Why “Just Use Open Source” Is the Most Expensive Sentence in AI
“It’s just a few APIs. We’ll use open source.”
You’ve probably heard this line said a few times if you lead engineering or tech at a bank. You have an incredible team creating agentic AI prototypes with LangGraph, LangChain or another open source framework.
Though the demo looks promising and the cost seems low, the prototype has evolved into a growing list of governance, audit, and model-risk questions, brittle services with low tolerance for stress, and a new obligation to maintain a system your team hadn’t planned on owning at scale.
Because of this, open source can become an expensive line item in your AI strategy.
In this piece, I explore how you can still get the upside without the compromise.
Governance in AI for Financial Services: The Hidden Engine of Speed and Scale
AI governance in financial services is often treated as a tax on innovation, a box to check after the “real work” of model building is done. In reality, the opposite is true. When it is designed into the fabric of your AI stack, governance becomes the engine that lets you move faster, scale bolder, and face regulators with confidence instead of hesitation.
Our view is simple: governance for AI is not a bolt‑on; it is the operating system for safe, scalable agentic AI in banking and capital markets.
Integrating Agentic AI Solutions with Core Banking Systems: A Strategic Guide for Enterprise Bank Leadership
Can you integrate agentic AI solutions with core banking systems? The short answer is yes, and for enterprise banks, it's no longer a question of "if" but "how" and "when." As financial institutions face mounting pressure to modernize operations while maintaining security and compliance, the integration of artificial intelligence with existing core banking infrastructure has emerged as both a strategic imperative and a complex technical challenge.
For Chief Information Officers and Chief Technology Officers at large enterprise banks, understanding the pathway to successful AI integration, particularly in overlooked operational areas, is critical to maintaining competitive advantage while managing risk.
Agentic AI for Financial Services: Why ‘Buy vs. Build’ Is Dead and the Future Is ‘Buy‑and‑Build’
For years, large banks and financial institutions have been stuck on the same architectural question: Should we buy an AI platform or build our own? In 2026, that framing is not just outdated, it is actively slowing down serious AI adoption in financial services. The institutions that will win with agentic AI in financial services will buy and build: buy the right foundation, then build their proprietary edge on top.
When leaders search for ways to deploy agentic AI for financial services, they do not need another abstract “AI strategy.” They need a practical path that fits the realities of regulated environments, legacy estates, and tight risk controls. That is where buy‑and‑build comes in.
From Fax Machines to Agentic AI in Banking: How AI Agents Are Transforming Middle- and Back‑Office Operations in Financial Services
Banks are already moving from “AI in the lab” to AI agents embedded in middle- and back-offices, and the firms that treat this as the next electronic‑trading revolution moment will pull away from the pack. Having lived through the historic fax‑machine era of trading and helped build some of the earliest electronic and ML‑driven systems at scale, I believe agentic AI in banking is the next structural shift in banking operations, and we built Artian for exactly this inflection point. In other words: yes—agentic AI and AI agents can automate meaningful portions of middle- and back-office operations in financial services when deployed with governance and humans-in-the-loop.
Dashboards Die, Chatbots Stall, Agents Learn
Enterprises today are flooded with data — dashboards, reports, chatbots — all promising intelligence and efficiency. Yet despite this abundance, meaningful change remains elusive. The problem isn’t access to information…
Vibe Coding Will Break Your Enterprise
Vibe coding tools like Lovable and Cursor don’t solve real problems in enterprise settings — they make them worse. In the world of sprawling systems, audit trails, and regulatory scrutiny, those “vibes” won’t cut it…
Challenges of Agentic AI in Finance
Building and deploying multi-agent architectures at enterprise scale is a deeply technical challenge that spans legacy constraints, integration complexity, governance requirements, and computational control…
Not All “Enterprises” Are Equivalent
The term “enterprise” is often used without precision. In reality, enterprise scale — especially in banking — carries specific implications that fundamentally change how AI must be developed and deployed…
Large Enterprises Aren’t Stupid, Stupid
They’re not dinosaurs, they’re not slow by choice, and they’re not willfully clinging to COBOL for the vibes. They’re deliberate. They’re complex. And — this is the part many Silicon Valley startups miss…