THE BLOG
Human-Centric Real Estate & Generative AI - Part 1
Here are three questions relating to the role and impact generative ai will have in the development and operation of human-centric real estate.
How is generative AI poised to transform human-centric real estate and shape the future of workspaces?
How will the relationship between generative AI and personalised workplace experiences evolve in the coming years?
What implications does the widespread adoption of generative AI have for traditional notions of office space and design?
In part 1 of this series of posts we’ll answer question 1.
How is generative AI poised to transform human-centric real estate and shape the future of workspaces?
First, we need to define what is meant by the term ‘human-centric real estate’. My definition would be that:
‘Human-centric real estate is a design and operational philosophy for buildings and spaces that prioritises the needs, health, and well-being of the people who use them. In this approach, the design, construction, and management of a building are all centred around creating an environment that is conducive to the comfort, productivity, and overall satisfaction of its occupants.’
In turn this description can be unbundled into representing six key pillars that build on the general description. These are:
1. Wellness and Health: Buildings that are designed to promote the physical and mental health of occupants. This can involve air quality management, natural light, green spaces, and facilities that encourage physical activity.
2. Ergonomics and Comfort: Ensuring that the physical environment (like temperature, lighting, and acoustics) is optimised for comfort and reduces strain or discomfort.
3. Technology Integration: Using Smart technologies to enhance the user experience. This can include automated climate control, adaptive lighting systems, and other innovations that respond to the occupants' needs and preferences.
4. Community and Connectivity: Designing Spaces to foster a sense of community and connectivity among occupants. This can involve communal areas, shared resources, and design elements that encourage interaction.
5. Flexibility and Adaptability: Human-centric spaces need to be flexible and adaptable to meet the changing needs of their occupants over time. Sam Altman has a sign above his desk that reads ‘no one knows what happens next’. If he doesn’t know, what hope have we. Flexibility and adaptability have never been as important as they are now.
6. Sustainability: Ensuring that the building is energy-efficient and environmentally friendly, is a non negotiable. Sustainable buildings are great enablers of the other five pillars, and they cannot exist without it.
Now, before we go on, it’s worth thinking about why we need to care about ‘human-centric real estate’. After all, for many decades, offices (and this is the core asset class under consideration here) were very much ‘investor centric real estate’ - we weren’t developing buildings for people but assets for owners. Occupiers needed to work somewhere so had to use offices, but the key purpose of these buildings was to produce secure and stable long term cash flows for investors. Every effort was made to secure ‘Tenants’ but once leases were signed real estate companies were famously uninterested in interacting with the humans who actually worked in these buildings.
This has now changed. The trend was building pre covid but the experience of the pandemic turbo charged the understanding that getting work done was no longer dependant on attending the office five days a week, for 40 or more hours. Technology has ‘slowly then suddenly’ ripped away the NEED for offices. Nowadays the game is all about making occupiers actually WANT to occupy their offices. And judging by the two or so years since society opened up following the pandemic, there isn’t that much WANT going on. In the US office attendance has flatlined for over two years at roughly 2.5 days a week. In Europe it is slightly more, and even in Asia it’s not back to 5 days a week.
The bottom line is we have vast quantities of office space largely unloved and unliked by customers. And on top of that, much of it is unsustainable environmentally with little or no clear financial pathways to becoming so. In short, we are drowning in space either currently obsolete or on the way there.
Which of course is where human-centric real estate comes in. We have a desperate need to create buildings and spaces that are ‘conducive to the comfort, productivity, and overall satisfaction of occupants.’
Many are still debating this point but it really is a fools errand. Not only is the evidence all around us, both anecdotally but also in the form of academic research, but with every day that passes the technologies that are the root cause of this dislocation between work and place are getting better, and as they do the imperative to focus on human-centricity grows and grows.
But, as we will see, the newest technologies are also here to help us turn a bug into a feature. We know people respond positively to human-centric spaces, and generative ai in particular can help us develop them.
Predictive AI also has a strong part to play but for now let us concentrate on how generative ai can help us with each of the six pillars mentioned above.
This is how:
Pillar 1. Wellness and Health
With Custom Generative AI models could design wellness programs or environment layouts tailored to individual health needs or preferences.
And Off-the-Shelf Generative AI, such as ChatGPT or individual GPTs could provide health and wellness tips, suggest ergonomic practices, or offer mental health support through conversational interfaces.
Pillar 2. Ergonomics and Comfort
With Custom Generative AI models might develop ergonomic furniture or workspace designs customised to individual user’s physical needs.
And with Off-the-Shelf Generative AI tools like ChatGPT could offer advice on ergonomic setups and comfort improvement based on user queries.
Pillar 3. Technology Integration
With Custom Generative AI they could be used to create personalised user interfaces for building management systems, adapting to individual preferences and usage patterns.
And Off-the-Shelf Generative AI can assist in troubleshooting technology issues, offering user support, and providing recommendations for tech upgrades.
Pillar 4. Community and Connectivity
With Custom Generative AI models might design communal spaces or community-building activities tailored to the occupants’ profiles.
And Off-the-Shelf Generative AI can offer advice on community engagement strategies and facilitate connectivity through digital platforms.
Pillar 5. Flexibility and Adaptability
With Custom Generative AI models could generate design modifications for spaces, that adapt to evolving use cases or occupant needs.
And Off-the-Shelf Generative AI can provide suggestions on how to make spaces more adaptable or multifunctional based on current trends and user input.
And Pillar 6. Sustainability
With Custom Generative AI we could help develop sustainable building materials or innovative green solutions tailored to specific environmental conditions. Think green roofs, living walls, solar panel layouts and geothermal systems.
And Off-the-Shelf Generative AI can educate occupants on sustainable practices and suggest eco-friendly changes as well as organise community sustainability initiatives, like recycling or shared renewable energy projects.
As you will have noticed the key focus in the use of generative ai is to ‘prioritise the needs, health, and well-being of the people’ who use our offices. It’s all about creating spaces that catalyse human skills, on an individual by individual basis.
Strategically it is not about the adoption of a silver bullet technology (the classic approach of too many ‘Smart Building’ advocates) but rather an ‘operational manifesto’ that is much more personal and intensive. Human-centric real estate is not a fixed and final product. It is much better thought of as a combination of physical, digital and human inputs carefully curated to maximise the health, happiness and productivity of its users.
That is the future of workspaces.
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In Part 2 we will answer the question ‘How will the relationship between generative AI and personalised workplace experiences evolve in the coming years?’
The future of commercial real estate is highly predictable …..
Midjourney / Antony Slumbers
Combine the need for:
Flexibility
Adaptability
Affordability
Sustainability
Health & Well Being
Productivity
With:
Pervasive connectivity
Exponential data growth
Advanced technologies
Generative (and non) AI
And you get:
Distributed working
More flexible, project-based, and collaborative work structures
Automated administration
Higher value human to human connections
Human + Machine workflows
Ecosystems over companies
Networks over defined spaces
Hyper productivity
Looser definitions of live/work/play
Leading to:
CBDs turning to CSDs (central social districts)
More mixed use development/neighbourhoods
Growth of great and/or liveable/walkable/affordable cities
Death of dull - cities, neighbourhoods, regions
Rise of suburban, near home, third party ‘places of work’
Preference for ‘latest/greatest’ buildings - flight to quality
As strong ‘flight to character’ - aesthetics matters
Focus on ‘catalysing human skills’
Human-centricity
Culminating in:
#SpaceAsAService as the defining characteristic of the modern ‘place of work’.
Not a niche within the real estate market, but THE market.
Form follows Function follows Technology.
Agree? Disagree? It’s complicated?
#GenerativeAIforRealEstatePeople
Mega Myth - To use AI you need a lot of Data
Midjourney / Antony Slumbers
It is generally assumed that to leverage AI you need to have a lot of data at your disposal. No data = no play! This is such a prevalent belief it’s almost assumed to be a Law of Nature. I’m sure you have heard and read it a million times.
But it is not true.
At least it is not when you are dealing with Generative AI.
With Predictive AI it most certainly is, as that is all about predicting, clustering and classifying. You are applying these actions to specific datasets. So obviously without data you are facing a brick wall.
But with Generative AI the data comes built in. Take ChatGPT - the GPT stands for Generative Pre-trained Transformer, where the training has already taken place on the near entirety of data that exists on the open Internet. And from that vast corpus of data has been developed a statistical model that allows for the creation (the generative bit) of new text, code, images, video, speech or actions.
So when you are using generative AI you have at your disposal if not ‘all the worlds information’ then pretty close to it. You have vast amounts of data at your beck and call. You can, if you have it available, augment this with proprietary data, but to a large degree that is not necessary, or at least does not bring as much as you think to the party. Your data is pretty small compared to what the ‘Large Language Model’ already has intrinsically.
The bottom line is that there is a huge amount you can do with Generative AI without the need for any other data. This is what is not generally appreciated and is why this technology is also known by a different interpretation of the acronym GPT - a General Purpose Technology. Which signifies, like electricity, the internal combustion engine, the Internet itself, a technology that is not a point solution but one that is or will become pervasive throughout society. Generative AI will seep in, often invisibly, to everything. There is little you will be doing within a few years that is not, in one way or another, mediated through AI.
Indeed, there is no need to wait. Below are examples of use cases, by business department, you can implement today. No data required.
McKinsey reckon 75% of potential productivity value and gains will come from the first four categories, but the others are included to show just how much is possible ‘out of the box’. Much of this can be achieved by an individual using public tools like ChatGPT, Claude, Google Gemini and Midjourney. Whilst other areas might require customised products or coding. But either way, almost all of this is available to anyone in your company. And again, with no data required.
Sales & Marketing
Content Creation: Generate engaging marketing copy, blog posts, and social media content.
Email Campaigns: Craft personalised email messages for different customer segments.
Market Analysis: Summarise market trends and news from publicly available sources.
Customer Segmentation: Predict customer preferences using open-source demographic data.
Product Recommendations: Suggest products based on general market trends.
Interactive Content: Create dynamic web content to enhance user engagement.
Predictive Analytics: Analyse customer behaviour for better targeting and segmentation.
Product and R&D
Idea Generation: Brainstorm product ideas based on market analysis.
Prototype Testing: Simulate user feedback on prototypes with AI-generated personas.
Research Summarisation: Compile relevant research to support R&D.
Competitive Analysis: Analyse competitors' product strategies.
Design Optimisation: Propose product design improvements using generative models.
Material Research: Summarise findings on new materials from public databases.
Customer Operations
Chatbots and Virtual Assistants: Implement AI-driven chatbots for customer support.
Feedback Analysis: Analyse customer feedback from public reviews.
FAQ Generation: Automatically generate FAQ content.
Operational Efficiency: Optimise workflows to manage high-volume periods.
Personalisation: Personalise interactions based on behaviour trends.
Software Engineering (Product Development and Corporate IT)
Code Generation: Generate boilerplate code and documentation.
Bug Fixing: Identify potential bugs using publicly available datasets.
Automated Testing: Adjust tests automatically to application changes.
Architecture Design: Suggest improvements based on public best practices.
Security Vulnerability Identification: Identify vulnerabilities from public databases.
Strategy
Trend Analysis: Identify emerging industry trends.
Scenario Planning: Generate business scenarios for planning.
Benchmarking: Benchmark against industry standards.
Innovation Tracking: Track industry innovation trends.
Strategic Diversification: Analyse potential diversification areas.
Legal
Contract Generation: Generate standard legal documents.
Legal Research: Summarise legal precedents from public databases.
Compliance Monitoring: Track changes in laws and regulations.
Dispute Resolution: Suggest resolutions based on similar public cases.
Policy Development: Analyse public compliance standards for internal policies.
Risk and Compliance
Regulatory Compliance Tracking: Monitor regulatory changes.
Risk Assessment: Conduct assessments based on public threat data.
Fraud Detection: Detect fraudulent activity patterns.
Ethical Compliance Monitoring: Monitor public sentiment for ethical issues.
Cyber Risk Analysis: Analyse public data on cyber threats.
Talent and HR
Resume Screening: Automate initial resume screening.
Employee Engagement: Analyse engagement trends to inform strategies.
Training Programs: Develop AI-driven training programs.
Workforce Planning: Use labour market trends for planning.
Diversity and Inclusion: Inform policies based on diversity data analysis.
So, as you can see, it’s time to bury the ‘you need data’ myth. You absolutely do to get the most out of a lot of AI, but with Generative AI the biggest constraint is not data, but your own curiosity, vision and willingness to just get stuck in.
Using AI to Turbo Charge Human-Centric Real Estate
Antony Slumbers & Midjourney
We are all aware of just how much Covid has transformed our collective attitudes towards work and the office. Four years after the start of Covid managers around the world are still struggling with how to assimilate into their operating procedures the desire amongst the vast majority of employees to not give up what they learnt was possible during the pandemic. That being in the office five days a week was absolutely not necessary, and that the costs and constraints around commuting into a workplace are self imposed and injurious to a better way of working and living.
This is still playing out, but the end point has been inevitable for years. Post Covid was never going to be a resumption of life pre Covid. We’ve all learnt too much.
We are now in the era of human-centric real estate. Unless the spaces and places we are being asked to spend our time are conducive to enabling people to be happy, healthy and productive they will increasingly become obsolete. They will no longer have any sort of product/market fit. Indeed we are already seeing this in the market - pitiful occupancy and utilisation levels are directly correlated to having lost any sort of human utility.
All of this is a known known. What is far less understood is just how much AI is going to:
Enable the creation of human-centric spaces and places.
Make these types of spaces even more important, as the ‘work we do’ is fundamentally reconfigured due to technological change.
Provide the tools for either existing companies, or new entrants, to be dramatically more productive than is the norm today.
In the following videos I dive deep into these rabbit holes and provide frameworks for how to utilise AI to create great human-centric real estate, how to rethink your own products and services and their delivery, and how to create a company culture appropriate to this new world.
In Part 1 of this video series I am going to introduce the subject, Define Human-Centric Real Estate, and explain why we need to care.
In Part 2 of this video series I am going explain what AI can actually do to impact the six pillars of human centric real estate, and what are the the direct and indirect financial benefits of this approach?
In Part 3 of this video series I am going to look at how we’d approach this topic if we were starting from scratch, and how productive that could make us.
And finally in Part 4 of this video series I am going to analyse the culture of a human-centric company, and wrap up everything in a set of conclusions.
Shaping the AI Economy: A Holistic Approach Beyond Zero-Sum Thinking
There is much commentary that AI is likely to enable fewer people to generate a given amount of economic output and therefore large numbers of people are going to be redundant, leading to a bifurcated, deeply unequal world, where a few do exceptionally well whilst the rest see their skills commoditised and downgraded in value.
Is this right?
I think not, because it overlooks the inherently dynamic nature of economies, particularly in response to transformative technologies. It harks back to the late 19th century lump of labor fallacy which considered there only to be a finite amount of work within an economy that can be distributed to create more or fewer jobs. That proved wrong then, and I hope and suspect it will prove wrong in the future.
How does the dynamic nature of economies play out?
In six ways:
Productivity Gains and Income Redistribution:
Increased Corporate Profits: AI-driven productivity improvements can lead to increased profits for businesses. Ideally, a portion of these profits can be redistributed to workers in the form of higher wages or reinvested into the business to stimulate further growth.
Consumer Spending: Higher incomes for workers can lead to increased consumer spending, which in turn stimulates demand across various sectors of the economy, creating a multiplier effect.
2. Cost Reduction and Price Elasticity:
Lower Production Costs: AI can significantly reduce production costs, leading to lower prices for goods and services.
Increased Demand: Lower prices often lead to increased demand (price elasticity). This increased demand can stimulate the need for more diverse services and products, leading to new job opportunities.
3. Investment in Innovation and R&D:
Reinvestment of Profits: The savings and profits garnered from AI integration can be reinvested into research and development, sparking innovation and the development of new products and industries.
Job Creation in New Sectors: This innovation can lead to the creation of entirely new sectors, which require human capital, thus generating new job opportunities.
4. Shift Toward High-Value Jobs:
Upgrading Skill Sets: As AI takes over routine tasks, there is a shift in the job market towards more complex and high-value tasks that require human input, such as strategic planning, creative problem-solving, and emotional intelligence.
Higher Value-Add per Employee: This shift can increase the value added per employee, leading to overall economic growth.
5. Global Economic Integration:
Opening New Markets: AI can break down barriers to entering global markets, allowing businesses to expand their reach and tap into new customer bases.
Global Supply Chains and Trade: Enhanced global integration can lead to more efficient supply chains and increased trade, boosting global economic activity.
6. Secondary Markets and Induced Industries:
Support and Maintenance of AI Systems: The growth in AI technology creates secondary markets, including the need for maintenance, updates, and support for these systems.
Training and Education: As AI evolves, so does the need for ongoing training and education, which itself becomes a significant sector.
So the causation of higher productivity due to AI leading inexorably to fewer jobs is not real. That does not mean it is not possible. The above IS very likely to occur but it does need to do so alongside a wide range of education and regulatory measures.
For example education systems must foster lifelong learning, teaching not just technical skills but also how to adapt, learn, and grow continuously. Adaptability is the key to thriving in an AI-driven economy, so we need to emphasise soft skills, such as critical thinking, creativity, and emotional intelligence, which become as crucial as imparting technical know-how.
As AI transforms industries, the need for re-skilling and up-skilling becomes paramount. We’ll need tailored educational programs that can help workers transition from declining sectors to emerging fields. And partnerships between educational institutions, governments, and businesses to identify skill gaps and develop targeted training programs.
And in terms of regulatory measures we’ll need comprehensive policies to support workers displaced by AI. These must include not just unemployment benefits but also access to retraining programs and job placement services. And these measures have to be proactive, anticipating changes in the labor market rather than reacting to them.
Proactive fiscal and regulatory policies can help in redistributing the gains from AI, ensuring that the benefits are widely shared across the economy. Government investment in public goods, infrastructure, and education can further stimulate economic growth.
Fostering innovation and sector development can be catalysed by governments that play a proactive role in nurturing new technology sectors through incentives, research funding, and infrastructure support. The approach has to encourage economic diversification, without which creating new job opportunities and industries will be difficult.
Entrepreneurship and Small Business Support is an imperative. We have to encourage entrepreneurship, especially in AI-enabled sectors that can drive job creation. Support can come in the form of tax incentives, grants, and access to resources. Small businesses are often more agile than larger ones and more likely to be incubators for innovative uses of AI.
Globally we need to work hard at generating collaboration and setting universal standards. AI’s impact transcends borders so international collaboration on standards, ethical guidelines, and best practices are required to create a more cohesive and responsible global approach to AI integration.
And finally regarding the role of governments we need to vastly increase public awareness and involvement in reshaping economies. Educating the public about AI’s potential and challenges is vital to ensure (hopefully) a well-informed citizenry. Public involvement in discussions around AI ‘should’ help in democratising its development and application.
And then of course we also need to think about the actions and behaviour of company management. The labour economist David Autor has written that "AI Could Help Rebuild Middle-Class Jobs” but “the key question to ask is for whom is AI a substitute and for whom is it a complement” and “We Have a Real Design Choice About How We Deploy AI”.
How companies behave, and how we as societies let them behave, is a critical factor in determining all of our futures.
All of them above has to be taken seriously. Our future is not pre-wired. We do have a lot of agency, and how AI integrates or dominates our lives is still largely up to us.
All of which is very tricky for the real estate industry. Just how many jobs are there going to be, what will people be doing, and where and what real estate are they going to need? Every answer depends on how much of the above occurs. And how.
If you see our futures determined by an AI mediated world that will inevitably lead to a society where, to quote Thucydides ‘The strong do what they can and the weak suffer what they must.’ then the amount of change will be immense, and any project you start today will very much have to pander to the ‘Strong’. The ‘weak’ will have to take what they are given.
Which feels very dystopian to me, even as I am aware many people, especially the more libertarian Silicon Valley types, see this absolutely as where we are going.
I see two other options. First, we do follow the guidance above and build a much bigger pie, with a piece for everyone. Or we either don’t try this, or it turns out that AI is going to take most of the pie regardless of its size, in which case we need to be thinking of a much more distributive, egalitarian, society where the few do not get to keep their massive gains, but they are spread far and wide.
Either way, the only guarantee is that big things are afoot, the next decade is going to be transformational, and that we really must hope for large doses of wise leadership across the globe.
And that last sentence worries the hell out of me!