Becoming #FutureProof: AI Literacy And YOU

‘AI literacy is the new future-proofing—those who learn to think with machines will shape what comes next.’

Last week we looked at ‘Understanding the New Value Equation’ within real estate, as it becomes ever more mediated through AI. This week we’re going to look at AI Literacy: what do you NEED to know about AI to be able to leverage it as a superpower, rather than be commoditised by it?

I think there are 11 building blocks to be aware of, and hopefully master. You can do so in many ways; take courses (like my #GenerativeAIforRealEstatePeople one), listen to podcasts, read articles, or simply ask your preferred language model to ‘explain X to a commercial real estate professional’. There is a lot to get to grips with, but a bit of time and application will get you ahead of your peers pretty quickly. Remember: most people in CRE are NOT being trained in any of this. Or training themselves. And that’s the open goal in front of you.

BUILDING BLOCKS

1. Foundational AI Knowledge

You need to understand what AI is (and isn’t), including key concepts like machine learning, neural networks, large language models, and generative AI. Importantly you need to focus on differentiating human and machine intelligence, and how AI should be treated as a function of well-structured data systems. 

Literacy begins with demystifying AI—knowing what it can realistically do, and what remains human terrain.

2. Data Fluency & Data Economics

Data is an economic asset, so understanding its collection, structuring, monetisation, reuse, and governance is important. Data enables both automation, and insight.

You cannot be AI-literate without being data-literate—and understanding how data compounds value over time. I mentioned him last week but Bill Schmarzo, the so-called ‘Dean of Big Data’ is an exceptional writer and teacher on data literacy, and if you don’t already, you should follow him. His 2023 book ‘AI and Data Literacy’, tells you pretty much all a non data specialist needs to know.

3. Problem Framing & Value Alignment

Involves translating business or operational problems into questions AI can help solve, starting with value creation not technical feasibility. You must “Start with the problem, not the model”, and this applies to functional automation and innovation. 

Design and Systems Thinking are excellent frameworks to help you gain the ability to break down complex problems into AI-solvable units aligned with business outcomes.

4. Use Case Fluency

It is useful to have a use-case first mindset. An understanding of where AI delivers value—identifying repeatable, high-ROI applications within CRE operations and strategy. Good use-cases are an engine of learning and scaling.

Literacy includes recognising where AI can augment real-world CRE value chains—from leasing to asset performance.

5. Prompting & Human–AI Interaction

The ability to frame prompts, iterate with AI systems, and extract value from conversational or generative interfaces is a super skill.

Prompting is the new digital fluency—knowing how to speak to machines to unlock creativity, insight, and automation.

6. Human Uniqueness & Judgment

Empathy, moral reasoning, creativity, spatial awareness, and strategic judgement—what remains uniquely human in a machine-enhanced workflow?

#HumanIsTheNewLuxury, as I repeatedly say. Knowing whether and where to put the ‘human in the loop’ is vital for developing AI system that are reliable and accountable.

It is essential that we preserve the integrity of human judgement, and maintain agency over deciding what really matters.

7. Decision Intelligence

How do you structure decisions for AI support? How do you diagnose, predict and prescribe integrating AI into CRE judgement frameworks. Are you able to decompose the decision-making logic. What will be the systemic impact on work; how will workflows change?

Knowing how to architect decisions is as important as knowing how to use tools.

8. Systemic & Strategic Thinking

You have to see AI not as a tactical feature but as a transformative force across business models, tenant expectations, and the entire CRE lifecycle. It must be a strong focus—redefining space, value, and experience. With links to data that enables economic and systemic transformation.

AI is not a productivity layer - it’s a catalyst for system-wide reinvention.

9. Ethics, Responsibility & Governance

Understanding bias, transparency, unintended consequences, and ethical design is a foundational AI skill. To maintain human agency and trust we need to develop systems that have decision integrity, and we must be responsible in our tool adoption and usage.

AI literacy includes the ability to anticipate and mitigate ethical risk—especially with tenant, community, or environmental data.

10. Organisational Enablement & Culture

And we need to create an environment where AI literacy is distributed, supported, and incentivised across our organisation. We need to advocate for citizen data scientists and AI marketplaces, and we need internal AI champions and shared tools. AI is going to lead to an enormous amount of cultural change in work and value perception, and handling this effectively is neither easy, or something that can be left to chance.

11. Curiosity, Experimentation & Learning Culture

Empowering low-risk experimentation, playful exploration, and rapid iteration, as a way to build AI muscle across an organisation, will deliver strong results. ‘Play is serious work!’

Literacy grows through doing—experimentation is the delivery vehicle of insight. As we discussed before, working with AI (in particular Generative) is more akin to working with humans than software, and that requires practice doesn’t it?

AND THAT’S IT

These are the 11 core building blocks you need to become instinctively familiar with. They are partly ‘ways of thinking’, partly about ‘mindset’ and partly about things you just have to learn. But none are rocket science. Anyone can become modestly capable in all of them quite easily. With a little application you could become highly capable in not much time at all.

AI literacy is about understanding the ‘rules of the game’, and how they interact. With that in place you’ll be much better placed to build high and wide; strong foundations, as we know, are a great enabler. 

Integrating AI into your thinking, your teams, and your workflows will become natural once you’ve internalised the above. Just something you do. 

Of course there is a lot more one could expand into - storytelling, personalisation and narrative, agent-based workflows and multi-agent orchestration, and metrics, ROI and measurement literacy, as well as all manner of domain specific imperatives, but these are all things one will build on top of these foundations.

For now, just nail these!

OVER TO YOU

How’s your AI Literacy? What about your friends and colleagues? Please circulate this. The real estate industry needs to be AI Literate. Let’s make it so, one person at a time.

Becoming #FutureProof: Understanding The New Value Equation

‘In an AI-driven industry, access to data will become more democratised, and the factors that determine competitive advantage will change.’

Last week we looked at the 10 Initial Steps all of us need to take to ensure we are #FutureProof in an increasingly AI-Mediated world. 

This week we are going to look at how the traditional ways in which commercial real estate (CRE) firms extracted value from data are shifting. 

THE HYPOTHESIS

This rests on a hypothesis being true: That AI access to data will become more democratised and AI models will be able to generate insights from open, and synthetic, sources.

The old, and existing, value equation relies on exclusive access to proprietary data as a moat. Firms can extract value by controlling rare or difficult-to-source information.

The new value equation will likely be based on how effectively firms leverage AI to generate unique, high-value insights and actions from data—regardless of whether that data is proprietary or widely available.

Wider access to data has been much debated, and called for, in CRE for decades. With limited results. Why might it ‘be different this time?’

DATA OPENS UP

There are five reasons:

1. AI is capable of extracting market insights from non-traditional sources (e.g., satellite imagery, IoT sensors, public filings, foot traffic analysis). For example, with pervasive building data, we’ll understand ‘the building’ and occupancy trends far more granularly than any ‘Lease Comp’. Furthermore the rise of publicly accessible alternative datasets will weaken the monopoly of proprietary CRE data providers. 

2. Decentralised & Crowdsourced Data Networks Will Grow. Increasingly companies with limited data will start to pool what they have with their peers. A current example, WIN, the Workspace Intelligence Network describes its mission as being to ‘Contribute to the growth and sustainability of the flexible workspace sector through data collaboration’. There will be others.

3. Regulations May Force More Transparency. The EU’s Data Act and similar regulations worldwide are moving toward mandated data-sharing requirements. Cities might require real estate firms to publish anonymised rent rolls for transparency in pricing & valuation.

4. Investor & Tenant Demand for Real-Time Insights Will Grow. Institutional investors want real-time data feeds rather than delayed quarterly reports. Tenants will expect more lease transparency and performance benchmarking, pushing landlords toward more open data ecosystems.

5. Causal AI and the increasing use of synthetic data generated by AI models. Causal AI models (which seeks to understand cause-and-effect relationships within data) need a lot of data, but Generative AI can create synthetic data when real-world sources are low. Which removes the need for proprietary datasets. Meaning AI-powered simulations will replace reliance on historical lease comps & transaction data.

And added to this is the simple fact that the multi-modal processing of data (text, imagery, video and audio) means that AI can ‘see’ a lot more than we are historically used to. Data is becoming hard to hide.

THIS WILL TAKE TIME

This isn’t going to happen quickly. Most likely it will happen à la the 'boiling frog, with no-one noticing much difference until it turns out everything has changed.

Incumbents, obviously, will fight to protect the value of proprietary data, but over the medium term, perhaps 3-7 years, AI will seep out through an increasing number of gaps and traditional data barriers will rot from within. After this, whilst full data openness is unlikely, we’ll be in a world where AI-driven decision execution is their new value proposition.

The world does not belong to those who can hoard data.

And that will be a good thing. The Finance industry is so much more dynamic than Real Estate BECAUSE data is open. How many poor people do you know working in the public stock markets?

As in software, open-source wins. Eventually.

THE OLD MODEL

So, we know the old, and current, real estate model derives a lot of its advantage from information asymmetry—having data that others do not. 

Which is valuable because data is hard to gather, expensive to acquire, difficult to analyse and insights take time to propagate. 

And this in turn allows for high fees for advisory and brokerage services, provides an edge in underwriting and investment decisions, and enables a power play with tenants due to superior knowledge of occupancy trends.

In this model, owning data = owning value. But …..

THE COMING MODEL

The new value equation will have none of this, and instead power and business will be acquired by those with the greatest insight and ability to execute. 

In this world competitive advantage won’t derive from "who owns the most data?” Rather the key questions will be:

• Who can generate the most actionable insights from data?

• Who can execute those insights faster and better than competitors?

• Who has the most AI-optimised decision-making frameworks?

This means the new value equation looks like this:

Value = (Data x AI Processing) + Execution Speed + Contextual Intelligence

In other words, CRE firms that succeed in the AI era will be those that:

• Develop the best proprietary AI decision models that make the smartest use of freely available (and some proprietary) data.

• Act on insights faster than competitors (e.g., instant underwriting, predictive leasing strategies).

• Demonstrate the strongest ‘Human+AI’ Judgement.

• Optimise workflows to make real-time data actionable, reducing lag in decision-making.

How well you use data in an AI-powered decision system is what will matter. Not merely access to that data.

Even if competitors have the same raw data, the firm with the better AI-powered decision system will win because they can act faster and with more confidence. Developing that ‘AI Synergy’ is going to be the super skill.

SIDEBAR
We've already seen this model succeed spectacularly in other industries. Renaissance Technologies revolutionised quantitative investing by focusing not on acquiring proprietary data, but on developing superior mathematical models to extract unique insights from public market data. With returns of approximately 66% annually over decades, they demonstrated that how you process data matters far more than exclusive ownership of it.

NEW DIFFERENTIATION STRATEGIES

To adapt to the new value equation, smart CRE firms will need to shift their strategies to:

Developing proprietary AI systems that leverage their unique capabilities and insights, and internal, operational data.

Removing friction from all transactions. Focussing on real-time data, and continuous processing, transactions should be as close to automated as possible.

Creating exceptional user experiences for clients, tenants and stakeholders. 

Developing exclusive AI-powered insight networks where they share data with complementary companies. As in ‘WIN’ above.

CONCLUSION

The old paradigm of "who owns the most data wins" is eroding. And exactly in accordance with Clayton Christensen's 'Law of Conservation of Attractive Profits' (which states that when one part of a value chain becomes commoditised, another typically becomes more valuable) we will see the increasing availability of proprietary asset data likely shift profits to new integration points or value-added services.

Profits will concentrate where firms integrate open data into proprietary systems—whether through AI-driven insights, IoT-enabled buildings, or hyper-personalised advisory services. 

It’s not that profits will disappear. They will move. To those who can position themselves as the new integrators.

The #FutureProof message is to not rely on data as a protector, and to start ‘skating to where the puck is going’.

OVER TO YOU

Do you agree CRE data will become more open? What’s your timescale? Who do you think wins, or loses?

Becoming #FutureProof: 10 Initial Steps

‘One needs to think of AI not as an existential threat (though it might be) but as a transformative enabler.’

Last week we talked about ‘Are YOU #FutureProof. This week we’ll cover 10 initial steps to take to ensure you are.

In every instance I am going to assume you have access to at least one frontier model: ChatGPT, Claude or Gemini. Preferably in paid mode, but you can, mostly, get limited use of these at full power for free. Every time a term is mentioned, or a concept, or hypothesis, or anything you are not sure about, DO ask an AI. They are brilliant at explaining complex matters at whatever level suits you. Personally, I coded software for many years, but I fundamentally think like the History and History of Art graduate that I am. So I often upload complex academic AI papers and ask the AI to ‘summarise and explain this to a Humanities graduate’. You’ll learn so much by doing this routinely and as a matter of course throughout your working day.

Starting with: 

Step 1: Develop AI Fluency (Not Just Literacy)
AI literacy is knowing what AI is. AI Fluency is knowing how to use, critique and apply it.

Literacy can get as complicated as you like, but I think, as business people, there are just a few things that are essential. What is Predictive AI, Generative AI and Causal AI? And what is AI Bias, and what are hallucinations? And the difference between automation and augmentation.

So:

Predictive AI is a branch of artificial intelligence that analyses historical data, identifies patterns, and uses machine learning algorithms to forecast future outcomes, enabling proactive decision-making across industries. Fundamentally this is an analytical tool.

Generative AI is a type of artificial intelligence that creates new content—such as text, images, audio, and code—by learning patterns from existing data and generating novel outputs that mimic human-like creativity and reasoning. Fundamentally this is a creative tool.

Causal AI is a branch of artificial intelligence that goes beyond correlation-based predictions by understanding cause-and-effect relationships, enabling more explainable, reliable, and intervention-driven decision-making. (This is a nascent field but will become important. For obvious reasons)

AI bias refers to systematic errors or unfair outcomes in artificial intelligence systems that arise from biased data, flawed algorithms, or human-driven assumptions, often leading to discriminatory or unbalanced decision-making in areas such as hiring, lending, and law enforcement. We joke about CRE being ‘pale, male and stale’ - which is a prime source of AI bias to look out for!

AI hallucinations refer to instances where an artificial intelligence system generates false, misleading, or nonsensical information that appears plausible but is not grounded in reality or factual data, often due to limitations in training data or model reasoning. There are increasingly good ways to mitigate this, but as with humans ‘Trust, but verify’.

The above alone, well internalised, is a good start but for AI Fluency it is different. Predictive and Causal AI are highly technical, complicated fields that frankly you are not going to be fluent in without a lot of study. Generative AI, however, is just a matter of practice. It does help to have some training in good prompting, but if you just interact with a frontier LLM as you would with a human, you’ll be well on your way. Just keep asking questions. After about ten hours of use you will have a very good feel for what works or doesn’t, and their strengths and weaknesses. Each model has a distinct ‘character’ and particular strengths: Claude excels at coding and natural writing, ChatGPT is strong for business-related writing and strategic thinking, while Gemini’s vast working memory makes it ideal for analysing large documents.

The key though is practice. You need those 10 hours under your belt. Incorporate asking a model about everything you do, and you’ll soon get there.

PS. For imagery, cough up $10 a month (at least once) to play around with MidJourney - it’s the best.

Step 2: Reframe Your Mindset: AI as a Co-Pilot, Not a Replacement
Make no mistake - AI is going to take a lot of jobs. Simply put, AI will dramatically improve productivity in many areas, and so unless the market for these areas grows significantly we will need less people to generate the outputs required. We need a bigger pie. I am confident this will emerge in many sections of real estate but certainly not in all. So we need to ensure we are in the growth areas.

And this is going to be where humans + AI can either do X better, faster, cheaper or where humans + AI enable things to be done that were hitherto impossible. This latter state is known as achieving AI Synergy. When combined the end result is better than either the best human, or the best AI, can achieve on its own.

An example, from Portfolio Optimisation: An AI might propose optimisation strategies (e.g., leasing adjustments or refurbishment needs), but human expertise is essential to evaluate feasibility and, crucially, align these with investor expectations. This AI Synergy enables a more data-informed, holistic outcome.

YOU have to be working where human + AI is required, but don’t kid yourself that is everywhere. Even if that’s true today, you need to be looking a year or two ahead. ‘Is what I am doing something an AI is likely to be able to do ... sometime soon?’

As you get more familiar using the tools this will become clear. You will ‘feel’ what is going to be possible.

Step 3: Focus on the Uniquely Human Skills
AI can handle pattern recognition, automation, and prediction. Anything ‘structured, repeatable, predictable’ will be eaten by AI.

AI can also simulate creativity, judgment, empathy, and complex problem-solving, but it IS simulating these things. And in certain situations one doesn’t need more than ‘simulated’. But you need to discern where first-principles thinking, nuanced decision-making, emotional intelligence, and adaptability is the real value-add, because these are human skills that AI cannot (at least yet) provide.

Being a ‘high quality’ human is going to become a super skill. Sounds odd but some humans are more human than others. And this is going to matter.

As is Critical Thinking - you need to be really good at this in a world where nothing, necessarily, is true. Take ‘The TDH Daily CRE Critical Thinking Challenge’ - https://chatgpt.com/g/g-67d6b0c27ad48191b36763997f2c09f4-the-tdh-daily-cre-critical-thinking-challenge

Step 4: Become a Master of 'Prompt Engineering'
The ability to interact effectively with AI will become as fundamental as typing or using the internet. You need to learn how to craft effective prompts that yield precise, valuable, and creative outputs.

Good prompting makes a real difference. Being able to ask a great question matters.

The joke is that LLMs are ‘the revenge of the Humanities graduate’ because all of a sudden understanding language is important, and the ability to use words clearly, to explain context, and to elucidate constraints, can be the difference between a genius or a trivial answer from an LLM.

You can scout the internet for good guidance on prompting—or simply take my #GenerativeAIForRealEstatePeople course. The latter is easier:)

Step 5: Understand Data and Its Value
Real estate has historically been data-poor, but AI is going to change that. We’ll soon have access to unprecedented amounts of information, and this will be much more democratically spread around. The days of data asymmetry are likely coming to an end (for reasons we’ll cover in another newsletter).

Which is all well and good but not much use if you know nothing about data science and analytics. Knowing how to interpret, validate, and apply data is a skill you must learn.

My top tips relate to one man, Bill Schmarzo, the so-called ‘Dean of Big Data’. He’s a leading authority on data science, analytics, and business value creation through data, and is known for his practical, business-focused approach to leveraging data as an asset.

Top tips are: read his book ‘AI & Data Literacy: Empowering Citizens of Data Science’ and his blog at https://www.datasciencecentral.com/author/billschmarzo/

Honestly, unless you’re a data science specialist, this will cover everything you, as a business person, need to know about data.

Step 6: Adopt an Experimentation Mindset
AI is evolving so rapidly that you shouldn’t get bogged down in perfection. A lot of the best outcomes, today, will come from experimenting. Using new tools, pushing them hard, and seeing what comes out the other end. Once again, you’ll get a feel for what is likely to be possible 6, 12 months hence.

Use AI as a sandbox for innovation—experiment with different use cases. Pro versions of ChatGPT, Claude and Gemini (which includes NotebookLM which we covered recently) are less than $60/£50 a month. Just as an individual this is money well spent. As a company leader, give these to as many of your employees as are interested. Let them experiment and watch what happens.

Step 7: Prepare for New Business Models and Paradigms
AI is or will be reshaping industries, and legacy business models are sure to be disrupted. Understanding quite how this will or might shift value creation is not easy, but I think there are some ‘directions of travel’ for the real estate industry that are clear. For example, AI will likely shift ‘knowledge work’ towards human-centric skills, leading to more hybrid and distributed working, but also more intensive, purposeful collaboration. ‘No one knows what happens next’ is a good mantra to follow, so adaptability and flexibility are going to be at a premium. And at a more fundamental level, the rise of AI is going to necessitate a mass of new energy infrastructure, and a heap of data centres.

I read a paper yesterday suggesting we might see ‘a century of change in a decade.’ That feels hyperbolic, but it’s certainly wise to anticipate significant transformation in the coming years. 

Step 8: Think in Terms of ‘Space as a Service’
Which brings me back nicely to my hobby horse ‘#SpaceasaService’.

In the world of AI-optimised work and cities, physical spaces will need to become smarter, more adaptive, and experience-driven. AI will drive dynamic demand forecasting, flexible workspaces, and real-time decision-making in urban environments, and being on top of the intersection of AI, automation, and physical infrastructure will be a key competitive advantage.

But above all else AI will enable us to provide real #SpaceasaService - Spaces that provide the services that enable every individual to be as happy, healthy and productive as they can be.

AI is going to enable us to ‘Build a Better Built Environment’. This is surely the point of being in the real estate industry?

Step 9: Cultivate an Anti-Fragile Career and Business Approach
As we’ve discussed, AI will automate many traditional jobs, but new roles will emerge. So we need to design and build careers that thrive in volatility—where adaptability, interdisciplinary knowledge, and AI augmentation are core. This will require a portfolio of skills, projects, and networks that position us as ‘AI-powered professionals’. Following the other 9 steps will, I think, deal with this one!

Step 10: Think About AI Ethics, Governance, and Human Implications
I am full of positivity about the next decade but it could go very badly wrong. WE need to ensure the future of AI is not just about what it can do but also what it should do. Sometimes it feels very dull and dreary but we must engage with discussions around AI bias, transparency, and responsible usage.

AI is a ‘General-purpose technology’ and these ‘affect an entire economy. They have the potential to drastically alter societies through their impact on pre-existing economic and social structures.’

Dull or not, this is serious stuff!

Final Thought: Be Curious, Not Fearful
One needs to think of AI not as an existential threat (though it might be) but as a transformative enabler. A technology that ‘enables’ us to do the currently impossible. With it we should be able to deal with bigger challenges. And looking around, the world is full of them. There is no shortage of work to be done. Should we choose to do it.

The winners in an AI-mediated world will be those who embrace learning, adaptability, and have a deep curiosity, and dare I say it, love for the world around them.

Hopefully that’s you. 

OVER TO YOU

Which of these 10 steps are you implementing first? Let me know. Need any help?

Are YOU Future-Proof?

‘Commercial real estate is facing its biggest transformation since steel-frame construction. Are YOU ready?’

This is the first in a series of articles looking at how to ‘Future-Proof’ yourself for a world changing at remarkable speed.

I am a super optimist about the promise of AI. In my mind I see it as enabling us humans to address, and fix, the most wicked problems on the planet. I look at the UN’s 17 ‘Sustainable Development Goals’ and think, with AI, we can actually achieve these. Today, we tinker around the edges of these grand challenges - but with super powerful AI we could conquer them.

Certainly this is something to aspire to. Ensuring we ‘make cities and human settlements inclusive, safe, resilient and sustainable’ is rather more aspirational than optimising ads on Facebook. AI will dramatically raise the bar of what is achievable.

My worry is timing.

I’ve been following AI since the early 2000’s. The term itself was coined in 1956, and has gone through a few ‘Springs’ and ‘Winters’ since then. For a long time progress was glacial. But the pace started to pick up with the rise of Deep Learning from 2012, and has grown in momentum since then. But it was the launch of ChatGPT at the end of November 2022, that saw the flood gates open. Since then progress has been extraordinary. Jenson Huang, CEO of Nvidia (upon whose GPUs the current AI industry is built) has talked about technology developing at ‘Moore’s Law Squared’. Seeing as the power of their chips has increased 1000X in the last 8 years, he’s maybe only exaggerating a bit.

Now this is amazing, but worrying. At least potentially. It means the future is coming at us much faster than ever before. If we are going to have access to 1000X more computing power in ten years time than today, what does that mean? For how we live, work, and play?

And what if it is even faster?

This was rammed home to me this week as I read a quotation from Jack Clark, one of the co-founders of Anthropic, the AI Research Lab behind Claude, the ChatGPT competitor.

He wrote:

‘People underrate how significant and fast-moving AI progress is. We have this notion that in late 2026, or early 2027, powerful AI systems will be built that will have intellectual capabilities that match or exceed Nobel Prize winners.

They’ll have the ability to autonomously reason over all kinds of complex tasks for extended periods.

They’ll also have the ability to interface with the physical world by operating drones or robots.

Massive, powerful things are beginning to come into view, and we’re all underrating how significant that will be.’

What if he is right?

That is the worry. Just a few years to adapt to ‘alien intelligences’ of extraordinary power.

Are you future proofed against that?
That is the career threatening question. Because you need to be.

Too many people talk of these AI’s taking over the menial work we all have to do, leaving us free for higher things.

But what if it's the higher things they can do?

Frankly speaking there is a general delusion around this. That grows out of a lack of understanding of how powerful these tools are today, and where they are likely to be shortly.

Technology is speeding up, but most of the world is oblivious how fast.

How then to become ‘Future Proof’?
The key here is to extrapolate forward, and build scenarios of the years to come that we can work back from. Only by anticipating what is likely to happen can we formulate ways to not just stay relevant, but to actively thrive in this coming world.

So let’s look forward and see what might/would be the consequences for the CRE industry IF Jack Clark’s prognosis comes to be.

Here are a few:

Strategic Transformation of Core Business Models
The advanced AI capabilities described in Jack Clark's statement might fundamentally redefine how properties are valued, designed, constructed, managed, and transacted.

Property Valuation and Investment
Analysis might evolve from art to science, with AI systems capable of processing billions of data points simultaneously to identify opportunities and risks invisible to human analysts. The resulting precision could compress cap rates for optimally positioned assets while dramatically widening spreads for properties deemed suboptimal by these systems.

Property Management
Traditional property management might transform into "intelligent asset optimisation”, with autonomous systems handling everything from tenant relations to predictive maintenance. Building management costs could decline through AI automation, whilst simultaneously improving tenant satisfaction through responsive, anticipatory building systems.

Physical Asset Evolution
The built environment itself would transform to accommodate and leverage advanced AI capabilities. New commercial developments would be designed from inception as physical-digital hybrids, with embedded intelligence throughout all building systems. The distinction between building management systems, IoT networks, and AI infrastructure would blur into unified intelligent environments.

Construction Methodologies
These might fundamentally change as AI-directed robotics handle increasingly complex building tasks. There might be significant cost reductions alongside a compression in build schedules for AI-optimised construction processes.

Energy Consumption
Energy consumption patterns might transform through microsecond-level optimisation of all building systems, potentially reducing energy usage considerably compared to current high-efficiency buildings. This would redefine sustainability benchmarks whilst creating new retrofit imperatives for existing stock.

Market Structure Disruption

Information Asymmetries
The competitive landscape might undergo tectonic shifts as traditional information asymmetries—long the basis of competitive advantage in CRE—are eliminated by ubiquitous market intelligence. Value will increasingly derive from implementation excellence rather than proprietary market knowledge.

Brokerage
Traditional brokerage faces existential challenges, with transaction processes potentially compressed from months to days through AI due diligence and documentation. Surviving brokerages will evolve from transactional facilitators to strategic advisors, helping clients navigate an increasingly complex technological landscape.

New Investment Imperatives
Capital allocation strategies would require fundamental recalibration. Properties designed without AI integration would face accelerated functional obsolescence, potentially creating a wave of stranded assets in certain submarkets. The premium for "AI-ready" buildings could mirror or exceed current sustainability premiums.

Investment horizons may compress as AI enables faster market clearing and reduces information friction. Simultaneously, modernisation capital expenditure budgets will likely need significant expansion to accommodate AI infrastructure implementation.

New asset classes optimised specifically for AI operations—such as buildings designed to house large language model computing infrastructure alongside human workers—could emerge as specialised investment categories with distinct risk-return profiles.

There would also be -

Changes in User Demand

Evolving Tenant Expectations
Commercial tenants would rapidly develop sophisticated expectations for building intelligence that go far beyond current "smart building" capabilities. Properties will be evaluated not just on location and physical attributes, but on their AI integration sophistication and adaptability.

User interfaces will transform from explicit controls to ambient intelligence that anticipates needs before they're articulated. Occupants will expect buildings to recognise them, remember their preferences, and proactively adapt to their needs—creating new standards for personalisation at scale.

The pandemic-accelerated hybrid work paradigm will evolve further as AI enables new collaboration models between in-office and remote workers. Physical space will increasingly be valued for its ability to facilitate human connections that AI cannot replicate, whilst routine tasks migrate to digital environments.

Spatial Requirement Transformations
Space utilisation patterns will undergo profound changes as tenant organisations implement their own AI strategies. Knowledge worker density requirements may decline by 30-50% as AI automation absorbs routine cognitive tasks, whilst collaborative spaces for human-to-human interaction may expand.

Facilities designed to house AI infrastructure alongside human workers will create demand for new building specifications addressing power density, cooling requirements, and physical security considerations that don't exist in current commercial building standards.

The rise of AI-powered autonomous delivery and service robots will necessitate new building interface designs, from loading docks capable of handling autonomous vehicles to internal navigation pathways optimised for robotic movement alongside humans.

Value Perception Shifts
Traditional valuation metrics like price per square foot might be supplanted by new measures such as "intelligence quotient," "adaptation capacity," or "computational density" that better reflect a building's ability to support AI-enhanced business operations.

Lease structures would likely evolve to include specific provisions for data rights, as building usage information becomes increasingly valuable. Tension between landlord and tenant interests regarding who owns occupancy data will create new negotiation dynamics and potentially new regulatory frameworks.

The perceived value of location may undergo significant recalibration, as AI enables new distributed work models whilst simultaneously creating premium districts where AI talent and infrastructure concentrate—potentially redefining prime markets in unexpected ways.

Implications
All of this would have huge implications for property owners and investors, industry professionals, policymakers and regulators.

Overall the commercial real estate industry would stand at an inflection point comparable to the introduction of electric lighting or elevator systems in terms of transformative potential. The timeline described by Jack Clark—late 2026 to early 2027—suggests an urgency that the industry has not fully internalised.

Now Clark might be right about what AI will be capable of by 26/27 but in all likelihood the CRE world is not going to look like the above in just a couple of years. However, I think the direction of travel is clear, and the CRE world WILL look like this in the not so distant future. Give it 10 years and I think much of the above will be a reality. Perhaps not across the board, but in terms of new buildings and the prime end of the market. With enormous consequences. Fundamental, structural ones. The future is not looking likely to be a mere extrapolation of the present.

Future-Proofing a Career in Commercial Real Estate Amid AI Disruption
All of this emphasises the imperative to become ‘future proof’, because it is not just going to happen. We are all going to have to make very conscious decisions about how we are preparing ourselves for a very different world.

So, over the next few weeks I am going to concentrate on just this. How to take agency over our fortunes.

I’ll be covering:

• An Immediate Action Plan

• Understanding the New Value Equation

• Developing Technical Literacy

• Positioning at Strategic Intersection Points

• Developing an Adjacent Skill Stack

• Strategic Career Positioning

I see these times as a dialogue between ‘bug and feature’. A lot of what we need to do will be hard and can be considered a bug. But in its very hardness it is actually a feature, as most people will give up, or not even begin. As I’ve said many times before these are great times for you, as individuals, to thrive. You can both protect your downside, and maximise your upside.

Ultimately, we are all going to need to adapt, and society will change considerably. But one step at a time. Start with you. I’ll show you how.

Over to You
What parts of this vision feel inevitable to you? Which parts seem further off? Are you already seeing signs of this shift in your own work? What scares you? What excites you? Let me know.

Four AI Tools Reshaping CRE

Four AI Tools Reshaping CRE

While many in CRE focus on AI as a tool for cost-cutting, the real shift is deeper: domain-specific intelligence, creative automation, and human-machine collaboration.

The four tools discussed here are exceptional efficiency boosters, but they are also indicators of a fundamental shift in how AI will impact CRE, transforming how markets are understood, assets are managed, and spaces are conceived. These tools, and others, will ‘slowly, then suddenly’ force a re-evaluation of work itself.

Introducing: NotebookLM, Operator, Deep Research and GPT-4.5

NotebookLM
Google’s NotebookLM is a research and note-taking assistant that uses their latest Gemini LLM, with its enormous 2 million token - 1,750,000 word - context window (think ‘memory’) to synthesise documents, generate summaries, and create podcast-style “Audio Overviews”. You can add hundreds of documents, videos, audio files, web site links or Google Docs/Slides and then interrogate them intensively and specifically. The system is ‘grounded’ in the sources you upload so has minimal hallucinations and provides citations.

Effectively, this gives everyone access to expert-level analysis—at the scale of an entire project.

Which has consequences: Imagine reviewing lengthy lease agreements or environmental reports as audio summaries while commuting or multitasking. Or using it as a collaborative research assistant for geographically dispersed teams, synthesising insights in real time. It could even redefine how companies share knowledge—leasing teams, for instance, might receive AI-generated podcast updates on market trends, technical due diligence, and portfolio changes, making internal communication more dynamic and efficientt.

NotebookLM will streamline the analysis of market reports, technical due diligence documents, even property portfolios.

Operator
OpenAI’s ‘Operator’ is an AI ‘agent’ designed to interact with websites and perform goal-based tasks, such as booking appointments or scraping data. Essentially it can operate your PC just as you can. It is programmable, meaning you can assign it tasks, and it will determine (either through explicit instructions or independently) the best way to complete them. So it could autonomously handle workflows that can proactively manage operations, anticipate issues, and optimise performance. Without constant human intervention.

So it might operate your building’s systems, go on deal sourcing hunts (24/7), or dynamically respond to Helpdesk requests.

Or it might be programmed (via natural language not code) to automate routine tasks such as data extraction from public property records or scheduling property inspections. Eventually it might integrate with CRE platforms and automate repetitive workflows (e.g., updating property listings or extracting market data from multiple online sources).

Think of it as a ‘digital employee’. But unlike traditional RPA, which follows rigid scripts, Operator is a digital employee that learns, adapts, and executes autonomously.

In this world, the human no long does daily tasks, but rather spends their time designing and overseeing these autonomous systems.

System design, high-value strategic thinking and client engagement become the killer skills.

Deep Research
OpenAI’s Deep Research (Google and Perplexity have similar products) is an AI research agent that autonomously browses, synthesises, and produces cited reports. Unlike what has previously been possible, these ‘reasoning’ systems take their time, anything from 5 to 30 minutes, to work their way up and down, back and forward, through a problem. They do pretty much what a human researcher would do, just much faster.

What they also do is open up the world of hyper-local market intelligence and niche market expertise. These systems can be instructed to go exceptionally deep—making them ideal for due diligence, competitor research, and trend analysis.

Are they perfect? No. As of today they can make mistakes and their work needs to be checked. As one would with an intern or junior researcher. But increasingly you will be able to give them secure access to all your internal knowledge and intelligence. And by doing so provide them with known facts and data, greatly reducing the likelihood of inaccurate or misleading information

GPT-4.5
GPT-4.5 is the latest, and last, non reasoning model produced by OpenAI. It is a model 10 times the size of GPT-4, with improved writing capabilities, greater world knowledge, and a more refined conversational personality. Whilst not seeming to be a major breakthrough on the AI Evals Leader Board, it is said to be the first model that really ‘feels’ like one is interacting with another human.

In practice it is going to be used as the ‘Teacher Model’ from which many, domain specific ‘Student Models’ will be ‘distilled’. Distillation creates smaller, faster models that retain most of the accuracy and capabilities of the original but are fine-tuned for specific tasks.

Within CRE we are likely to see it used to enhance communication and contracts. Its refined language abilities could revolutionise the drafting of contracts, marketing materials, and client communications, providing clear, persuasive, and data-backed narratives.

We know GPT-5 is coming and that will add ‘reasoning’ capabilities to what we are seeing in GPT-4.5. And from recent model development history, we know that the power of reasoning models is partly a function of the strength of the ‘pre-trained’ models they are built upon..

So expect to see more of us treating these LLMs as creative partners in CRE, augmenting human intuition and blurring the lines between human and AI creativity. We are going to redesign creative work in CRE, shifting it from individual creativity to collaborative AI-human creativity.

For CRE, GPT4.5 offers practical gains in everyday tasks—from generating property descriptions to automating client correspondence—thereby enhancing productivity while setting the stage for more transformative future models.

What These Tools Tell Us About the Future of AI

Domain Specialisation
First off there is domain specialisation. AI is moving toward tools that are not one-size-fits-all but are increasingly tailored to specific industries, blending general language capabilities with domain-specific data. Each of these tools can be targeted and fine-tuned for our very specific data, information and knowledge needs.

Hybrid Human–Machine Workflows
Secondly hybrid human–machine workflows will become the norm because, despite their impressive automation capabilities, these tools still require human oversight for quality control and ethical decision-making. The future lies in creating seamless collaborations where AI handles data-heavy tasks and humans provide judgment and strategic insight. With the caveat that this human input has to be of the highest quality. And that this does not apply to all workflows (though they will be the ones humans are actually interested in).

Incremental Evolution vs. Radical Disruption
Thirdly, we should expect more incremental evolution than radical disruption. Taking GPT4.5 as an example, its benefits will take time to evolve and emerge. But they will come bit by bit, so whilst a big bang should not be expected these small incremental improvements will have a cumulative impact and whilst a tad slower, one must still expect and anticipate significant transformation.

This is akin to the way Dave Brailsford adopted the strategy of the "Aggregation of marginal gains" to improve the Sky cycling team's performance. By improving hundreds of tiny factors by just 1%, his team achieved remarkable cumulative gains—winning the Tour de France two years ahead of schedule.

Operational Efficiency and Risk Management
Operator in particular will be a boon for operational efficiency and risk management but it is going to take some time for it to be ready for mainstream use. And then the design of the systems we apply it to, and where we place the ‘human in the loop’ is going to be critical. That said, once these are finalised we will have an AI that won’t stop doing whatever we want it to, and the productivity gains could be extraordinary.

Redesigning Workflows
How we reimagine work processes to leverage AI's efficiency is going to represent a super-skill amongst humans. It has been shown that adding an AI into a human workflow can actually make it work less well, can make it perform as well as ‘the best human’, or can make 2+2=5. Designing for the latter will take special, and very valuable, talents.

Curating Intelligent Outcomes
When humans don’t need to actually undertake tasks their input is going to be mostly around creating, and curating. Designing prompts, setting the parameters, and fine-tuning the outcomes to align with strategic business goals. Redesigning workflows and curating intelligent outcomes will become a new and large job category.

Reinventing the Office
And finally, while AI’s impact spans all asset types, the office will feel it most acutely. Routine tasks will fade, replaced by ideation, strategy, and creative collaboration. Offices must transform from places of execution into catalysts of human potential.

Conclusion: Embracing the Unseen Revolution
The four tools we’ve discussed are harbingers of the democratisation of expertise, autonomous workflows, niche market mastery, and AI as a creative partner. And this has broad implication for CRE:  we’re moving beyond automation, and are shifting towards a more data-driven, proactive, specialised, and creatively augmented future.

Automation will be everywhere but it will no longer be the end point, the goal. Instead we’ll be building products and services of a higher order. We’ll be looking to build a better built environment, not just optimise and manage what we currently have. These and other tools are going to allow us to raise the bar of what is possible, and what we aspire to.

As models improve we’ll increasingly be able to focus on human-centric goals such as well-being, community, sustainability, and economic opportunity. To be sure, we will have to as much of what we humans do in CRE today will be co-opted ‘by the machines’, but that is a feature not a bug. These tools are incredible enablers, and they will open the door to building a built environment we can barely imagine today.

What to do?
One can guess, probably pretty accurately, that certain areas are going to be impacted the most, and soonest: property management, investment, brokerage, development, and corporate real estate. So here are seven things to be doing to prepare:

TOP PRIORITY

1. Experiment with Tools: Use NotebookLM, Deep Research, Operator, and GPT-4.5 to gain direct experience.

Why: Hands-on experience is paramount to understanding the capabilities (and limitations) of these tools. Before you can effectively have internal conversations, identify opportunities, or plan strategically, people need to see what these tools can do. This builds conviction and sparks ideas.

2. Start Internal Conversations: Discuss the implications of AI within teams and across your company.

Why: Before anything else, creating internal awareness and alignment is crucial. Without a shared understanding of the opportunities and challenges, other efforts will be less effective. This helps build a culture of experimentation and innovation. It's also the least resource-intensive way to start.

3. Invest in Upskilling: Prioritise training programs for teams to leverage AI tools effectively, and boost AI literacy.

Why: AI tools are only as good as the people who use them. Upskilling ensures your team can effectively leverage these technologies, maximising their potential. This might involve internal workshops, online courses, or bringing in external experts.

4. Identify Automation Opportunities: Pinpoint workflows ripe for AI-driven automation.

Why: By identifying specific processes that could benefit from automation, you can focus your resources on implementing AI solutions that deliver tangible results. This can also help build a business case for further AI investments.

HIGH PRIORITY
5. Forge Strategic Partnerships: Collaborate with tech experts and AI solution providers - get to know and understand the ecosystem that is developing.

Why: Building relationships with tech experts and AI solution providers provides access to specialised knowledge, resources, and support. These partnerships can help you navigate the complex AI landscape and find the right solutions for your business.

MEDIUM PRIORITY
6. Assess Tech Infrastructure: Evaluate current systems for AI readiness.

Why: Understanding your current tech capabilities is essential for determining which AI solutions can be implemented and what upgrades may be necessary. This is more of a technical step that can be addressed once your automation opportunities are clearer.

7. Plan for Change: Develop a roadmap for integrating AI that maintains human oversight and creative input.

Why: Creating a roadmap ensures that AI integration is well-planned and aligned with your overall business goals. This helps avoid the pitfalls of implementing AI in a piecemeal or uncoordinated fashion.

But philosophically, really engage with the notion of "Building a Better Built Environment":  Reflect on what this means in an AI-powered context and how AI can help achieve our goals.

Be as aspirational as you can be. And then some more.

Everything IS possible.