AI can lift NOI across real estate, most directly where landlords run the operation themselves. Whether it does depends less on the technology than on what your people believe will happen to them.
On Friday I was asked how to persuade a board to fund AI redesign work. It sent me back to a deck I took to boards in 2023, called ‘Generative AI: Preparedness’. Rereading it was instructive and a little uncomfortable. Most of it was sensible at the time. Much of it has expired. And the section on people, which turns out to be the one that decides everything, was the weakest of the lot.
Executive summary
AI-driven productivity flows into NOI and gets capitalised, which makes it a capital argument for the board, potentially worth far more than a few salaries. The effect is clearest in operational real estate, and it reaches any landlord that manages its own buildings through voids, retention and service. The listed operators are already taking that gain, mostly as lower payroll. That creates the real obstacle: the people whose work has to change are afraid of the change, and new Gallup data shows the heaviest AI users are the most worried of all. The fix is incentives: a deal, written down before you start, on who gets the gain and how the change happens. Firms that settle it will compound; firms that don’t will pay for tools their people quietly route around.
WHAT I TOLD BOARDS IN 2023
The deck had six headings:
fostering a culture of innovation
laying the data foundation
harnessing tacit knowledge
AI literacy
pilot projects with ROI measurement
navigating a dynamic landscape.
Some of it holds up. Leaders should use AI visibly. Tacit knowledge is where the value sits. Outside expertise helps. Strategy needs revisiting.
The people section reads worse. It proposed innovation hubs, quarterly challenges, certificates and a ‘Knowledge Contributor of the Month’. It promised that education and demonstrated value would ‘guarantee universal buy-in’. Nothing in it asked the question every employee was silently asking: what happens to me?
A confession about this piece, too. What follows will date. In August I argued that ‘human judgement has a timestamp‘, and the same applies to advice. Treat what follows as a reading taken in October 2026, to be retaken.
THE EDGE MOVED
Four pieces of 2023 advice show how fast the ‘jagged edge’ of AI capability moves. Each was reasonable at the time.
Data foundation first. In 2023 the received wisdom was that AI needed tidy, structured data, and the models of the day could hold far less in view at once, so cleaning data first made sense. Today’s models handle large volumes of messy documents, emails and spreadsheets well, and a two-year data programme before anything else is a recipe for unnecessary delay.
What still matters is data quality where an agent acts. When a system takes a step on a rent roll or an arrears ledger, a wrong number gets executed, where in 2023 it would only have been reported. So dealing with data has become easier, but the stakes have risen.
Knowledge-capture sessions. Extracting what experts know was right then and is right now. What changed is the output. In 2023 it produced documents for people to read, and the expert usually lost ownership of the knowledge the moment it was written down. Now it produces ‘Skills’: the expert’s method, encoded so a system can run it. That raises the personal stakes for the expert considerably, which is why the rest of this piece is about the deal they are offered.
Pilots measured in time saved. Tools in 2023 were assistants that sped up tasks, so time saved was the obvious metric. Agents can now take bounded steps in a workflow, with checks. The measure that matters is the workflow’s outcome: days to let, renewals retained, arrears collected.
Literacy workshops. Capability now moves several times a year, so a one-off course decays almost as fast as my deck did. Training has to be continuous, delivered in the work, on the firm’s own workflows, and tied to redesigning them.
Fluency on its own is table stakes: any competitor can train or hire its way to parity. The advantage comes from what trained people then redesign and build.
The lesson is easy to state and hard to act on.
An AI plan without a date on it is a depreciating asset.
LABOUR IS NOI
When I started on this piece, my instinct was that labour savings make a weak argument in real estate. Real estate firms are asset-heavy and people-light, so a board hearing ‘productivity’ hears ‘a few salaries’ and shrugs.
That instinct was wrong for a large and growing part of the market. In operational real estate (such as build-to-rent, student housing, hotels, flex, senior living) site labour sits above the NOI line. Every pound of recurring operating cost removed, net of the software, the oversight and any slippage in service, is a pound of NOI, and NOI is capitalised. At a 5% yield, £1 of NOI is worth roughly £20 of value. That is illustrative arithmetic, and it is a capital argument. Boards understand capital.
The listed operators are already making it. AvalonBay in the US told investors in April that it is on track for $55m of annual incremental NOI from its operating initiatives by the end of this year, with further AI deployment and further optimisation of neighbourhood and centralised staffing next, on the way to an $80m target. The usual caveat applies: much of the model is centralisation that predates large language models, and AvalonBay doesn’t say how much of the NOI is down to AI.
Equity Residential’s March investor update is more explicit about people. It says its innovation programme delivered $55m of NOI between 2020 and 2025 alongside a headcount reduction of roughly 20%, and it expects a further $25m and roughly 10% from a second phase running to 2028, with AI leasing, AI service optimisation and AI-supported collections among the initiatives. NexPoint reported payroll down 4.3% in the first quarter, which it attributed to its centralised operating model and AI-enhanced leasing.
In the UK, Grainger is targeting EBITDA margin expansion from 56% to 60% by FY29 from its operating platform, and its chief executive has argued that Grainger is more likely to benefit from AI in its operations because of its data. Unite expects nearly £7m of annual operating cost savings from its technology platform upgrade. Both figures cover platform efficiency in general, and both show where operators expect the next margin to come from.
Two qualifications. First, structure decides who keeps the saving, and for an owner of a mixed, multi-let estate the levers move rather than disappear. Savings on recoverable costs mostly pass through the service charge to occupiers, and in hotels and senior living a management agreement splits them between owner and operator. What stays with the landlord is everything else: shorter voids, faster lettings, better renewals, fewer arrears, and a property team that can look after more buildings and more occupiers without growing. A well-run, competitive service charge is part of what an occupier pays rent for. On a long-hold estate, all of that compounds.
Second, the gain won’t stay exceptional. EliseAI says one in six US apartments now uses its software. It is my belief that when everyone runs the same leasing AI, the saving becomes the market’s baseline, and buyers will underwrite it. An asset run the old way will then be priced against an operating model it doesn’t have.
So the board case is strong. Look at how the gain is being reported, though: as payroll and headcount.
And that is where the problem starts.
YOUR HEAVIEST AI USERS ARE THE MOST AFRAID
In September Gallup published a study drawing on four waves of US panel data from 2023 to early 2026. Workers who use AI daily or several times a week were more than twice as likely to fear their job will be eliminated within five years as those who use it occasionally. Familiarity brought no reassurance. That fear costs money: as a worker’s displacement anxiety rose, their engagement fell, burnout rose and they became more likely to look for another job.
The people who lean in are the people most afraid of where they think leaning in leads.
What made the difference was management. Workers who felt respected, and felt their organisation cared about their wellbeing, showed a much weaker link between heavy AI use and fear of losing their job. Gallup reports this as an association, tracked in the same people over time, and its advice to leaders is to connect AI to team capability rather than headcount efficiency.
Meanwhile, people hide what they do. Deloitte’s survey of 25,000 UK workers, published last month, found that 31% of those using generative AI at work do so without their employer’s knowledge, around half have had no formal training, and 64% of weekly users worry their managers will think AI can do their jobs. When leaning in looks risky, people lean in privately. The firm then carries the data risk and can’t systematically capture the learning.
This piece rests on a concession. Some of these fears are correct. In a firm with a static portfolio, AI-driven productivity will mean fewer roles over time. Telling people otherwise will be found out, and quickly, and the cost will be every promise you make afterwards.
So the useful question for a board is who gets the gain, and how the change happens.
SHOW ME THE INCENTIVE
Charlie Munger’s favourite illustration of incentives, from his 1995 Harvard talk on the psychology of human misjudgment, was Federal Express. The whole system depended on moving every package through a central hub overnight, and the night shift kept failing to finish on time. FedEx tried moral suasion and everything else it could think of. Then someone noticed the night shift was paid by the hour, so a slow night paid better than a fast one. FedEx started paying by the shift, and the system began to work. It is the story behind the line he is usually quoted as saying: show me the incentive, and I’ll show you the outcome.
Your AI programme has a night shift. If the visible consequence of working faster is fewer colleagues, or your own role disappearing, you have built FedEx’s hourly rate into your transformation. No amount of ‘culture of innovation’ will outrun it. I proposed certificates in 2023. Munger would have laughed.
One warning before the remedy. FedEx’s output was easy to see: planes loaded by dawn. Outcomes in real estate lag and get muddied by markets, so pay tied crudely to them invites gaming. Measure tickets closed and you get tickets closed, solved or otherwise. Every outcome measure needs a quality check beside it.
With that caveat, here is the deal I would propose. It has six parts, and it should be written down before any redesign starts.
Say what happens to capacity. A growing firm can offer the bigger pie: more units, more services and better retention, run by the same headcount. I’ve argued before (’From Boardroom Privilege To Baseline Utility‘) that AI’s real prize is the demand it makes economic for the first time: services, customers and assets that were never worth serving at the old cost. For an operator that might mean resident services, smaller assets or third-party management. Redesign can create that growth, but someone still has to go and win it, and the extra capacity only protects jobs where they do. A firm with a static portfolio has to make a different offer, and make it honestly: change through attrition and redeployment, first claim on the new roles, and a real share of the gain. Real means specific: a team bonus paid from verified net improvement in the workflow, protected time to build new skills, a defined route into a more valuable role, and funded transition support where redeployment isn’t possible. Promise anything except ‘no change’.
Give decision rights to whoever redesigns the work. The person who rebuilds a workflow should own the rebuilt version. Progression should come from expanding decision rights, as I argued in ‘The Company After the Pyramid‘.
Pay on observable workflow outcomes, with checks. Days to let alongside the quality of the let. Resolution time alongside requests reopened. Arrears collected alongside tenancies lost.
Make skill authorship a career asset. Skills can be drafted from documents and examples, but only the specialist can validate the method, its limits and where it should be used, so they have to be central to designing the skills that run it. A skill complements the expert and substitutes for the expert’s repetition: once built, it handles the routine cases without them. That is the point of it, and pretending otherwise insults everyone’s intelligence. The firm will own the skill as intellectual property, so the author’s ownership has to mean authority: they decide how the workflow changes, approve revisions and can challenge what it produces. Name the author, and count authorship in promotion and pay, judged on use and quality, never on volume. Art history has a 400-year-old vocabulary for this kind of attribution (’workshop of’, ‘attributed to’): graduated authorship under a named hand.
Keep the library alive. Someone has to curate the skills, date them and review them as the edge moves, and that review sends the work back to the specialists. This is the System Architect role I described in ‘The Company After the Pyramid‘. It is a loop, never an event.
Stop punishing disclosure, and train continuously. Provide sanctioned tools, reward people for showing how they use them, and keep training. Continuous training is the firm’s side of the bargain: we keep investing in you, you help redesign the work, and you share in what it earns.
One test covers all six. Would someone still think this a fair deal if their current role eventually disappeared? If the answer depends entirely on keeping the job, the deal needs more work.
The deal also has a precondition: managers and executives have to be paid for it too. Gallup’s finding is that managers are the buffer, so their incentives come first, particularly in the middle layer whose job has been routing information. And if the executive committee is still rewarded for cutting headcount while managers promise people a share of the gain, staff will believe the executives’ incentives, and they will be right to.
THE RIGHT SIDE OF CHANGE
Now the individual, because the firm can only do half of this.
The change is coming whatever any of us thinks of it. What’s different now is how much agency a single person has to show what they can add. Every week I put a CRE or PropTech company through a Build/Back analysis, using Skills I designed and maintain myself, and my arguments get pressure-tested by a virtual board before they reach you. A few years ago that would have needed a research team and a fair amount of budget. The caveat matters for anyone doing this inside a firm: a tool one person built in a day, on a personal account, holding tenant data, is a liability until it is sanctioned and maintained.
For a domain specialist, leaning in now has a concrete form: designing and curating the skills that run your own method. That is a far stronger position than waiting to see whose method gets encoded.
Is it a moat? Only as a rate of learning. The fluency you acquired last year is worth less every month, and if everyone leans in, nobody is ahead. The durable advantage is the habit of keeping up, combined with domain knowledge competitors can’t download: being full stack, in CRE and in AI. The firm supplies the continuous training. The individual supplies the habit.
That habit only survives in a firm that makes leaning in safe. Which brings us back to the deal.
I should declare an interest. I run a training programme and help firms redesign workflows, so I benefit when you lean in. Weigh this piece accordingly.
BEFORE YOU START
For the board paper, six inputs, all settled before any build begins:
The NOI line you’re targeting, and roughly how much.
One workflow that’s already hurting.
An owner who carries the P&L for it.
A baseline, measured before anything changes. Without one, nothing can be proved later.
The people deal, written down and announced, including what people get if their role goes.
A staged budget with gates and kill criteria, covering continuous training as well as build.
The last question a board will ask is what it’s actually paying to build. My short answer, which I’ll be developing in a series of masterclasses at Cityscape in Riyadh next month: you can rent the brain and most of the harness. You can’t rent your context, your method or your accountability.
WHO GETS THE GAIN?
A prediction to hold me to. Within two years, a buyer’s underwriting on a UK build-to-rent or student housing asset will explicitly assume an AI-enabled operating cost base, and the seller’s legacy model will be priced against it.
When that happens, the operators who settled the people question early will have the cost base, the skills and the staff who built them. The others will have bought the same tools and watched their people work around them.
Get the incentives wrong and you buy tools. Get them right and you get capacity. Write the deal down and you get a firm that keeps learning.
So: who gets the gain in your firm, and do your people know the answer yet?