Market Overview: Industrial AI Analytics Solutions

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Executive Summary

This report outlines how industrial AI analytics solutions are revolutionizing the industrial sector by applying cutting-edge software and hardware techniques to manage, optimize and improve assets and processes. In this report, we provide a comprehensive overview of the current state and future trends of this dynamic market, based on an in-depth analysis of the drivers, challenges and opportunities for established industrial asset management (IAM) software vendors, AI-focused challengers, asset management service providers and customers. The report discusses how the market is segmented by vendor background, and analyses how each segment leverages its strengths and capabilities to deliver high-impact industrial AI solutions. In addition, it explores how vendors can develop more self-service MLOps (machine-learning operations), AI analytics and generative AI to meet the growing demand for scalable, flexible and user-friendly industrial AI solutions.

Table of contents

Basic Forms Of Artificial Intelligence Are Already Industry-Standard
Maturing Techniques, Cost Reductions And Funding Fuel AI Growth
Four AI Techniques Are Transforming Industrial Analytics
The Industrial AI Analytics Market Comprises Vendors From Three Key Backgrounds
Incumbents Leverage Decades Of Expertise And Physics-Based Asset Management
AI-First Vendors Use Cutting-Edge AI From The Ground Up
Service Firms Compose Disparate Solutions To Deliver High-Impact Industrial AI
Vendors Should Focus On Enhancing Self-Service MLOps And Generative AI

Table of figures

Figure 1. A Brief History Of Industrial AI Analytics
Figure 2. Segmentation Of The Industrial AI Analytics Market
Figure 3. AI Use Cases, Descriptions And Example Vendors
Figure 4. The Three Backgrounds Of Industrial AI Analytics Vendors
Figure 5. Comparison Of AI Analytics Workflows For AI-First Vendors vs Incumbent Vendors 

About the authors

Joe Lamming

Senior Analyst
Joe is a Senior Analyst in the Verdantix Operational Excellence practice. His current research agenda covers industrial DataOps, AI/ML analytics and applications of generative AI for industry and enterprise. Prior to joining Verdantix, Joe worked in the consumer electronics industry, where he gained experience in overseas manufacturing, product design and data science. Joe holds an MEng in Mechanical Engineering and Sustainable Energy Systems from the University of Southampton.

Henry Kirkman

Henry is an Analyst in the Verdantix Operational Excellence practice. His current research agenda focuses on connected worker solutions, technologies for industrial asset maintenance, and the industrial applications of AI, including generative AI and computer vision. Prior to joining Verdantix, Henry completed a Masters degree in Civil Engineering at the University of Exeter.

Malavika Tohani

Research Director, Operational Excellence
Malavika leads the Verdantix Operational Excellence practice. Her current research agenda focuses on digital technologies for Operational Excellence including digital twins and software solutions for industrial risk and asset management. Malavika has over 15 years’ experience in research and strategy consulting. Malavika previously worked at Frost & Sullivan, managing and delivering advisory projects for clients involving expansion, acquisition, benchmarking and product development strategies. Malavika holds a MSc in Economics from Madras School of Economics.

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