This CIO doesn't "recruit developers to code"—here are the 3 AI principles he focuses on instead

If you've ever wondered how a massive bank like Chase thinks about artificial intelligence, this article is for you. We'll walk through what Gill Haus, Chase's Chief Information Officer, has said about the bank's approach to AI, why he emphasizes getting the basics right before chasing flashy use cases, and what lessons other organizations — or simply curious readers — can take away from it. No technical background is needed; everything is explained in plain English.

Who Is Gill Haus and Why His View Matters

Gill Haus serves as Chase's Chief Information Officer, which means he oversees much of the technology strategy at one of the largest banks in the United States. When someone in that position talks about AI, it's worth paying attention, because banks handle enormous amounts of sensitive data, face strict regulations, and can't afford to experiment recklessly with customer money or information.

According to Haus, Chase's philosophy isn't about racing to adopt every new AI tool that hits the market. Instead, the focus is on discipline: understanding the technology, building on solid foundations, and applying AI where it genuinely helps customers and employees. This might sound less exciting than headlines about cutting-edge models, but it reflects a practical reality — in banking, trust and reliability come first.

Getting the Core Fundamentals Right

The central message from Haus is that AI success depends on mastering the fundamentals. What does that mean in practice? A few things:

Strong data foundations. AI systems are only as good as the data they learn from. If a bank's data is messy, inconsistent, or scattered across silos, even the most advanced AI model will produce unreliable results. Much of the groundwork for AI happens long before anyone deploys a model — it involves organizing, cleaning, and governing data properly.

Clear use cases tied to real value. Rather than asking "where can we sprinkle AI?", Chase's approach starts with a business problem or customer need, then asks whether AI is the right tool to solve it. This prevents expensive projects that impress internally but deliver little value.

Responsible use and governance. In a regulated industry, AI can't be a black box. Decisions that affect customers — loan decisions, fraud detection, customer service — need to be explainable, fair, and compliant with regulations. Building governance frameworks early is part of those fundamentals.

People and skills. Technology alone doesn't transform a company. Employees need to understand what AI can and cannot do, and teams need the right expertise to deploy it responsibly.

Why a "Fundamentals First" Mindset Makes Sense

It can be tempting to see this cautious approach as slow, especially when AI news moves at breakneck speed. But there's a strong argument for it. Companies that jumped into AI without solid data practices have often hit walls: biased outputs, hallucinations in customer-facing tools, security gaps, and projects that quietly get shelved.

For a bank, the stakes are higher than in most industries. A chatbot giving wrong financial information isn't just embarrassing — it can create real harm and regulatory trouble. By prioritizing fundamentals, Chase positions itself to adopt AI more confidently as capabilities mature, rather than constantly cleaning up after rushed rollouts. Haus's framing suggests the bank sees AI as a long-term capability to build, not a trend to chase.

That said, it's worth noting that public reporting on large banks' internal AI programs is always limited. We know the broad contours of Chase's stated strategy, but the full details of its deployments, tools, and results aren't publicly available, so readers should treat specific claims about outcomes with appropriate caution.

Lessons for Any Organization

Even if you don't work at a bank, Chase's approach offers transferable lessons:

1. Fix your data before your algorithms. Most AI failures trace back to poor data quality, not weak models.
2. Start with problems, not tools. Define what you want to improve, then decide whether AI fits.
3. Build guardrails early. Governance, security, and explainability are easier to design in than to bolt on later.
4. Invest in people. Training and upskilling determine whether AI actually gets used well.
5. Be patient with scale. Pilot small, learn, and expand only what works.

These ideas aren't glamorous, but they echo what many experienced technology leaders have discovered: sustainable AI adoption is an organizational discipline more than a technical trick.

Final Thoughts

Gill Haus's message about Chase and AI is refreshingly unglamorous: master the fundamentals — data, governance, clear use cases, and skilled people — and the rest follows. For a bank entrusted with people's money and personal information, that measured approach seems less like caution for its own sake and more like common sense. As AI continues to evolve, the organizations that invested in solid foundations are likely the ones that benefit most.

Note: This article reflects publicly stated views and general industry context. Details of Chase's internal AI systems are not fully public.