Deepak Nair
I write about how enterprise leaders turn AI capability into systems that improve how work gets done, earn trust, and get better over time.
My work connects models and systems, people, and enterprise readiness: the workflows, ownership, evidence, and operating choices that determine whether AI creates durable value.
I currently work at Google, shipping advertiser-facing AI agents. If you use Google Ads support, I’d love to hear your feedback on how we’re doing.
Working through an AI operating-model, workforce, or evaluation decision? Email me.
Operating thesis
Frontier models are improving quickly. Enterprise value depends on more than model capability: it depends on workflows, tools, policy, human oversight, evaluation, and trust. My work explores how those pieces come together in customer experience systems.
Three lenses
- Technology: What can models and agents actually do?
- People: How do people understand, supervise, trust, and work with them?
- Enterprise readiness: What policies, tools, workflows, and measures make deployment useful and safe?
Featured work
- The Work of Making AI Matter in the Enterprise
- SupportBench: evaluating AI-native customer-support systems.
- From Answers to Outcomes: the Support Agent Capability Ladder.
- Enterprise AI Readiness Studio: pressure-test one workflow and find the next uncertainty to reduce.
- Enterprise AI readiness worksheet
What I believe
- Real-world AI applications should be evaluated as systems, not just as LLMs.
- Making AI agents reliable in enterprise settings is hard but possible.
- Model capabilities are often ahead of how reliably we know how to use them.
- There is immense value in the enterprise waiting to be unlocked.
You can email me at hey@deepnair.ai, find me on X, or connect on LinkedIn.