Tech Roadmap: Enablers Of Industrial AI Applications (2025)

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Executive Summary

This Tech Roadmap report clarifies the business value, pace of innovation and maturity of 18 critical technologies that empower the development and deployment of AI in industrial settings. Verdantix conducted a fact-based assessment of the maturity, innovation, future prospects and business value of each solution, and provides investment advice for buyers. Managers responsible for selecting technologies for their operations should use this report to identify technologies that enhance industrial AI applications. The technologies identified range from those in Research and Development, such as quantum computing, to Growth stage technologies, such as edge computing and predictive analytics. Technology buyers can use the Tech Roadmap methodology to identify new technologies for pilot projects and to assess vendor product roadmaps. Risk-averse firms should focus on the potential of technologies with proven business value that are in the Growth or Maturity phases of the technology life cycle. Innovators and early adopters should take note of Launch phase technologies.

Table of contents

Introducing the Tech Roadmap analysis
Key questions answered by the Tech Roadmap analysis
Tech roadmap analysis aligns with operational optimization and automation
Tech Roadmap for enablers of industrial AI applications
Defining the market for enablers of industrial AI applications
Methodology overview
Identifying the five market phases of technology maturity
Three technology life cycles differentiate business value
Technology adoption and success influence positioning
Pace of innovation determines the time to the next lifecycle phase
Enablers of industrial AI applications in Phase 1: Research and Development
Quantum computing
Speech-to-speech AI
Enablers of industrial AI applications in Phase 2: Launch
AI agents
Explainable AI
GenAI copilots
Knowledge graphs
Multimodal AI
Synthetic data from generative models
Synthetic data from simulation environments
Enablers of industrial AI applications in Phase 3: Growth
Anomaly detection
Autonomous robotics
Computer vision
Edge computing
Hyperscale cloud platforms
Industrial DataOps
Industrial Internet of Things (IIoT) platforms
Predictive analytics
Retrieval-augmented generation (RAG)

Table of figures

Figure 1. Enablers of industrial AI applications: Tech Roadmap technology solution groupings
Figure 2. Tech Roadmap for enablers of industrial AI applications

Organisations mentioned

ABB, Accenture, AI21 Labs, Akira, Alibaba Cloud, Amazon, Amazon Robotics, Amazon Web Services (AWS), Anthropic, ANYbotics, AnyLogic, AspenTech, Augmentir, Augury, Avathon, AVEVA, Baker Hughes, Bentley Systems, Birds.ai, Bitfrost, BlueSkye Automation, Blynk, Boston Dynamics, Bridger Photonics, C3 AI, Capgemini, CARLA, Chetu, Cisco, CleanConnect.ai, ClearBlade, Cognex, Cognite, Cybus, Datadog, Datagen, DataKitchen, dataPARC, DataRobot, Deepgram, DeepSeek, Dell, Devo, Doosan Robotics, Duke Energy, D-Wave Systems, EdgeConneX, ElevenLabs, Falkonry, FANUC, Fero Labs, Fetch Robotics, Fiddler AI, Figure, Fogwing, Gazebo, GE Vernova, GFT Technologies, GlobalFoundries, Google, Google DeepMind, Gretel AI, H2O.ai, Hewlett Packard Enterprise (HPE), Hexagon, HighByte, Hitachi Energy, Hitachi Vantara, Honeywell, Huawei, Hugging Face, IBM, IFS, Inductive Automation, Intellico, IntelliView Technologies, Intenseye, IonQ, juna.ai, KPMG, KUKA, Kuva Systems, LandingAI, LangChain, Levatas, Litmus Automation, LlamaIndex, MachineMetrics, Marathon Oil, MathWorks, MetAI, Microsoft, Mobile Industrial Robots (MiR), Neo4j, nFlux, Noema, NVIDIA, Oden Technologies, OpenAI, Oracle, Overview, Oxford Quantum Circuits (OQC), Palantir, Particle, Peak, Plataine, Pragma, Praxis AI, Protex AI, PTC, QiO Technologies, Quantum Flagship, RapidMiner, Relimetrics, Retrocasual, Rhize, Salesforce, SAP, Scale AI, Schneider Electric, Siemens, Sight Machine, Smartia, Snowflake, SUS Corporation, SymphonyAI, Synthesis AI, Talend, Tonic AI, Trendminer, T-robotics, TTTech Industrial, UiPath, Unstructured, Uptime AI, Vanti, Y Combinator, Yokogawa, Zebra Technologies

About the authors

Henry Kirkman

Industry Analyst
Henry is an Industry Analyst in the Verdantix Industrial Transformation 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.

Robin Sureda-Tasis

Analyst

Robin is an Analyst in the Verdantix Industrial Transformation practice. Prior to joining Verdantix, he worked as an investment portfolio analyst at a private fund. He holds a BSc in Business Management, Digital Innovation and Entrepreneurship from Bayes Business School, and a Master's degree with Distinction in Digital Economy from King’s College London, where he specialized in AI, XR, and various disruptive innovations.

Malavika Tohani

Research Director, Industrial Transformation
Malavika leads the Verdantix Industrial Transformation practice. The team’s current research focusses on digital technologies and services that enable industrial operations become safer, efficient and sustainable such as asset maintenance and performance, industrial AI and data management, connected workforce and field service management, process safety and industrial engineering, design and construction. Malavika has over 20 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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