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.