Billtrust CTO Says a 1% AI Error Rate Can Create 100 Problems a Day

Clean data, strong governance and meaningful human oversight are essential to agentic AI, Billtrust Chief Technology Officer John Landy writes in a new PYMNTS eBook, “Building the Agent-Ready Payments Enterprise.”

The artificial intelligence terminology has shifted faster than most enterprise teams can adapt. Copilots became agents. Chatbots became autonomous workflows. And “agentic AI” went from a Gartner analyst term to a CFO line item in about 18 months. But the real question hasn’t changed: are you actually getting work done differently, or just describing the same automation with better branding?

Here’s what I’ve come to believe: the companies becoming truly agentic aren’t the ones deploying the most agents. They’re the ones that got serious about the infrastructure underneath them first.

The Data Layer Is the Foundation

Every company has a data moat: years of transactional history, customer behavior and operational signals. But volume alone doesn’t create leverage. AI doesn’t just need access to data; it needs context, metadata and domain knowledge layered on top. What’s the point of terabytes of records if the agent cannot distinguish a disputed invoice from a payment preference issue?

Most agentic deployments hit this wall first. The model is capable. Tooling is available. But the underlying data is messy, siloed or ungoverned. Bad data governance means agents make flawed decisions at scale, a debt you pay later in rework, model drift and customer friction.

A meaningful step forward is the Model Context Protocol, an emerging standard that lets AI tools query live enterprise data in structured, governed ways. We’ve built this into our platform: a CFO can ask their AI to summarize AR risk going into quarter end and get an answer from live data in seconds, without logging into a separate system. The AI gets access to the right things, permissioned appropriately — but not everything. That distinction matters enormously as enterprises scale agent access across functions.

Where Agents Are Working and Where They’re Not

Real impact is happening in bounded workflows: behavioral segmentation for collections outreach, payment policy optimization, anomaly detection in cash application. This works because the data is structured, the objective is clear and a wrong decision is recoverable. The next step is agents that don’t just answer questions but take action, sending outreach, applying payments, escalating disputes, all governed by role-based permissions and a full audit trail.

The toughest lesson is that full autonomy isn’t the right goal for most enterprise workflows right now. The failure mode isn’t artificial intelligence that can’t perform; it’s AI that performs without sufficient human visibility. When an agent makes 10,000 decisions a day, a 1% error rate is a 100-problem-per-day operation. The most mature teams design for “human-on-the-loop”: the agent executes, the human monitors exceptions and approves high-stakes actions. Decision rights haven’t disappeared; they’ve been restructured.

The Enterprise Fabric Question

Becoming an agentic enterprise is ultimately an organizational decision, not a technology one. It requires agreement on what agents are authorized to do, what data they can act on, how performance is measured and how governance extends to third-party partners in your AI stack. You can’t govern in the dark, and you can’t govern alone.

The companies that will lead aren’t the ones with the most ambitious agent roadmaps. They’re the ones building the right foundation: clean data, strong governance, meaningful human oversight and the clarity to know where autonomy earns its place.