Your Customers Already Hate Chatbots. Here Is What to Do Instead.

Sixty-four percent of customers say they prefer companies that do not use chatbots in their customer service. That number should alarm every operations leader at a lending institution who approved a bot rollout in the last five years. The gap between what borrowers expect and what most support automation delivers is not a technology shortfall. It is a design failure.

This article breaks down why legacy chatbots fail, what agentic AI customer support actually looks like in practice, and how the best operations teams are turning support from a cost center into a competitive advantage.

Most lenders deployed chatbots to reduce ticket volume and cut costs. The result: borrowers trapped in decision-tree loops, agents fielding the same escalated calls, and support teams no closer to proactive service. Agentic AI is a fundamentally different architecture. Instead of scripted menus, it reasons across context, takes action inside live workflows, and can even flag issues before borrowers know they exist. Built, the AI-native operating platform for real estate finance, is one example of how this shift plays out at scale. The lenders gaining ground are the ones rethinking the entire support model, not just swapping one bot for another.

The Chatbot Problem No One Wants to Talk About

“There’s nothing worse than that crappy chatbot that you input your name and issue, and it comes back and it can’t understand that, and it asks you the same question over and over again,” Zack Bowden, Director of Operations.

That frustration is not anecdotal. According to Gartner’s 2026 customer experience research, customers are now three times more likely to use third-party GenAI tools than company-provided chatbots, and only 27% are willing to try a chatbot again after a negative experience. The reason is straightforward: most “AI” in customer support is not intelligence at all. It is a rigid decision tree dressed up with a chat interface.

The old chatbot works like a phone menu with a text box. If your question fits one of twelve pre-programmed paths, you get an answer. If it does not, you get routed in circles until you give up or demand a human. For lending operations, where borrower questions span draw timelines, inspection status, insurance documentation, and budget reconciliation, a decision-tree bot can only cover a small portion of  inquiry volume without sending users down a seemingly neverending path of frustratingly simple questions.

The cost is not just borrower frustration. Every failed bot interaction generates a support ticket that a human agent handles manually, with the added friction of the borrower already being annoyed. According to Salesforce’s State of Service report, proactive service ranks among the top differentiators in customer experience, yet most organizations have not achieved it. That gap represents an enormous operational opportunity for lenders willing to rethink how support works.

What Agentic AI Actually Means (and Why It Matters for Lenders)

The term “agentic AI” gets used loosely, so here is the practical distinction. A legacy chatbot follows a script: if the borrower says X, respond with Y. An agentic AI system reasons across data sources, takes actions inside live workflows, and adapts based on context. The difference is between pressing buttons on a screen and having a real conversation with someone who can actually do something about your problem.

BCG’s September 2025 report, “Unlocking Impact from Agentic AI in Customer Service,” found that agentic AI measurably improves both resolution speed and agent effectiveness. The key finding: the improvement does not come from answering questions faster. It comes from the AI’s ability to act, not just respond.

In a lending context, that distinction matters because borrower inquiries rarely have simple answers. A borrower asking “where is my draw?” needs the system to check inspection status, verify documentation completeness, confirm budget alignment, and determine where the approval sits in the internal workflow. A decision-tree bot can tell the borrower to call their loan officer. An agentic system can surface the actual status, identify what is holding up the process, and in many cases resolve the blocker without human intervention.

Why this changes the economics of support

When agentic AI handles the routine, the human team spends their time on exceptions, relationship management, and complex judgment calls. That is a structural shift in how operations teams deploy their capacity. Instead of ten analysts answering the same “where is my draw?” question 200 times a month, those analysts work on credit analysis, portfolio review, and borrower relationships.

Proactive Support Is No Longer Mythical

“Proactive support. That used to be such a mythical term,” Zack Bowden, Director of Operations.

For years, proactive support was the aspiration everyone cited and nobody delivered. The reason: most support infrastructure is reactive by design. A borrower encounters a problem, submits a ticket, and waits. The best-case scenario is fast resolution. The typical scenario is a 24 to 48 hour response window and multiple follow-ups.

The shift to proactive support requires two things that legacy systems could not provide: real-time visibility into errors and workflows, and the ability to act on issues before the borrower reports them. Modern AI-native platforms monitor workflows continuously, flagging anomalies, documentation gaps, and process breakdowns as they occur.

The operational impact is measurable. When a team monitors errors in real time and contacts borrowers before they even know a problem exists, the entire dynamic of the support relationship changes. “You’ve never heard the smile in someone’s voice when they’re on a support call like when you’re the one calling them,” Bowden said.

That is not a soft benefit. Proactive outreach reduces inbound ticket volume, shortens resolution cycles, and builds the kind of trust that keeps borrowers in the portfolio. For lenders competing for builder relationships, it is a tangible differentiator.

What proactive support looks like in practice

Consider a construction draw where the borrower uploads insurance documentation that is expired or does not match the policy requirements. In a reactive model, the loan administrator discovers the issue during manual review, sends an email requesting updated documents, and the draw sits in limbo for days. In a proactive model, the system flags the deficiency at the moment of upload, notifies the borrower with specifics on what needs to change, and the documentation is corrected before the draw even reaches manual review.

The result: faster draw cycles, fewer back-and-forth emails, and a borrower who feels like the lender is working with them instead of against them.

How the Best Teams Are Making Human Agents Superhuman

“We want to utilize AI to make our human agents superhuman,” Zack Bowden, Director of Operations.

The most effective operations leaders are not asking “how do we replace our support team with AI?” They are asking a different question: how do we give every agent on the team the context, tools, and automation to handle twice the volume at higher quality?

This philosophy reframes support as a differentiator. When AI handles the data retrieval, document validation, status checking, and routine communications, human agents arrive at every interaction with full context and the ability to focus on what humans do well: judgment, empathy, and complex problem-solving.

The BCG research supports this approach. Organizations that deployed agentic AI alongside their existing teams (rather than as a replacement) saw the largest gains in both resolution speed and customer satisfaction. The AI makes the agent more effective. The agent makes the AI’s output more trusted.

For lending operations, the “superhuman agents” model means loan administrators who can manage 2 to 5 times more active loans without sacrificing quality. It means draw reviewers who spend their time on exceptions and risk signals rather than chasing documents. It means a support experience that borrowers and builders actually want to talk about.

How Built Helps

Built, the AI-native operating platform for real estate finance, was designed around this operating model from the start. Its AI Draw Agent processes draws 95% faster than manual review while flagging 2x more risk issues. That combination of speed and risk reduction means loan administrators are not choosing between moving fast and staying accurate.

The platform monitors every stage of the draw lifecycle in real time, surfacing documentation gaps, compliance issues, and process breakdowns before they become borrower complaints. That is what proactive support looks like when it is embedded in the workflow rather than layered on top.

Lenders on the platform have seen 2 to 5 times capacity increases per loan administrator, with 100% policy adherence enforced by AI trained on each institution’s own standard operating procedures. 45 of the top 100 US banks use Built to run their construction loan operations today.

The result: support teams that operate as a competitive advantage, not a cost center. Talk to our team to see how it works.

AI Customer Support FAQs

What is the difference between a chatbot and agentic AI?

A chatbot follows pre-programmed decision trees and can only respond to questions that match its scripted paths. Agentic AI reasons across multiple data sources, takes actions within live workflows, and adapts its responses based on context. For lenders, the practical difference is between a bot that tells a borrower to call their loan officer and a system that can actually check draw status, identify blockers, and resolve issues.

Why do customers dislike chatbots?

The core issue is that most chatbots cannot handle questions outside their narrow scripts, leading to repetitive loops and escalations that waste the customer’s time. In lending, where borrower inquiries are complex and context-dependent, scripted bots cover only a fraction of real support needs.

What does proactive customer support mean for banks?

Proactive support means identifying and resolving issues before the borrower reports them. In construction lending, this could mean flagging expired insurance at the moment of upload or alerting a borrower about a documentation gap before it delays their draw. Most organizations have not achieved proactive service, according to Salesforce research. The lenders who get there first gain a measurable advantage in borrower retention and builder relationships.

Can AI replace human support agents in banking?

The most effective approach is augmentation, not replacement. AI handles data retrieval, document validation, and routine communications while human agents focus on complex judgment, relationship management, and exceptions. BCG research found that organizations deploying agentic AI alongside existing teams saw the largest improvements in both resolution speed and customer satisfaction.

How does AI-native support differ from adding AI to an existing platform?

AI-native means the intelligence is embedded in the workflow from the ground up, monitoring processes in real time and acting on issues as they surface. Adding AI to an existing platform typically means a layer of automation on top of legacy processes, which limits what the AI can see and do. The distinction matters because AI-native systems can enforce policy adherence, flag risks proactively, and orchestrate entire workflows rather than just answering questions about them.