How Intelligent Systems Accelerate Enterprise Decisions - with Amar Akshat of Paysafe

In this episode of the AI in Financial Services Podcast, Nick Gurch hosts Amar Akshat, Senior Vice President of Architecture at Paysafe, to explore how intelligent systems can accelerate enterprise decision-making in financial services. Akshat identifies that large organizations often slow down due to fragmented architectural knowledge and decision-making silos, leading to 'architectural drift' and compounded technical debt. He emphasizes that the real bottleneck isn't technology, but the lack of codified, machine-readable organizational memory—what Paysafe calls 'org memory'—that captures architectural decisions, patterns, and constraints. By storing these in version-controlled, templatized repositories like ADRs and using standards such as llms.txt, enterprises can enable systems to understand context across distributed platforms, enabling faster, safer, and more consistent decisions. Akshat stresses that while LLMs are powerful for parsing intent, they must never orchestrate workflows—deterministic rule engines must govern execution to ensure reliability and compliance. He also envisions a future where regulators adopt intent-based frameworks and use AI to audit enterprise systems through standardized, declarative protocols like llms.txt, creating a transparent, auditable AI ecosystem. Key takeaways include: (1) Codifying organizational memory through machine-readable decision records is essential for speed and consistency; (2) LLMs should only pass intent, not orchestrate workflows—determinism and guardrails are non-negotiable in production; and (3) Standardizing context exposure (e.g., via llms.txt) allows teams to start from AI-first drafts based on proven patterns, drastically reducing rework and accelerating delivery without sacrificing compliance. The episode concludes with a forward-looking view of regulatory AI leveraging standardized org memory for auditability and trust.