Continuing the AI Conversation
Last week’s deep dive into Kenny Seaton’s AI-driven transformation at CSU Dominguez Hills revealed how AI machine learning is revolutionizing building operations – from achieving 96% reductions in gas usage to creating self-optimizing systems that outperform static OEM sequences. This week, we explored the inevitable industry tension this creates: How can proprietary innovation coexist with the open ecosystems needed for AI’s full potential?
1. The Proprietary vs. Open Dilemma
The discussion opened with a fundamental question: Does innovation require proprietary technology? Anto framed the challenge using a light bulb (innovation) and padlock (proprietary) metaphor:
Key Observations:
- Historical Context: Early proprietary systems (like pre-BACnet controls) often emerged from necessity, not malice
- The Lock-In Effect: Some vendors later weaponized proprietary protocols to restrict customer choice
- Today’s Balance: “Proprietary innovation isn’t evil, but its implementation can be”
2. Industrial Automation’s Playbook
Highlighting the stark differences between building automation and industrial systems:
| Factor | Industrial Automation | Building Automation |
|---|---|---|
| Procurement | Equipment-driven (no low-bid) | Project-based (value-engineered) |
| Standards Adoption | Often viewed as a cost center | Proliferation of competing protocols |
| Mission Criticality | Direct revenue impact | Often viewed as cost center |
The Takeaway: Industrial sectors standardized because equipment failures resulted in immediate production losses—a lesson for buildings as they become revenue-generating assets.
3. The Platform Solution
The Linux Foundation’s Tiger Team emerged with a potential path forward:
Progress Report:
- RDF Framework: Consensus reached on using Resource Description Framework for cross-system data modelling
- GitHub Momentum: Code repositories now being populated with interoperable building data examples
- 2525 Integration: Work underway to align with ASHRAE’s building performance standards
“We’re not asking vendors to open their secret sauce – just the pantry doors. Let owners mix ingredients from different chefs.”
4. AI’s Double-Edged Sword
The group grappled with AI’s paradoxical role:
The Challenge:
- Proprietary algorithms deliver unprecedented optimization (like Kenny’s Chiller AI)
- Black-box models create new lock-in risks and auditability concerns.
The Opportunity: Keith’s case study showed how open APIs can allow:
- Third-party validation of AI decisions
- “Mix-and-match” with best-in-class analytics
- Future-proofing against vendor obsolescence
5. An Owner-Led Revolution
The session closed with clear action items:
For Owners:
- Specify RDF compliance in procurement docs
- Demand measurable outcomes in service contracts
- Support pilot projects, intelligence layers
For Vendors:
- Embrace “open enough” models while enabling integration
- Invest in developer ecosystems around your platforms
- Participate in standards groups like the Tiger Team
The Bottom Line:
As buildings evolve from static structures to adaptive organisms, the industry must forge a new compact one where proprietary innovation and open interoperability aren’t rivals, but essential partners in progress.
Watch the Full Discussion:
Continue the Conversation: #SmartBuildings #OpenInnovation #AIforBuildings
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