Most enterprise AI projects don't fail because the AI can't perform. They fail because nobody documented what the AI is actually supposed to do, or how it should behave when things get ambiguous. Binny Gill, former CTO of Nutanix and now CEO of Kognitos, spent five years solving that by making English the programming language for business process automation, with a proprietary interpreter that extracts precision from ambiguity rather than demanding it upfront, the same way a parent figures out what a toddler wants.

His architecture deliberately avoids using LLMs during execution. For deterministic workflows like order-to-cash or vendor onboarding, injecting a creative model at runtime is a liability. The LLM earns its place at design time. He also makes a pointed argument about what AI governance actually requires that most companies are completely unprepared for: with AI, every model is a blank slate, which means the tacit knowledge that's always lived in people's heads has to be explicitly captured or it won't exist. And his 2030 prediction reframes the whole conversation away from technology capability and toward organizational architecture.

Topics discussed:

• Why the English interpreter was always the problem, not the language itself

• Neuro-symbolic architecture: separating creative AI at design time from deterministic execution at runtime

• Why the toddler model of ambiguity resolution beats forcing precise inputs upfront

• Why zero behavioral assumptions with AI models demand a new documentation standard

• How AI autonomously authors tribal knowledge rather than relying on humans to write it down

• The three enterprise buyer personas (CFO, CIO, Chief AI Officer) and what each one is actually evaluating

• Tailor shop vs. assembly line as the framework for assessing organizational AI readiness by 2030

• Why Binny predicts legacy company architecture, not technology, is what fails in the transition