Chris June frames IntelliSync’s operating principle simply: human review is not a checkbox; it is an accountability mechanism at specific decision points. In practical terms, meaningful human oversight means designing roles and interaction patterns so humans can intervene or take responsibility when AI outputs could be wrong or harmful. (nist.gov)
Can’t we just flag uncertain AI outputs for review
If your ERP-adjacent AI model is uncertain, that flag is useful—but not sufficient as a governance design. The operational question is: **what decision is being made, and who owns the consequences inside the ERP process?**In risk management terms, NIST treats governance as mapping and documenting how the organization manages AI risk across the lifecycle, including processes for human oversight. (nist.gov) In the EU AI Act, “human oversight” is explicitly framed as preventing or minimising risks and ensuring deployers have mechanisms that allow an assigned person to decide when to intervene. (artificialintelligenceact.eu)Proof (operationally): In an ERP workflow, “uncertainty” usually doesn’t map cleanly to “business harm.” A low-confidence classification may be harmless (e.g., internal tagging), while a high-confidence recommendation can be wrong in a customer-specific way (e.g., credit terms exceptions, product substitution rules, or contract-based pricing).
Implication: Build review gates by decision type and accountability, not by model confidence alone. Use a small set of review triggers tied to ERP actions: approval required, customer commitment creation, master-data mutation, and exception handling.
Put review where accountability meets the ERP decision
The right place for human review is at points where the ERP will do something consequential—create or change financial records, promise delivery or service, approve exceptions, or commit customer terms.Decision architecture matters here: the system must route “what to do next” based on the decision category, and the ERP process must record who approved what. Human oversight should be defined as part of AI risk management processes, not bolted onto the UI. (airc.nist.gov) The EU AI Act frames human oversight as a mechanism to allow intervention when outcomes are negative or not as intended. (artificialintelligenceact.eu)**Proof (where review helps):**1. ERP exception handling AI: When the model suggests an exception resolution (e.g., override a credit hold reason; choose an alternate SKU), a human verifies that the resolution matches company policy and the specific contract or customer context.2. AI approvals ERP process: When the model recommends an approval, the human checks authority, documentation, and downstream effects (inventory, revenue recognition, returns).3. Customer commitment points: When AI proposes dates, quantities, substitutions, or service terms, a human confirms feasibility and policy compliance because these commitments are externally observable and financially consequential.
Implication: Treat “human in the loop ERP workflow” as a small number of accountable gates—not continuous review of every step.
Where human review slows value and how to avoid itHumans
slow workflows when they’re asked to review routine, policy-safe decisions that the ERP already governs with strong rules (validation, master data constraints, approvals already designed for non-AI processes).Proof (failure mode): Many teams start with an “always review uncertain outputs” rule, then discover that:- Review volume explodes, so review becomes cursory.- Reviewers rubber-stamp to clear backlog.- The team loses evidence because decisions are logged inconsistently. NIST’s AI RMF is built for governance across the lifecycle and emphasizes defining processes for human oversight—meaning you should expect to formalize how oversight works, when it triggers, and what is documented. (nist.gov) The EU AI Act similarly requires mechanisms for human oversight for high-risk systems, supporting the deployer’s ability to intervene when appropriate. (artificialintelligenceact.eu)
Implication: Use a tiered approach:- No human review for decisions that are deterministic under ERP policy/constraints.- Automated with evidence capture for decisions that are reversible or low impact.- Human review for exceptions, approvals, and customer commitments.This keeps review focused, preserves turnaround time, and strengthens auditability.
Focused AI tool or lightweight custom software for ERP review gates
You don’t need a heavy custom platform on day one. The key is whether your “human review” requirement is primarily a workflow routing and evidence problem or a business-logic integration problem.Proof (trade-off):- If your needs are “show a recommended action, capture reviewer decision and reason, and update the ERP exception record,” a focused AI workflow tool can be enough because the hard part is operational gating and documentation.- If your needs are “apply company-specific exception logic across multiple ERP modules (pricing, inventory, order holds) with rigorous traceability,” lightweight custom software becomes necessary to enforce decision architecture inside the ERP integration layer.Implication (decision rule):- Start with a focused tool if you can express review gates with simple triggers tied to exception categories and approval types.- Move to lightweight custom software when your review gates must call multiple ERP services, compute impact, and enforce company policy beyond what generic workflow tooling can reliably do.To make this practical, ensure your chosen approach supports governance expectations around mapping and documenting human oversight processes. (airc.nist.gov)
A realistic Canadian SMB example: 6-person order deskImagine a 6-person
Quebec-based manufacturer using an ERP-integrated support desk. They receive customer requests for substitutions when items are out of stock.Operational need:- AI drafts a substitution recommendation based on BOM compatibility and recent purchasing history.- The ERP order desk needs to decide whether to accept the substitution.- Company policy requires an approval when substitution affects pricing or delivery commitments.Proof (how to place human review):- Let the AI propose substitutions automatically, but route to human review when the proposal would (a) change promised delivery date, (b) require a pricing override, or (c) resolve an exception category defined in your ERP process.- Capture: AI recommendation summary, referenced reason codes, the reviewer’s decision, and the ERP record updated. This matches the thesis: review sits where exceptions, approvals, and customer commitments require accountable judgment rather than automatic routing. Human oversight is treated as a defined mechanism in governance design. (artificialintelligenceact.eu)
Implication: You get faster “first draft” turnaround without forcing your busy order desk to review every non-exception suggestion.
View Operating Architecture
If you want governance_readiness, treat your “human review placement” as an explicit part of operating architecture: define the decision categories, implement review gates at accountable points, and record reviewer actions with evidence.Call to Action: View Operating Architecture.