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Review | Data Intelligence and Informatics Practice | Volume 1 - Issue 1 - 2026 | 1−18 |
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Industrial Artificial Intelligence: A Review of Technologies, Applications, Challenges, and Future Directions

Author(s): Rajiv Kumar Verma 1 , Priya Nair 2 , Amit Sharma 1
Author(s) information:
1 Indian Institute of Technology Bombay (IIT Bombay), IIT Area, Powai, Mumbai, Maharashtra 400076, India
2 National Institute of Technology Calicut (NIT Calicut), NIT Campus P.O 673 601, Kozhikode, 673601, India

Corresponding author

Data Intelligence and Informatics Practice

Volume 1 - Issue 1 - 2026

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Industrial Artificial Intelligence (IAI) has emerged as a transformative paradigm that integrates advanced computational intelligence with industrial systems, enabling unprecedented levels of automation, optimization, and decision support across manufacturing and process industries. This review provides a comprehensive synthesis of IAI technologies, spanning machine learning, deep learning, computer vision, natural language processing, reinforcement learning, digital twins, and explainable AI, and examines their deployment across diverse industrial sectors including automotive, aerospace, energy, logistics, and semiconductor manufacturing. Key application domains, including predictive maintenance, quality control, process optimization, and supply chain management, are critically analyzed in terms of methodology, performance benchmarks, and deployment maturity. Unlike prior reviews that focus primarily on a single technology family or a single application domain, this review integrates technology, application, and challenge perspectives within one evidence-based framework and benchmarks technology maturity against deployment readiness across eight industrial sectors. The review further identifies persistent challenges such as data scarcity, black-box opacity, legacy system integration, cybersecurity vulnerabilities, and workforce readiness, and proposes evidence-based mitigation strategies for each. A forward-looking perspective is offered, highlighting the convergence of large language models, federated learning, and Industry 5.0 human-centric frameworks as defining trajectories for the next decade. Drawing on 40 peer-reviewed journal articles identified through a systematic literature search, this article serves as a structured foundation for researchers, engineers, and policymakers navigating the complex IAI landscape.
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Lee, J.; Davari, H.; Singh, J.; Pandhare, V. (2018). Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20-23. https://doi.org/10.1016/j.mfglet.2018.09.002.

Peres, R.S.; Jia, X.; Lee, J.; Sun, K.; Colombo, A.W.; Barata, J. (2020). Industrial artificial intelligence in industry 4.0 — Systematic review, challenges and outlook. IEEE Access, 8, 220121-220139. https://doi.org/10.1109/ACCESS.2020.3042874.

Tao, F.; Zhang, H.; Liu, A.; Nee, A.Y.C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405-2415. https://doi.org/10.1109/TII.2018.2873186.

Bécue, A.; Praça, I.; Gama, J. (2021). Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities. Artificial Intelligence Review, 54(5), 3849-3886. https://doi.org/10.1007/s10462-020-09942-2.

Sisinni, E.; Saifullah, A.; Han, S.; Jennehag, U.; Gidlund, M. (2018). Industrial internet of things: Challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics, 14(11), 4724-4734. https://doi.org/10.1109/TII.2018.2852491.

Jan, Z.; Ahamed, F.; Mayer, W.; Patel, N.; Grossmann, G.; Stumptner, M.; Kuusk, A. (2023). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216, 119456. https://doi.org/10.1016/j.eswa.2022.119456.

Qiu, S.; Cui, X.; Ping, Z.; Shan, N.; Li, Z.; Bao, X.; Xu, X. (2023). Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review. Sensors, 23(3), 1305. https://doi.org/10.3390/s23031305.

Xu, J.; Kovatsch, M.; Mattern, D.; Mazza, F.; Harasic, M.; Paschke, A.; Lucia, S. (2022). A review on AI for smart manufacturing: Deep learning challenges and solutions. Applied Sciences, 12(16), 8239. https://doi.org/10.3390/app12168239.

Wang, H.; Zhang, W.; Yang, D.; Xiang, Y. (2023). Deep-learning-enabled predictive maintenance in industrial internet of things: Methods, applications, and challenges. IEEE Systems Journal, 17(2), 2602-2615. https://doi.org/10.1109/JSYST.2022.3193200.

Leng, J.; Zhu, X.; Huang, Z.; Li, X.; Zheng, P.; Zhou, X.; Mourtzis, D.; Wang, B.; Qi, Q.; Shao, H.; Liu, C.; Meng, W.; Tao, F. (2024). Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges. Journal of Manufacturing Systems, 73, 349-363. https://doi.org/10.1016/j.jmsy.2024.02.010.

Masood, T.; Sonntag, P. (2020). Industry 4.0: Adoption challenges and benefits for SMEs. Computers in Industry, 121, 103261. https://doi.org/10.1016/j.compind.2020.103261.

Khalil, R.A.; Saeed, N.; Masood, M.; Fard, Y.M.; Alouini, M.-S.; Al-Naffouri, T.Y. (2021). Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications. IEEE Internet of Things Journal, 8(14), 11016-11040. https://doi.org/10.1109/JIOT.2021.3051414.

Chen, T.; Sampath, V.; May, M.C.; Shan, S.; Jorg, O.J.; Aguilar Martín, J.J.; Stamer, F.; Fantoni, G.; Tosello, G.; Calaon, M. (2023). Machine learning in manufacturing towards Industry 4.0: From “for now” to “four-know.” Applied Sciences, 13(3), 1903. https://doi.org/10.3390/app13031903.

Baptista, M.L.; Goebel, K.; Henriques, E.M.P. (2022). Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artificial Intelligence, 306, 103667. https://doi.org/10.1016/j.artint.2022.103667.

Brusa, E.; Cibrario, L.; Delprete, C.; Di Maggio, L.G. (2023). Explainable AI for machine fault diagnosis: Understanding features' contribution in machine learning models for industrial condition monitoring. Applied Sciences, 13(4), 2038. https://doi.org/10.3390/app13042038.

Bécue, A. (2021). A review on the artificial intelligence in operational technology for cybersecurity of industrial control systems. IEEE Access, 9, 167247-167263. https://doi.org/10.1109/ACCESS.2021.3136543.

Dwivedi, Y.K.; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T.; Duan, Y.; Dwivedi, R.; Edwards, J.; Eirug, A.; Galanos, V.; Ilavarasan, P.V.; Janssen, M.; Jones, P.; Kar, A.K.; Kizgin, H.; Kronemann, B.; Lal, B.; Lucini, B.; …; Williams, M.D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002.

Wuest, T.; Weimer, D.; Irgens, C.; Thoben, K.-D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23-45. https://doi.org/10.1080/21693277.2016.1192517.

Brito, L.C.; Susto, G.A.; Brito, J.N.; Duarte, M.A.V. (2022). An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. Mechanical Systems and Signal Processing, 163, 108105. https://doi.org/10.1016/j.ymssp.2021.108105.

Li, W.; Huang, R.; Li, J.; Liao, Y.; Chen, Z.; He, G.; Yan, R.; Gryllias, K. (2022). A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges. Mechanical Systems and Signal Processing, 167, 108487. https://doi.org/10.1016/j.ymssp.2021.108487.

Kumar, P.; Hati, A.S. (2021). Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM. ISA Transactions, 111, 350-359. https://doi.org/10.1016/j.isatra.2020.11.009.

Li, H.; Zhang, Z.; Li, T.; Si, X. (2024). A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities. Mechanical Systems and Signal Processing, 209, 111120. https://doi.org/10.1016/j.ymssp.2024.111120.

Du, Z.; Chen, K.; Chen, S.; He, J.; Zhu, X.; Jin, X. (2023). Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system. Energy and Buildings, 289, 113072. https://doi.org/10.1016/j.enbuild.2023.113072.

Lee, J.; Azamfar, M.; Singh, J.; Siahpour, S. (2020). Integration of digital twin and deep learning in cyber-physical systems: Towards smart manufacturing. IET Collaborative Intelligent Manufacturing, 2(1), 34-36. https://doi.org/10.1049/iet-cim.2020.0009.

Pereira, A.C.; Romero, F. (2022). The duo of artificial intelligence and big data for Industry 4.0: Applications, techniques, challenges, and future research directions. IEEE Internet of Things Journal, 9(15), 12861-12885. https://doi.org/10.1109/JIOT.2021.3139827.

Radanliev, P.; De Roure, D.; Nicolescu, R.; Huth, M.; Santos, O.; Cannady, S.; Burnap, P. (2022). Digital twins: Artificial intelligence and the IoT cyber-physical systems in Industry 4.0. International Journal of Intelligent Robotics and Applications, 6(2), 171-185. https://doi.org/10.1007/s41315-021-00180-5.

Lee, J.; Su, H.; Yang, S. (2024). A unified industrial large knowledge model framework in industry 4.0 and smart manufacturing. International Journal of AI for Materials and Design, 1(2), 41-47. https://doi.org/10.36922/IJAMD025080006.

Choi, H.; Kim, D.; Kim, J.; Kang, P. (2022). Explainable anomaly detection framework for predictive maintenance in manufacturing systems. Applied Soft Computing, 125, 109147. https://doi.org/10.1016/j.asoc.2022.109147.

Zhang, D.; Pan, X.; Qu, T.; Zhu, H.; Liao, H.; Li, X.; Tao, F. (2022). A bi-level machine learning method for fault diagnosis of oil-immersed transformers with feature explainability. International Journal of Electrical Power & Energy Systems, 134, 107356. https://doi.org/10.1016/j.ijepes.2021.107356.

Sanakkayala, D.C.; Varadarajan, V.; Kumar, N.; Soni, G.; Kamat, P.; Kumar, S.; Patil, S.; Kotecha, K. (2022). Explainable AI for bearing fault prognosis using deep learning techniques. Micromachines, 13(9), 1471. https://doi.org/10.3390/mi13091471.

Serradilla, O.; Zugasti, E.; Rodriguez, J.; Zurutuza, U. (2022). Deep learning models for predictive maintenance: A survey, comparison, challenges and prospects. Applied Intelligence, 52(10), 10934-10964. https://doi.org/10.1007/s10489-021-03004-y.

Onchis, D.M.; Gillich, G.-R. (2021). Stable and explainable deep learning damage prediction for prismatic cantilever steel beam. Computers in Industry, 125, 103359. https://doi.org/10.1016/j.compind.2020.103359.

Rezaeianjouybari, B.; Shang, Y. (2020). Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement, 163, 107929. https://doi.org/10.1016/j.measurement.2020.107929.

Polverino, L.; Abbate, R.; Manco, P.; Perfetto, D.; Caputo, F.; Macchiaroli, R.; Caterino, M. (2023). Machine learning for prognostics and health management of industrial mechanical systems and equipment: A systematic literature review. International Journal of Engineering Business Management, 15. https://doi.org/10.1177/18479790231186848.

Soltani, Z.; Sørensen, K.K.; Leth, J.; Bendtsen, J.D. (2022). Fault detection and diagnosis in refrigeration systems using machine learning algorithms. International Journal of Refrigeration, 144, 34-45. https://doi.org/10.1016/j.ijrefrig.2022.08.008.

Faubel, L.; Schmid, K.; Eichelberger, H. (2023). MLOps challenges in Industry 4.0. SN Computer Science, 4(6), 828. https://doi.org/10.1007/s42979-023-02282-2.

Lee, J.; Gore, P.; Jia, X.; Siahpour, S.; Kundu, P.; Sun, K. (2022). Stream-of-quality methodology for industrial internet-based manufacturing system. Manufacturing Letters, 34, 58-61. https://doi.org/10.1016/j.mfglet.2022.09.010.

Alenizi, F.A.; Abbasi, S.; Mohammed, A.H.; Rahmani, A.M. (2023). The artificial intelligence technologies in Industry 4.0: A taxonomy, approaches, and future directions. Computers & Industrial Engineering, 185, 109662. https://doi.org/10.1016/j.cie.2023.109662.

Alomar, M.A. (2022). Performance optimization of industrial supply chain using artificial intelligence. Mathematical Problems in Engineering, 2022, 9306265. https://doi.org/10.1155/2022/9306265.

Gabsi, A.E.H. (2024). Integrating artificial intelligence in industry 4.0: Insights, challenges, and future prospects — a literature review. Annals of Operations Research, . https://doi.org/10.1007/s10479-024-06012-6.

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SUBMITTED: 13 June 2026
ACCEPTED: 31 July 2026
PUBLISHED: 4 August 2026
SUBMITTED to ACCEPTED: 48 days

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Verma, R. K. ., Nair, P. ., & Sharma, A. . (2026). Industrial Artificial Intelligence: A Review of Technologies, Applications, Challenges, and Future Directions. Data Intelligence and Informatics Practice, 1(1), 1−18. Retrieved from https://tecnoscientifica.com/journal/diip/article/view/1256
Keywords
Keywords: Artificial Intelligence machine learning transformative paradigm industrial systems
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