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Overcoming Gen AI Adoption Obstacles Across Process Industries


Process industries face many obstacles that are preventing the straightforward deployment and adoption of Gen AI. Here are some tips to overcome those obstacles.

Gen AI for customer service, Gen AI for content, Gen AI for…petrochemicals? As artificial intelligence continues to change everything we know about work and production, the most exciting applications will likely be in traditional industries rather than tech or digital ones. The use of Gen AI in these industries isn’t about automating existing processes; it’s about reimagining how these processes can be carried out more effectively and sustainably. As AI technology continues to evolve, its potential applications in process industries are expanding, driving significant transformations in these sectors.

So why aren’t more process industries taking part?

The challenges in implementing General AI (Gen AI) in process industries, despite its numerous potential benefits, arise from a variety of factors. These obstacles can be multifaceted and complex, often interrelated, and vary from industry to industry. Here are some key challenges:

  • Technical Complexity: Gen AI systems are inherently complex and require sophisticated algorithms and data processing capabilities. Integrating these systems with existing industrial processes can be technically challenging, especially in industries with legacy systems that are not easily compatible with advanced AI technologies.
  • Data Quality and Quantity: Effective AI requires large volumes of high-quality data. Process industries may struggle with collecting, storing, and processing the necessary data, especially if they have not digitized their operations over the years. This technical debt may take months or years to untangle.
  • Cybersecurity and Data Privacy: Implementing AI involves handling sensitive data, which raises concerns about data privacy and security. Process industries must ensure robust cybersecurity measures to protect against data breaches and cyberattacks.
  • Regulatory Compliance: Many process industries are heavily regulated, and ensuring AI systems comply with all relevant regulations and standards can be a significant challenge. There can be uncertainty about how existing regulations apply to AI or how new regulations might evolve.
  • High Initial Costs and ROI Uncertainty: The initial investment for implementing Gen AI can be substantial. Industries may face challenges in securing funding and may be uncertain about the return on investment, especially if the benefits of AI are not immediately clear.
  • Lack of Skilled Personnel: There is a shortage of skilled professionals who understand both AI and the specific needs of process industries. Training existing staff or recruiting new talent with the right skill set can be difficult and expensive.
  • Reluctance to Change and Cultural Barriers: There can be resistance to change within organizations, especially if employees perceive AI as a threat to their jobs. Overcoming these cultural barriers and gaining buy-in from all levels of the organization is crucial but challenging.
  • Scalability and Integration Issues: Scaling AI solutions from pilot projects to full-scale deployment can be difficult, particularly when dealing with complex industrial processes that vary significantly between different plants or sites.
  • Ethical and Social Implications: AI implementation raises ethical concerns, such as job displacement and accountability for decisions made by AI systems. Companies must navigate these issues carefully to maintain public trust and social license to operate.
  • Dependence on External Vendors and Technology: Many process industries rely on external vendors for AI technology, which can create dependencies and risks, including issues with compatibility, support, and vendor lock-in.

Addressing these challenges requires a comprehensive approach, including investing in technology infrastructure, focusing on staff training and development, ensuring compliance with regulatory standards, and developing strategies to manage the ethical and social implications of AI implementation.

Related: Why Is It So Hard to Trust Industrial Data? And What Can Be Done About It?

How can process industries make strides using Gen AI?

To successfully move forward with Gen AI in process industries, companies can focus on five key strategies:

Invest in Data and Infrastructure

Prioritize building a robust technological infrastructure that supports AI processes and invest in systems for efficient data management. This includes ensuring the quality and accessibility of data, which is crucial for AI effectiveness.

Develop or Acquire AI Expertise

Either build in-house AI expertise through training and hiring or establish partnerships with external AI vendors. This expertise is essential for the effective development, deployment, and management of AI solutions.

Implement Pilot Projects

Start with small-scale pilot projects to test and demonstrate the value of AI. This approach allows for manageable experimentation and learning, which can be scaled up to more extensive operations.

Focus on Cybersecurity and Compliance

Strengthen cybersecurity measures to protect sensitive data and AI systems and ensure compliance with relevant regulations and laws in the industry, particularly those concerning data use and privacy.

Foster an Innovation-friendly Culture

Cultivate a company culture that is open to change and innovation. Encourage employee engagement and adaptation to new technologies, and manage the organizational changes effectively to gain widespread acceptance of AI initiatives. In studies such as this one from Boston Consulting Group, process industries are not only starting to make top lists for most innovative companies but also doing that with forward-thinking in artificial intelligence.

Related: How Gen AI is improving Aker BPs data management practices

Overcoming hurdles is worth it

Integrating Gen AI in process industries is a journey marked by immense potential and significant challenges. However, each sector is finding unique ways to navigate this complex landscape. These developments highlight a crucial point: the successful implementation of Gen AI is not solely about the technology itself but also about the strategic approaches industries take towards its adoption. This includes addressing technical complexities, ensuring data quality, investing in cybersecurity, complying with regulatory standards, and, above all, cultivating a culture that embraces change and innovation. The journey is challenging, but the reward is unmistakable.

Elizabeth Wallace

About Elizabeth Wallace

Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain - clearly - what it is they do.

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