As AI agents become more common in commercial lending, banks face a critical question: which decisions can be delegated to technology, and where must human judgement remain central? While some lending journeys may be suitable for full automation, others require a banker who understands the client’s circumstances. Most will rely on a combination of both.
In an analysis from nCino, the banking technology provider explores how financial institutions can determine where AI agents add value across commercial lending while preserving human judgement and accountability. The analysis draws on Gartner’s Market Guide for Commercial Loan Origination Solutions to examine the technology landscape and the challenges banks face when integrating AI into lending workflows.
AI agents could help banks accelerate loan closings, serve more clients and improve quality control. However, these benefits depend on matching the technology to the right tasks and outcomes. Banks must determine whether each stage of the lending process calls for conventional automation, an AI agent working within defined boundaries, or direct oversight from a banker.
The challenge is compounded by a crowded technology market. Gartner’s Market Guide for Commercial Loan Origination Solutions identifies more than 60 vendors worldwide and describes the market as largely undifferentiated in terms of functionality. With vendors increasingly promoting AI capabilities, banks risk adding agents to existing workflows without establishing whether they deliver meaningful improvements. Gartner also cautions that enthusiasm around generative and agentic AI can exceed the technology’s current capabilities, making it harder for chief information officers to assess potential solutions.
Deploying agents in unsuitable areas can create three significant problems. First, customer relationships may suffer, particularly at community and regional banks where personalised service is an important competitive advantage. Second, automated systems may struggle with exceptions that fall outside established patterns. Third, banks risk creating accountability gaps when decisions must be explained to regulators or credit committees but the reasoning behind them cannot be reconstructed.
Gartner expects fully autonomous systems to remain unlikely in loan origination given the regulatory stakes. Instead, orchestrated multiagent systems designed for specific tasks are expected to become more common. Explainability is a particular concern, with Gartner noting that some vendors give limited attention to explainable AI in their marketing or product plans. Ultimately, banks remain responsible for decisions made using technology, even when the underlying models are supplied by third parties.
Reliable AI also depends on access to complete and consistent information. Combining tools from multiple vendors may offer flexibility, but it can fragment the records behind lending decisions. An agent working with incomplete customer or credit information may produce a confident response that fails to reflect the wider situation. Gartner advocates a single-platform approach that allows loan officers, underwriters and relationship managers to access the information and tools they need in one place.
A shared platform can help establish a consistent credit policy, a unified record of decisions and a clear audit trail. These foundations are essential if banks are to determine which activities can be delegated safely and demonstrate how decisions were reached.
Across the five stages of the credit lifecycle, client acquisition, underwriting, booking, servicing and loan management, lending activities can be divided into three broad approaches: automate, guide and tailor.
Automation is best suited to high-volume, rules-based tasks with verifiable outputs. Examples include document intake and classification, data extraction, financial spreading and covenant tracking. These activities can reduce manual processing and help eligible borrowers move through straightforward lending journeys more quickly, provided they meet the bank’s established criteria.
Guided workflows give AI agents responsibility for an initial task while leaving the final decision with a banker. Agents could draft credit memoranda, validate datasets, check compliance with credit policies or answer questions about internal requirements. A banker can then review the results before a decision progresses. This approach can help speed up analysis without removing human oversight, particularly when a borrower’s financial information requires further interpretation.
Tailored work should remain banker-led, with AI providing supporting intelligence rather than determining the outcome. Complex deal structuring, pricing exceptions, syndicated credit and relationship decisions can involve commercial considerations and client circumstances that require experienced judgement. For example, a business launching a new venture across multiple entities and credit facilities may need detailed analysis and a personalised approach that goes beyond a standard automated workflow.
The distinction is not simply between decisions that can and cannot be explained. Every lending outcome, including those generated through automation, must withstand scrutiny. Banks need to establish appropriate governance, transparency and data security before integrating AI agents into their operations. They should also prioritise solutions with safeguards built into their design rather than relying on controls added after deployment.
In its analysis, nCino uses Gartner’s Market Guide for Commercial Loan Origination Solutions to explore the vendor landscape and the direction of the market, providing a framework for banks assessing their options. The central challenge is not adopting as many AI agents as possible, but deciding where they can improve lending outcomes without compromising customer relationships, decision quality or accountability.
Copyright © 2026 FinTech Global
