The B2B space barely had time to wrap its head around generative artificial intelligence (GenAI) before the next iteration, agentic AI, arrived. And if GenAI jolted the B2B world with its ability to produce language, content and code at scale, agentic AI ups the ante.
These new systems are not just capable of completing tasks; they’re able to take initiative. From autonomously conducting sales outreach to managing procurement and even inching toward handling payment operations, agentic AI offers a leaner, faster, smarter B2B machine.
But there’s a paradox here that businesses are only beginning to grapple with: B2B runs on trust. And trust doesn’t scale easily.
While consumer markets might tolerate a bit of algorithmic overreach or a chatbot gone rogue, B2B relationships are often multimillion-dollar engagements built on handshakes, track records and human accountability.
B2B trust isn’t a soft concept. It’s codified in service level agreements, embedded in onboarding processes and measured in quarterly business reviews. The stakes are high: If a cloud service provider goes down, an entire eCommerce stack may crumble. If a payment gateway misfires, thousands of vendors might go unpaid.
As AI software advances on the consumer side, one core question is emerging: can autonomous AI agents earn a seat at the B2B table without disrupting the trust economy that underpins it?
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The Anatomy of B2B Trust
In B2B, the relationships are sticky and the risk tolerance is low. Firms rarely plug in a new solution because it’s smarter. Integrations, after all, are rarely as simple as “plugging” something in, anyway.
This scrutiny becomes even more complex with agentic AI: autonomous decision-makers, capable of orchestrating actions across departments, geographies and even partner ecosystems. And unlike human reps, they don’t come with an intuitive sense of when to escalate, pause or back track.
Picture an AI that doesn’t just generate an email but decides whom to email, when and how to follow up. Picture another that negotiates vendor contracts within pre-defined parameters or reorders inventory based on real-time market conditions.
Agentic AI systems combine large language models with workflow automation, orchestration layers, application programming interfaces (APIs) and guardrails. It’s the guardrails that are arguably the most important. In many industries such as banking and healthcare, introducing autonomous systems can create an existential risk. If an agentic AI system mishandles sensitive data or misinterprets compliance guidelines, the fallout could be catastrophic.
“The models are only as good as the data being fed to them,” Rinku Sharma, chief technology officer at Boost Payment Solutions, told PYMNTS. “Garbage in, garbage out holds even with agentic AI.”
Read more: How Embedded Finance, AI and Automation Are Redefining B2B Payment Networks
The New B2B UX Could Look Like Human-Plus-Agent
Rather than replacing human relationships, the emerging model is symbiotic. An account executive could work alongside an agentic AI that mines customer data, drafts proposals and predicts churn — but leaves the final call to the executive.
Treasury departments are experimenting with AI agents that monitor cash flow, flag anomalies and simulate payment scenarios. FinTech startups are piloting agentic layers that predict delays, reroute payments or even negotiate dynamic discounts with suppliers.
Still, no one is handing over the payment keys to AI just yet. But the technology is finding its way into the back office.
Chief financial officers of companies in the United States with more than $1 billion in revenue have found that generative AI is delivering returns, according to the February edition of PYMNTS Intelligence’s “The CAIO Report.”
The report found that the share of CFOs reporting a “very positive” return on investment from the technology leaped from 26.7% in March 2024 to nearly 87.9% in December.
The next wave of B2B AI likely won’t look like a single agent making bold decisions. It could look like an ecosystem of tools, processes and people — each with clear roles, shared language and mutual checks.
“If you’re going to experiment with agentic AI or any type of AI solutions, you want to focus on two things. One is the area where you’re most likely to have success. And two, is there going to be a good return on that investment?” WEX Chief Digital Officer Karen Stroup told PYMNTS.
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