Tushar Madan
Avianna · Product Adoption Lead, Databricks
I study how enterprises actually adopt AI — and what it takes, in systems and in organizations, for agents to do real work. At Databricks I run product adoption for Databricks Apps; at Avianna I turn what the field teaches into frameworks, prototypes, and writing. When a data and AI strategy stalls between competing goals, founders and executives bring me in to build clarity and alignment.
What I study
Enterprise AI adoption
How organizations move from AI demos to durable operating models — and why so many stall between the two.
Agent governance
What it takes to authorize, trace, and reverse agent-initiated work inside a real organization — the questions behind Concord and Lattice.
The field ↔ product loop
How signal from live enterprise deployments should shape what a data and AI platform builds next — grounded in a decade of building the platforms themselves.
What I build
Advisory
I advise a small number of startups and enterprise teams working through AI strategy, product architecture, agent workflows, and technical positioning. The teams I work with are usually overwhelmed — too many directions, too many stakeholders pulling at once — and want someone who can bring real clarity, build alignment across competing goals, and has actually shipped the systems and sold data and AI into the enterprise. The work is grounded in the lab’s research: we reason through where AI changes the product, workflow, architecture, and business model.
Areas
For advisory or collaboration inquiries, contact the lab.
Speaking
I speak to enterprise and practitioner audiences about cutting through competing priorities to get an organization aligned on one data and AI strategy it can actually execute — drawn from the field, not the deck.
Formats
Speaker bio
Tushar Madan helps founders and executives bring clarity and alignment to data and AI strategy — especially when competing priorities and overwhelmed teams have stalled it. He works at the intersection of field deployments and product at Databricks, turning what enterprises hit in the real world into strategy they can actually execute.
He writes about AI systems, agents, and the mathematics underneath at avianna.ai.
To invite Tushar to speak, contact the lab.
Background
A decade across the data and AI stack. At Databricks since 2019 — solutions architecture, then building and leading field-engineering teams that took enterprise data, AI, and ML workloads from prototype to production, now product adoption for Databricks Apps. Before that, principal architect for ML and big data at Atos in New York, and big-data engineering at FINRA, the U.S. broker-dealer regulator. The formal version is on LinkedIn.
More at the essays · avianna.ai