Standout UNSCRIPTED - Episode 373
Dave Mackerness, Director at Kaer and 2024 Stand Out 50 leader, returns to UNSCRIPTED five years after his first appearance to share what scaling cooling-as-a-service has actually looked like, from regional expansion to managing the customer behavior shift to an AI platform that makes their oldest plant their best.
Kaer has been in the cooling business for 70 years. The company began as a distributor, evolved into a system integrator, moved to performance-based contracts, and committed in 2018 to going all-in on cooling-as-a-service, a model it had been building since 2012. When Dave Mackerness last appeared on UNSCRIPTED in 2021, 80% of the business had transitioned. Today, Kaer operates across India, Malaysia, and Indonesia, with strategic investment from Patrizia and Mitsui backing the next phase of growth.
This conversation picks up where that one left off.
The Sweet Spot and Why Both Small and Large Companies Struggle
One of the most practically useful frameworks Dave shares is his thinking on which companies are well-positioned to offer as-a-service and which face structural barriers on both ends of the size spectrum.
Smaller companies often lack the capital. Cooling-as-a-service is capital-intensive, you build and own the infrastructure rather than sell it. And beyond capital, there is a trust issue: mission-critical infrastructure demands the confidence of large customers, which smaller companies may struggle to earn.
Larger MNCs face the opposite problem. They have the capital and the trust. What they lack is the ability to move. The bigger the company, the more legacy it has in the old model, factories producing hardware, revenue targets tied to box sales, reporting metrics on listed exchanges that shift uncomfortably if the model changes. Many have tried to test as-a-service in a separate business unit, found it hard, and watched investment get redirected back to the core product.
“That’s where I find the sweet spot. A medium-sized business in the middle, one that has the capital, has the trust, but isn’t so big that the legacy holds it back.” - Dave Mackerness
The Complexity-Cost Matrix: Is As-a-Service Right for Your Product?
The conversation that sparked Dave’s thinking on this came from a conference where he was seated next to someone in the crane and lifting industry who wanted to explore servitization. By the end of the conversation, Dave had talked himself out of recommending it.
That experience led him to a framework: plot complexity against cost. Products that are cheap and simple may benefit from as-a-service for customer experience reasons, music streaming being the classic example. Products that are complex and expensive sit in the sweet spot where the provider’s expertise and scale create genuine value the customer cannot replicate themselves. Cooling fits squarely there. So does aircraft maintenance, Rolls-Royce’s Power by the Hour model being the poster child. Cranes do not: the customer manages and operates the equipment themselves, leaving little room for the provider to add value through operations.
The Glass Box: Transparency as a Competitive Advantage
When Kaer started, they operated as a black box. This is my system, you receive the outcome, don’t ask how. Customers didn’t trust it. The model was right but the relationship wasn’t working.
The shift was to build a glass box instead, not giving customers control over how the outcome is achieved, but giving them full visibility. The result was the Kaer Connect app: supply temperatures tracked minute by minute, service reports signed by engineers, compliance documentation available at any time. In the old model, you report to the customer. In the as-a-service model, you make everything available and let them audit at will.
“80% of cooling-as-a-service is managing customers, making sure they’re happy, confident, and that what you’re delivering is auditable. We didn’t realize that at the beginning.” - Dave Mackerness
AI Has Inverted the Performance Curve
Kaer began building its AI optimization platform in 2017. Cooling systems are highly complex to operate, and small adjustments to set points across multiple pieces of equipment can have a significant impact on energy efficiency and carbon emissions.
The unexpected outcome: their oldest installation is now their best-performing one. Not because the hardware has been upgraded, it hasn’t. Because it has accumulated the most data. The models trained on that data now optimize the system better than any engineer could manually. The performance curve, traditionally a decline from day one, has been inverted.
“Your best plant is not the newest one. Your best plant is the one with the most data.” - Dave Mackerness
For mission-critical applications, the challenge is the variability AI can produce. Kaer’s solution was a second layer, a thermodynamic validation layer that checks any proposed action from the machine learning model against physical constraints before allowing it to execute. If the action would affect chilled water supply temperature, it does not happen. The result is a system engineers can trust.
The Energy Paradox and Why It Echoes Cooling’s Own History
Dave draws a direct parallel between how air conditioning was perceived a decade ago and how AI is perceived today. Cooling is vital but energy-intensive. The criticism: it’s good, but the carbon cost is too high. Kaer’s response was to decouple the two, delivering low-carbon cooling as an outcome.
AI faces the same challenge. The output is extraordinary, but energy consumption is increasingly scrutinized. Kaer’s position is unusual: they are using AI to reduce the energy consumption of the cooling systems they operate. The energy their AI platform consumes is, in their model, more than offset by the efficiency gains it drives.
What Comes Next
Dave’s clearest signal of where the model is heading: the conversation has shifted from buildings to portfolios. REITs, developers, and portfolio owners are approaching Kaer about outsourcing cooling across their entire estate, not building by building, but as a strategic decision made at CFO or CEO level. That conversation is in a different category entirely.
The other area of active development: agentic AI. Kaer is building compliance agents that monitor service and maintenance activity across the portfolio, with data going back to 1993. As the data library grows, so does the value of the models built on top of it.
“Once you buy Spotify, you’re not buying CDs anymore.” - Dave Mackerness
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