Chris June’s working rule for small teams is simple: start with AI where you already have a decision you can see, measure, and correct. In other words, an AI-first workflow should be a bounded system that turns operational signals into auditable routing and next actions through human review when confidence is uncertain. (airc.nist.gov)
The ERP workflow automation priorities that matter firstStart with the
ERP operations points that repeatedly fail in the same way: exception status handling, unclear ownership, missing or mismatched documents, and “handoff ping-pong” between queues. You’re looking for friction that is both frequent and costly in time or errors—so your first AI for ERP operations step can prove value without reshaping your whole operating model. Proof is available inside your own logs and tickets. If you can pull a list of the top 20 exception reasons over the last 30–60 days and you can map who resolves each one, you already have the operational signals needed to design operational intelligence mapping and decision architecture. AI is not required to find that mapping; AI is only required to reduce the effort of routing and coordinating once the mapping exists. (airc.nist.gov)
Implication: if you skip this mapping and build “AI features” broadly, you will not be able to attribute improvements to a workflow change. That is how small teams end up with tools that look busy but don’t reduce cycle time.
Which first use case fits “small system, measurable improvement”
Good first-use-case criteria for an ERP team are specific and operational:1) The workflow has a clear “status in → decision out → status out” shape.2) Inputs include documents or semi-structured text (emails, PDFs, packing slips) where manual interpretation creates delays.3) The operation already uses a human reviewer for edge cases, so you can introduce a confidence-based routing gate rather than a fully autonomous system.4) You can define a success metric you can measure weekly: straight-through rate, review queue volume, first-pass accuracy, or average time in exception.
Proof: modern document AI services explicitly support confidence-based thresholds for straight-through processing versus human review, which matches the “narrow first loop” approach small teams need. For example, Microsoft’s Document Intelligence transparency guidance describes using a confidence threshold for routing—high confidence goes straight through; lower confidence triggers human review. (learn.microsoft.com)
Implication: you can launch a workflow that reduces review effort without betting the business on perfect extraction. This also aligns with the NIST AI RMF idea that documentation and human involvement should support transparency and accountability in AI-enabled decision-making. (airc.nist.gov)
What not to automate first in ERP operationsDo not start
by automating core business-critical actions with no review gate. In practice, that means avoiding “autopilot” for decisions that change payment terms, purchase orders, inventory adjustments, customer billing, or compliance-relevant postings during your first pilot. Instead, reserve automation for:- Summarizing the document and proposing the extracted fields- Routing the exception to the correct human queue- Drafting the coordination message (what’s missing, what needs rework)- Preparing a structured audit trail for the human to approve
Proof: implementation trade-offs are real. Document AI and workflow automation must still handle confidence uncertainty, input quality variance, and edge cases; vendor guidance assumes thresholds and human review are part of a safe operational pattern. (learn.microsoft.com)
Implication: if you automate too early without a decision boundary, you increase operational risk and create more work during incidents. You also make it harder to run post-mortems because you can’t tell whether the model, the data, or the workflow rules caused the outcome.
When a focused AI tool is enough and when custom
routing software is necessaryA focused AI platform tool is enough when your first workflow is mostly “extract + route + log,” and you can tolerate the tool’s constraints on integration and review UX. Lightweight custom software becomes necessary when you need any of the following:- Tight ERP-specific status transitions (for example, a very specific mapping between ERP posting statuses and your exception states)- A custom review queue workflow (roles, SLAs, reassignments, escalation paths)- Consolidated “handoff packets” that bundle extracted fields, evidence links, and the recommended next action in the exact structure your team uses
Proof: confidence-based routing is a tool-friendly pattern, but it still requires a decision architecture around it—explicit thresholds, review gates, and traceability that connect the AI output to your operating cadence. This is consistent with ISO/IEC 42001’s framing of an AI management system as interrelated processes for responsible provision and use, including documentation and oversight expectations. (iso.org)
Implication: start by using a focused extraction/routing capability, then wrap it with small custom workflow glue only where the tool cannot express your decision structure and evidence trail.
How a Canadian SMB can launch ERP exception routing with
a 2-person ops teamExample: a 20–50 person Ontario distributor runs an ERP and processes ~300 invoices per month. Their operations team is two people. Most invoices arrive as PDFs from suppliers; 10–15% require exception handling because invoice numbers, PO references, or due dates are incomplete. The team currently uses an email thread plus manual entry into a spreadsheet to track what’s missing. A practical “ERP team AI first step” is:- Use document AI to extract key fields (invoice number, PO number, dates) with confidence scores.- If confidence is above a threshold, auto-create a structured “candidate record” for the ERP lookup step.- If confidence is below the threshold, route to a single review queue with a checklist of missing/uncertain fields and a one-click “request supplier clarification” message.- Record the model confidence, extracted values, and reviewer outcome in a lightweight audit log linked to the ERP document reference.
Proof: vendor guidance supports confidence thresholds with human review for documents such as invoices/receipts, and it is specifically designed to reduce overhead while keeping review under control. (learn.microsoft.com)
Implication: with only two ops staff, your measurement discipline matters more than feature breadth. You target one metric first—like reducing time-in-exception by 20%—and only expand to more exception types after the first workflow is stable.
What can fail in the first AI workflow and how to prevent it
Common failure modes for an ERP-first AI pilot are not “model accuracy” alone. They are decision and operations failures:- Thresholds chosen too low create a review queue overload.- Thresholds chosen too high increase wrong postings or wrong routing.- Evidence is not captured in a way the team can audit during a dispute.- The workflow is too wide (too many exception types) so you can’t isolate improvements.
Proof: NIST AI RMF highlights that documentation can enhance transparency and bolster accountability, and ISO/IEC 42001 defines an AI management system approach rather than one-off tool usage. (airc.nist.gov)
Implication: treat your first workflow as a decision architecture experiment. Keep the workflow narrow, enforce confidence-based routing, and design the audit log so a reviewer can reconstruct “why this action happened” without opening three systems.
Open Architecture Assessment
If you want to find your best first workflow automation priorities without overbuilding, open an Architecture Assessment with your ERP operations team. We will map your top exception states, define the first measurable decision loop, specify confidence-based routing and review gates, and produce an implementation trade-offs plan you can execute with a small budget.