KPMG’s push to turn ERP systems from passive ledgers into proactive decision engines is no longer an abstract vision — it’s materializing in production pilots and packaged use cases built on Microsoft’s Copilot ecosystem,
Dynamics 365, and the Model Context Protocol (MCP). Two compact but telling implementations — a
Goods Received Not Invoiced (GRNI) Chaser and a
supplier performance insight capability — illustrate how KPMG is composing
Copilot Studio agents, ERP connectors, and external data feeds to automate routine controls, accelerate matching, and surface supplier risk across global supply chains. These solutions promise measurable reductions in cycle time and clearer audit trails, but they also raise execution, governance, and independence questions that every CIO, CFO, and controller should evaluate before wide rollout.
Background / Overview
ERP systems have traditionally been transaction engines and record stores: they capture purchases, shipments, invoices, and payments, but they rarely act without human prompts. Microsoft’s Copilot strategy — embedded copilots for Finance, Supply Chain, and Sales plus low-code authoring in
Copilot Studio — changes that pattern by enabling
agentic workflows that can detect exceptions, draft human-reviewable actions, and nudge people inside everyday apps like
Teams,
Outlook, and
Excel. KPMG’s work with the Microsoft stack leverages this architecture to augment finance, procurement, and audit processes with automated follow-ups, grounded answers, and multi-source risk signals.
KPMG’s stated aim is to move clients from “systems that record transactions” to systems that “can take action” — converting repetitive ERP tasks into
automated, auditable agents that keep human judgment as the final control gate. The GRNI Chaser and supplier performance insight exemplify that shift: one automates exception chasing and invoice matching; the other blends ERP telemetry with external data to produce a richer supplier-risk view.
How the GRNI Chaser works (technical and operational anatomy)
What the GRNI problem is
The GRNI balance — goods received but invoices not yet matched — is a persistent finance pain that obscures true working capital, complicates month‑end close, and creates reconciling items that consume analyst time. Manual chase processes are slow and error-prone, typically involving emails, calls, and phone tags. Automating this triage reduces days-to-match and improves cash-flow visibility.
The agent pattern KPMG applies
KPMG’s GRNI Chaser uses a Copilot-style agent connected to
Dynamics 365 (or Dataverse) and published into
Microsoft Teams to proactively follow up with receiving teams or procurement owners when unmatched receipts persist beyond defined SLAs. The agent workflow typically follows these steps:
- Identify stale GRNI records by querying the ERP ledger and goods-receipt events.
- Enrich each record with contextual facts (PO number, receipt date, supplier, expected invoice days).
- Trigger an outreach in Teams to the responsible user with a templated request and suggested responses.
- Capture the user confirmation or correction and automatically update matching status or create a case for AP.
- Log all interactions and decisions in an immutable audit trail for controls and review.
This pattern reduces context switching by letting the user answer in Teams rather than jumping into ERP screens, and it preserves provenance — who confirmed a receipt, when, and what data the agent used to make suggestions. The approach maps to recommended Copilot Studio designs where agents are grounded in enterprise data and expose provenance for every answer.
Typical benefits observed
- Faster invoice matching and shorter days-to-pay or days-to-match for GRNI items, reducing working-capital uncertainty.
- Reduced manual volume for AP and receiving teams by automating routine confirmations.
- Stronger audit trails, because agent actions and human confirmations are logged in a governed store.
Early pilots have reported movement from multi-day reconciliation cycles to near-hourly exception triage for the same workloads, although implementation detail matters.
Caveats and technical dependencies
The effectiveness of a GRNI agent depends heavily on
master-data hygiene (accurate POs, consistent supplier IDs), timely posting of receipts, and robust connectors to the ERP. Where GL coding or receipt metadata are inconsistent, automation will thrust more exceptions to humans rather than reducing workload. Organizations should pair GRNI automation with a master-data remediation program to get sustained automation gains.
Supplier performance insight: blending ERP with external intelligence
The problem space
Supplier risk is multidimensional — financial health, delivery performance, geopolitical exposure, and reputational signals all matter. Traditional supplier scorecards often rely on historical ERP metrics (on-time delivery, quality rejects), but they miss real-time outside signals like credit filings, sanctions, or sudden news events.
KPMG’s approach
KPMG’s supplier performance insight solution ingests ERP-sourced metrics from Dynamics and enriches them with external feeds (financial filings, credit indicators, news sentiment, sanctions lists). A Copilot agent or dashboard then combines these signals into a holistic supplier reliability score and surfaces prioritized alerts to procurement and supply‑chain teams. The agent can also recommend mitigations — alternate sourcing, expedited shipments, or payment holds — and prepare justification narratives for procurement committees.
Why MCP and grounding matter
The
Model Context Protocol (MCP) and Microsoft’s agent frameworks are central here: they standardize how an agent queries multiple knowledge sources, reuses context across calls, and keeps grounding (i.e., the factual anchors) consistent. When suppliers are scored, every recommendation is linked back to the underlying facts (ERP delivery history, latest quarterly revenue, news excerpt), helping mitigate hallucination risk and improving explainability. This design is consistent with Microsoft’s guidance for agent grounding and KPMG’s architecture patterns.
Business outcomes and limits
- Proactive risk detection — earlier alerts reduce scramble time during supplier disruptions.
- Actionable mitigation — suggestions for alternative suppliers or contract clauses reduce decision latency.
- Improved stakeholder communication — pre-drafted supplier-risk narratives speed approvals.
However, external-feed fidelity, licensing for third-party data, and the correctness of mapping external identifiers to ERP supplier records remain practical constraints. Vendor claims about percent reductions in downtime or risk mitigation should be treated as targets to validate via pilot metrics.
The technical foundation: how KPMG and Microsoft stitch this together
Core stack components
- Dynamics 365 / Dataverse: transactional source of truth for POs, receipts, invoices.
- Microsoft Copilot Studio: low-code/pro-code authoring for agents and conversation flows.
- Azure AI Foundry / Agent Framework: model catalog, runtime protections, agent lifecycle.
- Azure Cosmos DB / OneLake (Fabric): durable stores for agent memory, chat histories, and telemetry.
- Teams, Outlook, Excel surfaces: user-facing channels for agent interactions and approvals.
Security and governance pillars
- Tenant-scoped identity and RBAC: agents respect the same role-based permissions as human users.
- Data-loss prevention (DLP) and controlled connectors: prevent leakage of sensitive fields into agent outputs.
- Auditable telemetry: every agent action, tool call, and human decision is logged for traceability.
- Human-in-the-loop controls: monetary or legally sensitive actions require explicit user approval.
Design patterns to reduce operational risk
- Constrain agent scope to read-only facts plus pre-approved actions that require sign-off.
- Present provenance and confidence with each recommendation.
- Implement per-agent service principals with narrow permissions rather than broad enterprise credentials.
These patterns reduce the attack surface for prompt injection, misconfiguration, and credential misuse.
Governance, regulatory and audit considerations
Independence and conflict-of-interest optics
KPMG’s deep engineering relationship with Microsoft accelerates product access but raises questions where KPMG audits or advises Microsoft customers. Firms must maintain transparent separation between advisory implementations and audit engagements, and disclose when vendor-built tools materially affect audit evidence or conclusions. Establishing explicit operational firewalls and documented governance is essential.
Model risk management
Enterprises should require red-team testing for hallucination scenarios, document model validation results, and define retraining and rollback SLAs. For finance and audit contexts, explainability is not optional; ensure the system can show which records and logic produced a recommendation.
Regulatory traceability
Audit regulators are increasingly focused on whether automation improves audit quality or simply speeds up poorly controlled processes. For external reporting and regulated submissions, keep human sign-off gates in place and preserve immutable links from AI-generated outputs back to raw ledgers. Regulators and professional bodies expect demonstrable KPIs and test artifacts showing that agentic tools do not degrade audit quality.
Practical implementation roadmap (recommended phased approach)
- Readiness assessment
- Inventory Dynamics 365 modules and versions; identify required connector capabilities.
- Run a master-data health check (suppliers, POs, receipt posting latency).
- Map stakeholders and define acceptance criteria for GRNI and supplier insights.
- Pilot design (1–2 focused processes)
- Start with a tightly scoped cohort (e.g., top 100 suppliers or single legal entity).
- Define KPIs: days-to-match, straight-through match %, reduction in manual follow-ups, number of supplier-risk alerts validated.
- Implement logging, human-approval gates, and a feedback loop for prompt tuning.
- Validate and harden
- Test for failure modes: connector outages, mis-mapped supplier IDs, hallucination cases.
- Verify audit logs and retention settings against compliance needs.
- Train users on how to interpret agent confidence and provenance fields.
- Scale with governance
- Define agent lifecycle CI/CD: unit tests, integration tests, and substantive scenario testing before production release.
- Set up usage telemetry dashboards and anomaly detection for agent behavior.
- Negotiate licensing and m-metering expectations with Microsoft and third-party data vendors.
Costs, licensing and operational considerations
- Copilot Studio / Copilot message metering: Copilot Studio commonly uses a m-meter billing model; plan pilots with conservative multipliers to avoid bill shocks. Prepaid capacity packs can smooth spikes for predictable workloads.
- Third-party data feeds: Supplier risk scores often depend on licensed financial-data or news sentiment feeds; budget for recurring fees and data-matching effort.
- Run and support: Expect to staff an ongoing agent product team that owns prompt libraries, CI/CD, telemetry, incident playbooks, and regulatory reporting. Operational scale is a non-trivial cost item, especially for global deployments.
Risks and countermeasures — a pragmatic checklist
- Risk: Vendor lock-in if solutions are tightly coupled to Microsoft agent frameworks.
Countermeasure: Document portability plans, separate business logic from platform APIs where possible, and retain data export paths.
- Risk: Model hallucination producing incorrect recommendations for financial posting.
Countermeasure: Require source-provenance display, confidence scoring, and mandatory human sign-off for entries that affect financial statements.
- Risk: Auditability gaps when agent actions are not preserved in immutable logs.
Countermeasure: Log every agent call, decision, and human approval; align retention with regulatory needs.
- Risk: Operational scale and talent — insufficient capacity to sustain global rollouts.
Countermeasure: Package IP into repeatable templates, invest in local delivery capability, and create a central-run managed service with regional specialists.
- Risk: Overstated ROI claims from vendor or marketing materials.
Countermeasure: Insist on measurable KPIs in contracts and run controlled pilots to validate claims against your data.
Readiness checklist for IT leaders (short actionable list)
- Confirm ERP version compatibility with Copilot connectors and Dataverse sync.
- Run master-data clean-up (supplier IDs, POs, receipt posting rules).
- Scope pilot users and SLAs; instrument telemetry and anomaly detection.
- Define governance: DLP, audit logs, role-based approvals, and agent lifecycle controls.
- Negotiate Copilot m-metering expectations and third-party data licensing.
Critical analysis: strengths, practical value, and open questions
Strengths
- Operationally focused value: Automating GRNI follow-ups and surfacing supplier risk reduces busy work and shortens decision loops in finance and procurement. KPMG’s use of Teams and Excel as interaction surfaces aligns with how work actually happens, improving adoption.
- Enterprise-grade architecture: Leveraging Azure services (Cosmos DB, Foundry, Dataverse) and MCP makes multi-agent orchestration and auditability feasible at scale. KPMG’s integration pattern follows platform best practices for governance and observability.
- Faster time-to-value for routine tasks: Early pilots report meaningful reductions in routine processing time for reconciliations and communications when upstream data is healthy.
Risks and unanswered questions
- Dependence on data quality: The core limitation is not the AI itself but the fidelity of ERP master data. Without remediation, automation will amplify errors.
- Governance and independence: For audit-related automation and for firms that both advise and audit clients, documented safeguards and transparent separations are mandatory.
- Vendor claims vs. verifiable outcomes: Many vendor or partner ROI figures are plausible but vendor-sourced; customers must insist on pilot KPIs and contractually verifiable outcomes rather than marketing statements.
Where claims are primarily vendor-provided and not independently audited, they are flagged as such and should be validated in live pilots. The architecture, however, is verifiable in public product and partner materials and aligns with Microsoft’s documented Copilot, Copilot Studio, and Azure agent frameworks.
Conclusion
KPMG’s use of
MCP for Dynamics 365 and
Copilot Studio to build a GRNI Chaser and supplier performance insights is a concrete example of how enterprise consultancies are turning ERP systems into
agent-enabled decision platforms. The practical gains — faster invoice matching, improved cash-flow visibility, and earlier supplier-risk detection — are real where master data is clean and governance is strong. Yet these benefits come with important tradeoffs: platform coupling, model-risk exposure, and the need for strong human-in-the-loop controls and audit trails.
For finance and IT leaders, the right path is incremental: start with narrowly scoped pilots, instrument measurable KPIs, harden governance before scaling, and treat vendor ROI claims as hypotheses to be validated on your own books and supplier networks. When done properly, KPMG’s agentic patterns — grounded in Microsoft’s Copilot ecosystem — can take many of the tedium and latency out of record-to-report and procurement workflows and leave skilled professionals doing higher‑value judgment work rather than chasing paper.
Source: KPMG
KPMG Microsoft Alliance – MCP Use Cases