model already built in Power BI, and drafting visuals or DAX expressions from a plain-language request | It does not build the data model or the connections; a team paying $14 a user a month for Power BI Pro, or $24 for Premium Per User, can still find Copilot unavailable until someone buys the underlying Fabric capacity, a separate and larger line item |
None of the seven tools in this section replaces an audit management platform’s engagement workflow, issue tracking or sign-off. They sit alongside one, doing a narrower job well, or half-well. The table below sorts every one of them, and the platforms above, by job instead of by vendor.
What AI is actually good at today, task by task
Ask what a tool is good at, not what it is called. Eight tasks cover almost everything shipping across the sixteen products named in this guide.
| Task | Where it shows up today | What a genuine result looks like | Where it breaks down |
|---|---|---|---|
| Draft a first-pass risk statement, finding or audit-plan section | Optro’s Audit Agent, IBM OpenPages’ audit-plan drafting, Fieldguide’s Planner and Auditor agents, Microsoft 365 Copilot generically | A draft that names the specific risk in the control’s own language, not a template that would fit almost any control | A generic-sounding draft is the surest sign the tool is not actually reading your control description |
| Summarize a long workpaper or evidence file | Trulli (Trullion), TeamMate+ AI Editor, Workiva’s evidence analyzer | A summary that flags what is missing or unclear, not only what is present | A confidently complete-sounding summary of an incomplete file |
| Extract and link data from source documents | DataSnipper’s Snips and Excel Agents, Blast Audit’s Snip and Matching, TeamMate Analytics’ Document Linker, Workiva’s Tie-Out | Every extracted figure traces back to the exact page or cell it came from, inside the tool itself | Table and row misalignment on scanned or low-resolution documents, a complaint that recurs in DataSnipper’s own G2 reviews |
| Score anomalies across a full population, not a sample | MindBridge’s Ensemble AI, Diligent One’s ACL AI Studio, LogicGate’s Automated Evidence Testing | A stated reason attached to every flagged item, not only a risk score | A score with no visible reasoning behind it, a limitation several of MindBridge’s own G2 reviewers name directly |
| Generate control-test steps or a testing program | DataSnipper’s four prebuilt SOX agents: segregation of duties, user-access changes, journal-entry controls and management variance | Steps specific enough to run, still checked against your own risk and control matrix before anyone relies on them | Steps generic enough to have been written before your control existed |
| Query standards, policy or a prior file in plain language | Trulli, MetricStream’s Policy Assistant, IBM’s watsonx Orchestrate chat, ServiceNow’s Now Assist and Otto | An answer with a citation back to the specific document it drew the answer from | An answer that reads exactly as confident whether or not it is grounded in anything real |
| Track, chase and pre-validate evidence requests | DataSnipper’s Collect and UpLink modules | Real-time status on what has been submitted, what is outstanding and who owns the follow-up | A feature that looks similar on a pricing page is not always the same job; Blast Audit, for one, ships a way to share signed evidence packages outward but nothing on its own feature list does inbound request-tracking |
| Find and merge duplicate controls across frameworks | ServiceNow’s On-Demand Rationalization and Proactive Clustering | A shortened control library that still maps every merged control back to each framework it used to satisfy separately | This is a narrow fix for a specific problem, control-library bloat inside a platform running several frameworks, and does little for a team without that problem |
None of the sixteen products named across both tables above do the parts of the job that still belong to a person: setting the risk rating, exercising professional skepticism on a management explanation, deciding whether a deficiency is significant, or writing the narrative that explains why a finding matters to this business, this quarter. The 7 percent “advanced” AI-implementation figure from IIA Vision 2035, cited above, is consistent with that: audit functions are mostly using these tools for the first three rows of the table, drafting, summarizing and extraction, not the judgment calls further down it.
One more distinction is worth keeping straight. Every tool in this guide is something your function might use. None of them is, yet, something your function tests as a control. Once an AI feature starts drafting risk statements or scoring transactions that a human signs off without re-deriving, it has become part of your control environment, a different kind of scrutiny than a purchase decision. The site’s guide to auditing AI and algorithms covers that question separately, once you are past the pilot stage this page is about.
The data-use questions to ask before you pilot anything
Put five questions in writing before any real evidence goes into a new tool. Is our data used to train the model, ours or anyone else’s, and where is that written down? Which named model and provider actually powers the feature, not a description like “proprietary AI”? How long are our prompts and uploaded documents retained, by the vendor and by any AI subprocessor behind it? Can every output be traced back to its source document inside the product itself? And is the feature included, an add-on, or gated to a tier you do not currently hold?
Answers vary sharply across the tools in this guide. DataSnipper states plainly that it does not train on customer data and names Azure OpenAI as the provider behind its Excel and Disclosure Agents. Microsoft’s own Copilot privacy documentation goes further than most of the standalone tools in this guide: it states that customer content is not used to train the underlying models, names the EU Data Boundary, and discloses Anthropic as one of its subprocessors. MindBridge’s own published pages name no model provider and no data-use statement for any of its three products. Trullion’s Trulli launch release names no model and states no training policy either. None of that means a vendor is doing something wrong with your data; it means you cannot check, which is a different problem with the same practical answer: ask in writing before you pilot, not after, and weigh the vendor’s own stability alongside its answer, since the site’s due diligence guide covers what to check about who is behind the tool, not only what it says about your data. The site’s Evaluating AI in Audit Software guide has the full twelve-question checklist, a five-task demo script that forces a live answer instead of a rehearsed one, and a usage-policy outline to have ready before the first call; this section is only the short version.
How to pilot AI in your audit function
A pilot with no plan turns into six months of one auditor using a tool nobody else adopted. The worksheet below is deliberately narrower than the site’s full 25-scenario demo script; fill it out before the first sales call, not after the first invoice. A handful of the mistakes covered in the site’s 15 buying mistakes guide apply directly to an AI pilot that skips this step.
The first AI pilot: a one-page worksheet
Pick one workflow, not the whole audit. Name a single, bounded task from the table above, summarizing evidence files for one engagement type, say, rather than “using AI in fieldwork” generally.
Name one owner. A single auditor accountable for running the pilot and reporting the result, not a rotating group.
Set a timebox and a stop rule. Thirty to sixty days, with a decision date already on the calendar and a plain statement of what stopping looks like, written down before the pilot starts, not negotiated after.
Write the success test before you start. A specific, comparable output, the same ten real files run by hand and by the tool, timed and checked by a second reviewer, not a general sense that it felt faster.
Get the vendor’s written answers first. The five data-use questions above, in writing, before any real evidence goes into the tool, using the full checklist in Evaluating AI in Audit Software if the vendor’s category calls for it.
Test on your own material. Ten real, redacted files from your own environment, never the vendor’s rehearsed demo sample; the site’s prompt library has reusable starting prompts if the pilot tool is a general-purpose assistant like Copilot rather than an audit-specific product.
Report the result either way. A one-page memo to whoever approves software spend, even, especially, when the answer is no; a documented no is worth more than an undocumented maybe that lingers for a year.
A pilot that passes still has to become a purchase. The site’s guide to the business case for audit software covers turning a memo like the one above into budget the CFO and audit committee will actually approve, and the guide to implementing audit management software picks up from there. If the pilot is happening inside a formal purchase process rather than a one-off trial, the site’s RFP method has a place to put these same questions in writing.
Worked example: Lakeshore Bancorp tests two evidence tools
Lakeshore Bancorp, the $9 billion regional bank with 60 branches and 212 key controls used for worked examples elsewhere on this site, tests those 212 controls every quarter and loses real hours to auditors re-keying numbers from bank statements and loan files into Excel workpapers. The audit manager runs the worksheet above against two candidates: DataSnipper’s Accelerate tier and Blast Audit’s single EUR45-a-month tier, picking one workflow, extracting and matching figures from ten real, redacted loan files rather than either vendor’s sample set, and timing a manual re-key of the same ten files as the baseline.
Blast Audit’s price is the easier one to plan around: a flat EUR45 a user a month with no seat minimum, though the team notes it is quoted in euros and confirms with Blast Audit how a US-dollar invoice would actually be billed before signing anything. DataSnipper’s own site names no price at all; the only public figures anywhere are Sacra’s low-confidence seat estimate and a separate add-on fee, so the team asks DataSnipper directly for a written quote rather than budgeting off a third-party number. Neither vendor’s public pages answer the data-use questions above for a document as sensitive as a loan file. DataSnipper states plainly that it does not train on customer data; nothing comparable turned up on Blast Audit’s pages during this guide’s research. That unanswered question, not the price difference, becomes the audit manager’s actual signing condition: a written data-use answer from Blast Audit before any real loan file goes into it, timed to arrive before the thirty-day pilot clock the worksheet above calls for starts running.
Questions about AI tools for internal audit
Is any of this actually “agentic,” or is it mostly automation with a new name?
Some of it meets the plain definition: software that carries out a multi-step task toward a goal with limited prompting along the way. Diligent One’s agentic-mode ACL AI Studio, Onspring’s Agentic GRC and DataSnipper’s Alwin all fit that description in their own vendor documentation. Most of what is labeled AI in the platforms table above is a single-shot feature, a draft or a summary, wearing a broader word because it demos well. The site’s Evaluating AI in Audit Software guide has the full method for telling the two apart before you sign anything; this page only catalogs what exists.
Which standalone tool should a small audit function try first?
Start with the job, not the tool. A team still re-keying numbers from PDFs is closest to a DataSnipper-or-Blast-Audit decision, and Blast Audit’s flat EUR45-a-month price is easier to model on a small budget than DataSnipper’s quote-only tiers. A team already inside Microsoft 365 with no budget for a new tool gets more from testing Microsoft 365 Copilot on real workpapers first, since the license may already be sitting unused. The site’s audit software for small teams guide covers the platform side of that same decision.
Does Copilot in Power BI replace a dedicated audit analytics tool?
No. It answers plain-language questions against a data model someone already built and connected; it does not build that model, connect to your ERP, or replace the 150-plus prebuilt tests in something like TeamMate Analytics or the full-population scoring in MindBridge. It also will not run at all without the underlying Fabric or Premium capacity most small teams do not already own, a separate cost from the per-user Power BI license.
Should a vendor’s AI ever draft something that goes straight into a workpaper?
Only with a named reviewer checking, editing and signing every output first, the same standard due professional care would apply to a junior auditor’s first draft. The site’s Evaluating AI in Audit Software guide has a full usage-policy outline built around exactly this requirement.
Why does Fieldguide show up here instead of in the platform reviews?
Fieldguide sells mainly to CPA and advisory firms doing outsourced or co-sourced audit work, with a corporate internal audit product as a second line, rather than being built primarily for an in-house function the way Optro or TeamMate are. It earns a place in this catalog on the strength of its Field Agents, not a full review on this site yet.
How often does this table need to change?
Faster than most of this site’s other pages. Several of the sixteen tools in the two tables above shipped or renamed an AI feature in September 2026 alone. Re-check this page, and the vendor’s own release notes, before relying on any single row for a purchase decision more than a quarter old.
internalauditguide.com has no commercial relationship with any vendor named on this page. We take no vendor money, run no affiliate links and accept no sponsored placements, and no vendor saw this page before publication. Product and company names are the trademarks of their owners. Corrections: desk@internalauditguide.com.
Sources and verification
- Gartner press release, 16 September 2026 — the 84 percent audit management software adoption figure (accessed 27 September 2026).
- Wolters Kluwer, AI adoption survey release — the 39 percent current AI use figure, 4,214 respondents (accessed 27 September 2026).
- IIA, 2025 North American Pulse of Internal Audit — the 41 percent generative AI use figure, 405 respondents (accessed 27 September 2026).
- Internal Audit Foundation, Vision 2035 — the 40 percent high-implementation and 7 percent advanced-AI figures, 6,506 respondents (accessed 27 September 2026).
- DataSnipper, internal audit product page — the four prebuilt SOX and controls agents (accessed 27 September 2026).
- DataSnipper, Alwin launch press release — the 22 September 2026 agentic platform launch and Elevate-tier gating (accessed 27 September 2026).
- MindBridge, agentic platform capabilities release — the 22 September 2026 Agentic Risk Assessment, Analysis Designer Agent and MCP server launch (accessed 27 September 2026).
- MindBridge, Fieldguide partnership release — the 20 August 2026 partnership and Fieldguide’s own scale and backers (accessed 27 September 2026).
- Vendr, MindBridge marketplace page — the $42,092 median annual price (accessed 27 September 2026).
- Fieldguide, solutions overview page — Field Agents and the top-100-firms claim (accessed 27 September 2026).
- Trullion, Trulli product page — Trulli’s research and summarization capabilities (accessed 27 September 2026).
- Businesswire, Trullion launches Trulli press release — the 7 May 2025 launch and module availability (accessed 27 September 2026).
- Blast Audit, pricing page — the EUR45-a-month price and feature list (accessed 27 September 2026).
- Microsoft 365 Copilot pricing page — the $30-a-user-a-month enterprise price (accessed 27 September 2026).
- Microsoft Learn, Microsoft 365 Copilot data, privacy and security — the no-training-on-customer-data statement and the Anthropic subprocessor disclosure (accessed 27 September 2026).
- Microsoft Learn, “Enable Copilot in Power BI” — the Fabric F2 and Premium P1 capacity requirement (accessed 27 September 2026).
Related guides
- Internal audit software: the independent buyer’s guide — every review, comparison and buying guide in one place.
- How we review audit software — the evidence levels, the scorecard and the fit-by-situation method.
- The audit software shortlist finder — eight questions, a shortlist with the reasons from each review.
- The requirements matrix — 156 weighted requirements and vendor scoring in a free Excel workbook.
- Evaluating AI in Audit Software: What Is Real and What Is Agent-Washing — the twelve-question checklist and demo script this page’s worksheet leans on.
- Best internal audit software — 25 platforms and tools compared by use case, AI features included.
- Types of internal audit software — what each category of platform is actually for.
- Internal audit software pricing — the base-platform prices this page’s AI tables build on.
- GRC suite vs standalone audit management software — the platform decision behind whichever AI feature you are evaluating.
- Internal audit software vs compliance automation — make sure the tool you are comparing sits in the right category first.
- The business case for audit software — turning a successful pilot into an approved line item.
- The audit software demo script — 25 scenarios to run alongside this guide’s pilot worksheet.
- Audit software due diligence — the security and vendor-stability questions this page’s data-use section does not cover.
- Audit software vs Excel and SharePoint — for a team still deciding whether it needs any of these tools yet.
- Implementing audit management software — what comes after a successful AI pilot turns into a purchase.
- 15 mistakes internal audit teams make when buying software — several apply directly to an AI add-on.
- Selecting an audit management system — where to put this guide’s data-use questions inside a formal RFP.
- Audit software for small internal audit teams — which of this guide’s standalone tools make sense on a small budget.
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