# The AI Era Just Changed the Build vs. Buy Equation _Published: 2026-05-27T00:00:00.000-04:00_ Building a lending system now means building AI infrastructure. Here’s what the build vs. buy debate really looks like in 2026. _Building a lending system used to be about workflow. In the age of AI, it’s about intelligence — and intelligence can’t be built from scratch._ When a bank decides to build a custom loan origination system today, the real decision isn’t about workflow anymore. It’s about whether to build permanent AI infrastructure, a standalone governance layer, and an intelligence foundation from zero context — or to partner with a platform where all three already exist. That’s the equation that’s changed. A conversation has resurfaced in banking boardrooms that most institutions considered settled. With AI capabilities maturing, banks are actively reevaluating whether to build or buy their loan origination system. CEOs and CTOs who closed this debate years ago are reopening it. The instinct is reasonable. AI changes what technology can do. If the tools have improved, the original answer may deserve a second look. But the question they’re reopening isn’t the same question anymore. ### **The Decision That Was Already Clear Before AI** Even before AI entered the picture, the math on building custom lending systems told a consistent story. In 2024, [Jude Schramm, Chief Information Officer at Fifth Third, put a number on it](https://finance.yahoo.com/news/why-fifth-third-cio-mostly-172656474.html): custom builds took five times longer and cost roughly twice as much as buying. That was the price of control, and for some institutions, the trade-off was worth it. You got exactly the workflow you wanted, on your own timeline, with no vendor dependency. But institutions that looked at the full picture often landed somewhere different. [Allied Irish Banks (AIB) made a deliberate choice](https://www.ncino.com/blog/how-aib-transformed-their-asset-finance-services-with-ncino) when they set out to modernize lending across asset finance, SME, and corporate lines. Before implementing the nCino Platform, AIB was running multiple systems that didn’t talk to each other. Frontline colleagues couldn’t see where an application stood in the process. The cost was measurable: as AIB COO Andrew McFarlane put it, “Our loan process took so long, we lost one in four loans. Customers would get frustrated and turn to our competitors.” Since choosing to partner with nCino, AIB has lent over €2 billion on the platform and reduced drop-off rates by 24%. McFarlane’s perspective on the long-term value of that decision is worth noting: “With nCino, we leverage the experience of 1,800 banks and financial institutions around the world, and the platform is always going to evolve.” AIB's decision was about workflow and visibility. In the AI era, the same choice involves a set of commitments that most institutions haven't fully accounted for. ### **What a Platform Partner Absorbs So You Don’t Have To** Institutions that choose a platform path gain access to three capabilities that could take years and tens of millions to build independently. Understanding what's included in that choice clarifies what the build path actually requires. **Continuous AI infrastructure, managed and evolving** A custom build doesn't end at launch. Model selection, fine-tuning, and deprecation cycles demand specialized engineering talent on an ongoing basis, and the refresh cycle has compressed from every 18 months to every six to 12 months as AI capabilities evolve. A platform partner absorbs that entire program across its customer base, which means each institution benefits from infrastructure investment that would be difficult for any one organization to sustain on its own. **Built-in governance and compliance** Any AI system influencing credit decisions requires audit trails, explainability, and model risk documentation. Building that governance layer from scratch is where most internal AI initiatives stall: [according to Gartner](https://www.gartner.com/en/articles/genai-project-failure), at least 50% of generative AI projects were abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. A platform with established governance means those frameworks exist on day one, tested across hundreds of implementations, not theorized in a single compliance department. **Network intelligence no single institution can build alone** This is the hardest gap to close. A custom build starts with one institution's data and one institution's history. The right platform brings anonymized, aggregated intelligence across its entire customer base: deal structures, workflow patterns, covenant data, and operational benchmarks that compound every quarter. That collective learning is what makes AI in lending actually useful rather than generically capable. ### **Over 14 Years of Context No Provider Can Replicate** As AI reshapes lending technology, a growing number of vendors are positioning themselves as an AI layer that sits on top of existing banking systems and orchestrates workflows. The pitch is appealing: keep what you have, add intelligence on top. At nSight 2026, our Chief Product Officer Chris Gufford posed the question every institution should ask before accepting that premise: “What was that platform built on? Because ours was built on over fourteen years of lending execution, compliance frameworks, and regulatory exams. We know the difference between a covenant breach and a technical default. And that matters when AI is operating inside of your institution.” Any institution can license the same foundation models that power today's AI features. The model isn't the differentiator. The context layer is: 14 years of deal structures, covenant data, workflow patterns, and anonymized intelligence from 1,600+ financial institutions. That context is what turns a general-purpose language model into one that recognizes which deal structures have performed well for institutions of a given size. Other partners can spin up a similar model tomorrow. They cannot spin up 14 years of banking context. ### **What Choosing the Platform Unlocks** Our [AI in Banking Benchmark](https://www.ncino.com/blog/ncino-ai-in-banking-benchmark-2026), released at nSight 2026, found that 84% of banking executives are already deploying AI at the enterprise level. That same group says they’re focused primarily on adoption, which means the gap between AI investment and AI outcomes is where the real competition plays out. Most financial institutions are somewhere on a progression from exploring what AI requires, to evolving their operations with AI-powered tools in lending and onboarding, to fully embracing AI as part of how the institution operates. Each stage builds on the one before it. Banks that choose to build custom systems are attempting to compress that entire progression into a single initiative, funded by their own engineering teams, against a technology curve that no internal team can match. ConnectOne Bank shows what choosing the platform path makes possible. CEO Frank Sorrentino built ConnectOne from under $1 billion in assets to a $14 billion institution, and at nSight 2026, he was direct about what drives that growth: “Efficiency is speed. And the faster I can get to the finish line for ConnectOne Bank, the more times we’ll win the tie.” With nCino as a partner, Sorrentino expects to make every frontline employee 50% more efficient, shifting a thousand hours per person per year from low-value tasks and toward the work that wins relationships. That level of operational gain requires a platform with intelligence already embedded, not a multi-year build starting from a blank codebase. ### **The Decision That Defines What Comes Next** [Our CEO Sean Desmond tested this principle firsthand](https://www.ncino.com/blog/i-asked-myself-an-honest-question-about-ai). On March 16, he sat down and began building the spec for a CEO agent stack: an AI-powered set of tools for the daily work of running the company. He used AI as his partner throughout the process. In 90 minutes, he produced a production-ready spec. The engineering team began development the following day and the system is now live inside daily operations. His reflection: “The velocity came from AI, but the judgment was mine.” That distinction is what this entire debate turns on. The AI delivered speed that would have required days or weeks of traditional delegation. But every decision along the way, every priority and trade-off, drew on years of context and experience that no model could replicate. That’s how AI works in banking, too. The institutions pulling ahead aren’t the ones building AI from scratch. They’re the ones pairing their own domain knowledge with an intelligence layer no single institution could build alone. The question isn’t build vs. buy anymore. It’s whether your institution is in the business of building banking software or in the business of banking. --- [View sitemap](https://www.ncino.com/api/markdown/sitemap)