
Articles
Director of Product, Univers
Published in the 25 May 2025
Your customer deployed an AI system that promised 30% energy savings. Six months later, their bills are higher than before. The finger-pointing begins:
Welcome to the contract crisis that’s paralyzing building tech deals.
Traditional building contracts were written for a simpler world. You bought an HVAC system, you got HVAC support. You bought lighting controls, you got lighting warranties. But today’s AI-powered building systems span five different technology layers:
Each layer has a different vendor, each vendor has a different contract, and each contract points liability somewhere else.
The result? When the system fails to deliver promised results, everyone is responsible for their piece, but nobody is responsible for the whole.
AI adds a whole new layer of accountability confusion:
Data Quality Issues: When AI underperforms, is it because sensors provided bad data, cloud storage corrupted information, or algorithms need retraining? Good luck proving which vendor caused the problem.
Black Box Decisions: Your AI system decides to shut down cooling in the middle of summer, causing equipment damage. The AI vendor says the algorithm worked correctly based on available data. But who validates that claim?
Continuous Learning Confusion: AI models that “learn and improve” create moving targets for performance guarantees. How do you hold vendors accountable for systems that change their behavior over time?
Explainability Requirements: New regulations demand AI decision transparency, but most vendors consider their algorithms proprietary. Who’s liable when you can’t explain why your building’s AI made costly decisions?
Building technology customers don’t want to manage vendor liability—they want results. At Univers, we’ve recognized this fundamental shift and designed our approach accordingly.
Rather than forcing customers to coordinate between multiple vendors for sensors, analytics, AI optimization, and system integration, Univers takes end-to-end responsibility for decarbonization outcomes. When a customer implements our platform to optimize their building portfolio or renewable energy systems, they work with a single point of accountability.
This end-to-end approach means we manage the complexity behind the scenes, coordinating our connected sensors, AI algorithms, and system integrations to deliver guaranteed results. Instead of customers navigating contracts with separate IoT vendors, software providers, and integration specialists, they get one contract, one relationship, and one team accountable for success. The pattern is clear: customers want partners who absorb integration liability instead of passing it along.
For sales teams: Are we making it easier or harder for customers to understand who’s responsible when systems underperform? There’s a tension between showcasing technical capabilities and simplifying accountability.
For product development: Each integration point we add creates another potential liability handoff. How do we balance innovation with the contract complexity we’re creating for customers?
For company strategy: As AI capabilities evolve rapidly, how do we structure partnerships and contracts that can adapt to changing technology without leaving customers stranded?
Some companies are experimenting with new models, though it’s early days:
AI-powered building systems deliver incredible value when they work. But “when they work” depends on flawless integration across multiple vendors who traditionally blame each other when problems arise. The companies winning enterprise deals aren’t building better AI—they’re building better contracts.
Your customers want smart buildings that deliver promised results. They don’t want to become contract lawyers to get them. The vendors who figure out how to absorb integration liability while delivering guaranteed outcomes will capture the market. Everyone else will keep fighting over who’s responsible for the last system failure.