How AI Is Reshaping Shutdown and Turnaround Operations - with Raghu Ahobilam of NOV

2026-03-03 · Source: The AI in Business Podcast · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Supply Chain & Logistics · Depth: Intermediate, long

Summary

Raghu Ahobilam, Global Director of Inventory and Assets at NOV, discusses how energy enterprises are leveraging data foundations and AI to transform maintenance, asset utilization, and cross-functional decision-making. The conversation highlights the shift from fragmented legacy systems to KPI-aligned, AI-ready workflows, emphasizing the role of scorecards, predictive insights, and integrated dashboards in improving utilization and reducing downtime across global fleets. Ahobilam details practical approaches for building KPI-driven dashboards, prioritizing predictive use cases, and strengthening ROI business cases across supply chain, manufacturing, and operations. He notes that while the energy sector is traditionally conservative, AI adoption is gaining traction, with companies focusing on realistic timelines and filtering overwhelming data to extract business-specific value.

Key takeaway

For Directors of AI/ML or Consultants guiding digital transformation in legacy industries, prioritize AI initiatives that directly align with clearly defined business metrics and scorecards. Focus on building data platforms as continuous capability layers, not one-off projects, to ensure long-term optimization of asset utilization and operational efficiency. Your strongest business cases will directly link workflow improvements in maintenance, manufacturing, and supply chain to tangible financial outcomes like cash flow and risk reduction.

Key insights

Unified data foundations and AI are crucial for optimizing asset management and decision-making in legacy industrial environments.

Principles

• Align AI initiatives with clear business metrics.
• Treat data platforms as long-term capability layers.
• Build strong business cases tied to cash flow and risk reduction.

Method

Define overarching company vision and quantifiable goals, then break them into bite-sized, measurable actions for different organizational levels, supported by KPI-driven dashboards.

In practice

• Use Hoshin-Kanri for strategic planning.
• Benchmark best practices from other industries.
• Establish platforms for ongoing optimization.

Topics

• AI in Energy Sector
• Predictive Maintenance
• Asset Management
• Digital Transformation Strategy
• Data Foundations

Best for: Executive, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.