代理人工智能与B2B支付的未来

Contents 3Executive summary 4Reframing B2B payments workflows in an agentic 7What agentic AI actually does in B2B payments 9Impact by stakeholder 11Supplier enablement: A critical agentic use case 12Guardrails, governance, and human oversight 13Conclusion Executive summary Agentic AI adds a groundbreaking new layer of delegated automation toB2B payments and acceptance: agents capable of executing tasks on thecustomer’s behalf within carefully defined policies, approvals and audittrails. While the opportunity is undoubtedly meaningful, the shape adoption Agentic AI adds agroundbreaking new layer of delegated automation Value will likely concentrate first in high-friction workflows, whereorganizations canrestrictwhat an agent is allowed to do andmeasure outcomes (e.g., invoice coding, policy checks, reconciliations, routinepurchasing, supplier outreach). Agents are inherentlyprobabilistic, and wherecustomers require highly predictable, low-variability execution, the pattern isusuallyhybrid: deterministic workflow automation enforces controls, while Adoption will vary across micro and small businesses, mid-market and largeenterprise segments because systems of record, integration maturity andgovernance capacity differ. Segment-specific deployment models will likely be Four themes determine scale: starting with measurable use cases; increasingautonomy over time (recommend → assist → execute); focusing on high-leverage value pools like orchestration and supplier enablement; and puttingthe right governance in place as the gating factor, including clear liability, Reframing B2B paymentsworkflows in an agentic01 Here, “B2B payments” covers how businesses initiate, authorize, execute andreconcile supplier payments across source-to-pay, accounts payable, invoiceprocessing and treasury controls. In an agentic context, the focus expandsfrom various payment rails to how policies, systems and counterparties govern Agentic AI introducesautomation into B2B Agentic AI introduces automation into B2B payments workflows. Becausemany corporates still run manual, exception-heavy processes, we can expectto see adoption that is progressive, not uniform. Governance maturity, liability Early adoption will likely start in high-volume workflows such asprocurementsupport(supplier matching) andexpense management(capture, coding,policy checks, approvals). The best “agent fit” is usually in thevariablepartsof the process: unstructured intake, missing context, exception handling, andcross-system coordination, while deterministic steps remain better served byexplicit rules. For example, an agent can read an invoice email thread, infer In the Agentic AI driven paradigm, AI agents transform how businessmanages their supplier relationships, reshaping their entire value chain Agentic B2B procurement lifecycle illustration Opportunities Agentic AI creates value by reducing friction and cost in repeatable workflows Early adoption will likelystart in high-volumeworkflows such as Data and intelligencemake messy payment data usable: extract and validateinvoice and remittance information, improve consistency across AP and ERPrecords, and use propensity signals to prioritize suppliers most likely to acceptcards and virtual cards. Instead of blanket outreach, an agent can flag high- Tools and enablersprovide deterministic controls: rule-based approvals (e.g.,auto-approving low-value renewals within threshold), treasury constraints andinfrastructure that executes and logs actions. Agents add value by triaging Worker agentsreduce coordination overhead in supplier onboarding andenablement and routine execution (e.g., initiating approved payments, sendingremittance, reconciling ERP/AP records). They should operate under explicit Risks Delegated execution changes the risk profile because decisions and actionscan propagate across systems faster than traditional workflows. The primaryrisks arecontrol failures: over-reliance that creates blind spots or falseconfidence in autonomy when human judgment is still required;security andliability exposure:agents acting across platforms increase liability complexity,and compromised or “rogue” agents can misuse credentials; andgovernancefragmentation: contradictory policies across ERPs, banks and networks 01.1Why adoption will lag technology When it comes to agentic,technology readiness is not the primary bottleneck.Adoption will be gated by (1) willingness todelegate decisions, especiallywhere money moves and accountability is high, and (2) the ability to capturevalue byredeploying resources, retiring legacy processesandmoving budgets, Two adoption modes In practice, adoption follows two modes. In standardized, highly integratedindustries (e.g., automotive, retail), agentic AI accelerates existing automationby optimizing workflows and exceptions (“Accelerator Mode”). In fragmented,low-integration industries (e.g., construction, home services), it is morefoundational, introducing automa