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I list them all in the Index of the Archive on this site‘

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AI And Bad Workmen

A new report has been caricatured as saying Generative AI is a failure - whereas it actually says that most ‘Enterprise’ users are simply using it badly

Don’t Believe Everything You Read

You might well have heard about a report that came out last week from MIT NANDA (Networked Agents and Decentralized AI) allegedly proclaiming that Generative AI was a busted flush and that 95% of enterprise pilots were failing.

The press, and a certain type of Linkedin ‘Expert’ were all over it. To the extent that, on August 19th, the UK’s Financial Times proclaimed that:

“US tech stocks sold off on Tuesday as warnings that the hype surrounding artificial intelligence could be overdone hit some of the year’s best-performing shares.”

Now, as Mark Twain quipped, one should "Never let the truth get in the way of a good story” but in this case I thought I better check on the veracity behind the cacophony.

And knock me down with a feather, it turns out that the report actually argues that Generative AI is a really useful tool that almost every large company is using incorrectly. Right there in the opening paragraph it states

“Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach.”

Not My Fault Guv’nor

Bad workmen are blaming their tools!

They are calling this the ‘Gen AI Divide’ - the stark gap between those generating considerable value and none at all.

And then they are very clear what the issue is:

“The core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time.”

And the solution:

“A small group of vendors and buyers are achieving faster progress by addressing these limitations directly. Buyers who succeed demand process-specific customization and evaluate tools based on business outcomes rather than software benchmarks. They expect systems that integrate with existing processes and improve over time. Vendors meeting these expectations are securing multi-million-dollar deployments within months.”

In order to ‘address these limitations’ they go on to explain how Agentic AI systems are what is required because these can be configured with memory, adaptability, and autonomous workflow integration.

And all this is in the first three pages of the report, which you would not have thought would be too taxing for any ‘expert’ to read.

But then, writing up a report saying enterprises have an incredible new tool at their disposal if used correctly maybe isn’t as click worthy as ‘the bubble has burst - yet again the emperor has no clothes’.

The Enterprise Prerogative 

Essentially, at enterprise scale, one has to ensure:

• Integration with internal systems (CRM, ERP, lease databases)

• Governance/traceability (audit trails, compliance logs)

• Continuous improvement loops tied to business outcomes.

They need embedded, auditable, adaptive systems. And off the shelf, or even lightly customised LLMs don’t provide these.

So the report emphasises that enterprises should ‘Stop building static, non-learning tools that require endless prompting’.

What’s Wrong with Custom GPTs?

So why not build Custom GPTs?

The answer is that a Custom GPT can hold static context (instructions, tone, reference docs), but they don’t adapt dynamically from user corrections or workflow usage over time. If you correct an output today, it won’t automatically do it better tomorrow unless you manually retrain or rewrite the system prompt. 

Which makes them configured assistants, rather than learning systems.

Big Is Boring - But Where The Money Is

WHICH IS FINE when using them for individual productivity, and within SMEs where the flexibility and adaptability is very much a feature rather than a bug, but in large enterprises you really need ‘process machines’. 

It’s noticeable in the report that they say whilst most budget is currently pointed towards front-office applications (sales & marketing, customer operations etc) most of the ROI actually comes from building agents systems to manage back-office operations. Enterprises are process factories, not havens of entrepreneurial spirit. Frankly they need boring systems to do boring jobs that currently bored humans do.

2 out of 9 IS Bad

In their research covering nine industry categories, they found only two, Technology & Media and Telecoms, were on the right side of the ‘Gen AI Divide’. In these two industries, the impact of Gen AI was such that it was acting as a forcing function for structural change, and entire business models and operating models were being reimagined. For all the rest it was a world of bolt-on pilots, static tools and little ‘change’. In addition the levels of training were poor (only 40% paid for ChatGPT or the like Licenses), leading to a large amount of shadow IT, and a lot of ‘build-it-ourselves’ thinking was leading to botched implementations.

Where AI Is Working: Individual Adoption

Where these companies were having success was in areas they weren’t focussing on. Individual use by employees. In interviews they found that drafting, analysis, summarisation and outreach were very popular use cases at the edges, and adoption of consumer tools like ChatGPT was way ahead of anything handed down from above.

It Probably Does Not Apply To You Anyway
This report is very much focussed on larger enterprises, with upwards of thousands of employees. Mostly CRE companies don’t fit in this bucket. For instance the ‘large’ UK REIT, British Land, only directly employed 634 people as of mid 2024, and most developers, asset managers, and agencies operate with dozens to a few hundred staff. CBRE, JLL, Cushmans et al. are of course a lot bigger, but en masse CRE is an industry of SMEs. That often thrive by letting the bottom up approach reign.

As such, whilst (as we’ve written about before) we are going to see a lot of use of ‘agentic systems’ within our industry, we are not going to be constrained by the same imperatives that much larger companies have to operate under. And therefore should, as we already are, see a lot of Gen AI use at an individual or team level. People just making stuff happen, and pushing AI models to help them with whatever they have to deal with. No massive, centralised, bureaucratic plan needed.

And agility is a super-power in CRE. There are so many workflow/operating model innovations possible - leasing teams piloting deal-structuring agents; asset managers automating ESG reporting; development teams using AI for scenario modelling, and on and on.

The Bottom Line
Ultimately, the media's misreporting of this study perfectly illustrates the report's actual findings. The report describes a failure of implementation at the enterprise level, where structural change is needed but not forthcoming. We're seeing a lot of 'sound and fury... signifying nothing'. A complete bum steer.

Frankly this is not surprising - this technology is a ‘disruptive’ innovation, and that hurts, harms and annoys many people. So it is little wonder we’re seeing a degree of backlash right now. Who wouldn’t rather be safe and secure than throwing themselves into the rough and tumble of the ‘new’?

I suspect most readers of this newsletter are happy to be somewhat unnerved by all the change going on. We’re not the complacent type are we?

OVER TO YOU
What’s your greatest success with Gen AI? Where are you seeing how you work, or think, or act, change? Does this bother you? Let me know.

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.

————————————————————————————————————————

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.