THE BLOG
10 Things That Matter In The Future Of Real Estate
The shift from incremental digital change to systemic AI-driven transformation
The real estate industry is moving:
From real estate as product to real estate as service
From design-intent to operational evidence
From human OR machine to human AND machine
From satisfying needs to creating desire
From providing space to enabling performance
In early 2022 I wrote a presentation, ‘10 Things that matter in the future of real estate’. Later that year ChatGPT was released, and since then AI has been the talk of the town. I’m revisiting my ’10 Things’ to see if anything has fundamentally changed.
The answer is yes and no. Some themes have been turbocharged, whilst others have been given new ‘superpowers’. In essence the shift is that today it’s less about incremental digital change and more about systemic AI-driven transformation.
The 10 Shifts at a Glance
Sustainability → Transparent, measured performance
Smart → Assurable automation
Flexibility → Adaptable + anti-fragile assets
Human + Machine → Human + Agent synergy
Productivity → Multiplication + superteams
Wellbeing → Cognitive comfort
Skills → AI fluency + judgement
Brand → Hyper-personalisation
Networks → Ecosystem orchestration
Innovation → Building a bigger economic pie
These shifts are interconnected: operational transparency enables productivity measurement; cognitive infrastructure enables human+AI performance; service delivery models capture the resulting value. Read them as a system, not a checklist.
Let’s look at these through three lenses.
PART I: THE MEASURABLE PERFORMANCE IMPERATIVE
(Why traditional real estate metrics no longer capture value)
The following three themes represent the shift from design-intent to operational evidence. They determine whether your asset survives the decade. Everything that follows determines whether it thrives.
1. Sustainability (2022) → Sustainability & Transparent Performance
Three years on from citing ‘Sustainability’ as a key theme it has become clear that green-washing got out of hand. So much virtue signalling about doing the right thing got caught out for being just that - virtue signalling. With no real heft behind it. This unfortunately gave the culture wars warriors liberty to decry the whole sustainability movement and as we’ve all seen, being ‘green’ is seen by many as a vice rather than virtue. As a result many corporate leaders have now sought to frame being unbothered by sustainability as the new reality.
But reality is reality and sustainability cannot be ignored. What is now needed is less interest in design-intent - the expected performance of a building based on its design assumptions, models and specifications, before it is occupied or operated - and a much greater focus on operational reality. We know this building ‘should’ perform in X manner - but does it? The Better Buildings Partnership has written about how ‘actual energy performance has no correlation with the EPC ratings of office buildings’
We need more NABERS style assessments:
Where you don’t get credit for promises; only for proven performance.
Where certifications shift from “predicted” to “measured”.
Where valuation models shift from EPCs to kWh/m², CO₂/m² etc
Where smart building claims must be auditable.
Where sustainability claims must be meter-verified.
Where adaptability must be demonstrated in practice, not just stated in design briefs.
And we need to shift capital and due diligence in this direction, and plan for pricing performance uncertainty at 10-20% discounts, whilst rewarding proven operational excellence. I.e we need to put our money where our mouth is. Prove it or suffer the consequences.
2. Smart (2022) → Assurable Automation (‘The New Smart Building')
The same applies to the notion of ‘Smart’. The value premium needs to shift from "having sensors" to "having auditable, cyber-secure systems that prove they deliver stated outcomes.”
We must make auditable, integrated building data platforms the new table stakes for institutional financing, treating unverifiable performance claims like missing financial statements.
Imagine a building with fully integrated, auditable data systems: every sensor, every system, every performance metric available for instant verification. Due diligence that currently takes weeks assembling data of questionable provenance becomes immediate access to cryptographically verified operational history. This is what 'assurable automation' means: not just sensors, but transparent, tamper-proof performance records that buildings can provide on demand. That's what we really need.
3. Flexibility + Resilience (2022) → Flexibility + Adaptability + Anti-Fragile
The requirement for real estate to be flexible and resilient was clear in 2022, but now, in 2025, we seem to be in a world where "No one knows what happens next." So our buildings face speedy obsolescence if they are designed for single-use and/or with limited flexibility. Adaptability is now a measurable financial asset.
So we need to explicitly value and design for structural and systems adaptability, and perhaps accept that 5-8% of additional capex will be required to protect against far greater drops in value if the future pans out in unexpected ways, which is almost inevitable.
Nassim Taleb coined the phrase ‘anti-fragile’ and it means system that becomes stronger when exposed to volatility, shocks or stressors. Not resilient, not robust, but improved by stress.
In real estate this means changing our thinking from stable, predictable, single-use and optimised for the average case, to being structurally, operationally, and economically designed to adapt. To being real estate that becomes more valuable when the world is unpredictable. So we need buildings that can be reconfigured quickly and cheaply when needs change. Think long spans, loose-fit floor plates, modular services, over-specified power & risers, demountable partitions, accessible infrastructure. Volatility becomes an opportunity (new uses, new layouts, new tenant segments), rather than a threat.
PART II: THE HUMAN PERFORMANCE MULTIPLIER
(How AI reshapes work and what buildings must enable)
These four themes address the fundamental question: what happens inside your building that couldn't happen elsewhere? As work becomes location-independent, only buildings that demonstrably enhance human+AI performance justify premium pricing.
4. Human + Machine (2022) → Human + Agent + AI Synergy
Three years in to the generative AI revolution, work is restructuring around human+AI teams. In 2022 we spoke a lot about humans and machines but mostly as separate, distinct entities. Today it’s become clearer that we’re moving to a world where humans and machines are going to work together much more holistically, more as cyborgs than centaurs. We’re entering the ‘Agent Boss’ era where each of us will have a series of virtual ‘agents’ to curate and manage.
In this world, cognitive support infrastructure becomes as important as physical infrastructure.
What does this mean? Think of it like this;
Physical infrastructure supports bodies.
Cognitive infrastructure supports minds.
Historically, offices have been optimised for desks, lighting, HVAC, lifts, meeting rooms and amenities. But in an AI-native world, what differentiates space is not its physical provision, but how well it enables thinking, decision-making, collaboration and AI-augmented workflows. This is what “cognitive support infrastructure” refers to: the spaces, systems and conditions that enhance human + AI performance.
So a building must be able to sustain exceptional environmental conditions (IAQ, high-performance acoustics, circadian lighting etc) AND provide digital/AI native infrastructure. Meaning guaranteed high-speed bandwidth, low latency, seamless device switching, local compute or edge environments for sensitive models, secure data access to building systems, integration with the BMS, tenant platforms and digital twins.
Companies ready, willing and able to pay premium rents in the future will be AI-native, so our buildings need to become the physical base-layer for these organisation’s AI operating systems.
5. Productivity (2022) → Productivity Multiplication + Superteams
Productivity is an area that has, under the radar, exploded since 2022. AI is not yet showing up in national productivity figures but we are seeing many examples of early adopters achieving very significant individual productivity gains. GitHub reports 55% faster completion rates for developers using Copilot; legal document review has compressed 70-80% in early adopter firms; customer service automation shows 40-60% capacity gains at scale.
It is hard to think of unchallengeable reasons why this won’t spread more widely amongst ‘knowledge workers’.
In the same vein, recent research from Eric Brynjolfsson, Stanford Professor, suggests that entry-level knowledge work, the tasks most exposed to AI automation, is seeing measurably slower hiring growth. While macroeconomic factors complicate attribution, the pattern aligns with early displacement hypotheses.
This gives one a steer as to where all this is going. Towards two potential real estate related scenarios:
(1) Companies need radically less space,
or
(2) Companies achieve same goals with smaller teams.
Both reduce office demand.
Whilst it does seem clear that premium space for high-performers remains valuable, and in much demand, it is undoubtedly the case that for a given level of output companies will be requiring fewer employees.
Which means we should prepare for a 15-25% demand reduction in high-exposure sectors, by repositioning from space provision to productivity enhancement, developing outcome-based pricing models that charge for performance rather than area.
Selling by the square foot is going to undervalue the very best space.
6. Health + Wellbeing (2022) → Wellness + Cognitive Comfort
The above means that we need to double down on ‘Health + Wellbeing’. We’ve known for a long time that indoor environmental quality directly affects cognitive performance, but historically that hasn’t been something most occupiers were all that bothered about. But in a much more aggressively AI mediated business world ‘cognitive comfort’ is likely to represent a key factor in a businesses success, because it affects employees output much more directly than it has to date. So buildings that measurably improve focus, decision-making, and mental stamina justify premium pricing. This is no longer amenity; it's core value proposition.
7. Skills (2022) → AI Fluency + Judgment
All of which means that the focus we had in 2022 on ‘Skills’ was correct but needs tweaking in light of the rise of AI.
The real estate industry faces a skills crisis. Traditional "real estate + spreadsheets" expertise is insufficient, not least of all because much of this work will be commoditised, if not entirely automated. Going forward we need people with very strong human skills, exceptional critical thinking, an ability to work through and solve complex, novel problems, and know enough about data science to be able to create and curate the aforementioned ‘Agents’ we’ll be working with.
This requires both workforce development, training existing staff in AI-augmented workflows, and non-traditional hiring from adjacent sectors. Firms without AI-native capabilities face competitive stress within 2-3 years as early adopters achieve productivity advantages that compound over time. The talent war is shifting from 'real estate expertise' to 'real estate expertise + AI fluency + design thinking.' Firms slow to recognise this will find themselves unable to compete for either talent or mandates.
If buildings can prove performance (Part I) and enable human+AI productivity (Part II), how do they capture this value? Through service delivery models that shift from product to platform.
PART III: THE SERVICE DELIVERY MODEL
(How real estate competes when space is optional)
These three themes represent the shift from real estate as product to real estate as service. They determine customer lifetime value, which now matters more than lease length.
8. Branding / Experience (2022) → Hyper-Personalisation + Human-Centric Experience
The idea of Brand being something worth getting stuck into was relatively new in 2022. For much of my career the mantra was always ‘you cannot brand real estate’. Which always struck me as bizarre, but was pretty much taken as gospel within the industry. The rise of WeWork (irrespective of the eventual outcome) and then Covid turbo-charged remote and hybrid working finally put a nail in this coffin. Of course you could Brand real estate - and branded real estate was what people actually wanted. ‘Flight to quality’ is all about Brand.
The difference today, as a result of the capabilities of Generative AI is that branding can be hyper personalised and spaces can be customised for individuals in far more granular ways than was previously even considered. In fact as AI increasingly handles routine work, authentic human connection becomes the premium offering - what I've called #HumanIsTheNewLuxury: the irreducible value of being in the room when creativity, judgment, and serendipity matter most. Real estate companies competing on experience differentiation, not location or specification, might well be the new normal.
So we need to be allocating far more budget to curation, programming and hospitality capability that creates defensible brand differentiation.
9. Relationships, Networks, Ecosystems (2022) → Ecosystem Orchestration + Community
It was clear in 2022 that each of us working in real estate had to work hard on building relationships, networks, and an ecosystem of other people and companies we could work with to deliver the full range of products and services our customers were demanding. In 2025 and beyond this trend is widening and becoming deeper.
We will increasingly be looking for the real estate we work in, or visit, to become part of this process. As the requirement to actually be in an office five days a week becomes ever less relevant we want more out of it when we are. Who else is here, what services can be accessed, what opportunities emerge from proximity to others? In this era space activation becomes more like space orchestration.
The building, and those operating it, need to understand who they are trying to attract and how they might assist in enabling them to do things they could not do elsewhere. It’s the idea of ‘Real estate as Maven’ - the space becomes an enabler of connection. The building becomes a matchmaker.
So significant budget needs to go towards facilitating shared events, and building opportunities to actively bring people together. What are people interested in, what are their motivations, what do they need to know and who do they need to meet.
It feels like a massive ‘brand extension’ for real estate but then real estate needs to make people want to be there. They no longer need to.
10. Innovation (2022) → Innovation + ‘Building a Bigger Pie’
Finally, the need for innovation, rightly considered a key aim in 2022, has become massively more important. As mentioned earlier, for a given level of output it is a certainty that businesses will require fewer people. So we absolutely do need to be actively working towards ‘building a bigger pie’.
Without innovation and the creation of new products and services we will reach a pretty dark place, with a lot of people under or unemployed. Incremental improvement is insufficient. Industry needs genuine innovation to create new value (not just capture existing value). We need to build!
AI is a bug and a feature in all of this. It might not eradicate many jobs entirely, but it will mean every job becomes reconfigured and each individual will be able to do far more. So jobs will go. On the other hand, each of us is being given intellectual firepower beyond our wildest dreams. The cost of intelligence is trending towards zero, and this is giving all of us, potentially, superpowers. We have unprecedented tools at our disposal to innovate. Talk of single person billion dollar companies might be over-egging it but we definitely have access to capabilities that enable us to punch way above our weight.
The downside though is that we MUST punch way above our weight. The only way forward for societies in an AI mediated world is to double down on innovation and invent the world as we want it to be. Otherwise we will have a world imposed on us that we really might not like.
CONCLUSION
Three years on, the fundamentals haven't changed, but the velocity has accelerated beyond what most of the industry has internalised.
The ten themes aren't discrete trends; they form an interconnected system. Buildings that cannot prove operational performance won't survive long enough to compete on productivity enhancement. Buildings that enable human+AI performance but price by area will see tenants capture all value gains. The service delivery model only works if underpinned by measurable infrastructure.
The timeline is compressed: Operational transparency becomes table stakes by 2027. Productivity-driven space compression materialises by 2030 - 15-25% demand reduction in high-exposure sectors. Outcome-based pricing models emerge for trophy assets whilst commodity stock faces structural obsolescence.
The uncertainty is substantial, but the directionality is clear enough to warrant defensive positioning now.
The industry faces a choice: Develop the operational sophistication to capture value in an AI-transformed world, or compete as commodity providers in a shrinking market. The organisations building measurement infrastructure, outcome-based pricing models, and genuine innovation capabilities today are positioning for defensible advantage tomorrow.
These ten things matter because they map the territory between today's industry structure and tomorrow's market reality.
The decade of reckoning has begun. Position accordingly.
OVER TO YOU
Start by auditing three things: how you measure performance, how your buildings enable human+AI work, and how your business model captures the value you create.
Beds, Sheds, Bytes - And The Future Of The UK
What the future of the real estate industry tells us about our own futures
CBRE Investment Management have just released a paper with an innocuous title and a quietly explosive implication: U.K. Real Estate in 2040: The Rise of Beds and Technology.
On the surface it’s a sector allocation piece: how the UK real estate ‘investment universe’ is likely to evolve over the next 15 years.
Underneath, it’s something else entirely.
If it’s even approximately right, it’s telling us what kind of country the UK is on track to become:
A ‘beds and infrastructure’ economy, where the dominant asset class is housing and social infrastructure, supported by logistics sheds, with a ‘small but important tech layer’ (data centres and life sciences), and a ‘structurally downgraded role’ for traditional offices and retail.
More than just a capital markets footnote, it’s a sketch of our long-term economic model. And it makes me very worried.
Let’s start with the structural shifts in the real estate universe, and then zoom out to what they imply for the broader economy – and for the choices we face.
A Slow Re-wiring
What the report actually says: the slow re-wiring of the UK investment universe.
CBRE IM start from the IPF’s 2023 estimates of the UK real estate universe, then run six scenarios out to 2040. They flex assumptions about:
Net addition of floorspace
Growth in value of stock
The share of each sector that sits in the investment universe vs owner-occupied or privately held.
Here’s the headline picture by 2040 (base case and scenario ranges):
From “urban core” to “beds-led” universe
Today’s universe is still dominated by the urban legacy:
Urban (offices + retail): ~ 49% of the investment universe
Industrial (“sheds”): ~ 34%
Residential (“beds”): ~ 16%
Tech (data centres + life sciences): ~ 1%
By 2040, across all scenarios:
Beds become the largest theme:
From 16% today to 32–54% of the universe
46% in the base case
Sheds shrink in share, but not in value:
From 34% today to 21–30% in 2040
Total value of industrial stock actually grows by ~ 47% - just more slowly than other sectors
Urban (offices + retail) halves in share:
From 49% today to 21–30% in 2040
Versus over 80% pre-GFC
Tech grows fastest but remains the smallest:
From ~ 1% today to 4–18% in 2040
5% in the base case; up to 12% in their “strong tech + lower resi penetration” scenario, and 18% in a “turbo tech” world
Two important details:
There is no scenario where beds are not the largest theme.
There is no scenario where tech is not the smallest.
Enormous Growth Potential in Residential
CBRE are very explicit about the drivers:
For beds, the constraint is investor appetite, not underlying demand.
Residential is an enormous underlying stock; only a small slice is currently institutional.
Today, they estimate only 1.5% of affordable and 4% of private rental stock is in the investment universe. But by 2040, they see ranges of 3.8–11.3% (affordable) and 7.5–22.5% (private rental).
Tech Needs Power!
For tech, the constraint is physical capacity - especially grid and water:
They assume 10–20% p.a. floorspace growth for data centres and 5–10% p.a. for life sciences, but note that power and planning constraints will decide where we land in those bands.
Overseas capital passes 50%
Finally, they project the ownership of the universe:
Overseas ownership has already moved from 15% (2003) → 25% (2013) → 40% (2023). They expect it to pass 50% around 2038.
A note on overseas ownership: The projected majority overseas ownership by 2038 is politically incendiary whatever its economic merits. Rationally, capital origin doesn't matter—Canadian pension funds and British insurers pursue identical strategies; regulatory architecture determines outcomes, not ownership nationality (see Singapore, Netherlands).
But politics isn't always rational. "Foreign landlords" triggers reflexes that constrain policy options even when the economic critique applies equally to domestic institutions. This makes Autopilot UK harder to defend politically whilst making Rewired UK's regulatory framework more urgent—it must be built before ownership shifts, not retrofitted during a backlash.
We need institutional capital at scale; origin is economically irrelevant. The question is whether we establish strong regulatory frameworks whilst we have political latitude, or wait until ownership patterns trigger nationalist politics that foreclose more sophisticated approaches.
Setting that political complexity aside for a moment, what does the base case actually project?
By 2040…
So by 2040, in their base case, we have:
A beds-dominated universe (46%),
Followed by sheds (25%) and urban (24%),
With a small tech theme (5%),
And a majority owned by overseas capital.
On its own terms, that’s a reasonable, internally consistent forecast.
But it clearly isn’t just a real estate story.
Whither the UK Economy?
Narrow your eyes and this is just sector rotation. Open them and it’s a sketch of the UK’s future economy. Read these numbers as if they were a macro scenarios document, not an allocation note.
You get something like this:
The UK becomes a ‘beds, meds and bytes’ economy:
Investing heavily in housing and social infrastructure (beds),
Keeping a strong base of logistics and industrial (sheds),
Building a niche but constrained tech infra layer (data centres and labs).
The historic role of CBD offices and high street retail as the capital market core never returns.
Urban’s share halves and stays there.
A large chunk of the built environment, especially mid-quality offices and secondary retail, looks destined for economic obsolescence unless something radical is done.
The whole system is increasingly owned by overseas capital, collecting rent on life’s essentials.
That equilibrium has characteristics:
Strong, defensive income streams for investors,
A real estate industry that wins by serving needs, not wants - housing, health, student beds, infra, logistics,
A consumption-led, low productivity economic model where a lot of value is captured through rents on non-tradable necessities.
In other words: you can absolutely read this as the architecture of a mature rentier economy.
And for the industry, that’s… not necessarily bad.
THE LOW ROAD
Autopilot UK: the easy path to a rentier future
Let’s be blunt.
If we do nothing more than let today’s incentives run, the CBRE picture is the Autopilot outcome:
Beds institutionalise
Housing, affordable, PBSA, senior living, healthcare become the main ballast of institutional portfolios.
The sector delivers exactly what investors want: long income, low volatility, demand that doesn’t go away in a downturn.
Sheds support consumption:
Logistics and industrial space primarily serve e-commerce, parcel delivery, and supply chains for imported goods. They are productive in an operational sense, but mainly as a distribution layer for a consumption-heavy economy.
Tech stays infra-only
Data centres and labs grow, but are treated as yielding boxes with power and cooling, not as anchors for new tech ecosystems. Our ability to host compute capacity is capped by the grid; our ambition for what to build on top of it remains limited.
Urban obsolescence accumulates quietly
Prime offices are refurbished, amenitised, and re-rated. A transitional band of stock is nursed along with capex and ESG upgrades. A large tail of 1980s–2000s offices in secondary locations slowly grind down in value, but rarely get a clean reset. And the same for secondary and tertiary retail.
From a real estate industry standpoint, this might look very attractive:
You end up holding assets people need, not assets you have to persuade them to want. Demand for beds, care, and basic infra is deeply inelastic, and you have repeatable, scalable platforms that match institutional mandates perfectly.
The losers are:
Owners of obsolete space, especially leveraged office and retail holders who will, eventually, be left “holding the baby”,
Places whose historic office/retail cores no longer have a clear purpose,
And, ultimately, households and workers, who see a rising share of their income channelled into rent and basic services, with limited productivity upside.
Layer on the foreign ownership point, and Autopilot UK looks like this:
A country where the dominant investment play is to securitise housing, care and infrastructure; a growing portion of the associated income flows offshore; and much of the remaining commercial stock decays in place.
That is not a forecast of collapse. It’s worse in some ways: a forecast of managed stagnation.
THE HIGH ROAD
There is another route: using the same assets as a modernisation programme.
None of the above is inevitable.
The really interesting thing about CBRE’s work is that the asset mix itself doesn’t have to change for the story to be different.
You can still have:
Beds at 32–54% of the universe,
Sheds and urban sharing 40–50% between them,
Tech at 4–18%,
Overseas owning more than half the lot,
…and yet end up with a radically different economic and social outcome.
Rewired UK
Call this alternative Rewired UK.
Rewired UK: same pie chart, different country
In Rewired UK, we use the “beds + sheds + tech + stranded offices” mix as a national modernisation programme:
Beds as social infrastructure, not just rent streams
Institutional capital still flows into PRS, affordable, PBSA, senior and healthcare. Beds are still the biggest theme. But those platforms are structured and regulated as social infrastructure:
Stable, predictable rent regimes;
Strong quality and energy standards;
Long-term, mission-driven operating partners: linkage to health, education, and labour-market outcomes.
The same capital that would otherwise fuel a pure rentier model is now paying for decency, efficiency and resilience in the living fabric of the country.
Sheds as a re-industrialisation platform
Logistics and industrial are no longer just “e-commerce plumbing”. Selected corridors and hubs are actively positioned as advanced manufacturing + logistics ecosystems: For robotics and heavy automation, connected to ports and energy assets, and tied into skills programmes and R&D.
The same 21–30% allocation to sheds becomes a platform for tradable production, not just parcel throughput.
Tech as anchor, not footnote
Data centres and labs are treated as anchors for innovation districts, not isolated boxes:
Co-located with AI and cloud engineering teams,
Adjacent to universities and FE colleges,
Surrounded by flexible offices, labs and maker space.
Grid upgrades and planning consents are conditional on local ecosystem commitments, not just rent rolls.
Stranded offices as a national retrofitting and repurposing fund
Instead of leaving obsolete offices to quietly depreciate, we treat them as raw material:
Some become housing, care, PBSA;
Some become creative and startup space;
Some become civic, health or education hubs;
Some are demolished to fix bad street grids and open up new mixed-use plots.
AI, modular construction and standardised pattern books could dramatically lower the transaction and design costs of these transformations and is something the industry is barely beginning to exploit.
Exactly the same asset themes - beds, sheds, tech, urban - suddenly support a very different national story:
Higher productivity, because the space we provide is a complement to high-value work, not a drag on it.
Better social outcomes, because housing and care are treated as infrastructure, not pure yield.
Healthier regional economies, because stranded stock is repurposed deliberately, not left to rot.
The difference is not the content in the CBRE spreadsheet. It is what we choose to do with it.
This really is a choice – and doing nothing IS a decision
What bothers me about the CBRE report is not the analysis itself. On its own terms, it’s thoughtful, careful, and methodologically transparent.
What bothers me is how easy it would be for the industry and the policy world to treat it as ‘just how things are’:
To optimise portfolios for the ‘Autopilot UK’ scenario,
To quietly accept that a residential-dominated, foreign-owned universe is an unalloyed good for the sector,
To wave away the obsolescence problem as someone else’s write-down, some time later.
But that isn’t a neutral stance. It’s a choice.
We are, implicitly, choosing between two futures built on the same basic building blocks:
Autopilot UK
Real estate as a machine for turning housing, care and infra scarcity into income streams,
A slow-motion write-off of obsolete offices and retail,
A consumption-heavy, low-productivity economy,
A majority of “life infrastructure” owned by overseas capital.
Rewired UK
Real estate as the backbone of a social and industrial modernisation programme,
Systematic retrofit and repurposing of stranded stock,
Housing, health, logistics and tech infra treated as platforms for human capital and productive firms,
A robust, investible universe that still works for its ultimate users.
The CBRE report doesn’t, and can’t, tell us which of those futures we should pick.
But it does something valuable: it removes the illusion that we are dealing in short-term market noise. The composition of the investment universe is slow-moving, path-dependent, and incredibly hard to reverse once set.
Which means the next decade is not just about:
“Do we like beds and sheds more than offices?”
or
“How many data centres can the grid handle?”
It is about what kind of country those bets add up to.
CONCLUSION
The unsettling conclusion is this:
Sleepwalking into a rentier future is not a forecast. It’s a policy, just not one we’ve admitted to.
And the hopeful one is:
We still have time to decide that we want something else – and to use the very same “beds + sheds + tech + stranded offices” mix to build it.
This is a choice WE, as a society, and very specifically, as a real estate industry, have to make. And the window for making it with full political latitude, before ownership patterns and asset lock-in constrain our options, is narrower than we think.
Human+Machine Organisational Architecture
A Framework for Sustaining Competitive Advantage in the Age of Capable AI
I’d like to propose a new organisational architecture for knowledge-intensive firms operating in an environment where artificial intelligence can execute most routine cognitive work at near-zero marginal cost. It’s a framework which addresses a critical paradox: AI automation creates immediate productivity gains but threatens long-term organisational capability by eliminating the traditional talent development pathway.
This introduces a framework I'll develop across several newsletters. We urgently need this, or something like it. How we run ‘knowledge’ companies IS going to be profoundly reshaped by the abundance of cheap intelligence AI will deliver us. We cannot go on as we are, and we absolutely must avoid becoming mere ‘slaves to the machine’. We need something better: I’d like to think what follows outlines what is possible, should we wish to take up the challenge.
THE GOAL
The foundational spirit of this organisational transformation is to treat AI as infrastructure for human capability development, and not merely a tool to reduce labour costs. Its purpose is to automate routine cognitive execution, so humans can direct their cognitive powers toward high-judgement work, creativity, design intelligence, and strategic thinking.
If the end point is not humans operating at a level above where they are today, doing work that did not exist before, and creating dramatically more productive companies, then it will have failed.
THE BROKEN PYRAMID
Efficiency Alone Leads to Collapse
Traditionally knowledge-intensive organisations have relied on a Pyramid Structure: a large base of junior staff executing routine work, an experienced middle layer, and a small top layer providing strategic judgment.
This structure served a dual function. First ‘Economic', where inexpensive junior labour effectively subsidised expensive senior expertise. And secondly ‘Developmental’ where juniors would learn by doing over a period of 8-12 years.
Capable AI fundamentally breaks this model by eliminating the economic justification for junior roles. Let’s look at this through the lens of a 30 person CRE investment company (or division).
AI can now perform tasks like data extraction and synthesis, first-draft document creation, and quantitative modelling at a cost of £2–10K annually, vastly undercutting the traditional junior analyst cost of £50–70K. So who needs juniors?
Delayed Catastrophe
This though sets us up for a paradoxical failure: The obvious optimisation (replacing juniors with AI) creates a delayed catastrophe: By removing said juniors and replacing them with AI, productivity surges in years 0–3, but after that we start to see a hidden erosion - no junior cohorts developing expertise. By years 8–12, a capability crisis hits as senior talent retires without qualified internal replacements. Feast then famine. Fine if you’re in the generation feasting, not so great for everyone else.
The Strategic Response
So we need a strategic response. If economically we don’t need, and benefit from, not having to employ juniors, but this in turn eventually kills us, maybe we need to be thinking of a better alternative.
A quick caveat here: many companies will luxuriate in dumping employees over the next few years. Because most of the C-Suite isn’t that bothered about what happens a decade out; their bonuses depend on results in the here and now. Shareholders might want to think hard about realigning incentives for this new actuality. And employees would do well to understand the time horizons of their bosses, and act accordingly.
Many is not all though, and this framework is for those types.
The Core Hypothesis
Here is the core hypothesis; organisations that deliberately design their operating model for human+machine collaboration, rather than substitution, will achieve 2–3x productivity improvements and 50% faster talent development (4–6 years vs 8–10).
These companies will have a new objective.
The traditional model focused on humans doing routine work while capability development was a side effect (learn by doing over time); the new model focuses on capability development as the primary objective, using AI to handle routine execution.
And this will require three fundamental shifts:
1. From execution to judgment: Junior roles need to shift from being about completing tasks, to supervised capability development. The role is no longer about ‘sucking up the grunt work’ but being rapidly developing talent. We’re trying to crack Bloom's 2 sigma problem: the educational phenomenon whereby the average student tutored one-to-one using mastery learning techniques can perform two standard deviations better than students educated in a classroom environment. We’re just substituting a place of work for the classroom.
2. From tacit to explicit: Senior expertise must become externalised organisational knowledge. The organisation needs to become the learning ‘organism’, collectively, and for the benefit of all. And suppliers of tacit knowledge need to be encouraged, and compensated, for spreading it around.
3. From time-based to competency-based progression: Advancement is driven by demonstrated capability, not tenure. It should no longer be a function of how long you’ve been in a job representing your career progression. If you’re good enough, you’re good enough.
A Three Layer ARCHITECTURE
The ‘Three-Layer Architecture’ proposed here very deliberately inverts the traditional pyramid model to focus on building and protecting human judgment and creativity.
Layer 1: Execution Engine (AI-Native): This layer automates systematisable, low-learning-value work (e.g., routine data analysis, compliance checking). It must be transparent, showing reasoning to preserve learning opportunities. That which is ‘structured, repeatable, predictable’ should be automated. But it is still important for the ‘Humans’ to understand what is being done.
Layer 2: Judgment Development (Human-Centric): Humans at all levels focus on non-routine work: strategic decision-making, creative problem-solving, quality assessment, and identifying edge cases where automation fails. The critical difference here (from a traditional model) is that everyone focuses on non-routine work. Juniors aren’t corralled into only dealing with ‘grunt’ work - from the start they are pushing their human-centric capabilities.
Layer 3: System Stewardship (Human+Machine): This is the meta-layer where humans design AI workflows, externalise expert knowledge, and continuously improve the system itself. All these systems are going to be iterative. The initial design, the creation, requires strong technical and domain-specific knowledge, but curation is going to be a major ongoing feature of work. Part of the point is that human+machine is a creative modality, not a write once then leave way of thinking. Competitive advantage will accrue through creation, but last through curation.
Redefining Organisational Roles
New Talent Structure: From Analyst to Learner and Architect
This organisational architecture will require new roles. We’re looking through the lens of an ‘Investor’ but variations on this can be developed for any type of knowledge work.
The New Roles
Resident Learners (Years 0–2): This role replaces traditional junior analysts. They will validate and review AI outputs (developing quality judgment) and practice judgment in simulations, focusing on documenting patterns and edge cases rather than routine data processing.
This is personalised learning at work: The AI will be doing the processing, but the humans will be learning how to recognise good from bad, and building their critical thinking capabilities. By also working with ‘simulations’, they will be exposed to a far greater variety of deals/problems/processes than is traditionally the case.
Critically the aim is that they will progress significantly faster, potentially reaching the next stage in just 3–4 years.
Autonomous Investors (Years 2–5): These will replace traditional associates. They will execute complex transactions, make independent decisions within limits, and mentor Resident Learners (teaching solidifies expertise and provides an extra flywheel for knowledge accumulation). All their cognitive powers are focussed on human-centric strengths. Being the ‘human in the loop’ is their purpose.
System Architects (Years 5–8): This new discipline doesn't traditionally exist. They are half knowledge engineer, half domain expert, focused on designing and refining AI workflows and capturing senior expertise into reusable frameworks, multiplying the organisation's effectiveness.
Strategic Leaders (Years 8+): Their work shifts from execution oversight to teaching, knowledge externalisation, portfolio strategy, and genuinely strategic problem-solving.
The Creative Dividend and Strategic Advantage
The New Moat: Institutional Intelligence and Imagination
The ultimate output of this architecture is that each layer of automation must return usable cognitive capacity to humans and generate a creative dividend.
Sustainable outperformance requires combining disciplined allocation of capital with distinctive creative capability (taste, imagination, narrative).
Key advantages are that this framework ensures sustained competitive advantage through institutional knowledge capture (less dependent on individuals) and greater resilience. The economic model will provides higher margins and faster growth because of AI augmentation and a reliable, accelerated talent pipeline. Talent density will increase, and that will spur far greater momentum than is traditionally seen. This is a commercial learning machine with a very human core.
Conclusion
A Hypothesis for Transformation
To reiterate: this transformation is critical because the alternative (short-term efficiency optimisation) will/would inevitably lead to a capability crisis.
Technology, paradoxically, is going to be the easy part; the transformation requires leadership conviction, patient capital, and cultural change.
In future newsletters we will cover the detailed Capability Development System (simulation and mentorship) and the Transition Roadmap.
An analogy to finish with
Think of the traditional knowledge firm as a clockmaker’s workshop: apprentices start by polishing gears (routine work) for years, slowly learning the art of clock assembly (judgment) from the master. When AI arrives, it can polish every gear instantly and perfectly. If the workshop eliminates the polishing job, it loses the training pathway, and future generations never learn how to assemble a clock.
The Human+Machine Architecture transforms the workshop into a flight simulator: AI handles the routine mechanics, freeing the apprentices to immediately practice complex landings (judgment) under the master's close guidance, reaching mastery in half the time, ensuring the firm always has expert pilots ready for novel missions.
The Great PropTech Flywheel: How To Achieve It
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: Decades-old COBOL (not all but much). 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.
Three Emergence Scenarios
Scenario A: Landlord Spin-Out
A forward-thinking European landlord (British Land, Derwent, Scandinavian institutional owner) builds an integrated stack, internally driven by regulatory pressure and sustainability commitments.
After 18-24 months: It works (18-22% OPEX reduction verified). But they can't run a software business. Other landlords want the capability. They spin out as separate entity with distinct governance, competitive compensation, autonomous leadership, external capital.
Challenge: Requires admitting competitive advantage should become someone else's product. As with Ocado above.
Scenario B: Big 4 Managed Service
Deloitte/PwC/EY/KPMG recognise they have Layer 4 (ESG practices), audit credibility (Layer 5), client relationships, and capital to acquire point solutions.
Build "Building Performance as a Service” - outcome-based managed service combining tools + advisory + ongoing verification.
Precedent: Accenture's acquisitions building hybrid consulting + technology practices.
Challenge: Consultancies struggle with product thinking and technical talent retention. Would likely acquire PropTech company for technology core, wrap in consulting delivery.
Scenario C: Purpose-Built New Entrant
Founding team from real estate + technology raises growth equity (not VC) to build orchestrator from scratch.
Team profile:
- CEO: Former COO of major landlord (customer credibility)
- CTO: From industrial IoT/energy management (technical execution)
- Chief Commercial: From Big 4 sustainability practice (customer relationships, audit credibility)
Capital: £50-100m from growth equity comfortable with outcome-based revenue lag.
Timeline:
- Year 1-2: Partner with anchor customer, deploy across 50-100 buildings, validate 15-20% OPEX reduction with Big 4 audit
- Year 3-4: Expand to 5-10 landlords, achieve £10-30m revenue, begin selective acquisitions
- Year 5-7: Prove model across building types, reach £100-200m revenue, path to IPO or strategic exit
Precedent: Palantir (integration and analytics layer for industrial and government systems, ~$60bn market cap) - think ‘Palantir for the Built Environment.’.
Why this path is most credible: Specifically designed to solve the market failure - patient capital, outcome-based pricing, orchestration model, real estate DNA, technology credibility.
Conclusion
Nobody predicted Stripe in 2010. Payments were “solved”, PayPal existed, banks existed. What Stripe did was articulate the structural logic: developer experience in payments is broken, here's where value should flow. Then they built it.
This newsletter isn't predicting the PropTech orchestrator. It's articulating the structural logic: data assembly, agent orchestration, and verification are where value flows in an AI-mediated real estate world. The exact implementation, who builds it, which path they take, what it's called, matters less than understanding that logic.
Because when the Stripe moment arrives in PropTech (and it will, even if it looks different than described here), you'll want to have been thinking about data, orchestration, and trust for the past 18 months.
The CRE AI Formula - Learning From Student Housing
Sometimes a niche reveals the whole system
I’ve just given a keynote entitled ‘AI or Die? The Silent Revolution Coming for Residential Living’, and I discovered whilst working on it that the future of AI in the PBSA sector provides a blueprint for most real estate asset classes.
The future is not as uncertain as we think.
We already know most of what we need to know about AI in real estate:
What works now
What’s likely possible within five years
The barriers and strategic risks
The implications for skills, technology, and human capability
The need for a staged roadmap
And, above all, that Human is the New Luxury.
What we don’t yet know:
Whether organisations can execute on the available capabilities
How long real transformation will take - timing is a fools errand!
Whether “fewer but better” employees will generate additional revenue
And whether the market will ultimately value “Human as Luxury” the way we think it will.
The Technological Flywheel
So let’s map this out. What do we know about the future of AI?
First, even if all AI progress was to stop today much of what is likely to occur by 2030 will happen anyway. In fact, there is probably ten years of optimising, fine tuning and ‘tweaking’ to be done on the frontier models as they stand today. OpenAI’s ChatGPT, Google’s Gemini and Anthropic’s Claude are already extraordinarily ‘intelligent’.
And before anyone jumps up and screams ‘But they’re not intelligent’, we all know they are not ‘intelligent’ in a human sense, but if they can reach the same destinations as much of human intelligence, frankly who cares. They’re intelligent.
Jensen Huang, CEO of Nvidia (whose chips power 80%-95% of AI output) a year ago stated that their power had increased by 1000X in just 8 years. And that currently they are improving at ‘Moore’s Law Squared’. And the highly regarded research company Epoch AI recently wrote that leading AI models are likely to grow another 1,000x between now and 2030, assuming current scaling trends continue.
A total of 5 current and former employees of Google and DeepMind have won AI-related Nobel Prizes in the last two years (2024 and 2025).
Both Google (specifically their AI research lab DeepMind) and OpenAI have achieved gold medal-level performance in the 2025 International Mathematical Olympiad (IMO), which is widely recognised as one of the most prestigious mathematics competitions in the world.
Mostly people do not realise just how far AI has already progressed or how fast it is continuing to develop.
So we already have enormously powerful AI to work with, and we know it is going to only get better, fast. Which underpins why change in real estate will accelerate. Guaranteed.
The Kernel Shift
Andrej Karpathy was one of the founders of OpenAI and head of AI for Tesla for many years. He is in the pantheon of AI researchers, and when he talks, everyone listens.
One of his signature ideas from late 2023 was that LLMs, Large Language Models, would develop to be the kernels of a new computer operating system.
Increasingly we would interact with computers using natural language (he quipped that ‘the hottest new programming language is English’), and the LLM would understand our requirements and pull in any extra tools needed to fulfil them.
Maybe a web browser, or a calculator, or Python coding terminal. The point being that the Language Model acted as our interface to any digital service we needed.
This is coming to pass at incredible speed. Far faster than was presumed two years ago.
Many of these ‘Agentic’ systems, as they are known, are already available to us within the frontier models (have you tried ChatGPT in ‘Agent’ mode?), and a whole industry of specialist engineers is developing, building domain specific ‘Agents’ and the ‘Orchestration’ layer needed to operate them.
Orchestration is best described as a way to synchronise the activities of multiple agents according to a desired plan. Like those drone displays becoming commonplace in replacement for fireworks.
And in this environment the individual tools become less important than the creation and curation of the workflows we want them to perform. This orchestration becomes the strategic battleground. Who has the best ‘orchestra’?
The PBSA Playbook
There is a ‘Low Regret’ AI playbook within the PBSA sector. Not pervasive today, but those using AI to date are mostly following it. It involves doing what we know works! Adopting applications mature enough to be tried and tested (many from other sectors such as multi-family).
These include utilising dynamic pricing (used in hospitality for a long time), predictive maintenance, and energy optimisation. With the latter two being enabled by ubiquitous connectivity and cheap yet powerful IoT sensors.
Simply put, you can’t really go wrong with these: they have been around a while, been heavily stress tested, and are available from multiple credible suppliers. Dynamic pricing ‘should’ provide 3-5% revenue uplift, preventive maintenance ends expensive ‘panic fixes’, and energy optimisation is perhaps the lowest hanging fruit with 10-20% savings being pretty easy pickings in most assets, and more in others.
As in PBSA, many other CRE asset classes should be utilising these as table stakes. It’s really quite delinquent if one is not.
The Future Frontier
Rapidly we should be moving to the next phase where the tech we use moves from being discrete apps to integrated systems, with AI becoming the “digital nervous system” of assets.
Within PBSA there are five obvious use cases, each of which, once again, are applicable across multiple CRE asset types.
1. Agentic Operations & Digital Twins
Integrated AI "agents" will orchestrate complex decisions simultaneously across entire portfolios, optimising pricing, maintenance, and energy usage based on real-time data and simulations run on digital twins. Leading to holistic, automated asset management.
Keep your eyes on the Finance sector for early instances of this, or the likes of Walmart.
2. AI-Assisted ESG & Grid Flexibility
PBSA buildings will evolve into smart energy nodes. AI energy managers will decide when to store, consume, or sell excess power back to the grid, enabling participation in virtual power plants (VPPs) and creating new ancillary revenue streams.
Look at what Octopus Energy are already doing with their vehicle-to-grid (V2G) technology.
3. Automated Inspections & Computer Vision
Drones or computer vision systems (analysing CCTV feeds) will perform regular inspections of roofs, façades, and communal areas to detect defects, monitor cleanliness, and automatically generate work orders.
Drones improve in line with AI, so they are getting to be very powerful tools, very quickly.
4. Hyper-Personalised Engagement
AI systems will act as proactive digital concierges, learning student preferences (e.g., study habits, event attendance) to provide tailored suggestions for study groups, events, or services.
Watch for the growth in AI model ‘memory’. Frontier models are growing in their ability to remember - so they can ‘get to know you’ in quite some detail.
5. Advanced Data-Driven Student Welfare
AI may analyse non-intrusive data (such as access card usage or facility logs) to identify students at risk of distress, dropping out, or loneliness, allowing staff to conduct proactive welfare checks. This application, however, faces significant privacy barriers.
Watch though for AI therapists with robust guardrails. Many bad actors want to sell personal AI, but so do many good actors. Regulation is likely to favour the latter over time, albeit probably not arriving until someone ‘misbehaves’ badly.
All of the above are nascent technologies but developing fast. They are complicated to get right but not rocket science. And every time the power of ‘natural language computing’ increases the easier it will be to implement them. Science fiction they are not.
The Human Dimension
An absolute certainty is that successful AI rollouts within CRE (as across all sectors) will be as much about ‘Change Management’ as technology. The brutal truth is that if AI can execute a task faster, smarter, and cheaper, that task is no longer for humans. And CRE is full of manual processes that should be automated away. And will be.
So all of us are going to be going through a process of upskilling and raising the quality of the jobs we can offer. And for a given unit of output we will definitely be needing fewer people.
I see this as a feature, not a bug. We want humans to be doing what humans are uniquely good at, and computers are not. It is pointless to be looking for anything else. This is ‘don’t bring a knife to a gunfight’ territory. We humans have to raise our game, maintain agency over ‘the machines’, and focus on where we add value.
#HumanIstheNewLuxury
I believe #HumanIstheNewLuxury, as a mindset, needs to be our default setting. We have to learn new ways of working, where we automate what we can, use technology to augment us wherever possible, and deeply appreciate where only 100% human will do.
Within PBSA the entire game is going to be about reducing costs through technology, but reinvesting in much higher levels of pastoral care, customer support, and fostering a sense of safety, connection, and belonging. AI cannot replicate the empathy, lived experience, cultural sensitivity, emotional intelligence, or gut instinct required for true wellbeing support. And providing that is what is going to safeguard excellent returns.
The critical factor offsetting staff displacement is the ability of these new, human-centric roles to directly influence revenue (RevPAB) and asset value in ways that transactional staff could not.
And is this going to be any different across other CRE asset classes? I don’t think so. My hunch is that the technology is going to develop faster than we imagine, but that once here, we’ll all get bored with it. It will be amazing for a while, but sooner or later no more exciting than turning a light switch on, or loading the dishwasher.
And then we’ll all be craving more humanity. And seeing as we spend 90% of our time indoors, maybe the real estate industry is in a good place to provide it?
Three Horizons for AI in Real Estate
PBSA and the rest of the industry will likely follow these timings:
Now (2025–2026): Low-regret moves: dynamic pricing, conversational leasing, energy optimisation.
Next (2027–2028): Integrated systems: AI agents orchestrating decisions across assets.
Beyond (2029–2030): AI-native portfolios: designed from day one to generate and govern their own data. And run themselves.
Conclusion
This blueprint for PBSA feels like a roadmap for much of the industry. Of course, none of it will be possible unless the industry gets its data in order, but I’m assuming, even if through gritted teeth, we’ll get there. Because we have no choice.
Once you’ve got near or real-time data, in a data lake rather than in silos, everything becomes possible. And it really is not that complicated. All of the above could be done today, with some difficulty. In 3-5 years it will become child’s play.
As stated at the top though, the things we don’t know are whether the industry can get to grips with all of this (surely it can), how long it will take, how the revenue/cost aspect of fewer but ‘better’ employees will pan out, and ultimately whether society develops to really value #Human, or we get subsumed under a technological tsunami.
Personally I am feeling very positive about all of this. I suspect a lot of the industry will mess it up, but they’ll fade out of existence and the future will belong to those who go ‘all in’.
OVER TO YOU
Where do you stand with embracing the future? Ready? Willing? Able? If you don’t believe in this vision, what are you counterfactuals? If we don’t move forward in this way, where’s our moat? As always, would love to hear your feedback.