Where’s the New Business?

A framework for finding where AI creates genuinely new ventures - not just better processes

Over the past three months we’ve built a collection of frameworks for addressing how to leverage AI in CRE: RIRA for strategy, the CRE Automation Matrix for analysis, and the Prompting Framework for execution. Together they answer: how are we creating value, what kind of work is this, and how do we get it done?

But there’s a question they don’t force you to confront.

When participants work through RIRA’s ‘Imagine’ phase - with its three horizons of Efficiency (H1), Capability (H2), and Transformation (H3) - they almost universally gravitate towards H1 and H2. Faster lease abstracts. Better evidence-linked analysis. Smarter compliance checks. All valuable. All necessary.

But none of them are new businesses.

H1 makes your current work faster. H2 makes it better. But H3 builds something that doesn’t exist yet.

And almost nobody gets to H3, not because they lack ambition, but because H1 and H2 are natural extensions of work you already understand. You can picture your workflows done more efficiently. You can picture them done with better evidence. But picturing a product or service that serves a customer who doesn’t yet know they need it, that earns revenue from a budget line that doesn’t currently exist - that requires a fundamentally different kind of thinking. It’s uncomfortable. It implicates your own business model.

Which is exactly why you need a provocation tool to get there.

The H3 Provocation Framework
The H3 Provocation Framework is a set of five questions designed to surface where AI creates genuinely new businesses, products, or market structures in commercial real estate. It sits within RIRA’s Imagine phase as a dedicated instrument for Horizon 3 thinking. It doesn’t replace the “Faster taxis / Better taxis / Uber?” diagnostic, rather it provides the provocations that generate H3 hypotheses in the first place.

WHAT H3s LOOK LIKE IN CRE

Before I describe the five provocations, let me show you what they can produce. I’ve run the framework against four of the most common products and services in commercial real estate - diligence, advisory, occupancy, and portfolio management - to illustrate the kinds of hypotheses that emerge. These aren’t predictions. They’re outputs of a generative process, and the same process can be pointed at any activity across the CRE landscape.

Continuous Diligence.
Due diligence exists as an industry because maintaining a verified, current information state across hundreds of documents requires sustained human attention over weeks. That’s a cognitive and temporal constraint. If AI removes it - keeping an asset’s entire information state continuously current and verified - the question “shall we commission a diligence report?” stops making sense. You don’t commission what already exists. The value migrates from “who can do diligence well” (a cottage industry of lawyers, surveyors, and consultants charging £50–100k per transaction) to “who maintains the verified state.” That’s a data infrastructure play, not a professional services play. Transaction speed becomes a product. The seller offering a continuously-diligenced asset with verified evidence chains commands a premium because they’ve collapsed the buyer’s time and risk.

Evidence-Based Advisory.
AI is about to flood the market with plausible-sounding investment analysis. The supply of professional-looking memos will explode. What won’t explode is the supply of trustworthy analysis - recommendations backed by evidence chains, auditable reasoning, and explicit assumption registers. The value migrates from “trust me, I’m experienced” to “trust the evidence chain, and I’ll interpret what it means.” The human adviser’s role shifts from analyst-packager to interpreter of verified outputs and owner of the final judgement call - pure Quadrant D work (see The CRE Automation Matrix Framework for details). The advisory firm that builds verifiable decision support first doesn’t just have a better product - they’ve made every competitor’s narrative-based approach look unaccountable by comparison.

Occupier-as-a-Service.
What if occupiers didn’t buy space but bought outcomes - guaranteed workspace performance delivered against SLAs rather than lease terms? Air quality, temperature responsiveness, service levels - all continuously monitored, verified, and priced on delivery. The purist version breaks the institutional capital stack (lenders can’t underwrite an SLA the way they underwrite a 15-year FRI lease). But the realistic transition is a hybrid: a conventional lease providing the contracted income floor, with a verified performance premium on top. The hotel sector already proved this model - variable income becomes investable once you have enough verified performance data to make it predictable. PBSA and BTR are moving the same way. Over time, as the data layer matures and investors learn to underwrite operational capability rather than just tenant covenant, the proportions shift. Value migrates from “location plus specification” to “verified performance delivery.”

Portfolio Intelligence as Product.
Large portfolio owners sit on enormous operational data - tenant behaviour, maintenance patterns, energy consumption, lease events - used solely for internal management. If the intelligence derived from that data became a product - benchmarking services, predictive models, optimisation algorithms - you’ve created a revenue stream that doesn’t currently exist in CRE’s business model. The moat is proprietary data combined with verification infrastructure that’s hard to replicate. The REIT doesn’t just earn rental income - it earns knowledge income. And unlike rental income, knowledge products compound: the more data you accumulate, the better the models become, the more customers they attract.

The Common Pattern
Notice what these four share. None of them improve an existing workflow. They each create something that didn’t previously exist as a product or service. They each involve a value migration - a shift in where the premium concentrates. And they each emerged from asking a specific, slightly uncomfortable question about the current state of things.

I chose diligence, advisory, occupancy, and portfolio management because they’re familiar to almost everyone in the industry. But the framework isn’t limited to these. Point it at development appraisal, debt origination, tenant representation, facilities management, fund reporting - any activity where AI is about to change the underlying economics - and it will surface H3 candidates specific to that domain. The five provocations are a lens, not a list.

One more thing worth saying plainly: the timelines on these are uncertain. Continuous diligence may be five years away from mainstream adoption, or three, or seven. The point of the framework isn’t to predict when. It’s to discern the direction of travel - to see where value is migrating so you can start positioning now rather than reacting later. The firms that recognised flexible workspace was a structural shift, not a fad, had years of advantage over those that waited for proof. The same dynamic applies here, across a far wider set of CRE activities.

Those questions are the framework.

THE FIVE PROVOCATIONS

The five provocations follow a narrative arc:
What becomes free →
What falls apart →
What gets built →
Who wins →
Who pays.

Each builds on the last, and the sequence matters.

1. Constraint Collapse - “What becomes free - and whose business breaks?”
Every workflow has a binding constraint - not always cost. Sometimes it’s time, cognitive bandwidth, scale, or access. AI is about to remove some of these entirely. The question isn’t “what gets cheaper?” It’s “whose revenue depends on this constraint existing?” If the answer includes your firm, this is where you need to be paying attention. The continuous diligence hypothesis emerged directly from this question: the constraint wasn’t that diligence was expensive, it was that maintaining a continuously verified information state was operationally impossible regardless of how much you spent. Remove that constraint and the entire episodic diligence model becomes a solution to a problem that no longer exists.

2. The Unbundling - “What are you actually selling - and which part is about to become worthless?”
Every service you charge for is a bundle of components, and you’ve probably never itemised them because the bundle is just “what we do.” AI will replicate some of those components to a verifiable standard. The ones it can’t replicate are where your future premium concentrates. The evidence-based advisory hypothesis came from this: an investment advisory mandate bundles market knowledge, analytical packaging, relationship access, and strategic judgement into a single percentage fee. AI commoditises the first two. The question is whether clients keep paying the same fee for the last two - or start buying judgement separately, possibly from someone who was never an “advisor” before but who now owns the best evidence infrastructure.

3. The New Entrant - “If someone started from scratch today, would they build what you’ve got?”
A well-funded team with no legacy systems, no existing relationships, but full access to frontier AI enters your market. They don’t need to respect how things currently work. What do they build? Who do they sell to? And the question that should keep you up at night: why can’t you build it first? Usually the honest answer is “because our current business gets in the way” - and that’s exactly the answer that should worry you most. The occupier-as-a-service hypothesis is a new entrant question: if you were starting a property management business today, would you build what property management currently looks like? Or would you build an AI-orchestrated operating platform with continuous monitoring, predictive maintenance, and verified performance delivery - that happens to manage buildings?

4. The Control Point - “After this shift, whose signature still matters?”
Value in CRE concentrates around accountability - whoever signs the recommendation, the valuation, the approval. Their signature carries weight because they’re standing behind judgements that can’t easily be verified any other way. When AI changes what can be verified and evidenced, that signature may carry less weight - or shift to someone else entirely. Whoever holds the accountability after the migration holds the value. Consider: a Red Book valuation requires a RICS-qualified surveyor’s signature because comparable selection, adjustment logic, and market judgement need a professional to stand behind them. If AI produces the analysis with full evidence chains, auditable reasoning, and verified comparables, the surveyor’s role shifts from “produce the valuation” to “validate the machine’s output.” That’s a different job, with different economics - and it may not need the same provider.

5. The Customer - “Who has this problem right now - and what are they currently paying to solve it badly?”
The most transformative new businesses don’t create demand from nothing. They serve demand that’s already there but currently met by expensive, slow, or inadequate solutions. The test isn’t “would someone hypothetically pay for this?” It’s “who’s already spending money or losing money because this doesn’t exist yet?” Every institutional buyer who has ever lost a competitive bid because their diligence took two weeks longer than the other side’s is paying the cost of the continuous diligence gap - they’re just paying it in lost deals rather than invoices. Every tenant who signed a lease based on a glossy brochure and discovered the building doesn’t perform is paying the cost of the missing performance verification layer. The customer already exists. They just don’t know yet that what you’re building is the solution.

HOW TO USE THIS

Work through the five provocations in sequence after a standard RIRA pass has produced its H1 and H2 outputs for a specific workflow. Those outputs become the foundation - you’re pushing further, not replacing them. Give each question time to breathe. The uncomfortable answers - the ones that implicate your own firm’s business model - are the ones worth pursuing.

Then classify what emerges. Using AI to produce lease abstracts faster is an H1. Building a lease abstraction system with evidence chains as a firm standard is an H2. Creating a continuously verified portfolio information state that becomes a product you sell to buyers, lenders, and insurers - that’s an H3. The distinction matters because H2s improve your current business while H3s build a new one. Both are valuable. Confusing them leads to underinvestment in the H3 and over-claiming on the H2.

Most ideas will turn out to be H2s. That’s fine. That’s expected. The framework has teeth precisely because it filters honestly. But the one or two ideas that survive all five provocations and still look like genuine H3s - those are worth serious investment of time and thought.

THE COMPLETE STACK

This completes RIRA. The framework has always had three horizons in its Imagine phase, but until now H3 has been a diagnostic label - something you aspire to - rather than a generative method. The five provocations give it teeth. They turn “are we building Uber or a faster taxi?” from a question you ask at the end into a structured process you work through to find out.

The stack remains three frameworks, each answering a different question. RIRA asks how we’re creating value - and now has the tools to push that question all the way to transformation, not just efficiency and capability. The Automation Matrix asks what kind of work this is and how it should be automated. The Prompting Framework asks how we actually get work done. Strategy, analysis, execution.

If your AI strategy starts and ends with doing current work faster, you’re playing an H1 game that everybody else will also be playing within two years. The firms that engineered verifiability into their cognitive work will be pulling ahead. And the firms that ran these provocations honestly, saw the value migration early, and started building - they’ll be playing a different game entirely.

The provocations don’t tell you which game to play. They help you see the games that are available.

Run the five provocations on your most profitable workflow - and note which answer makes you uncomfortable.