Compliance by design: scaling agentic back office with confidence

Barriers to realizing agentic AI benefits

Enterprise automation is transitioning from rigid, task- based automation to dynamic agentic systems capable of autonomous decision-making and cross-workflow execution. Autonomous systems can now access data, invoke tools, and execute workflows. A finance agent can investigate an invoice discrepancy. A procurement agent can evaluate supplier documentation. A facilities agent can interpret maintenance records and service-level requirements to prioritize intervention.

The opportunities are substantial, but so are the risks. Without enterprise trust, organizations may lack the confidence and control needed to allow AI agents to make business decisions. In fact, McKinsey’s 2026 AI Trust Maturity Survey found that nearly two-thirds of leaders cite security and risk as key obstacles to scaling agentic AI.

In related research, the Everest Group reported that the gap between data governance and AI governance is “where audit, operational, and reputational risks accumulate.” The risk profile grows considerably when AI agents must make decisions using unstructured and structured data.

Across shared services, four core friction points undermine enterprise trust.

1. Fragmentation occurs when information spans physical archives, legacy repositories, systems of record, and data clouds. Agents can access data without knowing what’s authoritative, causing validation delays. Processing cycle time slows, increasing cost per transaction, delaying cash conversion, and ultimately causing margin leakage. Establishing enterprise trust requires connecting agents to the right information by establishing unified access to authoritative physical and digital evidence before reasoning begins.
2. Context gaps open as unstructured documents lack the metadata and relationships AI agents need to reason correctly. Agents cannot reliably connect a document to the right customer, supplier, or transaction—lowering throughput and increasing cost-to-serve. Overcoming this friction requires supplying the right context, enriching unstructured assets with domain logic, obligations, and relationship metadata so agents can accurately interpret intent and business state.
3. Governance gaps exist around documents with unclear or varying permissions, retention rules, ownership, consent, and jurisdictional constraints. Agents default to manual escalation or halt entirely, driving up legal review costs and delaying contract cycle times. Addressing this friction demands embedding the right controls—integrating policy-as- code, dynamic permissioning, and human-in-the-loop (HITL) review directly into the execution flow rather than relying on manual checkpoints.
4. Explainability gaps leave regulators and boards unable to validate AI-driven outcomes. Without traceable evidence trails, an organization cannot show how an agent reached a decision or confidently deploy that agent in key workflows—limiting scalability and reducing return on AI investment. Overcoming this barrier requires traceable outcomes, with immutable audit trails and lineage records that prove compliance to regulators and leadership.

After-the-fact compliance review is no longer sufficient for back-office operations. To overcome the friction points, organizations need to know—and show—why a decision was made, which evidence and rules informed it, and who or what is accountable.

With governance built into workflows, compliance by design becomes the foundation for enterprise trust.

Compliance cannot be a checkpoint

Are AI agents ready to navigate the complexities of multi-region shared services centers? These organizations already struggle with cross-functional data discrepancies and inconsistent metadata across enterprise resource planning (ERP) systems, procurement platforms, and legal repositories.

Agents must adhere to localized privacy controls such as the General Data Protection Regulation (GDPR), along with policies for cross-border data transfers, data sovereignty requirements, and regional retention mandates. Frameworks like the EU AI Act and localized cloud-storage requirements expect organizations to document data use, maintain governance and traceability controls, and help ensure AI workflows do not move or expose regulated data outside approved environments.

Imagine an AI finance agent investigating an invoice exception. It finds a contractual provision allowing a disputed charge and approves the invoice. Was the contract current? Was the agent permitted to access it? Did the provision apply in the relevant country? Was the supplier’s legal entity correctly matched?

With incomplete governance, an agent could reason correctly and still produce the wrong outcome —triggering an incorrect payment, processing an invalid charge, or missing a duplicate invoice. When these errors leak into production, the business pays a double penalty: immediate financial losses through unearned payouts and missed early-payment discounts, followed by delayed processing across the broader workflow as panicked teams freeze automation to manually re-examine flagged transactions. Operations slow when manual, post-hoc reviews and “audit fatigue” set in—impacting velocity, inflating compliance management costs, and exposing the organization to regulatory penalties.