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.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

1. 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.

2. 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.

3. 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.

Stop Wasting Money On AI

Nearly eight in ten companies report using gen AI—yet just as many report no significant bottom-line impact. WHY?

“It took changing the system to suit the tool rather than adapting the tool to suit the system, that unlocked extraordinary gains.”

EXECUTIVE SUMMARY
Most companies are investing in AI but failing to see a significant return. The problem isn't the technology; it's the approach. Simply bolting AI onto existing, outdated processes is like replacing a steam engine with an electric motor but keeping the inefficient factory layout. True transformation—and a massive competitive edge—comes from fundamentally redesigning work around AI's unique capabilities. This article explains why this shift is critical and introduces a proven four-principle framework for achieving real ROI by reimagining roles, proving value in sprints, and empowering your people through a safe, human-centric transformation.

THE AI ROI PARADOX
‘Nearly eight in ten companies report using gen AI—yet just as many report no significant bottom-line impact’. According to a report from McKinsey this June 2025.

Which is not surprising, and was largely foretold. By many.

The big issue is that, as happens with all new technologies, companies tend to try and bolt them on to existing systems. We’ve spent, in many cases, decades ‘digitising the past’ and now many are set on ‘AI’ing the past’.

This approach either never works or doesn't get you very far, because the process itself is the impediment. A hammer is great if you have nails to hit, but less good if you’re working with eggs. Introducing a new technology can be as dramatic a change as moving from nails to eggs. 

The groundbreaking technologies enable you to do things you simply could not before, so the entire architecture of what you do can be fundamentally different. It’s only when you align all the moving parts, and redesign ‘the system’ do you see great returns.

A LESSON FROM THE AGE OF ELECTRICITY
The archetypal analogy is the steam factory at the dawn of the electricity age.

Steam powered factories essentially had one large engine powering a single large driveshaft which controlled winches and pulleys and formed the centrepiece of all activity. So swapping the technology powering the driveshaft from steam to electricity made very little difference. Work carried on as before. No-one really noticed.

So from the early 1880s when electricity started to become available as a power source, until the 1920s, the new technology had very little effect on productivity.

In the early 1920s though they started to redesign the factory itself. Instead of one central drive shift, work was split up (Adam Smith’s ‘Division of Labour’) into multiple workstations each with their own electric source of power.

And productivity exploded!

It took changing the system to suit the tool rather than adapting the tool to suit the system that unlocked extraordinary gains.

FROM ELECTRICITY TO AI: WHY THIS TIME IS FASTER
And today is exactly analogous to this. We are largely pre-redesign and getting exactly what could be expected.

Except in two ways. First, whilst electricity is a ‘General Purpose Technology’, meaning it has an effect across an entire economy, AI is a ‘GPT’ in a much more obvious way (the GPT in ChatGPT actually stands for ‘Generative Pre-trained Transformer’). Electricity was so novel it took a long time for its use to disseminate, but the pervasive consequences of AI are much easier to see. We can see where it can be leveraged very easily. This does not mean across the board adoption will be instantaneous but it does suggest that the timelines for adoption are probably much shorter. Maybe think a decade rather than forty years.

And secondly AI is something that certain industries (think Software, Marketing), especially startups, can leverage, at scale very very quickly. Hence the rise of the fast, agile, ultra-productive superteams we’ve discussed before.

So competition to all is going to come much faster. Yesterday I was at the CRETech London conference, and someone from JLL noted that they were already seeing ‘fee compression’, particularly in marketing. Customers are realising that suppliers have access to tools that should enable them to do more, much faster, and so are expecting lower costs.

Try competing in a market expecting different price points because of technology, when you are not using those technologies. It’s a very fast route to margin erosion, even bankruptcy.

So those 80% of companies have got to get their acts together and work out how to extract real ROI from AI.

We think we have the solution to that. With …

OUR FOUR GUIDING PRINCIPLES
Our methodology is designed to be both transformative and safe. It balances bold strategic goals with practical, human-centric execution.

The fundamental aim is to maximise your impact by enabling you to focus on where you add the most value. Automate routine tasks, augment human capabilities, and cultivate ‘AI Synergy’—where humans and machines together achieve outcomes greater than either could independently.

This is achieved through four core principles.

1. Reimagine the Role, Not Just the Task.

• The Principle: We don't just "AI the past." Our primary goal is to fundamentally redesign work by unbundling roles into their core tasks. We then analyse and rebundle the role around its highest-value, uniquely human functions—creativity, strategic thinking, and client relationships—while AI handles the rest. This creates new capacity and more fulfilling work.

• Why it Matters: This is how we move beyond simple efficiency gains to create a true strategic advantage and a more empowered workforce.

2. Prove Value in Sprints, Then Scale with Confidence.

• The Principle: Transformation starts with focused, evidence-based experiments. We don't bet the firm on an unproven idea. We use rapid micro-sprints (2-4 weeks) to test a new, AI-augmented workflow on a small scale.

• Why it Matters: We only scale what works. Every sprint must conclude with a clear "ROI Sketch" that demonstrates concrete value. This data-driven approach de-risks innovation and ensures we only invest in proven, next-generation workflows. This combines starting small with the discipline of proving value before scaling.

3. Empower the Person, Govern the Platform.

• The Principle: This is our core social contract. We empower your people with powerful tools and the autonomy to innovate, but we do so within a framework of strong governance. The human is always the expert-in-the-loop, accountable for the final output.

• Why it Matters: This builds trust. It tells your team that AI is a tool to augment them, not replace them. And it tells your leadership that we are managing risk, protecting IP, and ensuring security by governing the platforms, prompts, and data they use.

4. Capture & Compound the Learning.

• The Principle: A single success is a victory; a shared success is a compounding capability. The knowledge gained from every sprint—both successes and failures—is a valuable asset that must be captured and shared.

• Why it Matters: We build a living "Process & Prompt Library" that becomes your firm's central playbook for operational excellence. This creates a powerful flywheel effect, allowing the entire organisation to get smarter, faster, and more innovative with every cycle.

Bringing It All Together

• Principle 1: Strategic Vision

• Principle 2: Agile Process

• Principle 3: Human & Safety Ethos

• Principle 4: Organisational Learning Engine

Collectively, these principles form a coherent, resilient, and adaptive framework that transforms AI from mere technology deployment into strategic, human-centred competitive advantage.

THE HARD TRUTH: AI IS A CHANGE MANAGEMENT INITIATIVE
It is important to think of this process as a change management initiative as much as a technology program.

You HAVE to take your people with you. And these four principles allow for that, where each acts effectively as a fly wheel for the other. It’s an iterative process with strong feedback loops. 

It is also very upfront and honest. Too often in real estate I listen to people say ‘it’s not going to take your job, it’s going to augment you’. I think this is fundamentally dishonest, and your employees will see it that way, too. It is 100% certain that for a given unit of output, a company is going to need fewer people. It is only if collectively, or at an organisation level, we can BUILD A BIGGER PIE  that all jobs will be safe. And even then all jobs are going to change, as we change workflows.

All of which is very much a bug or a feature. For those who do not change, do not lean in to leveraging AI, bad things, frankly, are going to happen. Unless you have something very very special, unique and coveted in your armoury. But for those who do push hard now, I think they have two years, at least, to make hay. They’ll be dramatically more competitive than their peers and mostly their peers will take a good few years to change themselves enough to compete back.

THE 8 STEPS TO IMPLEMENTATION
The devil is in the detail (and there is a lot of detail behind these headline topics) but these four principles are manifested in these eight steps:

Step 1: Educate & Engage Stakeholders

Step 2: Co-Design with Workers

Step 3: Analyse Jobs, Prioritise Tasks and Prepare Data

Step 4: Communicate Role Impact

Step 5: Personalised Learning & Growth

Step 6: Redesign Jobs & Processes

Step 7: Monitor, Evaluate, Refine

Step 8: Foster Innovation & New Operating Models

All of which we will discuss in future newsletters, but for now I hope they give you a feel of the direction of travel you need to be going in.

OVER TO YOU
Where are you with your AI adoption strategy? Have you gone down many dead ends yet? Which principle do you think will be hardest in your organisation? Let me know. And if you’d like to go deeper into all of this with me, please get in touch.

The End Of Asking IT For Permission

“When everyone can code, the competitive advantage shifts from having developers, to knowing which problems are worth solving.”

EXECUTIVE SUMMARY

Why Real Estate Is Looking in the Wrong Direction The industry obsesses over where we work (office, home, hybrid) whilst missing the more fundamental shift: what work we do. 

Location is the output, not the input.

The microsoftware revolution changes everything. AI tools like Lovable and Cursor now let anyone create bespoke applications using plain English. The economics flip when software development drops from thousands of pounds to one hour of professional time plus minimal API costs.

Power shifts from IT to end users. When domain experts can solve their own micro-problems without coding or procurement, organisational dynamics transform. The "long tail" of previously uneconomical workflow needs becomes instantly addressable.

For real estate, this is seismic. As work becomes defined by orchestrating AI agents and custom microsoftware rather than location-dependent tasks, the link between productivity and physical space weakens further. Future demand will be shaped less by hybrid policies and more by the fundamental reimagining of work itself.

IT’S THE WORK WE DO THAT MATTERS

In real estate everyone is fixated on ‘where we work’ as a signal to the future nature of demand. In the office, at home, in 3rd places, or a hybrid of them all. The mix matters. Or so everyone seems to think.

There is though a much more important driver of the future of demand, and that is ‘the work we do’. The nature of the tasks we have to perform, to achieve the basket of goals we need to satisfy, to fulfil the requirements of our jobs, is a massively more important variable than where we do them. Because where we do something is a function of what it is we have to do. Where we work is an output, not an input. It is correlation, not causation.

And of course, it is technology that changes the ‘work we do’. Always has done. Electricity changed the nature of factories, and the computer, first the mainframe, then the PC, then the laptop, and then mobile devices all led to profound changes in offices and beyond. 

And the Internet and then WiFi did so again. 

And as hardware, and software, developed we all changed the ‘work we do’, which in turn led to the breaking of the link between work and space. We no longer need to be in place A to do task B.

So fixating on where we work is to miss the point. To be looking in the wrong direction.

 ALL CHANGE, ALL CHANGE

AI is set to change things again, probably quite dramatically.

A few weeks ago, in ‘Intelligent ‘AI’ Agents and Real Estate Demand’ we looked at the coming era of Agents and Agent Bosses, where each of us will be primarily working with, orchestrating, a slew of virtual assistants. But something else is also brewing.

The age of “Microsoftware” or “Workflowware.” 

And this will be as significant as the introduction of spreadsheets was.

THE ECONOMIC CONSTRAINTS OF THE PAST

Historically, software development followed a capital-intensive model. High fixed costs meant only problems shared by many got solved. The traditional software market serves the "head" of the demand curve - problems common enough to justify building a commercial product (e.g., a CRM).

So ‘the long tail’ never got serviced. An almost endless set of micro requirements that would be super useful, but perhaps only for a limited time, or for a small group of people.

AI-guided automation now makes it feasible to serve this long tail. And this opens up an enormous marketplace of heterogeneous unfulfilled needs.

LOVING LOVABLE

Let me give you an example.

On my website I have an index page of all my blog posts going back to 2013. Hundreds of thousands of words. I’ve long wanted to be able to extract the content of each of these posts, save them as pdfs, and move them into a folder on my Google Drive.

But how?

Until now I had no idea. I could do it manually but that would:

1. Take ages

2. I’d die of boredom doing it. 

So my drive remained empty.

This weekend though I used an AI powered software development tool called Lovable (Lovable.dev) and simply told it what I wanted to do, in plain written English. 

From there it produced a baby application I could use to fulfil this task. 

We went back and forth a bit (mostly with me saying ‘What do I do now?’) and I had to set up a new account with a ‘scraping’ service called ‘Firecrawl’, as well as set up a ‘Zap’ on my Zapier account. Both of which are cheap, non-tech friendly, and can be ‘mastered’ in not a lot of time.

But essentially the whole process was via ‘natural language computing’. I did not write any software. I just asked the existing software to write it for me.

THIS IS TRANSFORMATIONAL

The consequences of this - products like Lovable, but also Cursor, and many more - are, I think, transformational.

The quantity of software we use in our daily lives is set to explode. And we, as individuals, are going to be producing a great deal of it. The ROI calculation changes when the "I" (Investment) drops from hundreds/thousands of dollars/pounds etc in developer time to one hour of a business professional's time plus minimal API costs.  

This is so empowering for domain experts. You know your workflow inefficiencies better than any external developer, and with these new tools you can create a software solution easily. No begging IT, no long procurement process, just problem to solution in no time at all.

Each of us now has a toolkit which allows us to solve our own micro problems. Ones that were previously too small for IT but too complex for a manual approach.

I used to write about how every company needs to become a tech company, but now everyone can be a software creator. The dream is becoming true!

GIVE ME BACK MY AGENCY

What really appeals to me about this development is the amount of ‘Agency’ it returns to us, as individuals. If we can simply get on and quickly build the micro applications we need, on an ad hoc basis, that is a remarkable power. These could even be one offs - I just need an app to to X today. I can bin it after that. If I need it again, I’ll just recreate it.

It presages a ‘no permission needed’ world, where we have complete agency over what we need to do to achieve our desired outcomes.

It’s for us to work it out. The responsibility is ours, but so is the freedom. And anyone curious, progressive and ambitious will always be willing to assume responsibility if it comes with freedom to act.

So What Should You Do?

Start to adapt your mindset. Stop thinking "How can I do this faster?" and start thinking "How can this be done without me?" Identify the repetitive, rule-based parts of your job. 

Pick a personal pain point, just as I did. Maybe it's consolidating news about your top 10 clients from 5 different sources into one daily email. Build it. See it work. Then find the next one.

Find some time to play around with tools like Lovable and Zapier. Just pick one or two good ones like these and get confident in using them. Because you can ask them ‘How do I X, Y or Z’ the learning curve is not steep. Becoming really good at using them will take time but you can get to base camp pretty damn fast. And once there, you will see results. Not quite instant gratification, but not far off.

POWER IS SHIFTING

The real disruption here isn’t automation per se — it’s who gets to automate. When non-coders can build software to solve their own problems, the centre of gravity shifts. Which makes this not just a technological shift, but a power shift.

And power shifts, as we all know, always change the game.

OVER TO YOU

Which pain point would you like to eradicate? What software would you like to write?

Pick a few then try Lovable.dev (other options exist but this is particularly non-techie friendly). Whenever you get stuck, ask it what to do. Or use ChatGPT or Perplexity to explain things to you. Perplexity is particularly good at acting as a ‘Help’ function. Often better than using a sites own version.

The key is knowing what problems you can apply this to. Your domain knowledge is really valuable - adding these tech capabilities on top is a double boost to your competitive advantage.

And remember: most of your peers won’t be doing this!