Industrial AI isn’t really about AI at all

Key Takeaways

• Industrial AI is shifting from a focus on artificial intelligence to a deeper understanding of industrial systems and their context
• The emphasis is on interpreting context and understanding how industrial systems actually work, rather than just using smarter tools
• Octave, an industrial software firm, is framing this shift as a structural rewrite of industrial software itself
• The company's approach focuses on creating a full industrial lifecycle, from design to protection, and integrating fragmented tools and data models
• The constraint in industrial AI is not the AI itself, but rather the context and data quality

Introduction to Industrial AI

The industrial software industry is undergoing a significant shift, moving away from a focus on artificial intelligence (AI) as the primary story and instead emphasizing the importance of understanding context and how industrial systems actually work. This shift is driven by the need for systems that can interpret context, including decades of engineering decisions, maintenance histories, and tacit operational knowledge embedded in workflows.

From Fragmented Tools to Lifecycle Systems

Octave, an industrial software firm, is at the forefront of this shift, framing its approach as a structural rewrite of industrial software itself. The company's Chief Product Officer, Jay Allardyce, notes that AI is only as good as the data it is based on, and that context matters. Octave's approach focuses on creating a full industrial lifecycle, from design to protection, and integrating fragmented tools and data models.

Comparison of Industrial AI Approaches

The Constraint is Not AI - It's Context

While much of the software industry has focused on model capabilities and automation, Octave's framing is deliberately more conservative, emphasizing that the constraint in industrial AI is not the AI itself, but rather the context and data quality. Allardyce notes that customers want repeatability and understanding of signals and patterns based on potentially hundreds of design drawings or other inputs.

Conclusion

The shift in industrial AI from a focus on artificial intelligence to a deeper understanding of industrial systems and their context is a significant one. Octave's approach, which emphasizes integration, system readiness, and context, is well-positioned to address the constraints in industrial AI. By focusing on creating a full industrial lifecycle and integrating fragmented tools and data models, Octave is helping to unlock the true potential of industrial AI.

Bottom Line

In conclusion, the industrial AI landscape is evolving, with a growing emphasis on understanding context and how industrial systems actually work. Octave's approach, which prioritizes context and data quality over model capability, is a key part of this shift. As the industry continues to evolve, it is likely that we will see more companies following Octave's lead, focusing on integration, system readiness, and context to unlock the full potential of industrial AI. With 90% of industrial data currently going unused, the potential for growth and innovation in this space is significant, with companies like Octave poised to capitalize on this opportunity.