The Consequences for Offices of Artificial Intelligence

Midjourney / Antony Slumbers

Introduction

Making predictions is a mugs game, so they say. However, extrapolating out core trends, not fads, isn’t as precarious. Just as with #SpaceasaService, which I first wrote about over a decade ago, there are certain fundamental drivers of change which can only really end up in one direction. Timing might, often is, be a tricky nut to crack but certain inputs really do necessitate certain outputs. And so it is, I think, with the ongoing developments around Artificial intelligence.

Here we are going to look at what the key drivers of change are, and how they ‘will’ manifest themselves across the real estate industry, primarily focussing on the office market. My time focus is 4-7 years, the length of many office development projects. So we’re considering what might be towards the end of this decade.

To be clear, these drivers and consequences are what I believe will apply to the top end of the market. Maybe top 30%. In much of the market, change will come a lot slower, maybe even after complete obsolescence has set in. Of the offices and occupiers.

I am talking about the spaces and places leading companies, from start ups to multinationals, will want to occupy. Buildings that will support companies that are creating the future, that are leaning in to AI and other new technologies. Companies that are early adopters, to early majority. Companies that in 2030 will be where most are in 2040.

So let’s start with the key drivers that will impact the office market.

Key Drivers

1. AI-Enabled Decision Making:

Increasingly we will witness a democratisation of insights and decision-making processes. AI tools, often mediated through natural language interfaces, will provide data-driven insights to employees at all levels, not just top management. This will enable faster, more informed decisions across organisations and raise the potential for more innovative problem-solving as diverse perspectives are empowered by these AI driven insights. This may, nay should, lead to more agile and responsive organisational structures.

2. Automation of Routine Tasks:

AI will take over repetitive and some managerial functions, whilst freeing up, and augmenting, human workers to focus on more complex, creative, and strategic tasks (with an emphasis on those where humans retain primacy). This could lead to significant changes in job roles and required skill sets. There is great potential for increased productivity and efficiency in many business processes, as individuals and teams restructure their work to concentrate on where they truly can add value.

3. Enhanced Data Processing and Analytics:

We will be making more informed, real-time decisions. AI can process and analyse data much faster than humans (which is obvious) but more importantly AI can provide deeper insights and identify patterns that might be missed by human analysis. AI, or at least a major branch of AI, is all about prediction. Everywhere we should be able to raise the quantity, and dramatically lower the cost, of high quality predictions. This will both lead to more data-driven cultures within companies but also significantly raise the value of judgement. Machines are great at prediction - advanced humans likewise with judgement. Though note the caveat - advanced.

4. AI-Powered Collaboration Tools:

Now there is a huge market for them, a great deal of money and attention is being directed at developing improved, often AI powered, collaboration tools. Distributed, remote and hybrid working is only going to get easier, and therefore more effective. Anything done entirely remotely will be easy to distribute globally. Location will be meaningless for these tasks, though in practice most jobs will require meaningful human connection - after all few humans are so skilled that they alone can generate complete sets of value. And jobs that don’t require human connection will ultimately all be done by machines. AI will enable deep virtual connection and then it’ll be about ‘what else do we need’? One should assume that no-one needs to be located three feet apart: why might they want to is the key question?

5. Flattening of Organisational Hierarchies:

We’ll see a large reduction in traditional management layers, as AI takes over some traditional middle management functions like scheduling and performance monitoring. AI should enable more direct communication between top leadership and front-line employees and this could lead to more autonomous, self-managing teams. Ronald Coase wrote ‘The Nature of the Firm’ in 1937, and he focussed on ‘transition costs’, primarily:

• Costs of negotiating and drawing up contracts

• Costs of gathering information

• Costs of monitoring and enforcing agreements

All of these are bullseyes for AI, predictive and generative. If machines can do them, there is no requirement for human managers to do the same. Being in this middle zone is a very dangerous place to be. As it will be done away with, or at least hugely reduced in scale.

6. Reimagining of Business Processes:

The mechanism for point 5 above will be the ‘Unbundling and rebundling’ of work processes. AI will allow us to fundamentally rethink how work is done and value is created. In fact it it won’t be until we undertake this process that real value will be derived from AI. We need to stop iterating on the past, powered as it was by certain technologies, and turn our eyes towards redesigning work around what these new technologies enable. The direct analogy is the introduction of the electric engine which replaced steam power - great productivity gains didn’t manifest themselves until factories were fundamentally redesigned to take advantage of multitudes of standalone electric powered machines. This process took four decades - with AI it’s likely to take one at most. One has to assume and plan for rapid change on this front.

7. Continuous Learning Requirements:

There is going to be a huge need for upskilling and reskilling. AI is developing at such a pace that we all need to over index on continuous learning, with a very strong emphasis on adaptability. Currently organisations are not investing in nearly enough training. For every CEO proclaiming their companies AI initiatives only about a third of them are actually running the necessary training programs. Talk is cheap - training has to be taken seriously. The leading companies do, and this will result in significant competitive advantage.

8. Sustainability Imperatives:

This is already happening but the imperative will only intensify. As each year passes they’ll be an increased focus on energy efficiency and sustainable practices. Predictive AI should be a major influence here as it is very good at optimising resource use, reducing waste and energy consumption. This is a rapidly developing area and we ‘know’ what needs to be done, and largely how to do it. A growing area is generating and accessing renewable energy. Expect to see a lot of investment in energy infrastructure, particularly solar power married to battery storage. Self sufficient buildings has to be the north star being aimed at.

9. Evolving Employee Expectations:

We are changing. Always have of course, but it does seem that the global pandemic did act as a catalyst for many people. Demand for work-life balance and meaningful work environments has definitely grown since 2020.

Employees increasingly seek purposeful work and better integration of work and personal life. There’s a growing importance of company culture, values, and social responsibility and an expectation for more flexible work arrangements and personalised work experiences. Some say ‘wait for the next recession and all of this will disappear’ but that ‘feels’ unlikely. Adapting to this change in zeitgeist is a major requirement for the real estate industry.

Companies need to lead, but work happens within real estate.

10. Balancing Intangible and Tangible Business Aspects:

Not everything is going virtual. Yes much of modern business is focussed on intangibles but there still exists an awful lot of physicality. Life science and biotechnology space is very fashionable an area today, but there are many other areas where physicality is important. Healthcare, advanced manufacturing, aerospace and defence, the creative industries, education, training, personalised hospitality. Key is understanding which industries have these ‘special’ characteristics that demand very bespoke space. Their requirements are particular but at least they do actually ‘need’ office space.

11. Changing Economic Models:

And finally they’ll be a strong driver of change as work becomes ever more atomised. We’re already seeing larger companies make increasing use of ‘contingent’ workers, and a thinning out of full time, fully integrated workers. Tech companies have laid off large numbers over the last few years, with seemingly no impact on productivity and instead rising share prices. Sure they over hired during Covid but nevertheless it does seem like less people are required to maintain a steady state. As early adopters of AI perhaps we are seeing in tech companies the consequences of individually more productive individuals. One simply needs less people when each person operates at 5-10X what their peers did a few years ago.

Consequences - Anticipated Changes and Developments

All of the above is very likely to occur. It will evolve over time, and some companies will operate like this much faster than others, but betting against it happening is surely high risk.

So what will be the consequences of Artificial intelligence for the real estate industry, especially those dealing with still the sectors largest asset class, offices. What will be the nature of demand in the years ahead?

1. Shift to Flexible, Adaptive Spaces:

We simply do not know how space is going to be used in 3, 5, 10 years time. 10 years equates to 100X more powerful computers. How do we design for that? So much depends what becomes possible. People will use space based on that, and that is ….. just guesswork.

So we will definitely need to create spaces that that maximally flexible and adaptable. Reconfigurable with modular furniture and movable partitions. Spaces that can easily transform from individual work areas to collaborative zones. Where exceptional integration of technology allows for quick reconfiguration of digital and physical resources. We will of course be using AI to assist in all this space optimisation, that will create new configurations having learnt from usage patterns.

2. Increase in Collaborative Areas:

Much more space will be dedicated to teamwork and cross-functional collaboration. Which will require a variety of meeting spaces catering to different group sizes and work styles, with integrated technology to support both in-person and virtual collaboration. Informal gathering spaces to encourage spontaneous interactions and idea sharing will be a top focus. Most of the space will be the ‘water cooler’. Every space will be designed to catalyse ‘Human’ skills and capabilities. We are in the office to do what the machines, the AI, cannot.

3. Emphasis on Learning and Development Spaces:

A key ‘want’ in future offices will be exceptional areas dedicated to continuous skill development. In-house learning centres or corporate universities will become common. These will integrate AI-powered learning tools and simulators, and will be flexible spaces that can accommodate various training formats (lectures, workshops, hands-on practice). In many ways our places of work will become places of learning.

4. Enhanced Technology Infrastructure:

Our buildings are going to incorporate a lot of AI. We are moving to a world of intelligent, self monitoring and self optimising assets. All of which requires a lot of data, transmitted at very low latency. So every building will need advanced digital infrastructure to support all of this. Robust, high-speed networks capable of handling increased data loads. Edge computing, where analytics and AI inference (the running of AI applications) is done locally on devices incorporated into the buildings fabric, or attached to it, is likely to become a big thing. For a variety of reasons: Reduced latency, improved privacy, bandwidth savings, reliability and energy efficiency. All of this requires a high spec building.

5. Integration of AI-Human Interaction Spaces:

Our buildings will have dedicated areas for employees to work with AI systems that are designed for privacy and focus. With specialised equipment and interfaces for optimal AI-human collaboration, including immersive AI environments using AR/VR technologies. We’ll see the emergence of new spatial design principles for these AI-human workspaces. All of this will be a key reason to come to the office - to use equipment not available anywhere else.

6. Data-Centric Infrastructure:

Despite, or indeed because of, the focus on human-centricity, we’ll become far more data centric. We might even start to see the Integration of data centres within office developments. Buildings will be designed with enhanced cooling and power capabilities to support data infrastructure. There is potential for data centre heat to be repurposed for office heating, improving energy efficiency and this may lead to new approaches in building design that treat data infrastructure as a core component.

7. Reduction in Traditional Management Spaces:

As discussed above middle management is set to diminish so we will need less offices for this layer. Former executive spaces will be converted into collaborative or multi-purpose areas. With flatter organisational structures companies have the potential to design more open, democratic workplaces.

8. Decentralised Decision-Making Hubs:

Specialised and distributed spaces equipped with AI tools for decision support will emerge. Large companies may have their own but smaller companies will rent them as needed. These will take the form of 'war rooms' or 'decision theatres' with advanced data visualisation capabilities and integrated AI assistants to facilitate rapid, data-driven decision making.

These spaces will be designed to support both in-person and remote participation in decision processes. AI is really good at ‘modelling’ and ‘simulating’ and over time we’ll all become conversant with how to run these operations, but they will require bespoke setup and operational support.

9. Reimagined Workflow Layouts:

Offices will be redesigned reflecting the new realities of unbundled and rebundled work processes. Spaces are likely to be organised around work functions rather than traditional departments with the creation of 'neighbourhoods' for different work modes (focus, collaboration, learning, socialising). As happens today but more so. We will see the integration of AI-powered workflow management systems into the physical environment. Examples being smart meeting rooms, intelligent desks and workstations, AI-assisted collaboration spaces, automated asset tracking, personalised productivity environments and Intelligent wayfinding. Overall, layouts will become much more fluid but much more intelligent.

10. Focus on Employee Experience and Wellbeing:

Enabling people to be as happy, healthy and productive as they are capable of being will be the primary aim of any place of work. What this means is #SpaceasaService - spaces that provide each individual with the best possible environment to do whatever it is that they need to do, whenever they need to do it. Personalised, optimised, customised. Offices are likely to be designed more like hospitality spaces, prioritising user experience.

11. Sustainability-Driven Design:

Obviously sustainability is a non negotiable for offices of the future. Fortunately AI-powered systems will be massively helpful in this area, as they are excellent for predictive management and optimisation, as well as interacting with internal and external data sources. The potential for AI to manage complex sustainability initiatives across entire buildings or campuses is great. We know what needs to be done here and AI is very much our friend in getting it done.

12. Hybrid Work Support:

Everywhere spaces will be designed to integrate in-person and remote workers seamlessly. We will create 'Zoom rooms' and other tech-enabled spaces for virtual collaboration, and provide hot-desking and hoteling systems to manage flexible office usage. AI-powered scheduling and space management tools will be widely adopted and we’ll have enhanced audio-visual technology throughout the office to support impromptu virtual connections. As this form of working becomes deeply embedded we’ll use AI-driven tools to maintain company culture and employee engagement. We’ll spend years thinking about hybrid work but eventually it’ll just become ….. work.

13. Evolution of Urban Office Hubs:

We’re going to see a lot of the transformation of traditional office buildings into mixed-use spaces and the integration of residential, retail, and recreational facilities within office complexes. Some exist already but 'vertical villages' in urban skyscrapers will become commonplace. As will the repurposing of excess office space for other uses (e.g., vertical farming, educational facilities, sports venues, whatever we can imagine).

There is great potential for AI to optimise space usage and transitions between these different functions. This will start to strongly influence urban planning and zoning/planning regulations, leading to much more integrated city designs. CBDs will die off (with very few exceptions) as we gravitate towards more decentralised, polycentric urban development. AI will be at the heart of how we redesign our cities, but the guiding principle will be human centricity.

14. Rise of Mixed-Use Developments:

Everywhere we’ll be integrating office, residential, retail, and recreational facilities with a view to creating self-contained ecosystems that support diverse needs of workers and residents. This blending of work and living spaces to support changing lifestyle preferences will utilise AI for community management and service optimisation as these will be complex environments. There is potential for new types of leasing or ownership models in these mixed-use developments and this may lead to the emergence of new property categories that defy traditional classifications.

15. Location Strategy Shifts:

But where will we work. Yes, still in our newly forming poly centric major cities, but also in areas with strong AI talent pools and research connections. Traditional CBDs will have to compete with these emerging tech hubs or university adjacent areas. At a national level companies will give consideration to locations with advantageous AI and data regulations. There will be a balancing of physical accessibility with digital connectivity in location decisions. All of this may lead to the emergence of new tier-2 city hotspots with good quality of life and strong tech ecosystems and could result in a redistribution of office demand across wider geographic areas. Anywhere that can fulfil the technical and talent requirements, and is a ‘nice place to be’, will thrive. Agglomeration remains important but ultimately the internet is the master agglomerator. We’ll have more optionality and qualitative factors will become increasingly important.

16. Security and Privacy Considerations:

The more AI mediated our world becomes the more vital will be the implementation of advanced cybersecurity measures. Spaces that are designed to ensure privacy for confidential AI-human interactions will be at a premium. There will be pervasive integration of physical security measures with AI-driven surveillance and access control systems. It is likely we’ll see the emergence of new standards and certifications for AI-secure office spaces

17. Globalisation of Workforce:

Today the era of globalisation is receding but this’ll be short lived. Office spaces will need to facilitate international collaborations and virtual team management. The creation of 'global collaboration hubs' with advanced communication technologies and that have 'follow-the-sun' work models will impact on current office usage patterns. Particularly in major cities, where multinationals tend to congregate, offices will become more 24 hour. A 15 minute City, with a 24 hour lifestyle is rather wonderfully local and global at the same time.

Conclusion

The future of the office is a fascinating paradox. As AI automates routine tasks and reshapes traditional work patterns, it simultaneously amplifies the need for spaces that prioritise human connection, creativity, and well-being.

The offices that will thrive in this new era won’t simply be vessels for technology; they'll be vibrant ecosystems designed to cultivate uniquely human capabilities. They will be flexible, adaptable, and deeply integrated with AI, yet always centred around the needs and experiences of their human occupants.

This transformation represents both a challenge and an unprecedented opportunity for the real estate industry. By embracing AI not as a replacement for human-centric design, but as a powerful tool to enhance it, we can create workspaces that are not only more efficient and sustainable, but also more inspiring, engaging, and ultimately, more human.

And, I may add, more valuable.

Agree? Disagree? What have I missed? What have I got wrong? Let me know in the comments.

And thanks for getting this far!

Antony

The AI Optimist's Vision: A Transformative Future for Real Estate?

Antony Slumbers / Midjourney

I've been reading a provocative paper by ex OpenAI ‘Superalignment’ researcher Leopold Aschenbrenner titled "Situational Awareness". Now, Aschenbrenner isn't a household name in our industry, but his vision of AI progress is eye-opening, to say the least. It's the kind of forecast that makes you sit up and pay attention, whether you buy into it or not.

This 165 page paper has received a huge amount of coverage in the tech industry. And despite being wildly optimistic, some pushback but not as much as one might expect. I ‘think’ because it is possible. Maybe not likely, but possible.

Even if not right in terms of timing I think his direction of travel is spot on. And if it takes 16 years instead of six then all of this will probably happen whilst you are still working. Certainly your children will have to be dealing with the consequences, for good or ill.

What troubles me though is trying to get a grip on what this all means for real estate within just one cycle, maybe two. Here in London an unbuilt mega tower in the City of London has just applied to have its existing permission updated. Bigger, taller. Right next to an existing cluster of towers. If they get going it might be ready in 2030. Will anyone want such a thing by then? I’m totally ok with the notion that whatever happens we will be needing places of work, but will we want to work in ‘mono culture leviathans’? We’ll be needing places that catalyse human skills and I’m suspicious that these types of buildings will no longer have product/market fit.

What we want, and where we want it, and how we’ll use it all seem to me up for debate, and reinvention. Read below and let me know what you think we will need in this coming world.

These are Aschenbrenner’s 7 key trends:

2025-2026: AI Surpasses Human Experts

By 2025-2026, AI systems are expected to outperform human experts in raw problem-solving abilities across many domains. This is a critical milestone as it marks the point where AI transitions from being a tool that augments human capabilities to potentially replacing high-skilled knowledge workers in many tasks. For businesses, this means a dramatic shift in how work is done, with AI taking on increasingly complex and creative tasks previously thought to require human intellect. It could lead to significant productivity gains but also disrupt traditional workforce structures and skill requirements.

From Chatbots to Autonomous Agents

The evolution from current chatbot-style AI to autonomous agents represents a fundamental change in AI capabilities. While chatbots primarily respond to prompts, autonomous agents can proactively plan, reason, and execute complex multi-step tasks with minimal human intervention. This shift enables AI to handle more sophisticated workflows, make decisions, and even manage other AI systems or robots. For businesses, this could mean AI systems that can autonomously manage projects, conduct research, or even run entire departments with human oversight, dramatically increasing operational efficiency and innovation potential.

Rapid Automation of Cognitive Tasks

As AI capabilities advance, we're likely to see a rapid acceleration in the automation of cognitive tasks across industries. This goes beyond simple repetitive tasks to include complex analytical work, creative processes, and strategic decision-making. The implications for businesses are profound - entire job categories may be transformed or eliminated, while new roles focused on AI management and oversight emerge. Companies that can effectively leverage this automation wave could gain significant competitive advantages in efficiency, cost reduction, and innovation speed.

Enhanced AI Reasoning and Memory

Improvements in AI reasoning capabilities and "memory" (the ability to retain and apply information over long periods) will lead to AI systems that can engage in more sophisticated problem-solving and long-term planning. This could result in AI assistants that truly understand context, can learn from past interactions, and provide increasingly valuable insights over time. For businesses, this means AI systems that can tackle complex, multi-faceted problems, assist in long-term strategic planning, and provide more nuanced and contextually relevant support across all levels of the organisation.

Breakthroughs in AI Training

Expected breakthroughs in AI training methods, such as more efficient algorithms or novel approaches to data utilisation, could dramatically accelerate AI development and reduce the resources required to create powerful AI systems. This could lower the barriers to entry for AI development and deployment, potentially democratising access to advanced AI capabilities. For businesses, this might mean more accessible and cost-effective AI solutions, enabling even smaller companies to leverage cutting-edge AI technologies to compete with larger enterprises.

2030: The Possibility of 'Superintelligent' Systems

The potential emergence of 'superintelligent' AI systems by 2030 - those significantly surpassing human capabilities across all domains - represents both an enormous opportunity and a significant risk. Such systems could solve currently intractable problems in science, medicine, and technology, driving unprecedented innovation and economic growth. However, they also raise profound ethical, security, and control issues. Businesses need to be prepared for a world where the capabilities of AI may far exceed human understanding, potentially reshaping entire industries and the nature of work itself.

Exponential Economic Growth

The combination of these AI advancements is expected to drive exponential economic growth, potentially far exceeding historical rates. This growth could be fuelled by dramatic increases in productivity, entirely new industries and business models enabled by AI, and breakthrough innovations across all sectors. For businesses, this represents an era of unprecedented opportunity but also intense competition and disruption. Companies that can successfully integrate and leverage these advanced AI capabilities may see explosive growth, while those that fail to adapt risk becoming obsolete at an accelerated pace.

So …. A pretty optimistic view of the future. Or maybe aggressive is a better word. Either way, we need to contemplate it.

Because it might just happen.

Can we go from ‘Zero to One’ in Real Estate?

Antony Slumbers / Midjourney

Peter Thiel wrote a book about ‘Zero to One’, a term that represents a leap from nothing to something, creating a novel product, service, or idea that did not previously exist. And he compared it to ‘One to N’ which is about scaling, improving, and distributing existing products and services.

In real estate we’ve traditionally done a lot of ‘One to N’ - in the future, and as a consequence of AI, we need to be thinking more ‘Zero to One’. Technology is so fundamentally changing the nature of real estate demand that we need to be thinking much more boldly about the products and services we deliver, and how we deliver them.

We need to be developing entirely new models of real estate usage and development, redesigning our urban spaces to accommodate future works patterns and lifestyles, and thinking how we can offer unprecedented levels of efficiency, convenience and sustainability.

Instead, and for understandable reasons, we are mostly looking at how we can digitise the past. Can this new technology, this PropTech, enable us to do X better, faster, cheaper; rather than considering should we even be doing X.

With most technologies this makes sense. That is exactly what they are designed to do. Allow you to do more of what you already do.

AI though is different, especially Generative AI. This type of AI is probabilistic, not deterministic. To say it is just a ‘stochastic parrot’, a ‘next word prediction machine’ is to underplay how capable it is, but it is not in the business of 2 + 2 = 4, or performing a database lookup. It is, fundamentally, a creative tool. And that is what people find hard to get to grips with. We are used to getting answers that are ‘factual and neutral’ - that is not what this technology is all about. This root characteristic is why it is much more likely to lead to ‘Zero to One’ scenarios than ‘One to n’. It’s not dull enough for that. If you want to add up, get a calculator.

If you want to really innovate though, this is the way to go. It can enable things hitherto impossible. It doesn’t lend itself to doing things for you, though it often can. Where it shines is when you use it as a sparring partner, a confidant, a consigliere, a sounding board. At best it acts as a Socratic Mentor. Where it encourages deep thinking, self awareness and logical reasoning. It empowers you to take ownership of your learning and decisions. It’s a powerful flywheel of intellectual curiosity and critical thinking. And that’s where innovation is going to come from.

Probably you are not using it like this. And likely have not considered it thus. More likely it has been promoted to you as helping you reply to emails, or taking notes in meetings, or summarising or writing reports. All of which are useful and worthwhile, but ultimately very thin gruel. This is ‘One to n’ territory.

Richer use cases might involve:

Transforming Tacit into Explicit Knowledge - codifying all your companies expertise, whether structured or unstructured into dynamic knowledge graphs. These would democratise access to critical expertise, especially amongst less experienced employees, allowing them to leverage the organisation's collective intelligence.

Virtual Mentorship Programs - Use LLMs to simulate the decision-making processes of experienced professionals, creating interactive scenarios where less experienced employees can engage in problem-solving exercises guided by the model. And then backed up by in-person human mentoring. We hear so much about the supposed problems mentoring younger employees in a hybrid working world that completely ignores the potential of AI to develop hybrid mentoring programmes that are orders of magnitude better than the norm. Done well we should be able to turn 2 years experience into 20.

Autonomous Insight Discovery - Anyone can work hard and discern the obvious. What we want is to uncover the non obvious, the unknown unknowns. Financial data, market trends, consumer behaviour, and operational metrics can all be mined at unprecedented levels, uncovering opportunities for cost savings, investment, and strategic pivots that were previously hidden. The machines can act autonomously but the ‘human in the loop’ uses their experience to guide and cajole. Much of our future value will come from our curatorial skills and wisdom. Oftentimes ‘we know more than we can tell’ - AI can help us make visible the previously invisible.

Innovation Catalyst Systems - Real estate is famously siloed. Left hand does not talk to right hand. We cut ourselves off from discovering novel combinations of existing technologies, processes, and business models. Machines have no qualms about who they talk to; and they’re stupendous at synthesising data. Set them free.

Hyper-Personalised Real Estate Services - How good are we at analysing client behaviours, preferences, and market dynamics in real-time? AI enables personalisation that humans cannot. But humans are definitely better at face to face. The key here is playing to each others strengths. Few of us have staff constantly briefing us about what we need to know about a given situation or people we are set to meet. AI can give us all this superpower.

Enhanced Decision-Making Frameworks - all of the above can and should feed into our decision making. A continuous feed of relevant data, predictive analytics, and scenario simulations available to all.

Two key points come from all of this (and the myriad of other advanced AI use cases):

First is that AI is our most useful assistant as we strive to go Zero to One. It simply offers capabilities that did not exist before, and ‘should’ enable more of us to reach a level of innovative thinking that maybe we never thought we could. Being extremely well briefed and informed is becoming much easier.

And secondly, it emphasises the critical role of us ‘humans’ in shaping the AI tools we use. Creating and curating the AI we want is down to human agency. WE need to do it. WE need to decide what we want, how we want things to work, what we want to prioritise and what we want to deprecate. Everything about how we want our lives, our companies, and our societies to function is OUR responsibility. And is in our hands. Fundamentally the User Experience of the world is down to us.

So instead of thinking what tech you can buy to do what you do now better, faster, cheaper think about what you could do if you could do a lot more. Because with AI you will be able to do a lot more. It represents a massive software upgrade to humanity. Most notably it represents a massive upgrade in intelligence; we can access more, we can utilise it better, and we can spread it around like never before. But this requires a great process of redesign.

Let’s stop aiming at being 10% better - let’s 10X everything.

The Impact of AI on the UK Real Estate Sector's Productivity

Midjourney / Antony Slumbers

Our starting point when thinking about how AI is going to impact productivity across the real estate sector must be to understand what it is that we are dealing with.

AI fits into two main camps; Predictive AI and Generative AI. The former is an analytical tool, the latter a creative one.

Predictive AI is all about ‘Predict, Cluster, Classify’ and relies on learning from a very large corpora of historical data to predict what will happen in the future.

Generative AI is all about ‘Create, Synthesise, Innovate’ and relies on ‘Models’ that have been trained on extremely large corpora of data to create a statistical model that can then be referred to in order to ‘generate’ new data, mostly in the form of language, imagery or computer code.

The difference is critical in real estate. It is only when we have very large quantities of relevant data that we are able to utilise Predictive AI, whereas Generative AI can be used with our own proprietary data but does not require it, and has enormous utility ‘right out of the box’.

Given its nature Predictive AI has powerful but limited ways in which it can impact productivity within real estate. For years people have talked about using ‘AI’ to predict price movements and market dynamics: these have largely failed because the historic data is not large enough to successfully apply Machine Learning to (ML being the bedrock of Predictive AI). This is unlikely to change. So predicting the future of real estate for investment purposes will remain more a data science than an AI project.

However, the potential for Predictive AI to improve the performance (‘productivity’) of actual physical real estate is enormous. Because buildings can be configured to generate a tsunami of data. Given the current and future potential to ‘sense’ the world around us there are endless use cases for utilising Predictive AI to predict what will happen to A given B and C. 

We’ve hardly started optimising how our real estate operates. Ultimately the purpose of real estate is to enable its users to be as happy, healthy and/or productive as they are capable of being. We need to be creating spaces and places that provide the perfect environment for people to do whatever it is that they need to do, when and where they need to do it. It is the user experience of space that really matters. And that is something Predictive AI can really help with. And great user experiences are highly conducive to optimal productivity.

Generative AI on the other hand is much more of a GPT - a General Purpose Technology. Meaning a technology that permeates everything, rather than one designed for a specific purpose. If any task involves language, imagery or computer code then Generative AI has a use. It can help us read, write, create, analyse and understand at a scale and speed hitherto unimaginable. We can all do many things faster, better, cheaper. As a personal productivity tool Generative AI is a superpower.

A way to think about these two types of AI is that Predictive AI is for the quantitative aspects of our industry and Generative AI is for the qualitative. Sometimes we’ll need one, other times the other, and often we’ll need both. Between them there are few areas of the real estate industry that will not be impacted by their existence.

As such, leaning into ‘AI’ is likely to reap huge benefits. At an individual level it sets one apart, at a corporate level it adds differentiation and competitive advantage, and at a national level it could make the UK a magnet for talent, and position our industry as the global thought leader and exemplar of cutting edge real estate. This position is there to be had; no country is standing out as yet.

The real estate industry faces two huge challenges: decarbonising the built environment AND providing the right buildings for the ‘industries of the future’.There is no doubt that AI is going to be a major factor in both. Or should be.

Historically the real estate industry has been a technological laggard. Going forward this has to change. We have very long project cycles. A large development can easily stretch to 5, 10 years. And a decade in tech equates to 100X increase in computational power. So we need to be futurists. What is needed and what’ll have value in the future? Where will we work, what work will we do, where will we live, how will we live? All these questions will be answered through an AI powered lens. Demand, and supply, will be impacted by AI. We will be living in an AI mediated world. 

Leaning into AI should massively increase productivity across the UK real estate sector. It would be hard for it not to. The question is whether the industry has the leadership to make it happen? 

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Written as part of Bidwell’s ‘The Productivity Engine‘ Report. (https://www.bidwells.co.uk/insights-reports-events/productivity-engine-report/)

New Generative AI for Real Estate podcast!

Recently I had the real pleasure of taking part in a podcast for PropTech Denmark during their excellent symposium.

Links in first comment.

I genuinely learnt a lot. Hope you do to.

My co-presenters were:

* Rasmus Juul-Nyholm, Chair, PropTech Denmark & Co-founder, Home.Earth

* Leise Sandeman, Co-founder, Pathways AI

And our host was: Søren Vejby

It’s a very good primer.

Apple: https://lnkd.in/eVKxsvMH

Spotify: https://lnkd.in/eeAcGXqu