Let’s be honest: AI is everywhere. It’s the headline, the buzzword, the promise of a future that’s faster, smarter and more efficient. But as we sprint toward the next big thing, it’s easy to forget what actually makes all this possible. The truth? The fundamentals matter more now than ever.
In the rush to experiment with generative AI—whether it’s accelerating software development, automating workflows or reimagining entire business processes—the lesson that keeps surfacing is simple: real progress in AI isn’t about how fast you move, it’s about how strong your foundation is. That means your data, your systems and your people.
Generative AI is transformative, no doubt. But it’s also a reminder that the basics never go out of style. Like any complex system, AI is only as good as what’s underneath. Data modernization, automated testing practices and a focus on reliability—these are the difference between innovation that lasts and hype that fizzles. As we push the boundaries of what AI can do, going back to the basics isn’t just important—it’s essential.
Data Modernization: AI Is Only as Good as Its Foundations
AI doesn’t conjure intelligence out of thin air. It learns from data, guided by algorithms built and maintained by people. So, the quality, consistency and integrity of that data—and the systems that manage it—are what make AI useful and trustworthy.
Here’s where things get real. Poor data hygiene and architecture can quietly sabotage even the most advanced AI initiatives. Gaps in data connectivity, siloed ownership of data, inconsistent labeling and more are technical issues that turn into business problems. These issues are the seeds of bias and uncertainty in AI models that create risk. Every business, no matter the size and scale, needs to be thoughtful of their technology architecture and data modernization approach in order to capitalize on fast approaching opportunities.
Automation as the ‘New’ Discipline
In traditional software engineering, testing, version control and continuous integration have always been the backbone of smooth delivery. Now, as AI starts generating more code and embedding itself in development workflows, those principles are more relevant than ever.
AI can crank up the speed, but speed without discipline is a recipe for disaster. All code needs to pass rigorous checks for security, performance and compliance. A robust, fully automated, continuous delivery pipeline lets teams balance velocity and safety. It’s the guardrails that keep innovation aligned with standards and risk thresholds.
But automation isn’t just about preventing mistakes. It frees engineers from repetitive tasks that slow them down. When testing, deployment and monitoring are handled automatically, developers can focus on what matters—exploring new ideas, optimizing models and solving customer problems. The real productivity gains from AI don’t come from skipping steps. They come from automating the steps well.
All Roads Lead to Reliability
Here’s the heart of it: Reliability isn’t just a feature—it’s the foundation of what customers expect from us. The Chase mobile app and Chase.com have to work–full stop. If our systems don’t work properly (ideally with beautiful and intuitive design), customers lose trust, and trust is everything. Ensuring a service or product is available and ready 24/7/365 takes a lot of support. Manual observability, disconnected telemetry and siloed monitoring teams delay doing what’s right for the customer when issues do happen.
So, our teams don’t stop caring for their code after launch—they own it end to end. Ensuring the full product team has visibility into observability and reliability means the best people to fix any problem are on the front lines of defense. The industry calls this DevOps. We call it ‘You Build It, You Run It.’ As AI gets embedded into post-production activities, it’s important to have the right team culture –one that leans in to love a product after launch–already in place.
The Future Is Foundational
Every wave of technological change brings excitement, disruption and lessons that only time can teach. The AI era is no different. It’s a new world dictated by how fast organizations can deliver for their customers, and generative AI is already playing a significant role in unlocking speed.
If history is a guide, the technologies that endure are those built on strong foundations. AI will keep evolving, and so will our tools and techniques. What shouldn’t change is our commitment to the basics: data modernization, automated testing practices and a focus on reliability.
The fundamentals are what make the magic happen. In the age of AI, going back to the basics isn’t just smart—it’s essential. That’s how we build trust, drive real innovation and create technology that lasts.
