THE BLOG
AI Fluency Isn’t Enough
Within three years, your biggest competitor won't be the firm across the street—it will be a company that thinks about real estate in a fundamentally different way. While most of us are learning to use AI as a tool, they're using it to build a new kind of engine. Here's how to make sure you're the one building it.
Warning: This issue is not a snack—it’s a full meal. If you’re pressed for time, start with the Executive Summary below. If you’re crafting your firm’s AI strategy, read it all.
Executive Summary
Artificial Intelligence (AI) is rapidly shifting from an optional efficiency booster to an essential competitive foundation in commercial real estate. Currently, proficiency with AI tools like Large Language Models offers significant productivity gains—but only temporarily. Within 18–24 months, AI fluency will become commonplace, and this initial advantage will fade.
Long-term strategic advantage requires moving beyond merely adopting AI tools toward fundamentally rethinking and restructuring your business model. This involves a three-step strategic framework:
Unbundling: AI breaks apart traditional, integrated workflows (investment management, brokerage, development, property operations) into discrete automated tasks, boosting efficiency but creating complexity.
Emerging Constraints: Automation introduces new challenges—coordination of fragmented workflows, data interoperability and quality, trust in AI outputs, regulatory and ethical risk management, cognitive overload, and maintaining crucial human judgment and intuition.
Rebundling: Progressive companies address these new constraints by strategically "rebundling" around three key areas:
Data & Asset Integration: Build proprietary data platforms acting as a single, trusted source of truth integrating people, buildings, ESG, and operational insights.
Workflow Orchestration: Create intelligent orchestration layers to manage fragmented AI-driven workflows and optimise human-AI collaboration across the organisation.
Decision Support & Trust: Position the firm as a trusted advisor by embedding rigorous governance, transparency, and human oversight into automated decision-making processes.
Ultimately, CRE firms must choose between remaining mere "tool-users," destined to face commoditisation, or becoming strategic “engine-builders" - architects of new business systems and orchestrators of value creation in an AI-native landscape. This shift will create an unprecedented bifurcation within the industry, with profound competitive implications.
Forward-looking firms must start now. Those who navigate this rebundling effectively will dominate future markets, redefining industry leadership for the next era of commercial real estate.
——————————————————————————————————
Today a Super Power, Tomorrow a Commodity
Less than 10% of knowledge workers are daily users of LLMs. Circa 18% use them weekly, but if you are not using them daily they have not become part of your workflow. And if they are not part of your workflow, you’re not yet a power user. And won’t be enjoying their edge.
I think power users, or those seriously leaning in to using LLMs, have 18-24 months to ‘make hay’. They will be dramatically more productive than their peers and will be able to do the work of 2-10 people. We’ve written about ‘Fast, Agile, Ultra-Productive Superteams’ before and the evidence is mounting of companies generating $1,000,000+ of revenue per employee. This productivity boost from generative ai is very much a known known now.
This edge though will diminish over time, as the mainstream catches up and adopts the same tools. So the smart people in real estate need to be thinking beyond just the technology - they need to be thinking how this technology will change the fundamentals of our industry?
So with that disclaimer, that for now the fruit is low hanging, let’s look at how real estate is going to change.
Beyond Tools: Building a New Operating System for Real Estate
The current discourse often focuses on AI as a means to achieve incremental efficiencies, such as automating tasks like marketing copy generation or lease abstraction. However, this "tool view" is strategically incomplete and risks long-term commoditisation. The true power of AI in CRE lies in its capacity to act as a "coordination engine", fundamentally restructuring roles, firms, and competitive dynamics by radically reducing the friction inherent in knowledge work.
This transformation is characterised by a three-part dynamic: unbundling, emerging constraints, and strategic rebundling.
AI unbundles traditional CRE workflows across asset and investment management, brokerage, development, and property operations (in fact, all workflows), fragmenting what were once integrated roles into discrete, often automatable, activities.
This unbundling, however, is not frictionless; it introduces a new set of complex systemic constraints. Imagine that the old way of doing things in CRE had certain "frictions" or difficulties, often due to manual processes and scattered information. When AI steps in, it "unbundles" these tasks, meaning it breaks down traditional jobs into many smaller, automated pieces, which can make things more efficient. However, this fragmentation creates a new set of difficulties that the industry must navigate.
The most progressive CRE firms will not just use AI, but will actively "rebundle" capabilities around these emergent constraints to establish new control points where they can regain influence and competitive advantage by becoming indispensable in overcoming these new constraints.
The Great Unbundling: Fragmentation of CRE Workflows
Generative AI is actively re-architecting every major function within CRE, disaggregating bundled tasks into fragmented, AI-driven workflows.
Asset & Investment Management is seeing AI-driven analytics perform market research, deal sourcing, valuation, underwriting, and due diligence with unprecedented speed and accuracy, shifting roles from manual data processing to interpreting AI-generated insights.
Brokerage & Leasing workflows are being peeled off through AI-generated marketing materials, virtual staging, and AI chatbots handling initial client communication and routine inquiries. Lease administration tasks like abstraction, which once took days, can now be completed in minutes using AI, commoditising junior roles.
Development & Construction is being unbundled by generative design tools that rapidly produce feasibility studies, site plans, and building designs. AI also assists with project management, site monitoring, and compliance checks, moving tasks from manual creation to AI-assisted curation.
Property Operations & Facilities Management is shifting towards an "autonomous building" model. AI powers tenant service chatbots, optimises energy management and HVAC systems, and enhances security and reporting, reducing the need for large on-site staff.
Workplace Design & Operation (from the occupier perspective) leverages AI to generate office layout options, provide virtual assistants for room booking and help desk support, and analyse employee experience data for space optimisation.
This pervasive unbundling leads to increased efficiency but also creates a more complex ecosystem of tools and stakeholders.
We may not, mostly, be here yet but the direction of travel is very clear. All this is coming.
2. Emerging Constraints: The New Bottlenecks
Before AI, the CRE industry was characterised by "high-friction silos" and fragmented information, where critical data was scattered across many disconnected systems and manual processes. This created "coordination costs" – an immense overhead of time, labour, and cognitive effort needed to align people and synchronise workflows, leading to delays, duplicated work, and communication breakdowns. We all used to pay this "coordination tax”.
In an AI-mediated world, unfortunately, this ‘coordination tax’ doesn’t disappear, but rather it gets transformed. As we ‘unbundle’ tasks we make them intrinsically way more efficient, but we end up with many unbundled, super efficient workflows that need to be co-ordinated and managed.
And this fragmentation created by AI introduces new systemic challenges that become the "bottlenecks of a fragmented world". This though is where new competitive advantage will arise. Companies that can effectively manage this "new coordination tax”, which won’t be a trivial pursuit, will be in a very strong position.
There will be a lot to do to get there:
A. Coordination Frictions: With work spread across multiple AI tools and providers, orchestrating end-to-end workflows becomes a significant challenge. AI's high "clock-speed" can outpace human coordination, leading to breakdowns if not managed. When one part of the business runs much faster than another, trouble can follow.
B. Data Interoperability & Quality: AI thrives on data, but CRE data is notoriously siloed, non-standard, and often poor quality. Integrating disparate data sources and ensuring data governance and common standards are critical, as AI amplifies bad data. It’s hard to see how any real estate company will succeed without, at last, sorting out its data.
C. Trust & Transparency: As AI takes over more decision-making, ensuring trust in AI outputs becomes paramount. The "black box" nature of some models and instances of "hallucinations" can undermine confidence, requiring transparency. Firms that build a reputation for responsible AI use can turn trust into a competitive advantage.
D. Risk Management (Bias, Errors, Liability): AI introduces new risks, including algorithmic bias, outright errors leading to financial loss, and cybersecurity vulnerabilities. Regulations like the EU AI Act will impose constraints on high-risk AI uses, making robust risk management a non-commoditisable capability and a potential competitive moat.
E. Decision Complexity & Information Overload: Paradoxically, AI's ability to produce vast amounts of information can lead to "analysis paralysis" and decision fatigue. The new bottleneck is human cognitive capacity to process information effectively, making the simplification of choice a valuable service.
F. Loss of Tacit Knowledge & Human Intuition: As AI automates grunt work, there's a risk of losing the "human connective tissue" and intuition gained through hands-on experience. Over-reliance on AI could lead to homogenised strategies and a loss of diverse perspectives, making human judgment scarcer and more valuable.
G. Human Judgment Gaps & Ethical Oversight: Highly automated processes risk bypassing critical human judgment points, potentially leading to undesirable outcomes (e.g., an AI optimising for a single metric like NOI at the expense of tenant experience). Deliberately inserting human judgment ensures quality and sustainability, even if it introduces "positive friction”.
3. The Rebundling Playbook: Architecting the Next-Generation CRE Firm
The above are all new problems that we’ve not had to deal with in real estate before. But they will be real, and significant. And a major impediment. So we need to think about how to ‘rebundle’ our workflows to address them.
This will mean moving beyond merely adopting AI tools to architecting an entirely new system of value creation. And this will be the super power of the new winners in real estate. Most CRE companies will adopt an assortment of AI tools, but few will push on through to this rebundling. It’s just too much of a leap for the ‘average’ company. Too much of a break with ‘the way we do things here’.
Let’s take a look at what will be required.
Rebundle Around Data & Physical Asset Integration: Becoming the "Single Source of Truth" for People, Planet, and Pipes
The fundamental strategic move is to own the data layer. This means building a proprietary data platform that acts as the central nervous system, aggregating, cleansing, and standardising data from across the fragmented ecosystem. Beyond just traditional property data, this rebundling must incorporate:
Green-Data Flywheel: Combine ESG, embodied-carbon, and energy-profile datasets with AI models, using carbon-adjusted Net Operating Income (NOI) as a north-star metric. This involves piloting digital twins on assets to feed live HVAC, occupancy, and carbon data into the "Single Source of Truth", directly addressing the physical asset and ESG interlock gap.
Edge + Twin Architecture Blueprint: Publish an end-to-end reference stack from sensors to edge inference to graph databases and LLM retrieval for live-building data streams. This is the necessary technical plumbing to manage latency and compute costs for real-time digital twins.
By becoming the definitive "source of truth" for comprehensive, high-quality data (including building performance and environmental data), the firm establishes a proprietary and defensible moat, enabling superior AI-driven analytics that others cannot easily replicate.
A note here: This is not trivial, and frankly favours the large existing incumbents. However smaller companies can specialise in niches where ‘the big guns’ don’t go, or operate in a much more flexible, agile way. You don’t need all the data in the world, but what you do handle must be high quality, and valuable. And, as we’ve looked at before, more data is going to be public in the future.
There are ways every size of company can shine.
B. Rebundle Around Workflow Orchestration & Human-AI Collaboration: Becoming the "System Coordinator" for the Organisation
This move addresses the integration labyrinth and coordination frictions by building an intelligent orchestration layer above the multitude of PropTech tools. The focus here shifts beyond just connecting technologies to effectively integrating human capital:
Workforce-in-the-Loop Design: Embed "bounded autonomy" rules and role-based copilot policies to convert headcount savings into higher-order judgment capacity. This means mapping rebundled workflows to human decision checkpoints and defining companion skills roadmaps, addressing the significant human capital and change management gaps. If real estate really is a people business, now is the time to make your people the best they can be.
Restructure Roles and Teams Around Workflows: Shift from traditional departmental silos to more cross-functional, agile teams organised around products or client segments, with AI handling much of the cross-team coordination. This leads to a new operating model where AI agents can facilitate internal coordination, accelerating decision flows and enabling more synchronised execution. This aligns the organisation itself with the speed of AI.
By coordinating across formerly disconnected pieces (human and machine), the firm becomes the indispensable "operating system" for CRE transactions, simplifying decisions and ensuring seamless execution.
Outcompete by simply being the best orchestrators out there. Making the really complicated look easy.
C. Rebundle Around Decision Support & Stakeholder Trust: Becoming the "Trusted Advisor" with Ethical Oversight
This move directly confronts cognitive friction and decision fatigue by absorbing complexity and delivering curated, high-confidence recommendations. The firm's value proposition shifts to providing trusted judgment, underpinned by:
Stakeholder-Trust Charter: Move beyond mere compliance to proactive engagement through tenant data-rights dashboards and algorithmic impact assessments. Piloting a tenant transparency portal that explains how AI affects decisions (e.g., rent, maintenance, energy) builds social legitimacy and addresses the "social licence and externalities" gap. Honesty is the best policy.
Embed Governance and Human Expertise as a Value-Add: Develop robust AI governance, including audit trails for AI decisions, bias checks, and compliance certifications. Explicitly having chartered professionals review and sign off on AI-generated valuations, for example, provides an assurance layer that pure AI startups lack, turning governance into a competitive differentiator. This directly tackles the "trust and transparency" and "human judgment gaps" constraints by ensuring that decisions balance speed with wisdom.
This strategy establishes a powerful control point at the moment of decision, building deep, defensible client relationships based on confidence and reliability.
Conclusion: From Tool User to Engine Builder
The choice for CRE leaders is clear: will they remain "tool-adopters" competing on price with shrinking margins, or will they become "engine-builders" that establish new control points and capture a disproportionate share of the value created in the AI-native landscape?
The most convincing strategic lens for AI in CRE is the "unbundle → identify new constraints → rebundle around them" logic, as it effectively avoids the commodity trap.
However, competitive advantage will ultimately be won by firms that integrate people, planet, pipes, and portfolios into the same AI engine, recognising the uneven regulatory terrain on which this engine will run.
Addressing these identified gaps will transform a solid conceptual roadmap into an execution-ready strategy, allowing firms to not only adopt AI but to truly redraw the competitive map in the CRE industry.
This holistic perspective is essential for any company looking to capture the upside of AI transformation by becoming an "engine-builder" rather than just a “tool-user”.
A CAVEAT
So much of this, whilst I think spot on strategically, feels like a big ask for real estate companies today. And it is fair to say that as an industry with long time horizons, the short term might actually be rather long. This is definitely early adopter territory, and I don’t expect to see many truly ‘AI First’ real estate companies in the near future. So you might feel inclined to put this in the ‘future gazing’ bucket and think no more of it. And in many ways that would be rational. But… I’m also convinced we will see the emergence of companies that fit this bill. And they will be amazingly productive and will out-compete the mainstream when addressing similarly progressive clients. So either way, I think we’re heading for a bifurcated future, where real estate companies for the first time in history, don’t all look the same.
OVER TO YOU
Hit reply and share your biggest current AI-related constraint.
Are you on the path to becoming an “engine-builder”? Let's talk through your next steps.
Forward this to your most strategic colleague. Where does your firm sit on the “tool-user” to “engine-builder” spectrum?
PS My thinking on this framework was sharpened by the foundational work of two brilliant strategists. The "unbundling/rebundling" dynamic is heavily inspired by Sangeet Paul Choudary's work on platform economics, while the "where to play/how to win" approach to strategy comes from the indispensable Roger Martin. My goal was to build a bridge from their powerful theories directly to the challenges and opportunities facing CRE leaders today.
This Time Is Different: The Rise Of The Ghost Workforce
Work is in the throes of a punctuated equilibrium
We’re about to see work reinvented. We’re now at a moment in time when change goes from slowly to suddenly, and huge opportunities open up to multiply our cognitive capabilities and transform our productivity. Let the revolution commence.
A Sunday Epiphany: Watching Agents at Work
Last Sunday I was playing around with OpenAI’s new ChatGPT Agent service which, according to them, ‘can now do work for you using its own computer, handling complex tasks from start to finish.’
You give it a task, it fires up its own ‘virtual computer’ within your browser window and then gives you a running commentary about what it is deciding, autonomously, to do in order to fulfil the task you gave it. It feels like you have a ‘ghost in the machine’, an alien intelligence moving the digital world on your behalf while remaining utterly intangible.
And to me, it felt like a revelatory moment. This was something truly different.
A Break in the Timeline
In evolutionary biology there is a term, ‘punctuated equilibrium’, that suggests species typically experience long periods of little or no change—called stasis—interrupted by short, rapid bursts of significant evolutionary change. This model contrasts with the traditional idea that evolution happens slowly and gradually over time in a steady, continuous way.
And this is what the rapid development of generative AI is replicating. Since the launch of ChatGPT on November 30th, 2022 there has been a whirlwind of progress. And the whirlwind has even been speeding up. The arrival of ‘reasoning’ models in December 2024 has seemingly cranked up the gears across all the frontier labs, OpenAI, Google and Anthropic, and the cadence of new releases and features is now weekly.
The previous equilibrium has been rented asunder and we’re in a new era. The AI Age has truly arrived.
Not Just Another Boom
These four words are meant to presage a financial crash. During boom times whenever you get to the stage where people say ‘This Time is Different’ is usually the first ‘sell’ signal. And it has repeatedly proved true throughout history.
But I’m going to steal them anyway and apply the spirit to the world of work.
Using ChatGPT Agent strongly pushes me to say ‘This Time is Different’. This way of getting work done is something else entirely. It is not something we’ve ever had before, and it is going to redefine how we ‘get stuff done’ and how we conceive of a ‘workforce’.
Now, to be clear, ‘Agent’ is not the first service in this area. Notably, the Chinese-backed company Manus has had a service out there for some months doing similar things, if in a slightly different way. But you’ve probably never heard of them. Whereas you’re 99.9% certain to have heard of OpenAI and ChatGPT. With over 500 million weekly active users, this new way of working suddenly has massive distribution and public awareness. Meaning it will be taken up by significant numbers very quickly.
What This Means for Real Work
So what will that mean?
Well let me give you some examples of how I’ve used it to date, and some indications of where this is heading.
First, I asked it to visit the index page of this newsletter, read every edition, summarise each one, add these to a table and then separately write a meta summary of the topics I cover in the newsletter.
It took about 15 minutes, and did the job perfectly.
Secondly, I gave it a lengthy ‘Deep Research’ report into which companies within real estate were providing software services involving generative AI. I asked it to extract every company mentioned, find their website address, visit said website, and summarise the asset classes they operated in and the services they provide. And, again, put it all in a table.
Job done in 20 minutes.
Thirdly, I uploaded an Offering Memorandum and told it to find comparable sales within 5 miles. Then assess for sensitivity on rent growth 0% vs. 3% vs. 5%. Then provide a one-paragraph executive summary for investors. Then build a PowerPoint slide with deal highlights.
Job done in 15 minutes.
You might have read Arthur C Clarke’s Third Law where he writes ‘Any sufficiently advanced technology is indistinguishable from magic’. Well, that is exactly what I felt about all of this. Magic was afoot.
It’s really quite a bizarre thing to ask a multi step question, provide no other guidance, and then see a ‘machine’ itemise in text how it is going about arriving at an answer.
This Time is Different.
From toy to Power Tool
Obviously all of this does not work perfectly yet and one would not point it at mission critical work. But …. give it six months, or a year, and one can be pretty confident it will be ready for important work.
And when that happens, how we work might change fundamentally. I can see no reason why we won’t be running dozens of these ‘Agents’ 24/7 on repeatable questions, processes or workflows. Constant implementation of X, constant research about Y.
Let’s extrapolate a couple of potential use cases:
1. AI Underwriting Assistant
For input we might upload Offering Memoranda (PDF), Rent Rolls (Excel), or deal data to a shared drive or folder. And set a ‘trigger’ of a new file detected in the "Deals to Underwrite" folder (e.g., via Zapier or cloud API).
Then the ‘Agent’ would autonomously do the following -
Extract key data: lease terms, income, expenses, cap rate, market comparables
Perform rent growth/cap rate sensitivity
Benchmark against portfolio and market data
Flag inconsistencies or missing assumptions
Write a one-page underwriting summary
Set Risk flags (e.g., short WAULT, high OpEx ratio)
Provide a sensitivity matrix (e.g., IRR vs. rent growth)
Optionally create an IC-ready memo or PowerPoint slide deck
Notification : Email, Slack, or CRM update sent to Acquisitions team with summary and link to outputs.
2. Market Intelligence Synthesiser
For input we might have a predefined watchlist of locations, asset classes, tenants, and competitors.. And set a ‘trigger’ that runs on a daily schedule (e.g. 6:30am London time).
Then the ‘Agent’ would autonomously do the following -
Scrape news from key outlets (e.g., FT, Property Week, Bisnow, CoStar)
Pull comps and pricing signals from public data feeds
Aggregate macro indicators (e.g., Gilt yields, inflation prints)
Cluster insights by theme: pricing, regulation, supply, occupier trends
Produce a smart digest with summaries, headlines, and source links
Create charts for rent/yield movements and liquidity trends
Optional: brief commentary or strategy nudge
Notification :Delivered to email or Notion dashboard; summary ping in Slack.
And so on. Think of a process that follows this pattern. I suspect you have many. Maybe soon ‘Agent’ will be doing them for you.
How to Build Your Own Agent Workflows
So far I’ve only played around with this new tool. But the obvious direction of travel will be to define your own uses cases, then:
Define the use case
Craft and refine your prompt
Specify the required outputs
Provide data inputs (internal + third-party)
Upload presentation templates
Set automation triggers
Establish review and notification criteria
It will take some setting up, and some iteration, but these extensive workflows will be possible to achieve. If not today, then ‘soon’.
Are ‘Agent Bosses’ Here Already
A few weeks ago we talked about the growing notion of ‘Agent Bosses’ but Sunday was the first time I really understood what this might mean.
It is one thing setting up ‘agents’ to perform specific tasks, like creating Custom GPTs, or preset automations in ChatGPT ‘Projects’, but ‘Agents’ with a capital A are another thing entirely. The former is static and deterministic - you program it to do A then B then C in a predefined way whereas the latter are ‘alien entities’ that go off and do you bidding according to their own reasoning.
From Operator to Orchestrator
Imagine a year or two hence. You’ve offloaded glorified grunt work to your army of ‘Agents’, and your job now is to justify your relevance in the loop. So, beyond tending to your army, you’ll have been working hard on developing your ‘situational awareness’ of what living in an AI mediated business world means, deepened your critical thinking, data analytics and problem solving skills, and worked hard on elevating your uniquely human capabilities of empathy, judgement, imagination, creativity, curiosity, leadership and hope.
In short your work will both be on a higher plane than today, and somewhere else entirely. What ‘work’ is will have been redefined.
And I think I grasped this on Sunday.
OVER TO YOU
What would you wish to offload to an army of ‘Agents’? Have you got your data and documentation in order to point these virtual workers at?
But most importantly, what could you do with the time saved? What would an ‘Army of Agents’ enable you to do?
PS So as not to appear too ‘fanboy’ I hereby acknowledge the limitations of these current agent systems. They can be brittle, they might hallucinate and quite why they do what they do is somewhat lacking in transparency.
And yes there are strong data governance and security concerns, especially if one is operating in a regulated industry.
And clearly the ease of agent deployment is still uneven, requiring some degrees of prompt engineering finesse and no, we’re not at the easy plug-and-play stage yet.
But we know this. And nothing is insoluble. So glass half full, not half empty!
AI In CRE - Which Age Group Wins?
Would you rather be 22, 32, or 42?
If you’re 32 and mid-career in CRE, the next few years could make or break your trajectory. If you’re 22, this may be your golden ticket. And if you’re 42… well, you could be in the best position of all — if you act fast.
WE’RE GETTING IT ALL WRONG
There is an assumption in the business world, echoed within real estate, that the people most at risk from the rise in AI are the young, the first-jobber graduates. The thought being that AI will be able to do all the jobs that juniors did historically. And the need for junior employees is going to fall, possibly quite dramatically.
But we’re getting this all wrong!
In reality, the young are best placed to thrive in an AI-mediated business world.
You’d rather be 22, than 32 or 42 in the world we’re going into.
CRE IS BEING RESHAPED
AI is fundamentally reshaping CRE operations and career paths.
Value is on the move.
Take these examples:
In Valuation & Investment, faster and more data-rich underwriting is emerging via AI, and we’re seeing a shift from subjective models to real-time, data-driven insights.
In Asset Management we’re increasingly used to predictive maintenance, smart buildings and 24/7 tenant bots - reducing cost and boosting sustainability.
In Brokerage & Leasing AI-led lead generation is emerging, alongside content creation and negotiation support. Brokers increasingly rely on AI “copilots.”
And in Development & Construction site selection, heavily supported by AI, is arriving, alongside drones for progress monitoring and real-time risk analytics.
THE “AI NATIVE” IMPERATIVE
With everything becoming imbued with AI, we’re going to see some fundamental changes in how businesses operate.
For example, AI is breaking the age-old link between labour and output. It used to be that as your business grew you needed more people, but today that causal link has been broken. We’re entering the world of “Fast, Agile, Ultra-Productive Superteams” where individual productivity is multiplying, rivalling entire teams.
Likewise the old fallback of ‘we have data’ so you have to pay us, is going away. Having data isn’t going to matter much in the future. Unless you have very particular proprietary data it’s not going to have much value as such. AI will commoditise the aggregation and processing of data. We will be making more use of data in the future but given the new market dynamics brought on by AI, its value will trend towards zero. Competitive advantage will stem from strategic interpretation, not access to data. The profit is moving, but towards the canny human, not towards the hoarder of data.*
And across the board key human value will shift towards soft skills: strategic thinking, negotiation, storytelling, trust.
To operate successfully in this world you will have to be “AI Native”.
OUR ARCHETYPES
Which means what for our archetypal 22, 32 and 42-year olds?
Let’s do a SWOT analysis for each of them:
22-Year-Old New Entrant (Graduate Analyst / Junior Surveyor)
Strengths
High digital literacy & comfort with AI tools
No legacy workflows; high upskilling potential
Growth mindset orientation
Weaknesses
Lacks experience & market context
Limited network
Performs highly automatable tasks
Opportunities
Leapfrog career ladders via AI specialisation
Carve out niche new roles (AI Translator, PropTech Analyst)
Become indispensable to leadership by interpreting AI outputs
Threats
Entry-level work being rapidly commoditised
Risk of AI substituting foundational experience
May become “AI tool operators” with no strategic exposure
32-Year-Old Mid-Career Professional (Associate Director / Senior Manager)
Strengths
Deep domain knowledge & deal history
Strong professional network
Client management & team leadership
Weaknesses
Rigid legacy workflows
Excel modelling proficiency is devaluing
Time-poor for upskilling
Opportunities
Reframe role as human-machine orchestrator
Use AI to scale client work and spot model bias
Transition to tech-enabled strategic roles or into proptech
Threats
Skills from first decade are being automated
Risk of being squeezed between AI-native juniors and strategy-driven seniors
Devaluation of their proprietary info advantage
42-Year-Old Established Leader (Partner / Managing Director)
Strengths
C-suite influence, deep networks, strategic acumen
Authority to fund & lead enterprise-wide transformation
Proven in deal-making and capital raising
Weaknesses
Often distanced from day-to-day AI tools
May resist change due to legacy success
Entrenched in outdated models
Opportunities
Architect firm-wide AI adoption and new operating models
Forge alliances with tech leaders
Steer M&A for AI capabilities and AI-aligned assets
Threats
Misallocating resources due to limited AI literacy
Losing market relevance to AI-native firms
Internal resistance to organisational change
For all the archetypes I think the “Opportunities” quadrant is the most interesting. But whereas the 22 year-old just has to double down on being who they are and leveraging that, for the 32 and 42 year-olds they have to make very distinct changes to who they are to grasp these opportunities.
On the face of it, the older two need the younger one more than vice versa. Being naturally “AI Native” is a superpower.
That said, all of them are going to need a proactive and tailored strategy to adapt to these new realities. Being experienced actually feels like a bit of a bug whereas being inexperienced could be considered a feature.
Hard to grasp though it is I think we are at an analogous time to when the Model T Ford first rolled off the production line. When this happened in 1908 the US was producing somewhere between two and three million horse saddles a year, in an industry generating, in today’s money, $2-3 billion in annual revenue. But their market was about to collapse. Whilst I am not predicting a collapse in the CRE industry I do feel it operates somewhat like a posh saddlery. Awaiting a tsunami of change. But mostly looking elsewhere.
Except the 22 year-old. Perhaps?
STRATEGIC PLAYBOOKS
Either way, whatever the degree of change, one can always devise a “Playbook” to adapt.
So here’s what each archetype needs to do across three axes: Skills, Positioning and Network
For the 22 year-old they’ll need a “Agility Playbook”:
In terms of “Skills” they need to become deeply proficient in the major frontier AI models and assorted standout services, like Google’s NotebookLM, but also learn Python if possible (the new Excel…). In addition they should try to use as many of the leading AI PropTech tools as possible, such as those dealing with Lease Abstraction and Underwriting etc.
Regarding “Positioning” they should ONLY work for companies actively leaning into AI, and should be actively pushing for new roles involving working “Human + AI”. They have to be the go-to people for anything AI.
And for their “Network” they should still dive into traditional CRE groups but also try and join in any PropTech WhatsApp or Slack groups they can.
Reread their opportunities above - this is how they’ll make them happen.
For the 32 year-old they’ll need a “Pivot Playbook”:
In terms of “Skills” they HAVE to become AI Literate, and solidly skilled prompters. And a deep dive into “Change Management” wouldn't go amiss, as they’ll be at the centre of moving old to new.
Regarding “Positioning” their role is going to become less about doing, and more about “Orchestrating”. A few weeks ago we wrote about “Agent Bosses” and this is where our 32 year-old should be heading.
And their “Network” has to change considerably as well. Moving beyond real estate to product managers, AI VCs and AI Consultants.
This is going to be tough and is why this archetype is at the most risk.
For the 42 year-old they’ll need a “Architect Playbook”:
Their “Skills” will also need to encompass AI. Sure they absolutely must become daily users of AI and weave it into all their work, but they also need to think hard about AI ethics, governance and data. It’s going to be their job to provide the AI infrastructure foundations, strategic guidance, and AI Policies through which their companies will be working. If a company’s AI goes rogue it’ll be their heads on the block, so they need to know what they are doing.
“Positioning” is their big thing. Redefining their company’s operating procedures (following the unbundling and rebundling we’ve discussed many times) is at their door. They’ll need more data skills, and more AI technologists, and they’ll probably be looking to buy or partner with rising PropTech startups.
Their “Network”, as with the others, needs to extend beyond CRE and become much more focussed on keeping up with the latest technologies and thought leadership. Not least because all their customers are going to be down this rabbit hole, and they must know how to understand how they’re thinking, and how AI is likely to change the nature of their demand for CRE.
So the 42 year-old’s big worry will be being outflanked by more tech-forward competitors, and simply becoming obsolete.
They may be less exposed than the 32-year-old, not because they are immune to disruption, but because their seniority gives them the agency to shape responses — assuming they choose to use it, whereas their younger colleague could do exactly the right things but be snookered because their company messed it up.
MODERATING FACTORS
It’s not all about age groups though. There are four main moderating factors that will also have a large impact on all of them.
First off is “Mindset” - an individual with a lean-in, forward-thinking, curious and keen-to-learn (and unlearn) mindset, across archetypes, will outperform. Dramatically.
Secondly, your “Specialisation” counts. Recently we looked deep at where value is likely to move to in CRE - you must be cognisant of whether your specialism is about to be commoditised.
Thirdly, your “Organisational Context” matters - are you working for a company aiming to become fully AI Native? If not, beware. But also think about organisation types. Global firms will give you access to tools and training, but likely you’ll have little agency. A Boutique firm would suit those attracted to agile, risk-tolerant, somewhat unstructured setups. A Startup might be where you should be but we all know the game there - high immersion but high risk. Or perhaps an Institution? You’ll probably get access to those rare areas of proprietary data and decent budgets to play with, but like the Global firms these tend to be pretty top down places.
And fourthly “Geography” matters. Try and operate out of either “Mature Hubs” or “Emerging Markets”. The former offers rapid adoption but strong competition, the latter a bit more time, less exposure - but the opportunity to be where leapfrogging is possible.
Get all four moderating factors right and you are away!
TBH though, just get your ‘Mindset’ right. In all archetypes that will probably make the most difference. Get it wrong and the 22 year-old won’t get a job (worth having) and the 32 and 42 year-olds are at redundancy’s door.
CONCLUSION: STRATEGIC TAKEAWAYS
I think the first thing to try and really internalise is that we are entering a revolutionary era of change. We have had several decades of iterative change but what is going on now is something much more profound. Once again I’d like to hark back to how in real estate we need to be thinking 5, 10 or more years ahead, and the near certainty that a great deal is going to change by 2030, let alone 2035.
So for your CRE career to survive and thrive will probably require degrees of reinvention. The “Company” and how it operates is changing fast. Automation will target anything that is structured, repeatable, predictable, and increasingly with Generative AI a lot that is random, creative and unstructured. Value will still exist, but it will concentrate in synthesis, strategic judgement, and trust-building - areas where human-AI collaboration excels.
Our 22 year-old, contrary to public belief, is maybe the most advantaged by all of this. The smart ones at least have a growth mindset and AI fluency. Their value will be high and their progression much faster than we are used to. After all they don’t have to learn the past, just push for the future.
Most at risk are our 32 year-olds. Potentially stuck in a pincer move. Their old skills are becoming less valuable whilst they don’t have the new skills, or perhaps mindset for the new ones? If they don’t pivot they are in danger.
But most impactful could be our 42 year-olds. Their great opportunity is to rebuild their companies for the AI-native era. Not easy, but the few who manage it will do great business. Those who don’t will just fade away.
But overall, get that “Mindset” right and whoever you are, you’ll do great!
OVER TO YOU
Where do you stand? How vulnerable, or not, are you? Tell me about your mindset? How are you thinking about the future? I would love to hear people’s views on this.
* Of course this is a generalisation. There is high value data out there, but maybe 60% of the industry’s data needs will be wholly commoditised, with a sliding scale of value for the remaining 40%. People talk incessantly about “Data” in CRE (whilst actually doing little with it) and assume it equates to value. However, in an AI world, when something can be assembled quicker and faster, it tends to get used more, but its intrinsic value drops precipitously, as it has no scarcity value. That does not mean there are no profits to be made out of data, just that where they come from is moving.
From Boardroom Privilege To Baseline Utility
How AI Will Democratise Real Estate Value
EXECUTIVE SUMMARY
AI is going to enable us to unlock entirely new capabilities that were previously unavailable to most market participants, either because of high costs or complexity. What were once considered ‘cost centres’ are going to be redefined as ‘value drivers’.
Across real estate there is now much discussion about AI and its application. Mostly though this centres on doing what we do now, just better, faster, cheaper. Where we should be looking is how we can massively expand the market for real estate services, by taking what is currently bespoke and ‘white glove’ and making it available to all. Efficiency gains only get one so far. What we need is ‘a bigger pie’. This is how to bake it.
WELCOME TO THE 10X WORLD
In this newsletter we’re summarising a 127 page report covering 10 AI Agent Opportunities.
Everything below is expanded on in great detail, and I highly recommend you look at the full details of any opportunity that catches your eye.
THE CORE PARADIGM SHIFT: FROM BESPOKE LUXURY TO MASS-MARKET VALUE CREATION
Last week I was working on what new products and services were going to be enabled by the developments in AI. What were we going to be able to do that we couldn’t do before? After a while I realised I was alighting on areas where, in practice, most industry participants did not have access, but a minority, sub 10%, actually did. So the question should be not so much focussed on what was now possible, but what might be now possible at a price point where the potential market was 10X larger. What if we could serve needs that are currently unmet or only met for elite clients?
So, for example:
Using Generative AI to draft complex investment memos in minutes instead of weeks.
Using AI Strategy Agents to give under-resourced landlords institutional- grade asset plans.
Having an ESG Retrofit Roadmap that turned climate compliance into a self-service tool.
An always-on Market Scanner continuously hunting for mispriced deals.
KEY CHARACTERISTICS OF THE SHIFT
As one thinks about this it becomes clear that these use cases have ‘common themes’.
From White-Glove to Mass-Market: AI democratises services once exclusive to large institutions or high-end clients.
Data-Driven Decision Making at Scale: AI converts reactive, periodic, or "gut-based" decisions into continuous, data-driven processes, leveraging vast data beyond human capacity.
Proactive vs. Reactive: AI enables a shift from reactive "damage control" to proactive "prevention" and "opportunity seizure”.
Cost Centres to Value Drivers: Functions traditionally seen as overheads now become strategic differentiators or revenue generators.
Human + AI Collaboration: AI elevates human roles, handling routine tasks and freeing experts for higher-level strategy, relationships, and problem-solving.
Verticalised Knowledge and Integration: These are not generic AIs, but deeply domain-specific agents integrated with industry data and workflows, capable of bridging data silos.
DEFINING THE OPPORTUNITY
For each of the following ten opportunities we looked at:
The Problem Today (Luxury/Bespoke): What's currently expensive or inaccessible?
The Latent Demand: Who desperately needs this capability but can't get it?
The AI Agent's Vision: What the AI does?
The Strategic Shift/Value: How it impacts the business (e.g., faster, cheaper, better insights, new revenue)?
TEN AI AGENT OPPORTUNITIES
Here are the ten AI agent opportunities we unearthed.
Asset Management – Strategy Agent for Smaller Landlords: Transforms reactive small landlord operations into proactive, data-driven asset management, providing institutional-grade strategic guidance at a fraction of the cost.
ESG – Retrofit & Operational Upgrade Roadmapper: Shifts ESG from a compliance burden to a financial upside, enabling tailored action plans for energy efficiency and compliance for any building, driving energy savings and higher asset values.
Valuation – Narrative + Scenario Generator for SME Portfolios: Provides rich, scenario-driven valuations and narrative reports for smaller portfolios, moving beyond single-point appraisals to dynamic, strategic insights.
Leasing – Prospecting + Incentive Modeller Agent: Accelerates lease-up by proactively identifying prospects and optimising lease terms, democratising advanced tenant targeting and financial modelling for smaller players.
Development – Planning Risk & Local Sentiment Evaluator: Reduces costly surprises and delays in planning by assessing approval risk and community sentiment, enabling proactive design adjustments and stakeholder engagement.
Construction – Delay & Cost Overrun Early Warning Agent: Provides continuous monitoring and early alerts for project risks, leading to fewer delays and cost overruns, particularly for mid-size projects that lack sophisticated controls.
Finance – Lending Memo Generator for £1m–£10m CRE Loans: Dramatically increases efficiency and consistency in small loan underwriting, allowing lenders to scale business and improve risk management by automating comprehensive credit memo generation.
Occupier Strategy – SME Location + Workplace Optimisation Agent: Empowers SMEs to make data-informed decisions on location and workplace strategy, improving talent attraction, cost efficiency, and employee satisfaction.
Property Management – Digital Tenant Concierge Agent: Offers 24/7, instant tenant service, significantly boosting satisfaction and retention while increasing manager efficiency and scalability for all building types.
Investment Strategy – Always-On Market Scanner for Off-Market Deals: Provides an "edge" by continuously finding off-market, distressed, or mis-priced opportunities, democratising deal access and increasing transaction speed.
HOW WE SCORED THE OPPORTUNITIES
Five criteria were used to judge each opportunity. Latent Demand Intensity, Exclusivity of Current Offer, AI’s Ability to Commoditise, Integration Complexity, and Strategic Upside.
Every opportunity was considered to have a high ‘Latent Demand’ and to be currently an ‘exclusive’ offer - it seemed clear that if these services were economically available finding customers would not be a problem.
All are considered to be areas that AI ‘could’ commoditise. Meaning that the functionality of each opportunity strongly correlates with the capabilities of AI.
Integration, perhaps not surprisingly, is the toughest area, and scores varied widely here. Due to the need to access and assimilate disparate external data sources the Planning Risk Evaluator and Market Scanner would clearly be harder to integrate. Data Fragmentation is the industry's achilles heel and overcoming this, whilst not impossible, will certainly be a challenge.
However, all the opportunities were scored highly in terms of Strategic Upside. These aren’t incremental improvements - if they were achieved each one has the potential to materially change business outcomes.
STRATEGIC IMPERATIVES FOR CRE PROFESSIONALS
The absolute imperative, as we’ve discussed before, to ‘Build a Bigger Pie’ necessitates we adopt a 10X Mindset where opportunities like the 10 above are not summarily dismissed as too hard, or too speculative or a distraction from a focus on ‘efficiency’, and automating existing tasks.
ADOPT A 10X MINDSET
We know, even if we don’t want to acknowledge it, that the structure of the industry has to change to leverage all the new technologies, and to reflect the changing nature of demand in the market. We know that without a growth engine far fewer people are going to be needed in the industry than there are now. So we have to adjust our mindsets, stir up our entrepreneurial spirits and push into the future.
We actively need to be thinking of ‘value drivers’ not ‘cost centres’, investing in AI Fluency, and preparing ourselves, and our companies for a ‘human + machine’ future. All of the 10 opportunities above, as well as being potentially lucrative services, offer up great scope for marketing our human expertise - strategy, negotiation and relationship building.
PRIORITISE DATA
We need to prioritise data and modularity. We need to be able to access clean data, seamlessly via APIs, as and when it is required. Being able to use data in a cross functional way, mediated by AI, is going to be a superpower, and a huge differentiator.
And we need to work out how to pull all of this together. These 10 opportunities are not single source offerings. Their very beauty will be in the way they level the playing field by building ecosystems that allow the intelligence of AI to be applied pervasively across all our real estate workflows.
This will not be easy but the prize is great. Our ‘TAM - total addressable market’ will explode if we can really turn these luxury services into mass market offerings.
BUILD THE ECOSYSTEM
Neither real estate companies or PropTechs are likely to pull this off alone. The requirement for very strong technical skills, and perhaps even stronger domain knowledge, is very high. This will require partnerships, trust, and an ecosystem of interoperating vertical AI agents rather than monolithic software. It’s going to require a new way of thinking about business.
Whilst this way forward will not be for the faint hearted, not moving in this direction risks a steady decline. Someone will crack this, and when they do the purveyors of old school real estate services will be highly vulnerable. Even those luxury clients of today are not adverse to saving money, and they too will adapt, adopt and accelerate with AI.
OVER TO YOU
What luxury services do you provide that AI might enable you to sell to a much larger audience? What data do you have that could form part of an ecosystem? What part could you play in all of this?
And which of the 10 opportunities are you going to follow up with by reading their full details in the master report? You’ll be surprised how deeply they are addressed.
If you'd like to discuss how this applies to your business, I'd love to hear from you.
The Expertise Shock: Your CRE Future
How AI's strategic deployment will determine whether the value of your CRE expertise rises, falls, or transforms in the coming years.
EXECUTIVE SUMMARY
AI presents an 'Expertise Shock' for Commercial Real Estate, profoundly reshaping human expertise. Its impact varies based on whether it automates 'inexpert' or 'expert' tasks, causing roles to rise, fall, or transform in value and wages. Firms should strategically adopt AI as collaboration tools, focusing on enhancing human judgement, continuous learning, and uniquely human skills. Firms that strategically deploy AI as a collaboration tool to augment human judgement will thrive; those that don't risk devaluing their greatest asset: their people.
THE EXPERTISE PARADOX
Powerful artificial intelligence marks a pivotal moment for the commercial real estate industry. Its primary impact will not be a simple scarcity of jobs, but a profound and often paradoxical revaluation of human expertise. Which types of ‘expertise’ will remain valuable? The trajectory of any CRE role is going to depend on the type of tasks AI automates.
There is an ‘Expertise Paradox’ - certain roles that are seemingly similar in their exposure to automation (eg. Investment Analyst vs Valuer/Appraiser) may be on divergent paths due to AI’s specific impact on their task bundles.
As we’ve discussed before each job role consists of a set of goals, and then a bundle of tasks required to achieve that goal. How these bundles are configured will have a dramatic impact on the value of the ‘expertise’ they require.
A FRAMEWORK FOR THE EXPERTISE SHOCK
This newsletter will deconstruct the ‘Expertise’ framework, classify AI tools, and provide a ‘Rise, Fall, Transform’ outlook for CRE roles. It is underpinned by the June 2025 paper ‘Expertise’ released by famed US Labour Economists David Autor and Neil Thompson, which opens with this:
‘When job tasks are automated, does this augment or diminish the value of labor in the tasks that remain? We argue the answer depends on whether removing tasks raises or reduces the expertise required for remaining non-automated tasks. Since the same task may be relatively expert in one occupation and inexpert in another, automation can simultaneously replace experts in some occupations while augmenting expertise in others.’
BEYOND ‘EXPOSURE TO AUTOMATION’
Understanding AI’s impact on jobs requires a more rigorous framework that dissects the nature of work itself.
This paper particularly resonated with me because I have argued for many years that real estate’s obsession with ‘where we work’ has meant we’ve hugely under-indexed on ‘the work we do’. As we move into an AI mediated world, what it is we, as humans, actually do, becomes way more important than where we do it. In fact you cannot calculate the ‘where we work’ equation until you fully understand ‘the work we do’.
PILLARS OF THE FRAMEWORK
The framework is built around these pillars:
Expertise: Specialised knowledge and capability commanding a wage premium and acting as a barrier to entry.
Task Bundling: As mentioned above, each job's collection of required tasks, and the varying levels of expertise needed to fulfil them.
Two Critical Scenarios of Automation: How AI interacts with tasks within a professional’s ‘task bundle’ will lead to very distinct outcomes:
Automating the Inexpert: When AI automates routine, administrative, or supporting tasks, it frees the human expert to focus on their most valuable, judgement-based work.
Consequence: This augments the value of human expertise, leading to a rise in wages for those who remain, but a potential contraction in relative employment as fewer people are needed, and the barrier to entry becomes higher.
Automating the Expert: When AI successfully automates the core expert task itself—the very skill justifying a wage premium—it erodes the scarcity of that expertise.
Consequence: This devalues the expertise, resulting in a fall in wages for incumbents. However, it can lead to an expansion in relative employment as the removal of the expertise barrier allows a larger pool of less-qualified workers to enter the field with AI assistance.
AI'S DIVERGENT IMPACT ON KEY CRE ROLES: RISE, FALL, TRANSFORM
Whether AI automates the inexpert, or the expert, directly correlates to whether CRE roles will ‘Rise, Fall, or Transform’. So AI’s impact is much more complex and role specific than generally allowed for. Here are some:
Roles Set to "Rise" or “Transform”: These are roles where AI is likely to ‘augment’ the human by automating the inexpert.
Investment Analyst: AI can automate data collection, aggregation, and initial financial model population. This frees analysts to focus on strategic thinking, critical analysis, designing complex models, and interpreting data. Their value and wages are set to rise, though relative employment may contract as each analyst becomes more productive.
Acquisitions Officer: AI can automate lead generation and initial deal screening. This allows officers to focus entirely on negotiation, relationship cultivation, sourcing off-market deals, and strategic judgement. Their value and wage potential will rise, and relative employment may slightly contract.
Asset Manager: AI can handle data aggregation, reporting, and predictive forecasts. The role shifts to a higher-value, purely strategic function, focused on business planning, investor relations, and value creation. Wages are poised to rise, with likely employment contraction as managers oversee larger portfolios.
Broker (Tenant & Landlord Representative): AI can automate market analysis, listing summaries, and initial communications. This enables brokers to dedicate more time to client consultation, strategic advisory, and complex negotiation. Their value and commissions will rise, potentially leading to market consolidation and a "flight to quality” (this time of people, rather than buildings!)
Roles Set to “Fall”: These are roles where AI is likely to ‘automate’ away the value of the human by automating the expert tasks they traditionally perform.
Valuer/Appraiser: Sophisticated Automated Valuation Models (AVMs) can directly target the valuer/appraiser’s core expert task of applying valuation methodologies to standard properties. This erodes the scarcity of human valuation expertise, leading to a significant fall in wages. However, the role will likely transform and narrow, with a new elite tier of valuers/appraisers focusing on highly complex, unique properties, or high-stakes litigation/advisory work where nuanced human judgement is still critical. This group will retain ‘expert’ level incomes, whilst relative employment may expand for less-qualified users of AVMs, at lower rates.
This presents a stark trade-off. For the roles where AI automates the inexpert, people are likely to earn more as they can concentrate more of their time on high-value activities. But we will need less of them. Nice work if you’re one of the in-crowd, less so if you’re not.
And then, for the roles where AI automates the expert, we are likely to see currently highly paid people suffer a significant contraction in their earning potential, but then the chance for many less expert people, working with the AI tools, to probably raise their incomes. We can do a lot more appraisals, valuations as they become cheaper to do, but those doing them no longer need to be rare ‘experts’.
STRATEGIC CHOICES FOR FIRMS: AUTOMATION VS. COLLABORATION TOOL
Autor and Thompson make a critical distinction between two types of AI tools, which represents a strategic choice for firms with profound consequences for work organisation and careers.
Automation Tools:
Purpose: Designed to fully replace a human task by codifying specialised knowledge into software. Primary goal: efficiency and cost reduction.
Examples: Automated lease abstraction, automated rent roll processing, tenant communication chatbots, automated financial tools.
Impact: Over-reliance on these tools can lead to a "hollowed-out" organisation and the "ladder problem", where thinning junior ranks create a pipeline gap for future senior leaders who historically learned fundamentals through these tasks.
Collaboration Tools:
Purpose: Designed to augment and amplify human professional skills, acting as a "force multiplier". Primary goal: enhance human capability and judgement.
Examples: AI-powered underwriting and investment analysis which provide a starting point for human analysis, advanced AVMs used by expert valuers/appraisers to layer nuanced market expertise, predictive analytics for brokerage to identify leads, AI-augmented CRMs for relationship management.
Impact: Strategic adoption can strengthen firms by empowering professionals, democratising expertise, and potentially creating new "middle-skill" roles (e.g., "Deal Analytics Specialist," "Asset Performance Analyst") that leverage AI for sophisticated analysis. Career progression shifts to valuing an individual's ability to effectively partner with AI and perform "judgement work”. Note: Research generally shows that AI has a strong potential to raise the capabilities of lower skilled people more than highly skilled ones. Both do gain but the highest uptick is from those in the lower quartiles of competence. See ‘The Jagged Edge’ study for more on this.
STRATEGIC RECOMMENDATIONS FOR CRE FIRMS
We recommend a two-pronged strategy that addresses both talent and technology.
Talent Development:
Shift from Training to Continuous Learning: Develop a culture of "constant adaptation" through continuous, integrated learning, exploring experiential methods.
Cultivate "Judgement Work": Redesign curricula to teach professionals how to effectively work with AI – asking the right questions, spotting anomalies/biases, and applying contextual understanding to AI outputs.
Autor and Thompson emphasise the objective should be to help people “acquire judgement faster”. Which might grate with a certain old school ‘learn by doing’ type, but ‘learning judgement’ is something AI can enable by exposing individuals to countless simulations they can learn from. Role playing ‘games’ can be enormously effective.
Double Down on Inherently Human Skills: Invest aggressively in capabilities AI cannot easily codify: complex, multi-party negotiation; strategic relationship management and trust-building; persuasion; and creative, "out-of-the-box" problem-solving. These will be a firm's most durable competitive advantage. And again, AI can help develop these skills. For instance our own ‘The TDH Daily CRE Critical Thinking Challenge’ can be used to role play endless domain specific problems or tricky circumstances.
Technology Adoption:
"Collaboration First" Procurement Policy: Leadership must shift focus from "how many headcount can this tool replace?" to "how does this tool make our best people better?" Prioritise augmentation tools, especially for core, revenue-generating functions. This is a talent retention strategy.
Integrate, Don't Silo Data: Break down internal data silos to create a unified data environment. This fuels more powerful and accurate AI-driven insights, providing a significant competitive advantage.
Manage AI Risk with "Human-in-the-Loop" Governance: Implement a robust governance model that mandates human oversight for all critical decisions, positioning AI as a powerful advisor, but ensuring final judgement and accountability rest with a human professional. This mitigates risks like opaque decision logic, data privacy concerns, and AI "hallucinations".
CONCLUSION: THRIVING IN THE AGE OF AI
AI's impact is a nuanced story of task redistribution and expertise revaluation. The simplistic narrative of 'robots taking jobs' misses the point entirely. We need to think at a much more granular level, understanding where value will fall and where it will rise. Clayton Christensen’s "Law of Conservation of Attractive Profits” describes how the ability to earn attractive profits shifts within a value chain as products or processes become commoditised’. Profits don’t disappear, they move.
And they will in real estate, so we must work out in advance …. where to?
So real estate companies must pivot from a cost-cutting mindset (not everyone but often this is the default way of thinking) to one of value creation by strategically deploying AI as a collaboration tool, redesigning career paths for new middle-skill roles, and doubling down on investment in uniquely human skills that will remain scarce and valuable.
NOTE: Future Factors to Watch
The generally good news in this newsletter - higher value roles even if less of them - ‘might’ be cast aside if any of these three developments, or a combination of them, come to pass.
Data becomes ubiquitous and more open: Much of the ‘human’ edge remaining in an AI world revolves around knowing things that others do not. If this changes the edge diminishes. I’d rate this as likely to very likely, but over a decade rather than imminently.
Negotiating complicated Leases is something humans can do, face to face, far better than AI. So whilst this remains the norm, humans have a valuable edge. But if asymmetric negotiations become more commonplace an AI negotiator might well win the day. Again, I have this in the likely camp but over time.
And thirdly, as distributed working really starts to bed in more and more companies will be procuring their space on shorter terms, with more boilerplate agreements. The Leasing process will become simpler. This again would diminish the humans edge over the AI. I’d say this is highly likely, but again, over a decade rather than imminently.
But all this would mean is us humans will have to work where the profits are moving to again!
OVER TO YOU
Take a look at your own daily task list. Which bucket does most of your work fall into: 'expert' or 'inexpert'? Which tasks could an AI collaborator augment today? The answers will point to your future. If you're building a strategy to navigate this, I can help. Drop me a line.