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AI adoption has moved beyond experimentation. Enterprises are now focused on putting AI into production, strengthening data infrastructure and delivering measurable business results. The shift requires companies to combine technology expertise, domain knowledge and customer context to scale AI effectively.
In today’s episode of the Analytics Insight podcast, host Priya Dialani interviews Dinesh Venugopal, CEO of Tenerai, about how enterprises are moving beyond AI experimentation to focus on execution, scalability and measurable business outcomes. The episode explores how companies can move AI from pilot projects into core business operations.
Dinesh explains the way for organizations to formulate their AI strategies through improving their data infrastructure, blending customer context with domain knowledge and technology, and concentrating on revenue generation, cost efficiency, and the customer experience.
The expert also touches upon India’s capability to emerge as a world-class hub for AI engineering and execution, the necessity for technologists to be outcome-oriented problem solvers, and the significance of creating robust AI and startup ecosystems. The following are the excerpts from the interview.
Enterprises have already spent significant time testing AI models and pilot projects. The focus is now on moving those initiatives into production and using AI to improve revenue, reduce costs and enhance customer experience.
The three layers are the foundation layer, the AI readiness layer and the value creation layer. They cover LLMs, data and infrastructure, and applications designed to deliver measurable business value.
India needs to combine customer context, domain expertise and technology skills. Its workforce also needs to move beyond traditional technical roles and focus more on solving business problems and delivering outcomes.
The main challenge is not only technical skills. Technology workers need to understand customer problems and use AI to find better solutions. The shift is from writing code and completing tasks to solving problems and delivering results.
Companies need to strengthen their data and technology foundations before scaling AI applications. Modernising data infrastructure, building an AI enablement layer and connecting it to value-creating applications can support wider deployment.
