Engineering Enablement by DX
Mapping the new SDLC at BNY: Codifying AI into every step of the delivery lifecycle (Jason Valentino)
· Summarized
Key takeaways
- Embed AI into every step of the SDLC to maximize efficiency and quality.
- Foster a culture of experimentation and rapid sharing of AI innovations.
- Revisit and rewrite policies collaboratively with risk and audit teams to integrate AI responsibly.
- Use stress-testing and impact analysis to prepare infrastructure for increased AI-driven throughput.
- Support organizational AI growth through shared resources, showcases, and cross-team collaboration.
Summary
Innovating the SDLC with AI at BNY
Jason Valentino details how BNY is leveraging AI to transform the entire software delivery lifecycle (SDLC). He emphasizes moving beyond mere coding assistance to embed AI into planning, peer review, testing, change management, and compliance workflows. The goal is to increase throughput while maintaining rigorous controls in a highly regulated environment.
Scalability and Impact of AI Tools
BNY's experience with AI shows significant improvements: about 47% of code is now authored by AI, and AI-driven initiatives like improving code coverage have been highly effective. Jason discusses the importance of stress-testing systems, such as asking what would happen if throughput tripled, to identify bottlenecks and prepare the infrastructure for growth.
Embedding AI into Workflows and Culture
The podcast highlights the importance of integrating AI directly into SDLC workflows, like merge requests, testing evidence, and review processes. Jason underscores fostering a culture of experimentation, advocating for 'start saying yes' to innovative ideas — even small, disruptive projects — and establishing a shared platform for AI innovation showcases.
Handling Regulatory and Compliance Challenges
Given BNY's regulated industry, Jason describes how policies are being rewritten and policies are being co-designed with risk and audit teams to ensure compliance. Embedding AI into workflows helps streamline regulatory audits and controls, reducing friction and increasing transparency in compliance checks.
Future Directions and Organizational Support
Jason emphasizes the necessity of cross-department collaboration and continuous knowledge sharing through regular AI showcases. He advocates for platform teams to support AI initiatives by providing the right toolsets, shared resources, and governance policies, thereby enabling rapid innovation without chaos.
Sponsors
- Windsurf · We have our windsurfs, our co-pilots, our cloud codes, playing code X as well. We've gone ahead and built our shared resources for all of those to consume. We have our bratty intern in the middle. Don't tell Brian, I call it that. That's going ahead and running just a lot like a junior partner inside your teams, talking to you on soccer teams, executing tasks.
- Claude Code by Anthropic · We have our library of MCP servers that are approved for internal use. All of that is being built behind the scenes, but at the end of the day, this is kind of the category for creativity, fun part of the job.
- ChatGPT Codex · We have our bratty intern in the middle. Don't tell Brian, I call it that. That's going ahead and running just a lot like a junior partner inside your teams, talking to you on soccer teams, executing tasks.
Show notes
Jason Valentino is Head of Software Engineering Strategy at BNY, where he oversees developer tooling, DevEx, platform workflows, and software delivery governance across more than 8,000 engineers.
In this session from DX Annual, Jason shares how BNY moved beyond AI coding assistants to rethink the entire software delivery lifecycle. He explains how his team identified bottlenecks across the SDLC, prioritized automation opportunities, and applied AI to planning, peer review, testing, change management, and compliance workflows.
Jason also discusses what it takes to scale AI inside a highly regulated enterprise, including rewriting policies, partnering closely with risk and audit teams, and building a culture that encourages experimentation and rapid sharing of ideas.
Where to find Jason Valentino:
• LinkedIn: https://www.linkedin.com/in/jasonvalentino
In this episode, we cover:
(00:00) Intro
(01:20) Early results from AI coding tools at BNY
(04:08) The 3X stress test: What breaks if engineering throughput triples?
(06:56) Three ways to apply AI across the SDLC: IDE and CLI tools
(08:07) Using autonomous AI agents for repetitive engineering tasks
(09:16) Embedding AI directly into SDLC workflows
(12:27) Why leaders should encourage experimentation and “start saying yes”
(15:00) Q&A: How platform and productivity teams are evolving to support AI
(16:33) Q&A: Rewriting policies and controls for AI-assisted software delivery
(17:52) Q&A: How AI is affecting software quality and test ownership
(19:00) Q&A: What Jason is most proud of: Practical examples of AI across the SDLC
(20:30) Q&A: How BNY handles duplicated work across AI initiatives
(22:30) Q&A: How BNY uses AI to support regulatory and compliance work
(23:30) Q&A: Automating code reviews and change tickets
(25:55) Q&A: How increased AI-driven throughput is affecting on-call and reliability
(27:11) Q&A: How BNY works with risk and audit partners to move quickly with AI
(29:01) Q&A: How BNY scales successful AI use cases across the organization
(30:42) Q&A: What Jason is most proud of after BNY’s busiest year with AI
Referenced:
• AI-assisted engineering: Q4 impact report
• Measuring AI code assistants and agents
• Measuring developer productivity with the DX Core 4
• Windsurf
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