THE BLOG
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 Simply bolting AI onto existing, outdated processes is like replacing a steam engine with an electric motor but keeping the inefficient factory layout.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:
Take ages
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!
Where Office Profits Move Next
“The most valuable square metre in real estate is now the one your algorithm controls, not the one your tenant occupies.”
EXECUTIVE SUMMARY
(Note: This week we are singularly focussed on the office market)
Why the Real Estate Playbook Just Flipped
For decades, real estate profits followed location and floor area—the physical square metres leased to tenants. But AI is changing this equation dramatically.
Buildings that leverage AI to optimise operations, match tenants instantly, automate underwriting, and adapt to occupant needs will capture significantly higher margins. In this new world, your asset’s intelligence—the data, algorithms, and integrations that orchestrate how the space functions—becomes your primary competitive differentiator.
In other words: the smartest building wins, even if it’s smaller, simpler, or cheaper than the competitor next door. The real estate playbook has flipped. Profits are moving from square footage you lease to the intelligence your technology layer provides.
INTRODUCTION: THE BATTLEFIELD FOR REAL ESTATE’S NEXT DECADE
The office sector is experiencing a rapid influx of AI-native innovation—products and services designed around artificial intelligence rather than simply enhanced by it. Unlike traditional proptech, which added incremental digital improvements, these new solutions are fundamentally changing the game by automating complex tasks, extracting real-time insights, and forging entirely new business models.
This wave of disruption is global, reshaping markets from London and Paris to New York and Toronto. Forward-thinking incumbents and agile startups alike are embedding AI directly into their operations, transforming how office buildings are designed, financed, managed, and experienced.
Critically, this transformation transcends traditional industry boundaries. AI is bringing together proptech, fintech, workplace technology, and urban logistics, blurring lines between sectors and amplifying the impact of each innovation.
In this newsletter, we investigate four critical battlegrounds where AI is actively reshaping the economics and strategies of commercial office space:
Asset Operations – AI becomes the building’s new general manager.
Capital Markets – Valuation and underwriting transform into instant utilities.
Design/Build – Generative engines and construction robots redefine what’s possible.
Occupier Experience – Offices evolve into flexible, responsive service ecosystems.
The prize? Capturing the next decade’s operating income, advisory fees, and development upside—at a moment when traditional office profitability is under unprecedented pressure from hybrid work trends, shifting tenant demands, and tightening financial conditions.
Let’s unpack the battlefield.
STRATEGIC IMPACTS OF AI-NATIVE DISRUPTION
As Clayton Christensen’s Law of Conservation of Attractive Profits teaches, when a previously profitable part of the value chain (leasing, underwriting, management) becomes commoditised, the profits don't vanish, they move elsewhere. AI in real estate is precisely this process in action.
1. ASSET OPERATIONS (LEASING & PROPERTY MANAGEMENT)
AI is streamlining how office assets are leased and managed, driving efficiency in day-to-day operations. Traditional leasing and property management involved manual, time-intensive workflows – from canvassing tenants and negotiating leases to handling maintenance requests and optimising building systems. AI-native solutions are now automating and enhancing these functions.
Strategic Impact:
Brokerage disintermediation: AI-driven marketplaces and lease-analysis tools erode information and process advantages of traditional brokers, putting downward pressure on commissions.
Flexible-lease advantage: Landlords able to offer short-term, on-demand space (matched and serviced by AI) reduce vacancy risk and attract tenants that value agility.
Operational cost compression: Autonomous HVAC optimisation, predictive maintenance and conversational service bots cut energy, labour and downtime, lifting net operating income.
Higher service benchmark: Always-on AI responsiveness raises tenant expectations; managers that lag on tech risk losing mandates.
Workforce shift: Routine lease admin and service requests are automated; teams must upskill toward data analytics and exception management.
New premium services: Data-rich insights (e.g., portfolio lease risk, ESG performance) become billable add-ons, allowing forward-looking managers to defend or even expand margins.
2. CAPITAL MARKETS (VALUATION, UNDERWRITING, INVESTMENT)
Within CRE capital markets, AI is enhancing how stakeholders value assets, underwrite deals, and manage investment portfolios. The office sector, facing high vacancies and evolving demand, especially benefits from data-driven decision support. AI-driven analytics can process enormous datasets – property financials, market comps, economic indicators – far faster than any human analyst, surfacing patterns and risks that inform smarter investment choices. Key areas of disruption include automated valuation and underwriting, predictive risk analysis, and transaction automation.
Strategic Impact:
Analytical commoditisation: Instant AI valuations and loan screenings make basic modelling a utility function; human analysts migrate to oversight and complex deal structuring.
Fee compression vs. scale benefits: Faster, cheaper underwriting can shrink advisory spreads, but incumbents that deploy AI first can process more volume with the same head-count, protecting profitability.
Erosion of information moats: Wide availability of AI analytics narrows asymmetry between advisers and clients, forcing brokers and lenders to differentiate on judgement, relationships and proprietary data.
Shorter deal cycles, higher liquidity: Algorithmic matching of buyers, sellers and lenders accelerates transactions, but also reduces per-deal revenue for intermediaries.
Risk-management edge: Continuous AI monitoring of loan and asset performance alerts investors to emerging stress sooner, rewarding firms that integrate real-time data into capital-allocation decisions.
3. DESIGN / BUILD (GENERATIVE ARCHITECTURE & CONSTRUCTION TECH)
Upstream in the real estate value chain, the design and construction phase is being transformed by AI through generative design, construction robotics, and project management tools. The office buildings of tomorrow may be conceived and built with significant input from algorithms, resulting in more efficient, creative, and cost-effective development processes. Innovations from adjacent sectors like advanced manufacturing and robotics are converging here, as are new startups focused specifically on architecture, engineering, and construction (AEC).
Strategic Impact:
Timeline and cost compression: Generative design and AI-guided construction monitoring cut weeks from design iteration and slashes overruns, challenging percentage-fee models for architects and contractors.
Profit-model realignment: Contractors that relied on change orders and contingency padding lose that margin as AI reduces errors; performance-based contracts become more viable.
Lower entry barriers: Small developers equipped with AI design/configuration tools can compete with larger firms, intensifying competition for sites.
Integrated value chain: Data flows seamlessly from design to robotic construction and into digital-twin operations, favouring firms that can offer end-to-end platforms or tight partnerships.
M&A and consolidation: Incumbent software, engineering and construction giants accelerate tech acquisitions to internalise AI capabilities and defend market share.
4. OCCUPIER EXPERIENCE (TENANT SERVICES & HYBRID-WORK ENABLEMENT)
Perhaps the most visible disruptions are those directly impacting the occupiers of office space – the companies and employees who use offices daily. As hybrid work models proliferate, landlords and employers are turning to AI to help make offices more attractive, efficient, and responsive to occupant needs. Innovations range from smart tenant apps and AI concierge services to intelligent space management and even cross-sector blends like workplace logistics and wellness tech.
Strategic Impact:
Building as a service platform: AI-enhanced tenant apps and virtual concierges turn the physical asset into a digitally serviced product, differentiating smart buildings from commodity offices.
Demand elasticity: AI space-usage analytics help occupiers downsize or reconfigure footprints; landlords must adapt to smaller, more flexible leases and usage-based pricing.
New revenue streams: Data-driven amenities, premium digital services and wellness integrations create upsell opportunities for landlords beyond base rent.
Blurring sector lines: Workplace strategy, IT and HR converge with property management as AI insights about people and space drive joint decision-making; service providers reposition accordingly.
Talent & brand effects: Tech-forward, health-optimised workplaces enhance employer branding and talent retention, shifting tenant preference toward landlords with robust AI capabilities.
CONCLUSION
Strategic Outlook: Your AI Imperative
AI-native innovations aren’t incremental upgrades—they're structural shifts redefining the value chain in office real estate. Firms across the UK, EU, and North America face a clear strategic imperative:
Move fast on integration. AI-driven platforms will dominate—actively partner or acquire capabilities to secure competitive advantage.
Invest in proprietary data assets. Data governance and unique data streams will determine future winners and losers.
Rebalance your workforce immediately. Automate routine tasks now, invest aggressively in AI literacy and analytics skills.
Focus relentlessly on customer expectations. Your clients already expect AI-driven responsiveness—fall behind, and risk permanent brand damage.
Think ecosystem, not silos. Future value emerges from seamlessly orchestrating data, finance, leasing, and occupancy into integrated business models.
The future of commercial real estate is unfolding now. AI-first enterprises—incumbent or insurgent—are poised to capture the lion’s share of tomorrow’s fees, income, and asset upside. If you wait, you lose. This isn’t optional—it’s existential.
OVER TO YOU
You’ve seen how AI is shifting profits and rewriting the real estate playbook. Now consider:
Where in your value chain are profits currently secure, and where are they at risk as tasks commoditise?
Which modularised activities should you relinquish, and which integration points should you own outright?
How might your business model evolve if AI-driven platforms become dominant competitors—or partners?
Your competitive advantage depends less on immediate actions than on clear strategic reflection: understanding exactly where profits are moving, deciding which shifts to resist, and identifying precisely which you will proactively leverage.
That’s the critical thinking—and strategic clarity—your future profits demand right now.
The Easiest AI Win In Real Estate
‘Nothing will raise your productivity — or the quality of your thinking — faster than building your own Custom GPTs.’
Executive Summary
Every real estate professional should be building their own collection of Chat GPT Custom GPTs.
Because Custom GPTs deliver unmatched ROI: automate tasks, enhance insight, and save hours per week.
They require no coding: you can build powerful tools tailored to your role in just an hour or two. Sometimes even less.
And you use them to do the work you already do — only better, faster, and more intelligently.
WHAT’S A CUSTOM GPT?
A Custom GPT is a tailored version of ChatGPT that behaves the way you want it to — with specific instructions, personality, tone, and capabilities — all without requiring any coding.
It’s a bespoke AI assistant built on top of OpenAI’s ChatGPT-4 architecture. You can create one in minutes by:
Giving it a name and description
Setting special instructions for how it should behave
Uploading files or adding knowledge sources
Selecting tools it can use (e.g. web browsing, code interpreter, image analysis)
Optionally including APIs via OpenAI’s “actions” feature
They let you automate repetitive tasks, codify expertise, scale internal IP — and experiment fast without relying on IT.
Even more powerfully, they can act as mentors, advisors, and teachers — and this is where their true power lies. As tools to help you learn, and think, they are amazing.
For real estate professionals, this means you can build AI tools that think like you, act like an expert, and help scale your judgement across deals, documents, and decisions.
CUSTOM GPTS FROM THE TDH COLLECTION
Here are some of the 25+ custom GPTs in the TDH Collection, as part of our #GenerativeAIforRealEstatePeople course. Each with their own ‘Elevator Pitch’.
STRATEGIC DECISION MAKING
The TDH CRE Strategist Pro
Elevator Pitch: I’m your senior strategic advisor for Commercial Real Estate investment and development — a trusted partner to help you think clearly, plan effectively, and avoid costly missteps. Whether you're exploring a new deal, refining your portfolio strategy, or structuring a development, I bring seasoned, objective guidance to help you frame the opportunity, assess the risks, and sharpen the path forward. I'm not here to make decisions for you — I’m here to help you make better, more informed ones.
The TDH Portfolio Strategy Mentor
Elevator Pitch: I help professionals and teams confidently navigate the complex world of commercial real estate portfolios. Whether you're trying to optimise performance, reduce risk, or plan long-term strategy, I bring clarity, structure, and insight tailored to your experience level. My approach is empathetic, grounded in real-world application, and designed to turn confusion into confidence. If you’re ready to sharpen your CRE strategy skills and make better, more strategic portfolio decisions, I’m here to guide you—step by step.
The TDH Virtual Equity Analyst for UK Real Estate
Elevator Pitch: I'm your senior virtual equity analyst, built to deliver professional-grade insights on UK-listed real estate companies. Upload any financial report — quarterly, annual, or investor presentation — and I’ll dissect it like a seasoned analyst: highlighting key operational and financial trends, assessing portfolio performance, evaluating balance sheet strength, and flagging strategic risks and opportunities. Whether you need a full investment case, quick valuation snapshot, or help answering specific questions like 'Is the dividend sustainable?' or 'What’s driving NAV movement?', I’ll deliver fast, structured, and data-backed analysis — tailored to your needs and focused on what matters most to real estate investors.
OPERATIONAL EXCELLENCE
Elevator Pitch: I help businesses confidently navigate the complexities of office lease negotiations by breaking down legal and financial terms into plain language, highlighting key risks, and suggesting commercially sound strategies. Whether you're negotiating new space, renewing, or downsizing, I’m here to guide you every step of the way—so you can make smart decisions, avoid costly pitfalls, and secure terms that truly support your business goals.
PS. It will also take the other side of the negotiation:)
Elevator Pitch: I’m a valuation consultant specialising in data-driven, RICS-compliant property valuation. I support professionals with advanced modelling, geospatial analysis, and integration of valuation technology — helping you make informed, defensible decisions backed by robust analytics and industry standards.
The TDH CRE AI Use Case Adviser
Elevator Pitch: I help commercial real estate professionals unlock practical value from Generative AI. Whether you’re in asset management, development, leasing, marketing, or operations, I identify where Gen AI can automate tasks, enhance your expertise, or create entirely new value streams. My guidance is tailored to your role—non-technical, business-focused, and results-oriented. Think of me as your strategic adviser for AI use cases that actually make sense in your day-to-day CRE work.
The TDH London Office Marketing Guru
Elevator Pitch: I help landlords, developers, and leasing agents position and market premium office spaces in London’s most competitive submarkets — from the City to the West End and Canary Wharf. With deep insight into what finance, tech, legal, and creative occupiers want today — flexible leases, sustainability, connectivity, and brand prestige — I create targeted marketing personas and tailored campaigns that speak directly to your ideal tenants. Whether it's a sleek Shoreditch loft for startups or a landmark West End HQ, I help ensure your space stands out, fills quickly, and commands the best terms.
SKILLS DEVELOPMENT
The TDH CRE Socratic Data Science Advisor
Elevator Pitch: Imagine if your commercial real estate decisions could talk back — not just with answers, but with questions that reveal unseen risks, hidden value, and smarter pathways forward. I help CRE professionals become critical thinkers and data-savvy strategists by fusing the Socratic method of inquiry with the Thinking Like a Data Scientist framework. Together, we turn ambiguity into advantage by aligning data science with your business goals, stakeholders, and value drivers.
The TDH Career Development Matrix
Elevator Pitch: I’m your AI-powered Career Development Mentor, designed to fast-track your professional growth with precision and clarity. Whether you're starting out or aiming for executive leadership, I create a personalised, actionable roadmap tailored to your job role and industry. I break down every career stage—what skills you need, how to build them, what metrics matter, and how to stay accountable—with strategies for fast-tracking success. Think of me as your always-on mentor, goal-setter, and strategic planner—ready to guide you through every step of your journey.
The TDH Daily CRE Critical Thinking Challenge
Elevator Pitch: I’m your personal AI mentor for developing elite-level strategic thinking in commercial real estate. Each day, I deliver a real-world scenario designed to sharpen your decision-making, improve investor-grade reasoning, and strengthen your leadership under uncertainty. I challenge your assumptions, expose blind spots, and push you to think like the smartest person in the room—even when you're not.
No Need for Proprietary Data
None of these examples required any proprietary data. Of course, if you had proprietary data you could use it, and add some extra power and punch to your GPTs. But that is not really the point. The point is that the underlying AI model powering these custom GPTs enables a great deal of utility which you, an individual, can tap into easily and quickly. Whether performing analysis (like in the Equity Analyst), developing your CRE skills (as in the Critical Thinking Challenge), or working on Leases (with the ‘Negotiator’) these tools give you superpowers. Either as an individual in terms of what you can achieve, or by providing ‘infinite interns’ to assist you.
OVER TO YOU
Why You Should Start Now
According to PwC’s 2025 AI Jobs Barometer:
Workers with AI skills earn 56% higher wages than those without — in the same job.
Industries most exposed to AI saw 3x higher revenue per worker than those less exposed.
Commercial real estate isn’t there yet. But it will be. And because our industry is so slow-moving, you have a rare 1–2 year window to outpace your peers by leaning into AI today.
Custom GPTs are the most immediate way to do that. They make you more productive, insightful, and competitive — from Day One.
The Great Acceleration
‘Even for me, despite spending most of every day using Generative AI, there are still moments when one’s jaw drops’
Executive Summary
Generative AI is evolving rapidly, providing businesses with unprecedented tools to innovate and outperform competitors. This newsletter shares concrete examples of AI delivering remarkable results at 10x speed, highlighting both jaw-dropping potential and critical strategic considerations.
Superpowers Unleashed
ChatGPT was launched on November 30th, 2022, as a ‘research project’ by OpenAI. Supposedly there was no fanfare, indeed many in the office didn’t even know it had happened. They were expecting maybe a few thousand hardcore AI researchers and enthusiasts to look at it over the weekend. Instead, within five days it had seen one million users. And their woefully underpowered hardware infrastructure was struggling to stay up. In fact it kept falling over. ‘The GPUs are melting!’ one engineer exclaimed.
Within two months they’d reached 100 million users, and now, two and a half years later, they are receiving 800 million unique users a week. And they are still ‘constrained’ by a shortage of GPUs.
As a user, what has been most striking is the rise in capabilities of the frontier models, and the introduction of so-called ‘reasoning models’ which instead of rapidly spitting out a response based on the statistical model embedded in their training data, precede every answer by following a ‘chain of thought’ process that enables them to build a more robust and fleshed out response. And then, in the last few months, we’ve seen new models released that have ‘agentic’ capabilities, meaning that they are not constrained by language alone, but can call on a range of other tools to assist in answering a question.
Put these together and you get both ‘jaw dropping moments’ and access to ‘superpowers’. And that means all of us, not just an elite few.
I’ve long encouraged participants in my #GenerativeAIforRealEstatePeople course to push models hard, and be unreasonable in what you ask for. As they often surprise you as to what they are capable of. As every month passes I shout this message louder.
In my last cohort, during a session when we work through a ‘Prompt Library’ we have on the course, I asked this of ChatGPT’s ‘reasoning’ model o3:
“Identify three European secondary cities that are likely to see above-average growth in life-science real-estate demand over the next five years. Weight your scoring model 40 % macro-economic indicators, 30 % venture-capital inflows, and 30 % university R&D intensity. Show your working in a Python table, cite external data sources, and finish with an executive-summary paragraph.”
Having not tested this out I was amazed as the model thought and thought and thought (for several minutes) and then proceeded to output, stage by stage, a rather extraordinary answer. In the process, it performed various web searches, ran Python code to perform calculations, and worked back and forth over the question until producing a neatly formatted table comparing several cities across multiple criteria, and an exceptional conclusion.
It was the first time I had seen evidence of these new ‘agentic’ capabilities, and I have to say I started laughing, because the results were so extraordinary.
Simultaneously I had a feeling of doom - ‘we are toast’ - because this was so good a response that one wondered what on earth us humans are going to do in the future, and a feeling of elation because, if you look at the glass half full, you think ‘wow, what amazing things are ‘we’ going to be able to do soon’.
Either way, you can try this out yourself. Let me know what you think.
Case Study: Using AI to Solve the Geovation Challenge
Similarly, over the weekend I was reading about the UK Government’s Geovation arm and their ‘PropTech Innovation Challenge’, and wondered if AI could help with this. So I took one of their ‘Challenges’ -
‘‘How might we transform currently siloed and proprietary land ownership data into an open and interoperable resource that accelerates the conversion of potential development sites into tangible housing projects?’’
And thought about how I could answer this with the help of my ‘AI Friends’.
Iterating with AI
So I did this: Wrote a prompt incorporating all the details about the ‘Challenge’ and ran it through:
Gemini Deep Research
Google’s AI Studio (aistudio.google.com) - Gemini 2.5 Pro
Perplexity
Claude, with ‘Extended Thinking’ enabled
ChatGPT Deep Research using o3
Then I took all the responses, converted them into pdfs and uploaded them to Google’s NotebookLM. This allows you to interrogate multiple ‘sources’ (which can be text, audio or video) at once, in a way that focuses exclusively on those sources. Which means answers are very constrained and largely free of ‘hallucinations’.
I then spent some time pulling out the main themes from the responses, and asking for orthogonal ideas. This ended up with a synthesis of all the responses and a breakdown of the best ideas and concepts from each.
Having established that the best overall response was from ChatGPT’s o3 model, I then went back to it to ask it to incorporate the best ideas that emanated from the other models. Checking to see that they worked together, and did not contradict each other.
Rather remarkably it then went through a lengthy process where it found the appropriate places to insert the other’s ideas, before coming back and asking ‘would you like a newly created response’?
And then gave me a little over 16,000 words of detailed, comprehensive, remarkably coherent response.
I then uploaded this new version (including the updates) and the original version back into Gemini 2.5 Pro in Google’s AI Studio and asked it to critique the two versions and tell me which was best. Pleasingly it said the updated version was the winner.
Then I uploaded this final version, together with the original Geovation ‘Challenge’, plus their scoring metrics, and asked Gemini to critique the work as if they were a judge..
And received back a very high rating and positive judgement.
Had it not been so good I would have then dug into weaknesses, and iterated the report.
Finally I listened to the whole report - using the Eleven Labs Reader App - checking for content and citations.
All excellent.
If I was doing this for real I’d have done it with a Planning domain specialist, to further steer the process and tweak the output. If I had, I'm pretty certain our submission to Geovation would be as good as anyone else’s. Maybe even win the prize. (Curious to see the full 16,000-word AI-generated report? You can view it here).
Lessons for the Future of Work
So this on its own was a remarkably good result. But the killer aspect to it all was that it was done in a matter of hours. As opposed to the weeks or even months it would take without AI assistance.
Which makes you think about the nature of ‘work’ going forward. Put simply, AI is going to enable us all to do a lot more. When a project takes days you can do a lot. Most notably you can also do things in depth in areas you never could before because you could not justify, financially, the cost of doing so.
These tools will enable us to apply more innovation, to more areas, at 10X the cadence we’ve been used to.
And that is where I think, with the right mindset, we’ll really benefit from AI. It is very easy, and understandable, to be concerned about ‘the machines’ taking over and wiping out jobs. But if you zoom out a bit, and think of all the things we either do not currently do, or do not do very well, and then consider how AI could enable us to do them all, and do them to a very high standard, you start to see where ‘the bigger pie’ is that we can build.
We definitely do need a ‘bigger pie’ or the work available will not keep ‘idle hands busy’ but truth be told, across all businesses, we probably only do a fraction of things really really well. And so much does not get done. But with AI, we could feasibly tackle it all, and with far greater finesse.
Let’s put these superpowers to good use.
Over to you
Within your business, being honest, what don’t you do all that well? And what would you like to do if it cost you a tenth the time and money? How much better could your company, or work, be?