Capitalize on AI-driven rig automation to tighten procurement terms

What happened

Nabors and Corva have integrated Nabors’ RigCLOUD edge platform with Corva’s AI platform to provide a single source of operational KPIs and analytics. The combined system has been deployed in the field since March and is positioned to be used across non‑Nabors rigs via standard mapping, making it operationally real for multi-supplier programs. Watch whether vendors begin to require platform access as a priced service and how data-mapping agreements evolve

Why the category manager should care

Treat vendor-integrated analytics as an operational dependency that should be contractually managed, not a discretionary add-on

Key facts

• Integrated RigCLOUD edge + Corva AI platform
• Deployed in-field since March (operational deployments)
• Supports standardized KPI mapping for cross-party data exchange

What happened

Baker Hughes launched Kantori, an autonomous well-construction solution combining AI, physics models and live well data to guide drilling execution. Kantori has demonstrated material on-bottom performance improvements in trials and is positioned as a continuous optimization layer; watch how operators adopt it in live programs and how control handoffs are managed

Why the category manager should care

Autonomous tools introduce a performance-to-payment linkage and new acceptance criteria — require clear handoff definitions

Key facts

• Kantori autonomous well construction solution
• Combines AI, physics-based models and live well data
• Demonstrated field trials with improved ROP and reduced NPT

What happened

Cloud-based closed-loop drilling automation has moved from pilots to operational use across programs, delivering consistent performance improvements and exposing latency, control-hierarchy and safety-boundary issues. The industry lessons emphasize that integration, latency and safety limits determine what automation can safely control in the field

Why the category manager should care

Define which functions are supervisory versus direct-control in contracts and acceptance tests

Key facts

• Operational deployments across multiple operators and rig configurations
• Lessons: latency, control boundaries and integration drive success
• Cloud automation acts as supervisory layer above local control

What happened

Precision Drilling and partners have deployed asset-health and predictive maintenance solutions across large rig fleets to reduce nonproductive time and maintenance costs. The solution relies on multi-parameter models and broad sensor coverage, which makes data quality and calibrated thresholds the gating items for reliable predictions

Why the category manager should care

Require transparency on model inputs, sensor ownership and how predictive alerts convert to vendor interventions

Key facts

• Asset-health solutions deployed across rigs in multiple regions
• Reported reductions in nonproductive time and maintenance cost
• Models use 30+ parameters for equipment-specific behavior

What happened

Generative and agentic AI are moving beyond information retrieval toward planning, reasoning and adaptive execution in drilling workflows, with vendors building agentic assistants into analytics suites. This is an evolutionary signal rather than a disruption today; procurement should watch vendor roadmaps, governance and auditability commitments

Why the category manager should care

Treat agentic AI as an emerging capability that needs explicit governance, explainability and audit clauses in supplier engagements

Key facts

• Agentic AI adds planning, reasoning and execution capabilities to analytics
• Vendors integrating LLMs into operational workflows (examples cited)
• Focus shifting from retrieval to adaptive execution and agents