AI-Powered Agents Reshape B2B Payments, Demanding New โInfrastructure & Trust
NEW YORK – October 23, 2024 – A shift is underway in B2B payments, โฃdriven by the emergence of clever agentsโ capable of automating complex financial tasks. This evolution isn’t about replacing human expertise, butโ augmenting it with AI that operates in real time, demanding a โขrenewed focus on data quality, robust infrastructure, andโ a โredefined relationship between โhuman oversight and machine agency.
The adoption of these agents hinges on building trust, accordingโ to industry experts. Finance leaders, traditionally focused on risk assessment, are now grappling โขwith delegating decisions to software.”Unlike aโค lot of other emerging technologies or new โคthings,โ I think there’s broad consensus that โคAI is applicable to their โremit โคor their function,” saidโข [Name not provided], speaking to โthe changing landscape. “And so โฃeveryone’s focused on the โhow and were rather โthan the why.”
Criticalโค to successful implementation is ensuring data โintegrity – โscrubbing errors, filling gaps, and integratingโ datasetsโข to provide comprehensive context for decision-making. Beyond data readiness, the principle of least-privilege access is paramount: granting AIโค agents access only to the data and systems necessary for specific tasks.
Further infrastructure pillars include auditability, requiring โขdocumentation explaining an agent’s decision-making process, mirroring โthe current practice of interviewing human decision-makers. Additionally, system interface redesign, increasingly discussed under the banner of model contextโ protocols (MCPs), is crucial to avoid bottlenecks caused by legacy system design. “How do you โredesign or โaugment the interfaceโ of a system so that it’s able toโค be more efficiently interacted with by an agent?” asked โ [Name not provided], highlighting the differing data โand execution flow requirements of AI versus human operators.
Success, experts say, willโฃ depend less on developing complex algorithms โคand more on establishing solid “plumbing, policy and partnership.” โ Companies โขlacking extensive in-house AI talent should โprioritize application advancement over deep โคmodel exploration. “Unless you are in big techโฃ or in a very well-resourced company, your unlikely โto have the talent pool to actuallyโค go and dig into a model.It’s changing so quickly these days,” [Name not provided] stated. “Focus on the application bits.”
