Scheduling the right audit, catching a mismatched finding, and getting a root-cause response that actually holds up — that’s where most audit programs lose time. AI is starting to close that gap.
Omnex Systems has built AI directly into the audit process for customers of every size — from small quality teams running a handful of checklists to one customer running the software across more than 560 sites. The goal isn’t to replace the auditor’s judgment. It’s to make every stage of the audit cycle — scheduling, performing, responding, and closing — faster and more consistent, whether you’re auditing one plant or five hundred.
Here’s how that works in practice, stage by stage, plus how the same AI validation approach carries over into Problem Solver, Omnex’s module for concerns that never went through a formal audit at all.
Why Audits Need AI Now
Five things are pushing audit programs toward AI at the same time:
- Disruptive change across every industry. Products, processes, and supply chains are shifting faster than paper-based audit programs can track.
- Demanding compliance and regulatory requirements. Auditors are expected to catch more, with less room for a missed clause or an inconsistent finding.
- Evolving, proliferating standards. New and updated standards keep arriving on top of the ones your team already tracks.
- A mobile-first, tech-savvy workforce. Newer auditors expect to look something up on a phone or laptop, not dig through a binder.
- Virtual and distributed teams. Shop-floor audits increasingly happen without a live internet connection or a paper checklist in hand.
None of these forces are going away. What’s changed is that AI can now sit inside the audit tool itself — not as a separate system, but as a layer of feedback and recommendation at every step of the methodology: scheduling, performing, non-conformity findings, and root-cause/action-plan validation.
This is all built into Audit Pro. See the platform →
Scheduling: Let Historical Data Point You at the Right Audit
Instead of scheduling audits on a fixed rotation, Audit Pro’s AI looks at your historical non-conformity data and recommends which type of audit to run where. A process with a history of non-conformities gets flagged for a layered process audit (LPA); the software also recommends which poor-performing processes or areas to prioritize for an LPA, process audit, or system audit.
In a live demo of the scheduling workflow, choosing a layered process audit brought up a list of processes with the AI-recommended ones highlighted in red, based on the number of non-conformities logged against them historically. In that example, coating, casting, forging, and silver coating were the processes flagged for attention. From there, scheduling continues as normal — audit number, area, system, process — and the audit agenda itself again reflects which processes the historical data says to prioritize.
The effect is simple: instead of every process getting an audit on the same fixed schedule, the processes with a real track record of problems get looked at more often.
Performing the Audit: Real-Time Feedback While You Work
During the audit itself, the auditor works through a checklist — an IATF checklist, for example — with defined checkpoints and questions. When something doesn’t pass, the auditor logs the non-conformity, enters objective evidence, selects the relevant clause, sets the category (major or minor), and assigns responsibility.
Here’s where the AI adds a second set of eyes: when the auditor clicks “validate,” the system checks whether the objective evidence actually matches the stated non-conformity, based on how similar findings have been logged before. If something doesn’t line up, it’s highlighted in red with an AI recommendation button offering feedback and a suggested correction.
From the Demo
One non-conformity was deliberately entered with mismatched evidence — “FMEA in control plan for product PXYZ” attached to a finding that problem-solving wasn’t fully effective. The AI caught the mismatch and recommended the category be changed from “major” to “minor,” based on how this type of finding had historically been categorized — with a disclaimer that this content is generated by AI and may not always be accurate, so critical information should be verified independently.
The three other non-conformities entered in the same demo, all with well-matched objective evidence, were validated by the AI as consistent — no flag, no extra step. That’s the point: the AI isn’t there to slow down the findings that are already right. It’s there to catch the ones that aren’t, before they move any further down the line.
Responding to Findings: AI-Assisted PDCA and 8D
Once a non-conformity is raised, the auditee has to respond — through a PDCA (Plan-Do-Check-Act) or an 8D, depending on how the organization is set up. For the root cause and corrective actions, the auditee can click “AI validate,” and the system highlights fields that look inconsistent — for example, a stated root cause that doesn’t actually align with the non-conformity it’s supposed to explain, or “why” reasoning that doesn’t connect back to the finding.
When the AI has a specific suggestion, it offers one. When it doesn’t, it says so plainly — “sorry, I don’t have any recommendations to offer” — rather than guessing. That honesty matters: it means a suggestion that does show up is worth paying attention to.
Once the auditee submits the PDCA, the auditor gets a notification and reviews the response’s effectiveness before closing the audit out. Both sides stay in the loop by email throughout — when the non-conformity is raised, when the auditee responds, and when the auditor reviews and closes it.
Every step of this cycle runs inside Audit Pro. Explore Audit Pro →
The 8D Report: For Major Non-Conformities and Issues Outside an Audit
Major non-conformities — and issues that come up outside a formal audit entirely, like a non-conforming part found on the line — go through an 8D-style (“AD”) report. The auditee populates team members, the problem description, root cause, and action plan, then runs AI validate.
The AI checks for the gaps that are easy to miss under deadline pressure:
- Team members with no department identified — often a sign the team isn’t genuinely multidisciplinary
- Actions with no responsibility assigned
- Missing containment-action information
- A root-cause description sitting at 0% contribution — meaning the field hasn’t actually been filled in with substance
- Missing verification dates for permanent corrective actions
Every flagged field comes with an AI suggestion attached, so the team knows exactly what to fix before the report moves forward.
Problem Solver: When the Concern Didn’t Come From an Audit
Not every issue starts with an audit finding. Customer concerns, internal concerns, health-and-safety issues, and general product issues can be raised from any department — and Omnex’s separate Problem Solver module handles exactly that, using the same AI validation approach as the 8D report above.
Take a customer-reported external leak as an example. It gets logged with a category and subcategory (functional / customer complaint), a problem description with customer contact details, process information, status, and quality-alert details, including how many non-conforming parts were identified. From there, the investigation runs through the same 8D-style structure, and clicking “AI validate” highlights issues across team identification, root cause, and corrective actions — in red for what’s clearly wrong, yellow for what might not be right — with an AI suggestion available for each flagged field.
The intent behind Problem Solver is broader than auditing: it’s meant to help quality engineers, process engineers, and managers across every department suggest, recommend, and validate their own data as it gets digitized — not just the auditors running the audit program.
AI-assisted validation follows the auditor from the shop floor into every finding, response, and corrective action.
Built Around the Standards and Checklists You Already Use
None of this requires starting from scratch. You can define your own audit standards, checklists, and checkpoints, or Omnex can supply standard ones — IATF, ISO 9001, ISO 13485, and layered process audit (LPA) checklists among them. Checklists can be imported directly from Excel, or new checkpoints and questions can be defined manually. Whichever route you take, the AI recommendations described above run on top of it, learning from the non-conformity history your team is already generating.
Licensing That Extends to Your Supplier Base
Audit Pro licenses on a concurrent-license model. An OEM that purchases 50 concurrent licenses, for example, could use 25 internally and allocate 25 to suppliers — the licenses stay owned by the OEM, but suppliers can use them. Suppliers who need to respond to non-conformities or corrective-action requests need their own login to the software, which the OEM sets up and provides; suppliers don’t need to purchase the software separately.
That matters in practice: a non-conformity raised against a first-, second-, or third-party supplier can route straight back into the OEM’s software, and the supplier can respond directly through their own access — no separate system, no manual re-entry, no purchase required on the supplier’s end.
See AI-Assisted Auditing on Your Own Data
Audit Pro and Problem Solver bring AI into scheduling, performing, and responding to audits and concerns — without taking the auditor out of the decision. Explore either product below, or request a free demo and we’ll walk through it using audits that look like yours.
Frequently Asked Questions
What is AI-assisted auditing?
AI-assisted auditing is when software recommends, checks, and validates decisions throughout the audit lifecycle — which audits to schedule based on historical non-conformity data, whether objective evidence matches a stated finding while performing an audit, and whether a root-cause or corrective-action response actually holds up — while the auditor and auditee stay in control of every decision.
How does AI validate audit findings?
When an auditor logs a non-conformity and clicks validate, the system compares the objective evidence and category against how similar findings have historically been logged. If something looks inconsistent — for example, evidence that doesn’t match the stated non-conformity, or a category that doesn’t match historical patterns — it’s highlighted with an AI recommendation. A disclaimer always accompanies AI-generated suggestions: the content may not always be accurate, so critical information should be verified independently.
Will an OEM receive supplier responses to non-conformities directly in its own software?
Yes. Suppliers can receive notifications and respond directly if they have access to the software. The OEM owns the licenses and can allocate a portion of them to suppliers, providing the login; suppliers do not need to purchase the software themselves.
Can we create our own audits and checklists, or do we need to use pre-loaded standards?
Both are supported. You can import your own questions and checklists from Excel and define your own standards, or Omnex can supply pre-loaded standards such as IATF, ISO 9001, and ISO 13485, along with layered process audit (LPA) checklists, or build custom checkpoints and questions for you.
How does the concurrent-license model work for suppliers?
Licenses are concurrent rather than assigned to one person. An OEM purchasing 50 concurrent licenses could use 25 internally and give 25 to suppliers — the OEM still owns the licenses, but supplier accounts can use them. The OEM sets up supplier usernames and passwords and provides them, so suppliers can log in and respond to non-conformities or corrective-action requests.
Is Problem Solver the same as the problem-solving tools inside Audit Pro?
No. The 8D-style response tools inside Audit Pro are tied specifically to audit findings. Problem Solver is a separate module for concerns that don’t start with an audit at all — customer concerns, internal concerns, health and safety issues, and general product issues raised from any department — using the same AI-assisted validation approach.