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deepmind.google·6d ago
AI Engineering
OpenAI Blog·Dec 12·5 min read
BNY builds “AI for everyone, everywhere” with OpenAI
TL;DR
BNY Mellon faced barriers to enterprise-wide AI adoption, with most employees unable to build AI solutions. The bank deployed Eliza, a platform enabling 20,000+ employees to construct AI agents using OpenAI's models without requiring deep machine learning expertise.
✦ Why It Matters
Engineers can learn how abstraction layers and low-code platforms unlock AI adoption at enterprise scale without sacrificing capability.
Key Takeaways
- Build low-code platforms to enable non-experts to create AI solutions without deep ML knowledge.
- Design abstraction layers that hide complexity while preserving access to powerful models like GPT.
- Measure adoption breadth and business impact metrics when rolling out AI tools enterprise-wide.
- Prioritize user experience and intuitive interfaces to drive adoption among diverse employee skill levels.
Full Summary
BNY Mellon, a major financial services firm, identified a gap between AI's potential and its actual deployment across the enterprise—most employees lacked access to tools or skills to build AI applications. The bank partnered with OpenAI to create Eliza, a platform that abstracts away technical complexity and allows non-specialist employees to design and deploy AI agents (autonomous software systems that perform tasks with minimal human intervention) powered by OpenAI's language models.
The approach democratizes AI development by providing intuitive interfaces and pre-built components rather than requiring employees to write code from scratch. Over 20,000 BNY employees now use Eliza to build custom AI agents tailored to their workflows.
Results include measurable gains in operational efficiency and improved client outcomes, demonstrating that broad-based AI adoption drives tangible business value when barriers to entry are removed.