The AI War in Process Industries in 2024

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Technology companies are always searching for new technologies to add to their portfolios and add to their abilities to solve real industrial issue to save money to their customers. However on the other side, the competition between these technology companies in the AI field, already fierce,  and is likely to intensify in 2024 and beyond, driven by several factors:

Competition landscape:

  • Industrial Automation Companies: They are investing heavily in AI research and development, aiming to dominate the market with comprehensive solutions and deep knowledge of the process industry.
  • Mega Technology Companies: While they pioneer in deploying several AI based projects, yet they are working hard to add more deeper knowledge  and increase their footprint in the process industry.
  • Startups: Nimble and innovative, they are bringing specialized solutions to niche areas within the process industries.
  • Industry incumbents: Traditional players are investing in AI to improve efficiency and productivity, not wanting to be left behind and die sooner.

Technological advancements:

  • Emergence of next-gen AI: We might see advancements in emotionally intelligent AI and simulation technologies, creating more robust and adaptable solutions.
  • Generative AI: This has immense potential for product design, aftermarket services, and supply chain management, offering cost reduction and innovative solutions.
  • Digital twins: These virtual replicas of physical processes will become more comprehensive and integrated with AI, leading to better optimization and predictive maintenance.
  • Widespread cloud adoption: This allows for easier data storage, sharing, and collaboration, accelerating AI development and deployment.

Industry trends:

  • Sustainability: Pressure to reduce emissions and waste will drive demand for AI solutions that optimize energy use, minimize waste, and enable cleaner production processes.
  • Labor shortages: AI can automate tasks, improve worker safety, and even create new jobs, addressing current labor challenges.
  • Personalization and customization: Customers expect tailored products and services. AI can personalize production processes and optimize product offerings, giving companies a competitive edge.

Challenges and regulations:

  • Data access and ownership: Sharing sensitive process data can be a concern. Data governance and ownership models will need to be addressed.
  • Explainability and trust: Transparency in how AI models reach decisions is crucial for building trust in their outputs.
  • Regulation: Governments are developing frameworks to ensure responsible AI development and deployment in critical industries like process manufacturing.

End users and Consultants are expecting to see:

  • More specialized AI solutions catering to specific needs within the process industries.
  • Develop AI solutions that automatically identify and recommend detailed corrective actions for specific types of anomalies for operational problems, improving efficiency and reducing downtime.
  • More integrated solutions via enhanced collaboration platforms
  • Increased collaboration between tech companies, traditional players, and researchers to accelerate innovation and provide unified multi-functional solutions.
  • A focus on responsible AI development, addressing data privacy,  explainability, and ethical considerations.

This competition will ultimately benefit the process industries by driving innovation, efficiency, and sustainability. Companies that embrace AI strategically and address the challenges effectively will be best positioned to thrive in this dynamic landscape.


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