Strategic Focus: Five Barriers To Industrial AI Analytics Adoption – And How To Overcome Them

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Executive Summary

This report is written for technology buyers evaluating industrial AI analytics software. It examines five common barriers to adoption: poor and fragmented data; difficulty demonstrating return on investment (ROI); skills and capability gaps; governance and trust concerns; and the challenge of scaling beyond proof-of-concept in complex legacy environments. The report sets out practical steps to address these barriers, emphasizing the importance of strengthening data foundations, establishing governance and security-by-design, linking AI outputs to measurable operational outcomes, building workforce capability, and designing for production deployment and multi-site scale.
Adoption of industrial AI analytics software is accelerating
Multiple barriers impact industrial AI project success
Practical pathways to scaling industrial AI
Figure 1. Overcoming barriers to AI adoption
Figure 2. Approach 1: Data hub
Figure 3. Approach 2: DataOps platform
Figure 4. Approach 3: DataOps ontology

About the Authors

Jatinder Devgun

Jatinder Devgun

Senior Analyst

Jatinder is a Senior Analyst at Verdantix, providing insights on emerging technologies and market dynamics for technology buyers and software vendors. His expertise lies in as...

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Malavika Tohani

Malavika Tohani

Research Director

Malavika is a Research Director at Verdantix, guiding research that explores how digital technologies and services are reshaping industrial operations to become safer, more ef...

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