Nidhi Vichare

Most data platforms measure impact through correlation. I built a four-step causal attribution framework with prospective baselines, holdback controls, per-stream comparison, and auditable attribution. It proved $1B+ in ad revenue at a major global AdTech company. Every dollar provable to finance, not estimated by the data team.

A field experiment from INSEAD and Harvard Business School proves the bottleneck in AI adoption is not the technology — it is discovering where to deploy it. 515 startups, causal evidence: 44% more use cases, 1.9x revenue, 39.5% less capital. Why this compounds in enterprises with organizational inertia, compliance constraints, and legacy data estates.

A spec-driven architecture for enterprise AI agents using LangGraph as the execution engine. Declarative YAML schemas (Agent, AgentWorkflow, KnowledgeBase) as Kubernetes-style CRDs with conformance profiles, policy-as-code enforcement, and GitOps deployment. Includes a nine-framework ADK comparison (LangGraph vs Google ADK vs OpenAI Agents SDK vs AutoGen vs Bedrock vs Claude SDK vs CrewAI vs Agno), production scorecard, and downloadable spec templates.

Between January 6 and March 7, 2026, the AI industry compressed two years of sequential development into ten weeks of simultaneous movement. Products shipped. Infrastructure commitments hit $660-690B. Regulators adapted. The data platform became the AI platform. A continuous story from the perspective of a CDO.

A CDO's perspective on AI agents, productivity hype, and the security trade-offs no one is talking about. Why installing OpenClaw is like handing a stranger root access to your machine.

Strategy decisions, execution details, and why DataOps maturity is your AI readiness. From cloud platform commitment and transformation layers to cross-department visibility and the BI layer as an AI launchpad.