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-StandardMaturing 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 AnalyticsFigure 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
Organisations mentioned
3d Signals, ABB, Accenture, Adarga, AES, Altair, Amazon Web Services (AWS), Arcadis, ArcelorMittal, AspenTech, Augury, Avanseus, AVEVA, Baker Hughes, C3 AI, Clarifai, Cognite, Dataiku, DataProphet, DataRobot, Deloitte, Delta Bravo, Electric Reliability Council of Texas (ERCOT), EthonAI, Falkonry, Flutura, General Electric (GE), Goldman Sachs, Google, Grafana Labs, H20.ai, Hitachi Energy, Hitachi Vantara, Honeywell, Hortifrut, IBM, Iguazio, inmation, Intel, Intelecy, Itus Digital, Kelvin AI, MaxGrip, McKinsey & Company, Meta, Microsoft, Midjourney, Mtell, NVIDIA, OpenAI, OYAK Cement, QiO Technologies, RapidMiner, Samotics, SAP, Schneider Electric, Seeq, Senseye, Siemens, Sight Machine, SLB, SmartSignal, Software AG, SparkCognition, Sparta Systems, Stanford University, SymphonyAI Industrial, Trendminer, Uptime AI, Veracel, Wipro, Yokogawa, ZementisAbout the authors
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