Implementer’s Guide to AI: Manufacturing, Automotive & Energy Leaders Move from Pilots to Scale
ResearchNXT, in partnership with Salesforce, has released the latest edition of the Implementer’s Guide to AI, focused on Manufacturing, Automotive, and Energy enterprises. Based on insights from 400+ survey responses and 5 expert interviews, the report explores how AI adoption is shifting from experimentation to scaled execution — and what’s still holding transformation back.
AI Has Moved from Optional to Essential
Across these industries, six in ten organisations already use AI for efficiency, automation, and smarter decision-making, yet only one in ten have achieved enterprise-wide scale. While predictive maintenance, AI-enabled quality control, and digital twins are delivering measurable results, many companies remain stuck in pilots due to data silos, governance gaps, and workforce challenges.
Efficiency Before Growth
The first wave of AI adoption has focused on reducing downtime, minimising waste, and improving operational reliability. The next wave is moving toward demand forecasting, dynamic pricing, and customer personalisation, turning efficiency gains into strategic growth.
Key Insights from the Report
6 in 10 companies have adopted AI; only 1 in 10 have scaled it successfully
22% remain in pilot projects
65% cite data quality and integration as the top barrier
61% have AI-ready infrastructure but lack formal governance frameworks
Highest ROI seen in predictive maintenance, IT–OT integration, and digital twins
The report also highlights case studies from leading manufacturing and energy enterprises, showcasing how AI is transforming production, design, and customer engagement in measurable, outcome-driven ways.
From Automation to Agentic AI
The report also introduces Agentic AI as the next leap in enterprise transformation. Built on Salesforce Agentforce, these AI agents can automate complex workflows, connect data across systems, and act autonomously to deliver measurable business outcomes — marking a shift from simple automation to multi-agent orchestration.
Explore data-driven insights, benchmarks, and industry best practices to scale AI responsibly across Manufacturing, Automotive, and Energy enterprises.
