(96b) Reimagining Industrial Knowledge Management Using AI-Support with Knowledge Capture, Pruning, and Distribution

The Energy and Chemicals sector operates in a high-risk environment where the effective management of knowledge is critical to safety, regulatory compliance, and operational success. However, traditional methods of capturing, maintaining, and distributing industrial knowledge often fall short in addressing the challenges of today’s fast-paced and increasingly complex landscape. This abstract explores a set of persistent challenges in industrial knowledge management and proposes potential strategies and frameworks for tackling them through innovative approaches, including AI support.

Challenges in Knowledge Management

1. Knowledge Capture:

   • Loss of Expertise: With a significant portion of the workforce nearing retirement, the "Silver Tsunami" threatens to erode institutional knowledge. Studies show that 57% of retiring professionals have shared less than half of their critical knowledge, and 21% have shared none.
   • High Workforce Turnover: Frequent employee transitions hinder the development of tribal knowledge, leaving organizations vulnerable to operational inefficiencies.
   • Resource Constraints: Engineering teams often lack the time and tools to systematically capture nuanced knowledge from experienced professionals.

2. Knowledge Refinement:

   • Inconsistent Information: Conflicting internal documents, outdated standards, and misaligned processes result in operational confusion and potential safety hazards.
   • Knowledge Expiration: Organizations struggle to identify and retire stale information, undermining decision-making and compliance.
   • Continuous Improvement: A lack of feedback loops and iterative processes limits the evolution of best practices, impeding operational excellence.

3. Knowledge Distribution:

   • Accessibility Barriers: Inaccessible or poorly organized information leaves workers disenfranchised, reducing engagement and productivity.
   • Training Inefficiencies: Traditional training programs fail to meet the needs of modern professionals who benefit more from on-demand, context-specific knowledge.
   • Knowledge Transfer at Scale: Distributing expertise across teams and geographies in a dynamic industrial environment remains a persistent challenge.

Potential Solutions

To address these challenges, we propose a conceptual framework that integrates human-centric workflows with technology-driven efficiencies:

1. Improving Knowledge Capture:

   • Develop tools that enable seamless integration of voice, video, and text to capture expertise in real-time.
   • Pair senior experts with junior employees to co-create knowledge assets, leveraging the technical proficiency of digital natives.
   • Organize targeted "knowledge capture blitz" events to systematically document critical processes and insights.

2. Enabling Knowledge Refinement:

   • Use AI-driven systems to identify inconsistencies and redundancies within internal and external documents.
   • Establish expiration dates for knowledge assets, with workflows for SME review and updates.
   • Promote continuous improvement through accessible feedback mechanisms, allowing employees to propose updates and refine best practices collaboratively.

3. Transforming Knowledge Distribution:

   • Deliver "micro-doses" of knowledge in a question-and-answer format tailored to real-time needs, minimizing interruptions to workflows.
   • Create dynamic visualizations, such as knowledge activity maps and heat maps, to highlight gaps and confirmed areas of expertise.
   • Facilitate recognition of individual contributions to enhance engagement and build a culture of knowledge sharing.

Role of AI in Knowledge Management

Artificial Intelligence (AI) has emerged as a critical enabler for addressing these challenges by enhancing both efficiency and scalability:

• Computationally Frugal AI: Leveraging lightweight, cost-effective algorithms allows organizations to implement AI solutions without excessive infrastructure demands.
• Human-in-the-Loop Systems: AI complements human expertise by automating repetitive tasks while ensuring SMEs retain control over critical decisions.
• Domain-Specific Workflows: AI systems tailored to the Energy and Chemicals sector can streamline regulatory compliance, identify inconsistencies, and support troubleshooting.

Toward a Safer, Smarter Future

By integrating these strategies, industrial organizations can mitigate risks, enhance safety, and improve productivity. The proposed framework aligns with the principles of Manufacturing 5.0, which emphasizes human-centric digital transformation. Beyond technology, fostering a culture of continuous learning, collaboration, and recognition will remain critical to unlocking the full potential of industrial knowledge management.

This session aims to stimulate discussion around these challenges and invite participants to contribute insights and experiences. Together, we can reimagine how knowledge is captured, refined, and shared, paving the way for a more resilient and innovative Energy and Chemicals sector.