The next AI payments boom may happen in the back office

In recent years, discussions surrounding generative AI have predominantly revolved around consumer applications, such as chatbots, image creation tools, and copilots for summarising meetings. However, a less conspicuous yet potentially more significant transformation is unfolding in the realm of finance teams, procurement departments, treasury divisions, and enterprise payment systems.

According to a report titled "Beyond Automation: Defining Agentic Global Payments" by Sunrate and Mastercard, agentic payments—transactions orchestrated, managed, or optimized by AI agents operating within prescribed guidelines—could experience a more robust growth trajectory in business-to-business commerce compared to consumer transactions.

The report anticipates that B2B agentic commerce transaction value will expand at a compound annual growth rate of 335 percent over five years, contrasting with 211 percent growth for consumer-to-business transactions. This notable disparity also challenges a prevailing belief about AI adoption, suggesting that businesses, typically perceived as slower to embrace technological advancements, may instead serve as fertile ground for agentic AI implementation.

The rationale behind B2B payments being more conducive to agentic AI lies in the nature of business transactions. Unlike consumer spending, which is often influenced by personal preferences, emotions, and external factors, business payments adhere to specific rules, including supplier contracts, approval thresholds, cash flow policies, compliance checks, and audit requirements.

Consequently, an AI agent operates within a structured environment, devoid of the need to gauge human preferences. Instead, it focuses on verifying invoice accuracy against purchase orders, assessing whether payments align with approved limits, ensuring adherence to treasury policies for foreign exchange conversions, and confirming that suppliers comply with contractual conditions.

The report identifies three key attributes that make B2B commerce particularly suitable for AI agents: structured decision criteria, rule-based workflows, and diminished emotional engagement. In essence, businesses prioritize the accuracy, compliance, timeliness, and cost-efficiency of payments over the user experience. This is crucial because B2B payments are rarely uncomplicated.

A single transaction may involve an intricate web of enterprise resource planning systems, bank portals, procurement platforms, internal approvals, currency conversions, tax documentation, and reconciliation processes. For many organizations, especially those with a global presence, the payment itself is merely one component of a series of manual checks and fragmented systems.

In Southeast Asia, this complexity is magnified by the region's diverse economic landscape. Companies operating across multiple countries may grapple with various currencies, banking systems, tax regulations, documentation standards, and supplier practices. Even for tech-savvy enterprises, finance operations frequently involve cumbersome spreadsheets, email authorizations, and manual data transfers among disparate systems.

This scenario presents an ideal environment for AI agents, not for replacing human finance professionals entirely, but for automating repetitive, rule-based tasks that currently impede efficiency. The initial opportunities for agentic payments in B2B commerce primarily manifest in "quick win" areas characterized by structured data, repetitive workflows, and clear business rules.

Invoice processing is a prime candidate. Many corporations still rely on manual data entry to extract invoice details, match them with purchase orders, and route them for approval. An AI agent could streamline this process by handling extraction, validation, and flagging discrepancies, thereby facilitating the smooth transmission of clean invoices for payment.

This approach not only mitigates errors and averts duplicate payments but also significantly reduces the time finance teams allocate to mundane tasks. Foreign exchange conversion poses another opportune area for AI automation. Companies involved in cross-border transactions often face challenges in determining the optimal time to convert currencies, as this decision can impact profit margins.

Traditionally, this responsibility has been shouldered by treasury teams or finance managers who manually apply internal policies. An AI agent could automate conversions based on predefined rules, such as when exchange rates reach a specified threshold, as payment deadlines approach, or when exposure limits necessitate action. Additionally, procurement workflows are well-aligned with AI-driven automation.

Businesses engaged in procuring software licenses, spare parts, or raw materials must typically gather vendor quotes, compare offerings, verify budgets, secure approvals, and arrange payments. AI agents can expedite these procedures by scrutinizing requirements, directing requests, and ensuring compliance with established policies before funds are disbursed.

The potential productivity gains are noteworthy. Research cited in the report indicates that AI agents can shorten procurement cycle times by up to 40 percent, leading to cost savings of 12 to 15 percent while enhancing policy adherence. For entrepreneurs, these figures translate into tangible commercial advantages in a sector that has historically garnered less attention than consumer finance solutions, lending platforms, or buy now, pay later services.

In Southeast Asia, where numerous small and medium-sized enterprises are still in the process of digitizing their financial operations, agentic tools could represent a pivotal layer of B2B fintech infrastructure. However, the unfolding of this opportunity is not devoid of cautionary considerations. The greater autonomy granted to AI agents necessitates robust governance mechanisms.

While a chatbot making an erroneous product recommendation may be an inconvenience, an agent executing an incorrect payment, violating policy guidelines, or committing fraud poses substantial risks to organizations. Effective governance ensures that the benefits of agentic payments are realized without compromising regulatory compliance or financial integrity.