In conversation with CIO&Leader, Anand Deodhar, Group CIO at Force Motors shares how his team transitioned AI from experimental pilots to enterprise-grade solutions that deliver real business impact. From predictive maintenance to GenAI-powered assistants, Deodhar breaks down the strategy, architecture, and workforce transformation that helped embed AI into the core of plant operations.
CIO&Leader: How do you define success when transitioning an AI initiative from pilot to production?
Anand Deodhar: In manufacturing, success isn’t defined by shiny dashboards or isolated pilots. It’s when AI genuinely improves plant performance—be it uptime, efficiency, or bottom-line savings. I look at hard KPIs like OEE improvements, reduction in downtime, or process optimization that holds up under real-world pressure.
One such example was predictive maintenance model. After two months of rigorous piloting—including stress testing and failure scenario analysis—it was deployed across plants and helped cut unplanned downtime by 15%. For me, success is when the shop floor, IT, and leadership teams all see value and start relying on AI as part of their daily decision-making—not as a side experiment, but as core to how we operate.
CIO&Leader: What are the core pillars of your current enterprise AI strategy?
Anand Deodhar: My AI strategy is grounded in four solid pillars—built from lessons learned on the shop floor and the boardroom alike:
Business-Aligned Use Cases: We start with what matters to the business—targeting bottlenecks with high impact and low resistance.
Data Governance & Security: With GenAI, ensuring data privacy and compliance isn’t optional—it’s foundational for me
Scalable Infrastructure: A hybrid cloud-edge model ensures that we can scale AI with both speed and efficiency, without disrupting legacy operations.
Skill Enablement: I had made AI learning a priority—not just for IT, but for operations, quality, and procurement teams too.
To me These aren’t just strategy buzzwords—they’re working principles that help us scale AI without losing sight of business value.
CIO&Leader: What key AI use cases have successfully moved into production, and what measurable impact have they delivered?
Anand Deodhar: AI is no longer a pilot exercise in setup—it’s already implemented in some cases , Some of the key use cases now embedded in production include:
Predictive Maintenance (CNC Machines): Brought down maintenance costs and improved uptime as well
Machine Vision for Defect Detection (Paint Shop): Helped cut down rejections significantly
AI-Driven Demand Forecasting: Improved procurement planning and helped reduce inventory by good value freeing up working capital.
Each project started with a business challenge—not a tech pitch. That’s what ensured real-world impact.
CIO&Leader: What infrastructure or architectural changes were necessary to scale AI effectively within your organization?
Anand Deodhar: To scale AI sustainably, we had to rethink our IT backbone—because models alone don’t deliver results. We focused on:
Hybrid Cloud & Edge Stack: For seamless model training and fast real-time inference.
Kubernetes & Containerization: Ensuring models can run across diverse environments smoothly.
Modular AI Libraries: Reducing time-to-market by reusing proven components.
Dedicated VLANs & GPU Workstations: Ensuring low-latency AI even in bandwidth-constrained areas.
These upgrades weren’t just tech decisions—they were enablers of future-ready operations.
CIO&Leader: What are the biggest challenges you’ve faced in operationalizing AI, and how have you addressed them?
Anand Deodhar: Every AI initiative meets roadblocks—and we had our share too. The major ones?
Data Silos: built standardized ETL pipelines with tagging, so data stays clean and consistent.
Skill Gaps: invested in reskilling, brought in vendor support, and created cross-functional squads.
Trust & Governance: From the start, embedded explainability and compliance checks to win stakeholder confidence.
Having said this , I never treat these as obstacles—we treated them as part of the journey toward enterprise-grade AI.
CIO&Leader: How are you preparing your workforce for scaled AI adoption, and what organizational shifts have been required?
Anand Deodhar: Technology can only go so far—people make or break transformation. That’s why My plan is
To deliver 2,000+ hours of AI/ML training across functions.
Appoint an AI Champions in each plant—practitioners who bridge IT and operations.
Make AI performance reviews part of our monthly production meetings.
This approach has shifted AI from being “someone else’s project” to something everyone owns.
CIO&Leader: Looking ahead, what does your AI roadmap over the next 12–18 months look like — especially in terms of GenAI or foundation model deployments?
Anand Deodhar: Looking ahead, most of the mfg AI roadmap is focused on scaling further and tapping into GenAI:
GenAI Assistants: To help technicians quickly access SOPs, logs, and manuals.
Digital Twins: for real-time simulation and production line optimization.
GenAI in Supply Chain: To improve planning precision and reduce supplier risk.
LLM Governance Frameworks: Ensuring responsible and secure GenAI usage.
ERP/MES Integration via APIs: embedding AI deeper into workflows—targeting a boost in line-level productivity.