The System We Need (But Don't Have)

Picture the ideal state: Sensors detect HVAC degradation. AI predicts failure 47 days out. The system auto-generates a work order, routes it to a pre-qualified contractor, schedules intervention during low occupancy, and logs the prevented failure in the ESG ledger. The documented improvement feeds into green loan pricing, triggering a margin step-down. Total time from detection to resolution: 36 hours. Human intervention: one approval click.

THE SIX LAYERS

This isn't science fiction. Every component to make it happen exists. In 6 layers. Within these are the entire ecosystem of products and services we need to ‘build a better built environment’.

Layer 1: Data Collection: IoT sensors, BMS integration, digital twins capturing real-time performance.

Layer 2: Optimisation: AI analytics predicting failures, optimising HVAC, lighting, space utilisation.

Layer 3: Execution: Modular retrofits, automated FM workflows, augmented maintenance teams.

Layer 4: Governance: ESG platforms, carbon accounting, continuous performance verification.

Layer 5: Finance: Green debt priced on verified operational data, not BREEAM certificates.

Layer 6: Human Layer: Occupant analytics linking environment to productivity, retention, wellbeing.

As components in an ecosystem they would each act as flywheels for each other: Better data → smarter optimisation → validated execution → credible ESG → cheaper capital → funds improvements → generates more data.

The problem is that despite everything existing, and the existence of ‘Smart’ buildings from London to Singapore, we have components, not systems. Nowhere is this flywheel in motion. It is a ‘known known’ that this is what we need, but to date we’ve just not managed to make it happen.

Why the Flywheel Doesn't Spin

There are multiple reasons why the flywheel doesn’t spin:

1. Fragmented Ownership

Each layer has different buyers making independent decisions:

• Layer 1: Procurement teams optimising sensor unit costs
• Layer 2: Engineering teams proving AI ROI
• Layer 4: Sustainability officers meeting compliance deadlines
• Layer 5: CFOs negotiating debt terms
• Layer 6: HR measuring employee satisfaction

No single decision-maker sees the compounding value. The procurement team buying sensors doesn't benefit from reduced debt costs three layers later. The CFO accessing green finance never sees occupant retention data that justified the improvements. They work together, but apart.

2. The Adoption Trap

Layer 2 (AI) needs data from Layer 1 (sensors) - but sensor deployment won't scale until AI proves ROI. Layer 5 (finance) needs verified data from Layer 4 (governance) - but governance platforms struggle until they unlock cheaper capital. Layer 3 (execution) needs proven savings from Layer 2 before landlords commit retrofit budgets.

Each layer faces adoption friction individually. Network effects only materialise at system scale - but no rational actor deploys the full stack speculatively.

3. Capital Structure Mismatch

The layers require incompatible funding:

• Layer 1 (sensors): Hardware capex, slow payback, 10-15% margins → VC won't fund
• Layer 3 (execution): Services business, 5-12% margins → PE finds unattractive
• Layer 5 (finance): Regulatory-heavy, capital-intensive → Requires institutional capital
• Layers 2, 4, 6 (software): High-margin, scalable → This is what VC wants

But the flywheel only spins across all six layers. You can't fund it with capital that only wants three spokes.

The Sovereign Solution (That We Don't Have)

There's a straightforward solution, in theory. And one that I had high hopes to see emanate from the Gulf.

Sovereign-scale orchestration could solve all three problems. A Gulf sovereign wealth fund committing £5-10bn to create companies across all six layers, with guaranteed government procurement, mandated interoperability, and 30-50 year capital horizons.

The Gulf has executed this playbook before: Emirates catalysed ground handling, catering, training into a £100bn+ ecosystem. Ma'aden seeded downstream aluminium, phosphates, industrial clusters. When you control both supply and demand, you solve chicken-and-egg problems by mandate.

Unfortunately it seems the guiding force in the Gulf real estate sector remains a short-term, build-to-flip model, incentivised by a need to project rapid, noteworthy ‘progress’, a desire for ‘capital velocity’ and a reliance on real estate as a major component of GDP and employment.

With energy and water subsidies removing price signals and the preference for importing proven solutions, all incentives are towards being rationally irrational and not building for the future. Everything needs to be done today, or tomorrow at the latest.

In fact the Middle East is solving the wrong problem superbly. World-class at rapid deployment, capital mobilisation, iconic architecture whilst not addressing durability, adaptability, resource efficiency or knowledge creation.

So sovereign-scale orchestration is unlikely to move from theory to reality.

In western markets we certainly can't make it happen. We don't have sovereign entities that can mandate integration across thousands of private landlords or force technology interoperability.

British Land can't compel Landsec to use the same protocols.

Which means the West needs market-driven orchestration, not sovereign coordination.

The Failed Paths Forward

The PropTech Unicorn

Brilliant predictive maintenance AI forecasts HVAC failures with 94% accuracy. It generates an alert:

Chiller 3 will fail in 47 days

Then what? Without Layer 3 (execution) integration, the alert goes to an inbox. Gets forwarded. Quote requested. Finance approves three weeks later. Scheduled for next maintenance window, six weeks out. By then the chiller has failed.

Without Layer 4 (governance), even if repair happens, there's no ESG logging. Without Layer 5 (finance), accumulated improvements never feed into debt pricing.

The AI was correct. The technology worked. But the *system* didn't activate.

Venture-backed PropTech optimises individual layers superbly. But venture structures - 7-year duration, software margin requirements - prevent ecosystem orchestration.

The Landlord Builds It Internally

Landlords have the right incentives (often 20-50 year holds) and captive testing grounds (millions of square feet/metres). But building enterprise software requires completely different DNA: agile development, product management, technical talent retention.

And the talent economics work against them, especially now AI is such a key technology. Machine Learning engineers are likely to be considerably more expensive than asset managers, which won’t go down well. And the best technical people will likely leave, or be poached, within 18 months.

Historical precedent is harsh: Tesco built supply chain technology, which they never managed to sell externally and eventually outsourced. Sainsbury's built a whole banking arm but ended up selling that to NatWest. These were structural mismatches between core competency and market requirements.

Exceptions do exist. Ocado spun out fulfilment technology as a genuinely separate entity - distinct governance, compensation, leadership. But this requires admitting your competitive advantage should become someone else's business.

The Incumbent Platform Extends

Yardi could acquire point solutions across all six layers and force integration through ownership.

But they’d face acute innovator's dilemma:

• Architectural legacy: Adding AI is effectively limited to either greenfield rebuild (politically impossible) or wrapper strategy (orchestrating *around* the platform).
• Business model conflict: Yardi makes money from seat licenses. The flywheel model is outcome-based (which we’ll come to): "We reduce OPEX 20%, take 30% of savings." This cannibalises their revenue.
• Channel conflict: Routing work orders to specific contractors competes with customers' FM teams and vendor relationships.

Repositioning from administrative infrastructure to strategic partner would alienate their current user base - often the people whose jobs the flywheel would automate.

Private Equity Roll-Up

PE can consolidate supply (buy 8-10 PropTech companies), but not demand (thousands of independent landlords with different priorities).

Combining high-margin software (Layers 2, 4, 6) with low-margin hardware and services (Layers 1, 3) destroys the blended margin profile PE requires. And who buys an integrated PropTech conglomerate? Too large for strategic acquisition, too operationally complex for public markets.

What Actually Works: The Orchestrator Model

What actually would work is a new entity designed for orchestration, not ownership.

Think Uber: doesn't own cars or manufacture GPS, but orchestrates the system and captures coordination value. Think Stripe: doesn't own banks, but orchestrates developer access to payment infrastructure.

The orchestrator's function:

• Ingests data from *any* Layer 1 source (sensor-agnostic)
• Runs optimisation models (proprietary or wrapping best APIs)
• Routes execution via Layer 3 partners (FM platforms, contractor networks)
• Feeds governance/ESG reporting automatically
• Provides verified performance data to finance
• Surfaces insights to occupants through existing workplace apps

Why This Becomes Viable Now

Five years ago, integration required armies of engineers building bespoke connectors. Today, three shifts change the economics:

1. LLM-Based Integration

LLMs interpret unstructured data - maintenance logs, sensor feeds in proprietary formats, PDF contracts, email complaints - and route information across systems without bespoke APIs.

Example: Predictive alert → LLM queries BMS history (any vendor format) → checks warranty terms (reads PDF) → identifies contractors → generates work order → routes to approval → updates ESG ledger → notifies occupants → logs for loan calculation.

Every step previously required dedicated integration. Now the LLM handles interpretation and routing. The integration complexity changes fundamentally.

2. API-First Modern PropTech

The 2015-2023 PropTech wave produced hundreds of API-first point solutions, unlike legacy systems. Modern ESG platforms expose RESTful APIs. Sensor networks use standard protocols. Even legacy systems now have third-party connectors.

3. Vertical AI Agents

Facilities maintenance and procurement involve multi-step workflows with conditional logic and exceptions. This previously required manual execution or brittle workflow engines.

Now AI agents manage these dynamically, adapting to context, interpreting policies, handling exceptions without explicit programming for every edge case.

The Business Model That Changes Everything

The orchestrator uses ‘Outcome-Based Pricing’.

I.e ”We reduce operational costs 15-25%. We take 30% of verified savings for five years."

This is radically different from SaaS subscriptions:

For landlords:

• Zero upfront cost (eliminates budget approval friction)
• Zero implementation risk (only pay if it works)
• Aligned incentives (orchestrator only profits from actual savings)

For the orchestrator:

• Captures value from ‘integration’ across layers
• Revenue scales with customer value, not seat count
• 18-24 month revenue lag (requires patient capital, creates moat once cash flows)

Why this was impossible before: Outcome-based pricing requires verified measurement, attribution clarity, and continuous monitoring - all require the integrated stack. Point solutions can't verify outcomes in isolation.