AI is generating a lot of discussion in the audit world right now. And if you're an EBP auditor, you've probably started wondering whether it belongs in your workflow.
The honest answer is: maybe. But not yet for most firms.
Not because AI isn't capable. Because AI is only as useful as the data and workflow it's operating on. And in EBP audits, that's exactly where many firms are still struggling.
When people discuss AI in audit, they jump straight to the exciting stuff: drafting narratives, flagging risks, surfacing anomalies in large data sets. It sounds compelling.
What often gets overlooked is that all of this assumes the underlying data is already clean, consistent, and structured in a way that can actually be used.
In EBP audits, that assumption breaks down fast.
You're pulling data from dozens of recordkeepers, each with their own report formats, field names, and export quirks. No two recordkeeper reports look the same, and what works for one provider won't map cleanly to the next. Payroll data has to be manually reconciled against recordkeeper data. Census reports get reformatted from scratch on every engagement. Before any AI tool can do something useful with any of it, someone has to make sense of it first.
The challenge isn't the technology itself. It's the work required to create a consistent, usable workflow around the data. And that's a challenge many EBP firms are still working to solve.
This tends to play out in a predictable way.
A firm gets excited about AI, starts experimenting with ChatGPT or another tool, and runs into an immediate wall: the inputs are a mess. Data is formatted inconsistently. Workpapers vary by staff member. There's no standard starting point to build from.
At that point, firms often discover that the obstacle isn't the AI tool itself. It's the lack of consistency in the underlying workflow.
If data is formatted differently across engagements, workpapers vary by staff member, and there is no standardized starting point, the AI tool has little reliable information to work with.
For AI to add real value in your EBP practice, a few things need to be true first.
Your data has to come in clean. That means recordkeeper reports are being ingested and formatted consistently, regardless of which recordkeeper is involved. Not manually reformatted each time by whoever happens to be on the engagement.
Your workpapers have to be standardized. If every staff member is building workpapers differently, there's no consistent output for any tool to work with or build on.
Your team has to know where judgment is required. AI can support structured, repeatable work. It can't tell you what matters in a specific plan environment. That still takes an experienced auditor.
When those things are in place, AI starts to make sense as a layer on top of a structured workflow. Not before.
A lot of firms are asking whether AI belongs in their EBP audit workflow. But a better question might be: are we actually set up to use it effectively?
Before investing in AI tools, firms should take a step back and evaluate the foundation underneath their process first.
1. Is our data consistent across engagements?
AI works best when data follows repeatable patterns. But in many EBP audits, every engagement starts with different report formats, naming conventions, and export structures depending on the recordkeeper involved.
If your team is still manually cleaning up or reorganizing data engagement by engagement, AI tools will struggle to produce consistent or reliable outputs. The firms getting the most value from automation are usually the ones that have already created a standardized process underneath it.
Many firms focus on how AI might speed up testing or analysis, but a large amount of audit time is still spent much earlier in the workflow: formatting reports, reconciling payroll data, rebuilding census files, and organizing supporting documentation.
If the process leading into the audit is still heavily manual, AI may not solve the real bottleneck. In many cases, improving the workflow itself creates a bigger operational impact before advanced AI tools even enter the picture.
3. Are our workpapers built consistently?
If workpapers vary significantly depending on the engagement team or preparer, there's no reliable structure for AI tools to support or build from.
Consistency matters not just for efficiency, but for reviewability, defensibility, and long-term scalability. AI performs best when outputs are built on repeatable processes rather than individual preferences or inconsistent documentation styles.
In auditing, faster isn't enough. Teams still need to understand how conclusions were reached, what supporting data was used, and whether outputs can stand up during peer review or inspection.
AI should support auditor judgment, not replace it. The ability to review, validate, and defend the work remains just as important as efficiency gains.
Those goals sound similar, but they often lead firms in different directions.
Some firms adopt AI hoping to save time. Others are trying to create a more consistent process across plans, providers, and engagement teams. The firms seeing the most long-term success are usually the ones that focus on building repeatable workflows first, because AI tends to amplify the strengths or weaknesses already present in the process.
Before evaluating any AI tool for your EBP practice, ask yourself: is our data structured and audit-ready enough to give a tool something real to work with?
If the answer is no, that's where to start. And that's exactly what our on-demand session, Why Your Firm Can't Standardize 100% of Its 401(k) Audits—And What to Do About It, is designed to help you do. See how firms are moving away from inconsistent, manual processes and toward a single approach that works across every plan and provider.
