Are You Building the Right AI Foundation-or Just Another Pilot?

Ashok Jade, CIO from Kirloskar Group in a conversation with CIO&Leader shares hard-won lessons on what it really takes to move Al from experimentation to enterprise-grade deployment.

- Musharrat Shahin

MANY BUSINESSES are still having trouble scaling AI beyond pilots, even as it transitions from a buzzword to a boardroom priority.

The CIO of Kirloskar Group, Ashok Jade, offers important insights on transforming AI prototypes into enterprise-ready solutions in this exclusive interview. He describes the practical difficulties faced by CIOs, ranging from handling model drift and fragmented data to negotiating old systems and vendor gaps. In a time when GenAI hype rules the roost, Jade's viewpoint serves as a reminder that effective AI is about governance, infrastructure, and long-term benefit rather than experimentation. Still, it's unclear if you're constructing the proper AI base or merely doing another little pilot.

CIO&Leader: What has been your most significant learning while moving Al projects from pilot to production?

ASHOK JADE: I feel, moving AI projects from pilot to production is a journey of recalibration of technical and strategic rigor.

One of the most significant learning is that model accuracy often deteriorates in real-world environments compared to controlled pilot settings. This drop is mainly because inconsistencies in data quality, misalignment between the model's assumptions and actual business workflows, and the evolving nature of user behaviour and operational contexts. The gaps show the gap between theoretical or limited thinking of problem and AI model with practical consumption.

Another critical learning is the underestimated cost of infrastructure. While pilots may run on shared or minimal resources, production-grade AI demands scalable, secure, and resilient infrastructure—often involving cloud orchestration, GPU acceleration, and real-time monitoring. These requirements can inflate operational costs significantly, challenging the projected ROI and necessitating a more careful while productionising AI projects.