AI transformation
AI use-case discovery, agents, workflow automation, proof-of-concept design and the data and operating foundations required for production.
About / M Miglani
Ex-Databricks depth.Founder-focused AI growth.
An AI Transformation & Product Growth Consultant combining experience from Infosys Limited, Cognizant, Wells Fargo US, major financial organizations in the Netherlands and Databricks with a forward-looking focus on AI agents, product launch, PMF and distribution.
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Builder / Operator / Advisor
Close enough to the product to challenge assumptions. Technical enough to make the system real. Commercial enough to keep the market in the room.
Enterprise engineering evolved into AI product transformation.
From 2011 to 2018, the foundation was built through work with Infosys Limited, Cognizant and Wells Fargo US: Java and Spring platforms, production support, observability, database performance, secure delivery and large-scale data migration.
During 2018–2019, work with major financial organizations in the Netherlands expanded that view across Azure, AWS and GCP. The focus included infrastructure automation, Kubernetes, incident intelligence, chaos engineering, secure CI/CD and governance.
From 2019–2024, the work moved into Databricks-centred Data & AI as a senior specialist. The experience included contributing through a period when a smaller specialist team grew into a larger organization—bringing exposure to both focused delivery and scale.
From 2024 onward, the position is AI Transformation & Product Growth Consultant: helping founders turn AI use cases into products, launch them, learn toward PMF, build distribution and automate sales and marketing—without losing production reliability.
Product launch + PMF
Customer problem definition, product wedge, launch planning, market evidence and iteration toward product-market fit.
Distribution + automation
Founder-led distribution, sales and marketing workflows, AI-assisted research, follow-up, reporting and feedback loops.
Enterprise foundations
Ex-Databricks Data & AI, multi-cloud SRE, DevOps, observability, data engineering, security and production operations.
Start with the operating problem
Technology choices should follow the workflow, users, constraints and business outcome—not precede them.
Design for production early
Reliability, security, observability and ownership belong in the architecture before a proof of concept becomes critical.
Automate repeatable decisions
Automation is most valuable when the process is understood, controls are explicit and exceptions remain visible.
Leave systems maintainable
Clear scope, documentation, knowledge transfer and pragmatic technology choices reduce long-term dependency.
In motion / AI transformation
Thinking, building and explaining the next move.
Strategy becomes valuable when the product, system and route to market move together.