KPMG LLP has formed a strategic relationship with Uniphore to embed AI agents directly into enterprise workflows, beginning with procurement, contracting, finance, and other process-intensive functions. Announced this week at the World Economic Forum Annual Meeting, the collaboration focuses on integrating AI within existing enterprise systems rather than layering it on top. The move reflects mounting pressure on large organizations to demonstrate measurable returns from AI initiatives while maintaining governance, auditability, and control across regulated operations.
From Advisory Insight to Embedded AI Delivery
Uniphore positions itself as a Business AI provider focused on deploying agentic systems in complex enterprise settings. Its platform connects structured and unstructured data, fine-tunes small language models (SLMs), and governs AI agents operating across business processes. The company has worked with a number of large enterprises, particularly in customer experience and operations, and is now extending that model into more regulated, rules-driven domains.
Under the new relationship, KPMG will use Uniphore’s Business AI Cloud alongside domain-specific SLMs to design and deploy AI agents that reflect industry regulations, internal policies, and institutional knowledge. The intent is not to replace existing systems, but to allow AI agents to operate within them, integrating with current data environments, controls, and approval structures.
This approach aligns with KPMG’s broader effort to equip its global workforce with AI-enabled delivery models that combine consulting judgment with machine execution. Rather than distributing generic tools, the firm is developing an internal model in which teams design, deploy, and govern AI agents tailored to specific industries and use cases.
“We are thrilled to align with Uniphore’s vision for AI as a transformative force for business as we focus on helping clients move from experimentation to operational value,” said Prasad Jayaraman, Advisory Principal at KPMG. He emphasized that the collaboration is designed to support regulated industries where scale, auditability, and governance are non-negotiable.
Procurement as a Test Case for Agentic AI
One of the earliest areas of focus is procurement and contracting, where high document volumes, fragmented data, and policy complexity continue to strain teams. According to trade reporting, contract review and obligation management remain persistent sources of cycle-time delays and compliance risk, particularly in organizations operating across multiple jurisdictions.
KPMG and Uniphore are developing AI agents to classify large volumes of contracts, extract obligations, compare terms against approved standards, flag deviations, and route exceptions for human review. By operating inside existing workflows, the agents are intended to reduce manual handling without bypassing established controls.
The model is designed to address common procurement pain points: extended contract review cycles, inconsistent application of standards, and revenue or margin leakage caused by overlooked terms. By surfacing risks earlier and more consistently, the agents aim to shift human effort toward negotiation, supplier engagement, and strategic decision-making rather than document triage.
Beyond procurement, the partners plan to extend the agent model across functions such as finance, workforce optimization, claims processing, and customer experience, as well as across sectors including financial services, healthcare, telecommunications, and energy. Central to that expansion is what both firms describe as an “SLM factory” approach, transforming regulatory frameworks, process playbooks, and institutional knowledge into reusable AI components.
Where Agentic AI Will Be Tested First
One area that may shape enterprise adoption is how effectively AI agents capture policy drift, those small, frequent adjustments to standards and procedures that accumulate across large organizations. According to recent governance research, policy drift is a leading source of operational inconsistency, yet it often goes unnoticed until an audit or exception exposes it. AI agents built to detect and reconcile these incremental shifts can help stabilize processes that typically fragment over time. That capability could determine whether agentic systems become a dependable part of routine operations or remain confined to isolated use cases.