Real Estate's Great Rewiring: From Opaque Assets To Transparent, Self-Optimising Investments

From agentic facilities management to valuation 2.0, the next ten years will see real estate become cheaper to run, radically more transparent, and far more investable.

THIS IS A SPECIAL REPORT

It’s becoming clearer how AI and other technologies are likely to manifest themselves within the real estate industry over the next 10 years.

We have grouped 18 emerging trends into timelines: ones that are happening now, those that’ll develop over the next 2-5 years, and ones 5-10 years out. Several overlap but the trends demonstrate different use cases, representing meaningful workflows within the industry that we didn’t want to set aside.

If anything we are being conservative in these timings and they might occur faster, but it is safe to say that by 2035 we’ll be seeing an industry familiar and comfortable with a whole range of new technologies, workflows and behaviours.

As it is rather long, I’ve added this executive summary, which says it all in a nutshell. For the details, read the rest or skip to the trend you’re interested in.

Executive Summary
Overview
Over the next decade, AI and related technologies will transform commercial real estate from a fragmented, opaque sector into one defined by automation, transparency, and continuous optimisation. The adoption curve is compressing: many changes are already underway. By 2035, real estate assets will be cheaper to operate, higher performing for occupiers, and more transparent for investors—cementing the industry’s shift into a modern, data-driven future.

Key Timelines & Trends

• Now – 2 Years (Already in play): Passkeys drive tenant app revenue; NABERS-style operational evidence replaces EPCs; provenance-first artefacts* accelerate deals; agentic FM triage cuts ticket times; AI-powered lease intelligence gains trust; controls optimisation pays back in <12 months.

• 2 – 5 Years (Scaling across portfolios): Budget-bounded AI agents handle procurement and property management; audit-ready, provenance-signed AI outputs become the norm; plant CapEx includes optimisation with M&V guarantees; live, signed data feeds flow directly to valuers and lenders.

• 5 – 10 Years (Systemic transformation): AI orchestrates O&M by default; portfolio-wide disclosure becomes mandatory; live operational data streams feed directly into DCFs; “assurable automation” emerges as a premium in secondary markets.

Why This Matters

• Efficiency → AI agents cut portfolio operating costs by 20–40%.

• Transparency → Provenance-signed, performance-based data shortens sales cycles and eliminates pricing games.

• Capital → Assets with auditable automation and transparent operational evidence secure cheaper finance and higher liquidity.

• Talent → Real estate finally looks like a modern, data-rich industry—attracting the calibre of people it has long struggled to retain.

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The Trends

Present/Near-Term (Already in play or with rapid impact):

1. Passkeys As A Revenue Lever

What/Why?
Passkeys, or device-based logins, eliminate passwords and reduce user friction. This in turn leads to more frequent sign-ins and higher engagement, as users authenticate via biometrics or device PIN. Less friction is highly likely to translate into higher ‘Monthly Active Users’ (MAUs) and more opportunities for personalised offers and transactions, effectively removing the "password tax" to unlock untapped revenue.

What to watch for:
Growing support from major tech companies. As of late 2024 20% of top websites support passkeys and Amazon reports 175 million customers use them.

Value hook:
It would not be unreasonable to see 15-30% more active users of a tenant engagement app with perhaps a 5-10% boost in revenue from increased amenity sales. This would transform identity from a security cost centre into a revenue lever, enabling "agentic" (AI-driven, personalised) interactions and improving security by virtually eliminating phishing risks.

2. Operational Evidence Beats EPCs in Due Diligence

What/Why?
Traditional due diligence relies on static metrics like Energy Performance Certificates (EPCs), which are based on design specs. However, investors and tenants realise these don’t tell the full story and will increasingly demand actual operational evidence, such as 12 months of utility bills, real consumption, and indoor environmental data, which is far more revealing of a building’s true performance.

We are already seeing this reflected in the increasing use of the NABERS rating system, which uses metered energy.

The great unlock is that providing a digital trail of operational evidence reduces uncertainty and would help speed up sale proceedings.

What to watch for:
Increased use of NABERS-style ratings but most especially investors explicitly pricing deals on in-use efficiency, with compliance to operational carbon targets influencing bid levels.

Value hook:
The lack of transparency of operational evidence costs time and money when it comes to sales, or investments. No-one trusts EPCs (rightly) and any good negotiator will chip away at the price based on lack of robust data.

3. Provenance-first Artifacts* Shorten Sales Cycles

What/Why:
A major time sink in real estate deals is verifying document authenticity and accuracy. Provenance-first artefacts are documents and data files cryptographically signed at the source, creating tamper-evident records. This immediately establishes trust in data integrity, collapsing the audit burden in due diligence by making cross-checking redundant. Knowing each artefact is "as originally issued" accelerates deal processes.

What to watch for:
Startups working on "blockchain-verified proofs" for private real estate data rooms, Real estate platforms using technologies like blockchain, digital notaries, or secure data escrow for due diligence, the emergence of ISO standards or industry protocols on digital building documentation, insurers or Big Four auditors requesting digitally signed building performance data, and sellers advertising a "verified data package" in their deals.

Value hook:
The core value is speed and confidence. By removing doubt one is effectively monetising trust.

4. Privacy-Preserving Occupancy Beats Camera Analytics (Total Cost of Risk)

What/Why?
Monitoring occupancy is vital, but camera-based analytics present high privacy and compliance risks (e.g., GDPR fines, legal liability, tenant backlash) due to collecting identifiable data. Privacy-preserving occupancy solutions (e.g., thermal sensors, infrared detectors) avoid collecting personal data by only counting signals, drastically lowering compliance overhead and risk. These solutions deliver necessary occupancy metrics without identifying individuals.

What to watch for:
Fewer employee complaints regarding surveillance, faster regulatory approval for sensor projects, with privacy-friendly sensors "sailing through" internal compliance, companies removing cameras due to staff backlash and Insurance or legal advisors recommending non-camera solutions to reduce liability.

Value hook:
The value is primarily in risk avoidance and operational savings. Face recognition is a GDPR compliance nightmare, involving throwing money down the drain. Avoiding it isn’t hard and makes little or no difference to how you operate.

5. Agentic FM Triage Under Spend Caps Cuts Cycle Time (Without Scaring Risk)

What/Why?
Facilities Management (FM) involves numerous small issues requiring quick resolution. Agentic FM triage uses AI "agents" to automatically handle routine work orders up to a defined spend limit, dramatically speeding up response times. These agents classify and action requests instantly, handling the routine 80% of tickets. Spend caps and rules keep risk in check, ensuring complex or high-value issues are escalated to humans, thereby lowering risk exposure.

What to watch for:
Early deployments in large portfolios as FM teams use their scale to re-orient around doing more with less. Change management programs looking at where the ‘human in the loop’ needs to be and how much leeway, in terms of budget, an AI can be allowed. Software companies will surely be building in such capabilities soon.

Value hook:
It’s a straight numbers game. If AI handles 30% of tickets automatically, FM teams can be 20–30% more productive, managing more buildings with existing staff. The budget caps keep everything manageable.

6. Controls Optimisation (Pick One Plant Loop) Pays Back < 1 Year

What/Why? Most large buildings’ HVAC and plant systems run sub-optimally. Controls optimisation applies advanced algorithms to run equipment more efficiently. While owners may fear costs, targeting a single plant loop or system yields such significant energy savings that the project pays for itself within a year. This "low-hanging fruit" approach is a bite-sized project with minimal disruption, as it involves improving existing controls rather than replacing equipment.

What to watch for:
Successful pilots, “Optimisation” becoming a dedicated budget line item in CapEx plans, and performance-based contracts (e.g., "No savings, no fee") for building optimisation becoming common.

Value hook:
The economics are compelling: energy costs in real estate are a significant line item, and everyone knows there is a lot of low hanging fruit in terms of badly configured systems.

7. Lease/Contract Intelligence with Citations

What/Why? Manual legal and financial analysis of leases and contracts is slow and costly. And the bane of everyone’s life in real estate. AI intelligence tools are now good enough to overcome the ‘trust problem’ and are "bankable" for decision-making by lenders, investors, and asset managers.

What to watch for:
More deals and workflows explicitly incorporating AI outputs, often noting they were "generated with AI and verified”, regulatory and court acceptance of AI-generated contract analysis as evidence, provided sources are verifiable, insurance or lender guidance accepting AI-flagged risks or waiving full attorney review if AI tools with citations have been used and major real estate brokerages and services firms partnering with AI providers for lease abstraction.

Value hook:
Dedicated lease extractions services, with direct citations, are good enough that no-one should be manually extracting leases. The cost savings are considerable, and the savings in time are important for ‘getting deals done’. Contract intelligence is similar though it’s likely a ‘human in the loop’ will be required. For now.

8. Converged Identity Extends to Visitors and Contractors

What/Why?
Traditional systems for employees, visitors, and contractors are often fragmented, creating inefficiencies and security gaps. Converged identity management provides one unified platform for anyone needing access (physical or digital), streamlining processes. This improves security and compliance by enforcing uniform policies and enhancing convenience with a single digital credential. It closes security vulnerabilities by automatically deactivating access when contracts end or IT accounts are disabled.

What to watch for:
New solutions from tech suppliers, policies requiring visitor and contractor compliance, regulatory drivers in sectors like healthcare and data centres, and visitor kiosks issuing credentials managed in the same system as employee badges.

Value hook:
Major gains are in security and efficiency. A unified identity reduces "orphan" accounts and badges, lowering breach risk and avoiding incidents. Possibly reduced insurance costs but significant efficiency savings in onboarding and compliance. Quantitatively, converged IAM can lower identity management costs by ~30%. It ultimately protects asset value and reduces costs from fragmented systems.

9. Performance-Based Disclosure (NABERS-Style) in Particulars & Term Sheets

What/Why?
Investors, tenants, and regulators are increasingly demanding actual performance data (especially energy and carbon) over design intents. In the near future, marketing documents for sales or leases will prominently feature performance-based ratings (e.g., NABERS, ENERGY STAR scores, actual consumption figures) as standard. This shift is driven by existing mandates (e.g., Australia), investor ESG mandates, and regulation (e.g., NYC’s Local Law 33), as stakeholders require real, measurable performance data.

What to watch for:
Appearance of energy use intensity (EUI) figures, operational carbon numbers, or NABERS/ENERGY STAR ratings on the first page of offering memorandums and broker brochures. Plus new industry standards (e.g., RICS, NCREIF), tenants requesting performance data during leasing, and "Particulars Plus" data rooms offering live data feeds supporting headline figures.

Value hook:
This enables market differentiation and pricing of efficiency. Buildings with strong performance disclosure will enjoy a "green premium" or better liquidity, while laggards might face a "brown discount”. Once performance-based disclosure becomes common, failure to be open will be punished, a shift that will be transformational for the market in data.

10. Insurers/Lenders Begin Pricing ‘Control-Plane Maturity’

What/Why?
Insurers and lenders are refining risk assessment. As buildings become smarter, their "control-plane maturity" (how advanced, reliable, and secure automation and control systems are) is an emerging risk factor. A building with robust, well-integrated, and cyber-hardened controls is deemed lower risk for property insurance and operational disruptions. Insurers already discount for IoT monitors; this will extend to pricing in the overall control environment.

What to watch for:
Insurers rolling out new products or endorsements for smart buildings, underwriting questionnaires asking detailed control-system questions, green financing criteria expanding to consider operational tech certifications or smart scores and rating agency commentary positively viewing portfolios with high digital infrastructure maturity.

Value hook:
Lower insurance premiums, attractiveness to investors and improved access to capital. This trend effectively monetises good operations.

Mid-Term (2–5 Years):

11. Budget-Bounded Agents Run Part of PM/PPM and Low-Value Procurement (2–5 Yrs)

What/Why? This is pushing on from trend 5. In 2–5 years, autonomous AI agents will handle a significant portion of property management (PM), preventive maintenance (PPM), and procurement tasks within defined budget limits. This extends beyond reactive triage to proactive management, like ordering consumables when inventory is low or scheduling routine maintenance within an annual budget. The "bounded" approach prevents overspending, ensuring financial risk is controlled while boosting efficiency.

What to watch for:
Formal policies authorising AI-driven spending, with CFOs setting expenditure limits for AI agents, startups marketing "auto-procurement bots" for spare parts or AI scheduling assistants for preventive maintenance, vendor consolidation of physical and digital ID systems allowing agents to manage contractors as just-in-time resources, and integration of IoT, inventory systems, and procurement, enabling AI agents to orchestrate sequences from fault detection to scheduling technicians.

Value hook:
This is when costs start to tumble. Automating 40–70% of procurement/PM tasks will substantially cut administrative overhead. Agents operate 24/7 with instant response, ensuring faster maintenance and preventing issues from escalating, avoiding downtime or damage costs. AI can reduce maverick spending and secure bulk discounts by consistently following plans. Every AI action is logged, providing detailed audit trails and consistency, reducing mistakes and mitigating risk.

12. Audit-Ready, Provenance-Signed AI Workpapers Become Normal

What/Why?
As AI increasingly informs business decisions, especially in regulated areas like finance and real estate accounting, there's a need to trust and verify its outputs. In 2–5 years, AI-generated analysis ("AI workpapers") will come with a robust audit trail, metadata, and cryptographic signatures proving its generation process, data, and integrity. This "provenance-signed" approach ensures auditors can confirm legitimacy, enabling "assurable automation".

What to watch for:
Auditors and regulators starting to mandate these provenance mandates, tech suppliers enabling them, industry consortiums defining standard formats, and insurers requiring audit logs.

Value hook:
The value is trust and compliance, directly impacting the bottom line by reducing the risk of compliance failures and improving decision quality. Audit-ready AI unlocks greater AI ROI by enabling confident automation. No more black boxes and immediate explainability.

13. Optimisation Becomes a Line-Item in Plant CapEx – M&V or Don’t Pay

What/Why? Historically, CapEx for plant upgrades focused on equipment, not ongoing optimisation. This is changing: in 2–5 years, it will be standard to include an "optimisation" line-item in plant CapEx, often under a "Measurement & Verification (M&V) or you don’t pay" performance contracting model. The industry recognises that new equipment needs continuous tuning (via analytics, AI optimisation) to deliver promised savings. RFPs will bundle multi-year optimisation agreements, linking payments to verified energy savings. See previous newsletter about ‘Outcomes as a Service’.

What to watch for:
RFPs and contracts using language like, "Vendor will provide continuous commissioning software... Payment tied to performance subject to M&V”, accounting treatment that allocates part of plant CapEx to an "opex-as-a-service" model, consultants recommending "optimisation or bust" for any plant upgrade, ASHRAE and CIBSE incorporating M&V and optimisation tuning periods into major plant retrofitting standards and insurance or lenders preferring projects with performance guarantees.

Value hook:
The value is ensuring projected savings are actually achieved and persist. An optimisation line-item, though adding 5–10% to project cost, can hugely improve ROI by guaranteeing 20–30% energy reduction. Making vendor payment contingent on M&V eliminates the risk of wasted CapEx on the optimisation part.

14. Data Rooms Offer Live, Signed Feeds to Valuers and Lenders

What/Why?
Real estate due diligence has relied on static documents. In 2–5 years, data rooms will commonly include API endpoints or live CSV links to key building performance data, cryptographically signed or blockchain-verified for integrity. This allows valuers and lenders to pull real-time data (e.g., hourly energy consumption, occupancy rates) directly from building systems, rather than relying on summaries. This enables dynamic analysis, speeding up diligence and improving accuracy.

What to watch for:
Inclusion of data APIs in deal marketing, valuation firms tooling up to consume API data from buildings, adoption of blockchain, regulatory moves requiring continuous provision of KPIs, and formalising the practice of providing BMS logins into read-only APIs with digital signatures in VDRs.

Value hook:
Value is in speed and confidence in transactions. Live, trusted data can reduce due diligence by weeks, enabling more precise and faster underwriting and faster closing for sellers. And be even more transformational than trend 9.

Longer-Term (5–10 Years):

15. Agent-Orchestrated O&M Becomes Default; Humans Manage Exceptions

What/Why?
Within 5–10 years, AI agents will coordinate most routine Operations & Maintenance (O&M) tasks by default, with humans managing only exceptions, oversight, and vendor relations. This is the logical culmination of earlier trends, where AI systems become the "autopilot" of building operations.

What to watch for:
Significant changes in staffing ratios (e.g., 1 facility manager per 50 buildings instead of 5), FM providers (e.g., JLL, CBRE) offering "AI-driven FM" solutions, case studies of buildings operating "95% autonomously”, and advances in robotics for building maintenance (window cleaning drones, floor scrubbers) as physical actuators commanded by AI agents.

Value hook:
Potential for huge efficiency gains from reducing human labour by 50–70% for routine tasks, leading to net savings of ~30% of O&M costs. Tenants benefit from a more responsive building, potentially leading to higher rents or retention. It is also scalable, allowing portfolio growth without linear OpEx increases.

16. Portfolio-Wide In-Use Disclosure Is the Market Norm

What/Why?
In 5–10 years, large real estate owners and REITs will routinely disclose portfolio-wide operational performance metrics (energy, water, emissions, occupancy) to investors, regulators, and the public as standard practice. This goes beyond individual building disclosure to aggregated, portfolio-level openness.

What to watch for:
Regulatory mandates requiring large real estate owners to measure and disclose operational carbon annually, major firms signing onto net-zero commitments requiring transparent progress reporting, tenants demanding owners share building performance data portfolio-wide and stock exchange requirements for ESG metric disclosure for REIT listings.

Value hook:
Can yield lower cost of capital and higher valuations for leaders. Transparent performance management often leads to a "transparency premium" from investors. Public data forces internal focus, spurring efficiency improvements and cost savings.

17. Valuation 2.0: Live Operational Inputs Flow into DCFs by Default

What/Why?
Traditional property valuation relies on static inputs. In 5–10 years, valuations will shift to a dynamic model where live operational data streams flow directly into DCF software by default. Appraisers will pull real-time data (occupancy, traffic counts, energy costs, air quality) from building systems, enabling more accurate and forward-looking valuations. This is driven by the emergence of supporting technology (APIs, digital twins, data standards) and industry calls to integrate sustainability and operational performance into valuation methodology.

What to watch for:
Valuation software (e.g., Argus Enterprise) integrating APIs for data ingestion, advisory firms advertising "data-enhanced valuations" using live building data, valuation standards (e.g., RICS Red Book) evolving to encourage use of operational data, digital twin platforms partnering with appraisal firms to feed them data and forward-thinking investors requiring actual sustainability performance data in cash flow analysis.

Value hook:
The most direct value is improved accuracy and confidence in valuations. This leads to better investment/lending decisions and less risk of overpaying/underselling or loan defaults. Live data helps catch declines in performance early, allowing for "real-time adjustment" of valuations and operational intervention to preserve value.

18. Secondary-Market Premium Emerges for ‘Assurable Automation’ Buildings

What/Why:
In 5–10 years, buildings with highly automated systems that are demonstrably assurable (reliable, safe, verifiable, cybersecurity hardened) will command a premium in the secondary market (sales, REIT/share price). This evolves from "green premiums," recognising the value of automation proven to be trustworthy.

What to watch for:
Brokers highlighting “SmartScore Platinum” in offering memos, actual sale comparables showing assets with verified automation selling at lower cap rates, ”Assurable automation" becoming a due diligence item, with buyers hiring consultants to audit a building's tech stack, and top tenants demanding fully smart-enabled buildings and paying more rent for them.

Value hook:
Quantitatively, an “assurable automation” building could have operating costs 10% lower and command 5% higher rent, significantly boosting NOI. It rewards buildings that are effectively "future-proof" and high-performing due to tech, leading to formal adjustments in valuations.

CONCLUSION

By 2035, most buildings will run largely on autopilot: self-describing, continuously optimised, and radically more transparent. The strategic stakes are clear:

• Efficiency → Operating costs fall 20–40% as AI handles the bulk of routine work.

• Transparency → Hidden inefficiencies disappear; pricing chips and data droughts become relics.

• Capital → Valuations and financing will hinge on live, audit-ready performance data.

• Talent → An industry once seen as slow and opaque becomes a magnet for modern, data-native professionals.

The winners will be those who act now - doubling down on today’s trends, positioning for mid-term automation, and ruthlessly questioning which assets and business models can survive in a world of transparent, self-optimising real estate.

OVER TO YOU
Which trend can you double down on today? Which one can you lean into for tomorrow? Which assets does this make you want to sell, and which to buy? And how might this change how you work?

It’s all doable, and will happen. And a lot of it already is. Where is your place in this world?

* Provenance-first artefacts mean documents or digital objects that are cryptographically signed at the source, creating tamper-evident records.

Short-Term Wins Are Still Wins!

The Hidden Alpha: Reasoning Models + Great Prompts

Last week we went into a deep dive about how to create long-term strategic advantage with commercial real estate. This week we’re going to look at short term tactical advantages. 

As readers of this newsletter you probably are aware that OpenAI released its long-awaited upgrade to ChatGPT - Version 5, last week. And the incredible hullabaloo it created. People were expecting the moon and when the undeliverable was not delivered they got very animated. At times you’d think the world was about to end.

The advantage is bigger than we thought.

There was though one short tweet (X if you really must) that Sam Altman put out that contained some extraordinary, and important, information:

“the percentage of users using reasoning models each day is significantly increasing; for example, for free users we went from <1% to 7%, and for plus users from 7% to 24%.”

I was not surprised that almost no free users were ever using the ‘reasoning’ models but rather staggered that only 7% of ‘Plus’ paid users were. These models, notably o3, were so much more powerful than the default model, 4o, yet were going almost unused.

The biggest thing about GPT-5 is that for the first time hundreds of millions of people will have access to frontier reasoning models and, at last, will be able to see what these things can do! This IS a big deal.

Paid users, though, had access, but 93% were not using them. And perhaps this will remain the case for a majority at least.

So it seems likely that the gap between capability and day-to-day utilisation remains huge—and that gap is exactly where your personal and organisational alpha sits.

Most teams still use AI like search. They dabble in chat, copy/paste the output, and hope for the best. Which is such a waste because the edge comes from using reasoning-capable models with excellent prompting discipline, applied to high-leverage work. This is where GPT-5 shines—and where a small set of patterns will yield outsized results.

For now, this is a huge short-term win for you, your team, and your company—if you act. Below is some practical advice about how to make the most of this ‘capability gap’.

From “using AI” to “using it well”

I use a simple stack to explain why prompt quality matters:

WHO / WHAT / HOW

• WHO: Do you want the AI to act as? Adding context is important. Be clear who you want to be interacting with.

• WHAT: Do you want the AI to do? Think clarity, context and constraints. LLMs cannot read your mind - you need to tell them what you want.

• HOW: Do you want the output to be returned? Length, format, structure. How do you want information presented?

These fundamentals turn a vague ask into a brief. They’re the front door to all good prompting.    

The Importance of Patterns
And on these foundations you overlay certain ‘Patterns’ - types of prompt addressing particular circumstances.

Examples of these patterns include ‘Cognitive Verifier’, ‘Self-Refine’, ‘Self-Consistency’, ‘Flipped Interaction’, or ‘Outline-to-Synthesis’. These are reusable “moves” that force better thinking. 

You then add ‘Verification’. Ask for a plan first, then a critique of that plan, then a final answer. This dramatically improves accuracy and defensibility for board-facing work.  

And ‘Evidence handling’. For research, require citations, uncertainty labels, and explicit counter-arguments.    

And finally, a ‘Deliverable spec’. Decide the end product upfront: one-pager, IC memo, risk log, table, or slide deck. The model writes to the format you define.

When you combine these habits in GPT-5, the model stops being a clever autocomplete and becomes a co-pilot for judgement work. The difference really is chalk and cheese, and normally amazes people when they first see it all in action. Expect this to go mainstream in the coming months.

Five GPT-5 patterns that change outcomes 

Let me walk you through five GPT-5 patterns that change outcomes (with CRE use-cases).

1) Cognitive Verifier — Plan → Critique → Answer

Where to use it: IC memos, board papers, credit submissions, asset strategies.

Why it works: It reduces blind spots and shortens review loops by building a self-check into the workflow.

Example prompt: “Act as a senior investment committee adviser. First outline your plan to evaluate [insert asset/market information - more is better]. Pause. Critique your plan for missing assumptions, weak evidence and risks. Only then produce a one-page recommendation with mitigations.” 

2) Self-Refine — Draft → Self-Review → Redraft

Where to use it: proposals, quarterly packs, client comms, investor letters.

Why it works: Quality rises across controlled iterations without human micro-management.

Example prompt: “Draft the [insert required document details - again more is better]. Self-review against these criteria: accuracy, clarity, evidence, senior readability. Produce a revision plan, then redraft and list the top five changes and why.” 

3) Self-Consistency — Multiple paths → Converge on best

Where to use it: valuation sensitivities, strategy options, scenario planning.

Why it works: Running independent reasoning paths reduces idiosyncratic error; you see consensus and dissent clearly.

Example prompt: “Run three independent analyses of [insert deal details]. Use different assumptions/paths. [you could define this if you wish]. Show consensus, the strongest dissent, and a recommendation with what evidence would overturn it.”

4) Flipped Interaction — Socratic stress-test

Where to use it: lease strategies, transformation roadmaps, go-to-market plans.

Why it works: The model interrogates you, forcing clarity on constraints and hidden assumptions.

Example prompt: “Act as [insert the role of your interlocutor]. Ask sequenced questions to pressure-test my plan [insert plan details]. Don’t proceed until I answer. Be firm but fair. Summarise gaps and offer corrections.”

5) Outline-to-Synthesis — From bullets to a defensible plan

Where to use it: converting outlines + leases/EPCs/notes into an asset plan or ESG roadmap.

Why it works: You expand structure without losing the brief; sections stay evidence-linked.

Example prompt: “Using the outline and attached sources, expand into a six-page asset plan. For each section: decisions, evidence, owner, KPI, and 90-day actions. Include a one-page executive summary.”   

And More!

And beyond these five it is worth considering the ‘Vision & “Reasoning-Plus” research capabilities of these models.

Vision. Ask the model to critique masterplans or dashboard screenshots and list “unknowns to resolve before pre-app”. You’ll be surprised how competent frontier models (GPT-5, Claude, Gemini) are at reading visuals in context.    

Agentic research. Today’s models can plan, gather sources, synthesise, and self-check. Use this to compress the first 60-70% of a company brief or market note, then spend your time on judgement and negotiation. In particular Gemini and ChatGPT both have ‘Deep Research’ modes, and ChatGPT has a separate ‘Agent’ mode where it can actively visit websites and your own computer network (with permissions of course).

This is where you get an advantage others aren’t taking.

Where the edge shows up in Commercial Real Estate

And here’s how these capabilities directly translate into CRE workflows.

Underwriting & valuation. Multi-scenario cashflows, covenant testing and comps coherence, packaged as a defensible memo. (Verifier + Self-Consistency.)    

Leasing strategy. Trade-offs (rent-free vs capex), anchor-tenant sequencing, adversarial planning for negotiations. (Flipped Interaction.)  

Asset management. Quarterly plans that reconcile OPEX, tenant feedback, and BMS data into a single action map. (Outline-to-Synthesis + Verifier.)    

ESG/retrofit. Pathway design with standards cross-walks, capex phasing and risk logs. (Outline-to-Synthesis + Self-Refine.)    

Workplace strategy. Hybrid policy, utilisation analysis, change-management comms. (Self-Consistency + Flipped Interaction.)    

 The Window is Open (for now)

The above are just a starter set of tools you can leverage. There is almost no aspect of real estate where the application of excellent prompts will not yield strong results.

Yet, to repeat - ALMOST NO-ONE IS TAKING ADVANTAGE

Over time (I guess - 12-18 months) the default setting will shift towards reasoning; rate limits will rise, friction will fall, and more people will use these tools. But capability ≠ competence. Habits, libraries and governance are durable moats. If your top five workflows don’t yet have standard reasoning templates, you’re leaving results on the table. The easy pickings are there.

OVER TO YOU

A Personal challenge: pick one high-stakes task this week and make reasoning-by-default your norm—using a verifier step and a defined deliverable. Measure cycle time and revision count.

Team move: nominate three pattern owners to maintain a shared prompt library measure internal use of reasoning.

If you want to institutionalise these wins before your competitors catch up, the next #GenerativeAIforRealEstatePeople cohort starts on the 5th September and includes the full GPT-5 Prompt Library, with live practice on the patterns above. 

The advantage is real—and still underpriced.

Short-Term Wins are still Wins!

But the window WILL close, so act now.

AI Fluency Isn’t Enough

Within three years, your biggest competitor won't be the firm across the street—it will be a company that thinks about real estate in a fundamentally different way. While most of us are learning to use AI as a tool, they're using it to build a new kind of engine. Here's how to make sure you're the one building it.

Warning: This issue is not a snack—it’s a full meal. If you’re pressed for time, start with the Executive Summary below. If you’re crafting your firm’s AI strategy, read it all.

Executive Summary

Artificial Intelligence (AI) is rapidly shifting from an optional efficiency booster to an essential competitive foundation in commercial real estate. Currently, proficiency with AI tools like Large Language Models offers significant productivity gains—but only temporarily. Within 18–24 months, AI fluency will become commonplace, and this initial advantage will fade.

Long-term strategic advantage requires moving beyond merely adopting AI tools toward fundamentally rethinking and restructuring your business model. This involves a three-step strategic framework:

• Unbundling: AI breaks apart traditional, integrated workflows (investment management, brokerage, development, property operations) into discrete automated tasks, boosting efficiency but creating complexity.

• Emerging Constraints: Automation introduces new challenges—coordination of fragmented workflows, data interoperability and quality, trust in AI outputs, regulatory and ethical risk management, cognitive overload, and maintaining crucial human judgment and intuition.

• Rebundling: Progressive companies address these new constraints by strategically "rebundling" around three key areas:

• Data & Asset Integration: Build proprietary data platforms acting as a single, trusted source of truth integrating people, buildings, ESG, and operational insights.

• Workflow Orchestration: Create intelligent orchestration layers to manage fragmented AI-driven workflows and optimise human-AI collaboration across the organisation.

• Decision Support & Trust: Position the firm as a trusted advisor by embedding rigorous governance, transparency, and human oversight into automated decision-making processes.

Ultimately, CRE firms must choose between remaining mere "tool-users," destined to face commoditisation, or becoming strategic “engine-builders" - architects of new business systems and orchestrators of value creation in an AI-native landscape. This shift will create an unprecedented bifurcation within the industry, with profound competitive implications.

Forward-looking firms must start now. Those who navigate this rebundling effectively will dominate future markets, redefining industry leadership for the next era of commercial real estate.

——————————————————————————————————

Today a Super Power, Tomorrow a Commodity

Less than 10% of knowledge workers are daily users of LLMs. Circa 18% use them weekly, but if you are not using them daily they have not become part of your workflow. And if they are not part of your workflow, you’re not yet a power user. And won’t be enjoying their edge.

I think power users, or those seriously leaning in to using LLMs, have 18-24 months to ‘make hay’. They will be dramatically more productive than their peers and will be able to do the work of 2-10 people. We’ve written about ‘Fast, Agile, Ultra-Productive Superteams’ before and the evidence is mounting of companies generating $1,000,000+ of revenue per employee. This productivity boost from generative ai is very much a known known now.

This edge though will diminish over time, as the mainstream catches up and adopts the same tools. So the smart people in real estate need to be thinking beyond just the technology - they need to be thinking how this technology will change the fundamentals of our industry?

So with that disclaimer, that for now the fruit is low hanging, let’s look at how real estate is going to change.

Beyond Tools: Building a New Operating System for Real Estate

The current discourse often focuses on AI as a means to achieve incremental efficiencies, such as automating tasks like marketing copy generation or lease abstraction. However, this "tool view" is strategically incomplete and risks long-term commoditisation. The true power of AI in CRE lies in its capacity to act as a "coordination engine", fundamentally restructuring roles, firms, and competitive dynamics by radically reducing the friction inherent in knowledge work.

This transformation is characterised by a three-part dynamic: unbundling, emerging constraints, and strategic rebundling. 

AI unbundles traditional CRE workflows across asset and investment management, brokerage, development, and property operations (in fact, all workflows), fragmenting what were once integrated roles into discrete, often automatable, activities. 

This unbundling, however, is not frictionless; it introduces a new set of complex systemic constraints. Imagine that the old way of doing things in CRE had certain "frictions" or difficulties, often due to manual processes and scattered information. When AI steps in, it "unbundles" these tasks, meaning it breaks down traditional jobs into many smaller, automated pieces, which can make things more efficient. However, this fragmentation creates a new set of difficulties that the industry must navigate.

The most progressive CRE firms will not just use AI, but will actively "rebundle" capabilities around these emergent constraints to establish new control points where they can regain influence and competitive advantage by becoming indispensable in overcoming these new constraints.

1. The Great Unbundling: Fragmentation of CRE Workflows

Generative AI is actively re-architecting every major function within CRE, disaggregating bundled tasks into fragmented, AI-driven workflows.

Asset & Investment Management is seeing AI-driven analytics perform market research, deal sourcing, valuation, underwriting, and due diligence with unprecedented speed and accuracy, shifting roles from manual data processing to interpreting AI-generated insights.

Brokerage & Leasing workflows are being peeled off through AI-generated marketing materials, virtual staging, and AI chatbots handling initial client communication and routine inquiries. Lease administration tasks like abstraction, which once took days, can now be completed in minutes using AI, commoditising junior roles.

Development & Construction is being unbundled by generative design tools that rapidly produce feasibility studies, site plans, and building designs. AI also assists with project management, site monitoring, and compliance checks, moving tasks from manual creation to AI-assisted curation.

Property Operations & Facilities Management is shifting towards an "autonomous building" model. AI powers tenant service chatbots, optimises energy management and HVAC systems, and enhances security and reporting, reducing the need for large on-site staff. 

Workplace Design & Operation (from the occupier perspective) leverages AI to generate office layout options, provide virtual assistants for room booking and help desk support, and analyse employee experience data for space optimisation.

This pervasive unbundling leads to increased efficiency but also creates a more complex ecosystem of tools and stakeholders.

We may not, mostly, be here yet but the direction of travel is very clear. All this is coming.

2. Emerging Constraints: The New Bottlenecks

Before AI, the CRE industry was characterised by "high-friction silos" and fragmented information, where critical data was scattered across many disconnected systems and manual processes. This created "coordination costs" – an immense overhead of time, labour, and cognitive effort needed to align people and synchronise workflows, leading to delays, duplicated work, and communication breakdowns. We all used to pay this "coordination tax”.

In an AI-mediated world, unfortunately, this ‘coordination tax’ doesn’t disappear, but rather it gets transformed. As we ‘unbundle’ tasks we make them intrinsically way more efficient, but we end up with many unbundled, super efficient workflows that need to be co-ordinated and managed.

And this fragmentation created by AI introduces new systemic challenges that become the "bottlenecks of a fragmented world".  This though is where new competitive advantage will arise. Companies that can effectively manage this "new coordination tax”, which won’t be a trivial pursuit, will be in a very strong position.

There will be a lot to do to get there:

A. Coordination Frictions: With work spread across multiple AI tools and providers, orchestrating end-to-end workflows becomes a significant challenge. AI's high "clock-speed" can outpace human coordination, leading to breakdowns if not managed. When one part of the business runs much faster than another, trouble can follow.

B. Data Interoperability & Quality: AI thrives on data, but CRE data is notoriously siloed, non-standard, and often poor quality. Integrating disparate data sources and ensuring data governance and common standards are critical, as AI amplifies bad data. It’s hard to see how any real estate company will succeed without, at last, sorting out its data.

C. Trust & Transparency: As AI takes over more decision-making, ensuring trust in AI outputs becomes paramount. The "black box" nature of some models and instances of "hallucinations" can undermine confidence, requiring transparency. Firms that build a reputation for responsible AI use can turn trust into a competitive advantage.

D. Risk Management (Bias, Errors, Liability): AI introduces new risks, including algorithmic bias, outright errors leading to financial loss, and cybersecurity vulnerabilities. Regulations like the EU AI Act will impose constraints on high-risk AI uses, making robust risk management a non-commoditisable capability and a potential competitive moat.

E. Decision Complexity & Information Overload: Paradoxically, AI's ability to produce vast amounts of information can lead to "analysis paralysis" and decision fatigue. The new bottleneck is human cognitive capacity to process information effectively, making the simplification of choice a valuable service.

F. Loss of Tacit Knowledge & Human Intuition: As AI automates grunt work, there's a risk of losing the "human connective tissue" and intuition gained through hands-on experience. Over-reliance on AI could lead to homogenised strategies and a loss of diverse perspectives, making human judgment scarcer and more valuable.

G. Human Judgment Gaps & Ethical Oversight: Highly automated processes risk bypassing critical human judgment points, potentially leading to undesirable outcomes (e.g., an AI optimising for a single metric like NOI at the expense of tenant experience). Deliberately inserting human judgment ensures quality and sustainability, even if it introduces "positive friction”.

3. The Rebundling Playbook: Architecting the Next-Generation CRE Firm

The above are all new problems that we’ve not had to deal with in real estate before. But they will be real, and significant. And a major impediment. So we need to think about how to ‘rebundle’ our workflows to address them.

This will mean moving beyond merely adopting AI tools to architecting an entirely new system of value creation. And this will be the super power of the new winners in real estate. Most CRE companies will adopt an assortment of AI tools, but few will push on through to this rebundling. It’s just too much of a leap for the ‘average’ company. Too much of a break with ‘the way we do things here’.

Let’s take a look at what will be required.

1. Rebundle Around Data & Physical Asset Integration: Becoming the "Single Source of Truth" for People, Planet, and Pipes

The fundamental strategic move is to own the data layer. This means building a proprietary data platform that acts as the central nervous system, aggregating, cleansing, and standardising data from across the fragmented ecosystem. Beyond just traditional property data, this rebundling must incorporate:

Green-Data Flywheel: Combine ESG, embodied-carbon, and energy-profile datasets with AI models, using carbon-adjusted Net Operating Income (NOI) as a north-star metric. This involves piloting digital twins on assets to feed live HVAC, occupancy, and carbon data into the "Single Source of Truth", directly addressing the physical asset and ESG interlock gap.

Edge + Twin Architecture Blueprint: Publish an end-to-end reference stack from sensors to edge inference to graph databases and LLM retrieval for live-building data streams. This is the necessary technical plumbing to manage latency and compute costs for real-time digital twins.

By becoming the definitive "source of truth" for comprehensive, high-quality data (including building performance and environmental data), the firm establishes a proprietary and defensible moat, enabling superior AI-driven analytics that others cannot easily replicate.

A note here: This is not trivial, and frankly favours the large existing incumbents. However smaller companies can specialise in niches where ‘the big guns’ don’t go, or operate in a much more flexible, agile way. You don’t need all the data in the world, but what you do handle must be high quality, and valuable. And, as we’ve looked at before, more data is going to be public in the future. 

There are ways every size of company can shine.

B. Rebundle Around Workflow Orchestration & Human-AI Collaboration: Becoming the "System Coordinator" for the Organisation

This move addresses the integration labyrinth and coordination frictions by building an intelligent orchestration layer above the multitude of PropTech tools. The focus here shifts beyond just connecting technologies to effectively integrating human capital:

Workforce-in-the-Loop Design: Embed "bounded autonomy" rules and role-based copilot policies to convert headcount savings into higher-order judgment capacity. This means mapping rebundled workflows to human decision checkpoints and defining companion skills roadmaps, addressing the significant human capital and change management gaps. If real estate really is a people business, now is the time to make your people the best they can be.

Restructure Roles and Teams Around Workflows: Shift from traditional departmental silos to more cross-functional, agile teams organised around products or client segments, with AI handling much of the cross-team coordination. This leads to a new operating model where AI agents can facilitate internal coordination, accelerating decision flows and enabling more synchronised execution. This aligns the organisation itself with the speed of AI.

By coordinating across formerly disconnected pieces (human and machine), the firm becomes the indispensable "operating system" for CRE transactions, simplifying decisions and ensuring seamless execution.

Outcompete by simply being the best orchestrators out there. Making the really complicated look easy.

C. Rebundle Around Decision Support & Stakeholder Trust: Becoming the "Trusted Advisor" with Ethical Oversight

This move directly confronts cognitive friction and decision fatigue by absorbing complexity and delivering curated, high-confidence recommendations. The firm's value proposition shifts to providing trusted judgment, underpinned by:

Stakeholder-Trust Charter: Move beyond mere compliance to proactive engagement through tenant data-rights dashboards and algorithmic impact assessments. Piloting a tenant transparency portal that explains how AI affects decisions (e.g., rent, maintenance, energy) builds social legitimacy and addresses the "social licence and externalities" gap. Honesty is the best policy.

Embed Governance and Human Expertise as a Value-Add: Develop robust AI governance, including audit trails for AI decisions, bias checks, and compliance certifications. Explicitly having chartered professionals review and sign off on AI-generated valuations, for example, provides an assurance layer that pure AI startups lack, turning governance into a competitive differentiator. This directly tackles the "trust and transparency" and "human judgment gaps" constraints by ensuring that decisions balance speed with wisdom.

This strategy establishes a powerful control point at the moment of decision, building deep, defensible client relationships based on confidence and reliability.

Conclusion: From Tool User to Engine Builder

The choice for CRE leaders is clear: will they remain "tool-adopters" competing on price with shrinking margins, or will they become "engine-builders" that establish new control points and capture a disproportionate share of the value created in the AI-native landscape?

The most convincing strategic lens for AI in CRE is the "unbundle → identify new constraints → rebundle around them" logic, as it effectively avoids the commodity trap. 

However, competitive advantage will ultimately be won by firms that integrate people, planet, pipes, and portfolios into the same AI engine, recognising the uneven regulatory terrain on which this engine will run. 

Addressing these identified gaps will transform a solid conceptual roadmap into an execution-ready strategy, allowing firms to not only adopt AI but to truly redraw the competitive map in the CRE industry. 

This holistic perspective is essential for any company looking to capture the upside of AI transformation by becoming an "engine-builder" rather than just a “tool-user”.

A CAVEAT

So much of this, whilst I think spot on strategically, feels like a big ask for real estate companies today. And it is fair to say that as an industry with long time horizons, the short term might actually be rather long. This is definitely early adopter territory, and I don’t expect to see many truly ‘AI First’ real estate companies in the near future. So you might feel inclined to put this in the ‘future gazing’ bucket and think no more of it. And in many ways that would be rational. But… I’m also convinced we will see the emergence of companies that fit this bill. And they will be amazingly productive and will out-compete the mainstream when addressing similarly progressive clients. So either way, I think we’re heading for a bifurcated future, where real estate companies for the first time in history, don’t all look the same.

OVER TO YOU

Hit reply and share your biggest current AI-related constraint.

Are you on the path to becoming an “engine-builder”? Let's talk through your next steps.

Forward this to your most strategic colleague. Where does your firm sit on the “tool-user” to “engine-builder” spectrum?

PS My thinking on this framework was sharpened by the foundational work of two brilliant strategists. The "unbundling/rebundling" dynamic is heavily inspired by Sangeet Paul Choudary's work on platform economics, while the "where to play/how to win" approach to strategy comes from the indispensable Roger Martin. My goal was to build a bridge from their powerful theories directly to the challenges and opportunities facing CRE leaders today.

This Time Is Different: The Rise Of The Ghost Workforce

Work is in the throes of a punctuated equilibrium

We’re about to see work reinvented. We’re now at a moment in time when change goes from slowly to suddenly, and huge opportunities open up to multiply our cognitive capabilities and transform our productivity. Let the revolution commence.

A Sunday Epiphany: Watching Agents at Work
Last Sunday I was playing around with OpenAI’s new ChatGPT Agent service which, according to them, ‘can now do work for you using its own computer, handling complex tasks from start to finish.’ 

You give it a task, it fires up its own ‘virtual computer’ within your browser window and then gives you a running commentary about what it is deciding, autonomously, to do in order to fulfil the task you gave it. It feels like you have a ‘ghost in the machine’, an alien intelligence moving the digital world on your behalf while remaining utterly intangible.

And to me, it felt like a revelatory moment. This was something truly different.

A Break in the Timeline
In evolutionary biology there is a term, ‘punctuated equilibrium’, that suggests species typically experience long periods of little or no change—called stasis—interrupted by short, rapid bursts of significant evolutionary change. This model contrasts with the traditional idea that evolution happens slowly and gradually over time in a steady, continuous way.

And this is what the rapid development of generative AI is replicating. Since the launch of ChatGPT on November 30th, 2022 there has been a whirlwind of progress. And the whirlwind has even been speeding up. The arrival of ‘reasoning’ models in December 2024 has seemingly cranked up the gears across all the frontier labs, OpenAI, Google and Anthropic, and the cadence of new releases and features is now weekly.

The previous equilibrium has been rented asunder and we’re in a new era. The AI Age has truly arrived.

Not Just Another Boom
These four words are meant to presage a financial crash. During boom times whenever you get to the stage where people say ‘This Time is Different’ is usually the first ‘sell’ signal. And it has repeatedly proved true throughout history.

But I’m going to steal them anyway and apply the spirit to the world of work.

Using ChatGPT Agent strongly pushes me to say ‘This Time is Different’. This way of getting work done is something else entirely. It is not something we’ve ever had before, and it is going to redefine how we ‘get stuff done’ and how we conceive of a ‘workforce’.

Now, to be clear, ‘Agent’ is not the first service in this area. Notably, the Chinese-backed company Manus has had a service out there for some months doing similar things, if in a slightly different way. But you’ve probably never heard of them. Whereas you’re 99.9% certain to have heard of OpenAI and ChatGPT. With over 500 million weekly active users, this new way of working suddenly has massive distribution and public awareness. Meaning it will be taken up by significant numbers very quickly.

What This Means for Real Work
So what will that mean?

Well let me give you some examples of how I’ve used it to date, and some indications of where this is heading.

First, I asked it to visit the index page of this newsletter, read every edition, summarise each one, add these to a table and then separately write a meta summary of the topics I cover in the newsletter.

It took about 15 minutes, and did the job perfectly.

Secondly, I gave it a lengthy ‘Deep Research’ report into which companies within real estate were providing software services involving generative AI. I asked it to extract every company mentioned, find their website address, visit said website, and summarise the asset classes they operated in and the services they provide. And, again, put it all in a table.

Job done in 20 minutes.

Thirdly, I uploaded an Offering Memorandum and told it to find comparable sales within 5 miles. Then assess for sensitivity on rent growth 0% vs. 3% vs. 5%. Then provide a one-paragraph executive summary for investors. Then build a PowerPoint slide with deal highlights.

Job done in 15 minutes.

You might have read Arthur C Clarke’s Third Law where he writes ‘Any sufficiently advanced technology is indistinguishable from magic’. Well, that is exactly what I felt about all of this. Magic was afoot.

It’s really quite a bizarre thing to ask a multi step question, provide no other guidance, and then see a ‘machine’ itemise in text how it is going about arriving at an answer.

This Time is Different.

From toy to Power Tool
Obviously all of this does not work perfectly yet and one would not point it at mission critical work. But …. give it six months, or a year, and one can be pretty confident it will be ready for important work. 

And when that happens, how we work might change fundamentally. I can see no reason why we won’t be running dozens of these ‘Agents’ 24/7 on repeatable questions, processes or workflows. Constant implementation of X, constant research about Y.

Let’s extrapolate a couple of potential use cases:

1. AI Underwriting Assistant
For input we might upload Offering Memoranda (PDF), Rent Rolls (Excel), or deal data to a shared drive or folder. And set a ‘trigger’ of a new file detected in the "Deals to Underwrite" folder (e.g., via Zapier or cloud API).

Then the ‘Agent’ would autonomously do the following -

• Extract key data: lease terms, income, expenses, cap rate, market comparables

• Perform rent growth/cap rate sensitivity

• Benchmark against portfolio and market data

• Flag inconsistencies or missing assumptions 

• Write a one-page underwriting summary

• Set Risk flags (e.g., short WAULT, high OpEx ratio)

• Provide a sensitivity matrix (e.g., IRR vs. rent growth)

• Optionally create an IC-ready memo or PowerPoint slide deck 

• Notification : Email, Slack, or CRM update sent to Acquisitions team with summary and link to outputs.

2. Market Intelligence Synthesiser

For input we might have a predefined watchlist of locations, asset classes, tenants, and competitors.. And set a ‘trigger’ that runs on a daily schedule (e.g. 6:30am London time).

Then the ‘Agent’ would autonomously do the following -

• Scrape news from key outlets (e.g., FT, Property Week, Bisnow, CoStar)

• Pull comps and pricing signals from public data feeds

• Aggregate macro indicators (e.g., Gilt yields, inflation prints)

• Cluster insights by theme: pricing, regulation, supply, occupier trends

• Produce a smart digest with summaries, headlines, and source links

• Create charts for rent/yield movements and liquidity trends

• Optional: brief commentary or strategy nudge

• Notification :Delivered to email or Notion dashboard; summary ping in Slack.

And so on. Think of a process that follows this pattern. I suspect you have many. Maybe soon ‘Agent’ will be doing them for you.

How to Build Your Own Agent Workflows

So far I’ve only played around with this new tool. But the obvious direction of travel will be to define your own uses cases, then:

• Define the use case

• Craft and refine your prompt

• Specify the required outputs

• Provide data inputs (internal + third-party)

• Upload presentation templates

• Set automation triggers

• Establish review and notification criteria
  

  It will take some setting up, and some iteration, but these extensive workflows will be possible to achieve. If not today, then ‘soon’.

Are ‘Agent Bosses’ Here Already
A few weeks ago we talked about the growing notion of ‘Agent Bosses’ but Sunday was the first time I really understood what this might mean.

It is one thing setting up ‘agents’ to perform specific tasks, like creating Custom GPTs, or preset automations in ChatGPT ‘Projects’, but ‘Agents’ with a capital A are another thing entirely. The former is static and deterministic - you program it to do A then B then C in a predefined way whereas the latter are ‘alien entities’ that go off and do you bidding according to their own reasoning.

From Operator to Orchestrator
Imagine a year or two hence. You’ve offloaded glorified grunt work to your army of ‘Agents’, and your job now is to justify your relevance in the loop. So, beyond tending to your army, you’ll have been working hard on developing your ‘situational awareness’ of what living in an AI mediated business world means, deepened your critical thinking, data analytics and problem solving skills, and worked hard on elevating your uniquely human capabilities of empathy, judgement, imagination, creativity, curiosity, leadership and hope.

In short your work will both be on a higher plane than today, and somewhere else entirely. What ‘work’ is will have been redefined.

And I think I grasped this on Sunday.

OVER TO YOU

What would you wish to offload to an army of ‘Agents’? Have you got your data and documentation in order to point these virtual workers at?

But most importantly, what could you do with the time saved? What would an ‘Army of Agents’ enable you to do?

PS So as not to appear too ‘fanboy’ I hereby acknowledge the limitations of these current agent systems. They can be brittle, they might hallucinate and quite why they do what they do is somewhat lacking in transparency.

And yes there are strong data governance and security concerns, especially if one is operating in a regulated industry.

And clearly the ease of agent deployment is still uneven, requiring some degrees of prompt engineering finesse and no, we’re not at the easy plug-and-play stage yet.

But we know this. And nothing is insoluble. So glass half full, not half empty!

AI In CRE - Which Age Group Wins?

Would you rather be 22, 32, or 42?

If you’re 32 and mid-career in CRE, the next few years could make or break your trajectory. If you’re 22, this may be your golden ticket. And if you’re 42… well, you could be in the best position of all — if you act fast.

WE’RE GETTING IT ALL WRONG
There is an assumption in the business world, echoed within real estate, that the people most at risk from the rise in AI are the young, the first-jobber graduates. The thought being that AI will be able to do all the jobs that juniors did historically. And the need for junior employees is going to fall, possibly quite dramatically.

But we’re getting this all wrong!

In reality, the young are best placed to thrive in an AI-mediated business world.

You’d rather be 22, than 32 or 42 in the world we’re going into.

CRE IS BEING RESHAPED

AI is fundamentally reshaping CRE operations and career paths.

Value is on the move.

Take these examples:

In Valuation & Investment, faster and more data-rich underwriting is emerging via AI, and we’re seeing a shift from subjective models to real-time, data-driven insights.

In Asset Management we’re increasingly used to predictive maintenance, smart buildings and 24/7 tenant bots - reducing cost and boosting sustainability.

In Brokerage & Leasing AI-led lead generation is emerging, alongside content creation and negotiation support. Brokers increasingly rely on AI “copilots.”

And in Development & Construction site selection, heavily supported by AI, is arriving, alongside drones for progress monitoring and real-time risk analytics.

THE “AI NATIVE” IMPERATIVE

With everything becoming imbued with AI, we’re going to see some fundamental changes in how businesses operate.

For example, AI is breaking the age-old link between labour and output. It used to be that as your business grew you needed more people, but today that causal link has been broken. We’re entering the world of  “Fast, Agile, Ultra-Productive Superteams” where individual productivity is multiplying, rivalling entire teams.

Likewise the old fallback of ‘we have data’ so you have to pay us, is going away. Having data isn’t going to matter much in the future. Unless you have very particular proprietary data it’s not going to have much value as such. AI will commoditise the aggregation and processing of data. We will be making more use of data in the future but given the new market dynamics brought on by AI, its value will trend towards zero. Competitive advantage will stem from strategic interpretation, not access to data. The profit is moving, but towards the canny human, not towards the hoarder of data.*

And across the board key human value will shift towards soft skills: strategic thinking, negotiation, storytelling, trust.

To operate successfully in this world you will have to be “AI Native”.

OUR ARCHETYPES

Which means what for our archetypal 22, 32 and 42-year olds?

Let’s do a SWOT analysis for each of them:

22-Year-Old New Entrant (Graduate Analyst / Junior Surveyor)

Strengths
High digital literacy & comfort with AI tools
No legacy workflows; high upskilling potential
Growth mindset orientation

Weaknesses
Lacks experience & market context
Limited network
Performs highly automatable tasks

Opportunities
Leapfrog career ladders via AI specialisation
Carve out niche new roles (AI Translator, PropTech Analyst)
Become indispensable to leadership by interpreting AI outputs

Threats
Entry-level work being rapidly commoditised
Risk of AI substituting foundational experience
May become “AI tool operators” with no strategic exposure

32-Year-Old Mid-Career Professional (Associate Director / Senior Manager)

Strengths
Deep domain knowledge & deal history
Strong professional network
Client management & team leadership

Weaknesses
Rigid legacy workflows
Excel modelling proficiency is devaluing
Time-poor for upskilling

Opportunities
Reframe role as human-machine orchestrator
Use AI to scale client work and spot model bias
Transition to tech-enabled strategic roles or into proptech

Threats
Skills from first decade are being automated
Risk of being squeezed between AI-native juniors and strategy-driven seniors
Devaluation of their proprietary info advantage

42-Year-Old Established Leader (Partner / Managing Director)

Strengths
C-suite influence, deep networks, strategic acumen
Authority to fund & lead enterprise-wide transformation
Proven in deal-making and capital raising

Weaknesses
Often distanced from day-to-day AI tools
May resist change due to legacy success
Entrenched in outdated models

Opportunities
Architect firm-wide AI adoption and new operating models
Forge alliances with tech leaders
Steer M&A for AI capabilities and AI-aligned assets

Threats
Misallocating resources due to limited AI literacy
Losing market relevance to AI-native firms
Internal resistance to organisational change

For all the archetypes I think the “Opportunities” quadrant is the most interesting. But whereas the 22 year-old just has to double down on being who they are and leveraging that, for the 32 and 42 year-olds they have to make very distinct changes to who they are to grasp these opportunities. 

On the face of it, the older two need the younger one more than vice versa. Being naturally “AI Native” is a superpower.

That said, all of them are going to need a proactive and tailored strategy to adapt to these new realities. Being experienced actually feels like a bit of a bug whereas being inexperienced could be considered a feature.

Hard to grasp though it is I think we are at an analogous time to when the Model T Ford first rolled off the production line. When this happened in 1908 the US was producing somewhere between two and three million horse saddles a year, in an industry generating, in today’s money, $2-3 billion in annual revenue. But their market was about to collapse. Whilst I am not predicting a collapse in the CRE industry I do feel it operates somewhat like a posh saddlery. Awaiting a tsunami of change. But mostly looking elsewhere.

Except the 22 year-old. Perhaps?

STRATEGIC PLAYBOOKS

Either way, whatever the degree of change, one can always devise a “Playbook” to adapt.

So here’s what each archetype needs to do across three axes: Skills, Positioning and Network

For the 22 year-old they’ll need a “Agility Playbook”:

In terms of “Skills” they need to become deeply proficient in the major frontier AI models and assorted standout services, like Google’s NotebookLM, but also learn Python if possible (the new Excel…). In addition they should try to use as many of the leading AI PropTech tools as possible, such as those dealing with Lease Abstraction and Underwriting etc.

Regarding “Positioning” they should ONLY work for companies actively leaning into AI, and should be actively pushing for new roles involving working “Human + AI”. They have to be the go-to people for anything AI.

And for their “Network” they should still dive into traditional CRE groups but also try and join in any PropTech WhatsApp or Slack groups they can.

Reread their opportunities above - this is how they’ll make them happen.

For the 32 year-old they’ll need a “Pivot Playbook”:

In terms of “Skills” they HAVE to become AI Literate, and solidly skilled prompters. And a deep dive into “Change Management” wouldn't go amiss, as they’ll be at the centre of moving old to new.

Regarding “Positioning” their role is going to become less about doing, and more about “Orchestrating”. A few weeks ago we wrote about “Agent Bosses” and this is where our 32 year-old should be heading.

And their “Network” has to change considerably as well. Moving beyond real estate to product managers, AI VCs and AI Consultants.

This is going to be tough and is why this archetype is at the most risk.

For the 42 year-old they’ll need a “Architect Playbook”:

Their “Skills” will also need to encompass AI. Sure they absolutely must become daily users of AI and weave it into all their work, but they also need to think hard about AI ethics, governance and data. It’s going to be their job to provide the AI infrastructure foundations, strategic guidance, and AI Policies through which their companies will be working. If a company’s AI goes rogue it’ll be their heads on the block, so they need to know what they are doing.

“Positioning” is their big thing. Redefining their company’s operating procedures (following the unbundling and rebundling we’ve discussed many times) is at their door. They’ll need more data skills, and more AI technologists, and they’ll probably be looking to buy or partner with rising PropTech startups.

Their “Network”, as with the others, needs to extend beyond CRE and become much more focussed on keeping up with the latest technologies and thought leadership. Not least because all their customers are going to be down this rabbit hole, and they must know how to understand how they’re thinking, and how AI is likely to change the nature of their demand for CRE.

So the 42 year-old’s big worry will be being outflanked by more tech-forward competitors, and simply becoming obsolete.

They may be less exposed than the 32-year-old, not because they are immune to disruption, but because their seniority gives them the agency to shape responses — assuming they choose to use it, whereas their younger colleague could do exactly the right things but be snookered because their company messed it up.

MODERATING FACTORS

It’s not all about age groups though. There are four main moderating factors that will also have a large impact on all of them.

First off is “Mindset” - an individual with a lean-in, forward-thinking, curious and keen-to-learn (and unlearn) mindset, across archetypes, will outperform. Dramatically.

Secondly, your “Specialisation” counts. Recently we looked deep at where value is likely to move to in CRE - you must be cognisant of whether your specialism is about to be commoditised.

Thirdly, your “Organisational Context” matters - are you working for a company aiming to become fully AI Native? If not, beware. But also think about organisation types. Global firms will give you access to tools and training, but likely you’ll have little agency. A Boutique firm would suit those attracted to agile, risk-tolerant, somewhat unstructured setups. A Startup might be where you should be but we all know the game there - high immersion but high risk. Or perhaps an Institution? You’ll probably get access to those rare areas of proprietary data and decent budgets to play with, but like the Global firms these tend to be pretty top down places.

And fourthly “Geography” matters. Try and operate out of either “Mature Hubs” or “Emerging Markets”. The former offers rapid adoption but strong competition, the latter a bit more time, less exposure - but the opportunity to be where leapfrogging is possible.

Get all four moderating factors right and you are away!

TBH though,  just get your ‘Mindset’ right. In all archetypes that will probably make the most difference. Get it wrong and the 22 year-old won’t get a job (worth having) and the 32 and 42 year-olds are at redundancy’s door.

CONCLUSION: STRATEGIC TAKEAWAYS

I think the first thing to try and really internalise is that we are entering a revolutionary era of change. We have had several decades of iterative change but what is going on now is something much more profound. Once again I’d like to hark back to how in real estate we need to be thinking 5, 10 or more years ahead, and the near certainty that a great deal is going to change by 2030, let alone 2035.

So for your CRE career to survive and thrive will probably require degrees of reinvention. The “Company” and how it operates is changing fast. Automation will target anything that is structured, repeatable, predictable, and increasingly with Generative AI a lot that is random, creative and unstructured. Value will still exist, but it will concentrate in synthesis, strategic judgement, and trust-building - areas where human-AI collaboration excels.

Our 22 year-old, contrary to public belief, is maybe the most advantaged by all of this. The smart ones at least have a growth mindset and AI fluency. Their value will be high and their progression much faster than we are used to. After all they don’t have to learn the past, just push for the future.

Most at risk are our 32 year-olds. Potentially stuck in a pincer move. Their old skills are becoming less valuable whilst they don’t have the new skills, or perhaps mindset for the new ones? If they don’t pivot they are in danger.

But most impactful could be our 42 year-olds. Their great opportunity is to rebuild their companies for the AI-native era. Not easy, but the few who manage it will do great business. Those who don’t will just fade away.

But overall, get that “Mindset” right and whoever you are, you’ll do great!

OVER TO YOU
Where do you stand? How vulnerable, or not, are you? Tell me about your mindset? How are you thinking about the future? I would love to hear people’s views on this.

* Of course this is a generalisation. There is high value data out there, but maybe 60% of the industry’s data needs will be wholly commoditised, with a sliding scale of value for the remaining 40%. People talk incessantly about “Data” in CRE (whilst actually doing little with it) and assume it equates to value. However, in an AI world, when something can be assembled quicker and faster, it tends to get used more, but its intrinsic value drops precipitously, as it has no scarcity value. That does not mean there are no profits to be made out of data, just that where they come from is moving.