When the 'Expert' is a Machine - What Then?

And this is going to upend the 'work we do', our business models, and where value and competitive advantage is to be found. When an AI can work autonomously for 8 hours with minimum human supervision whole swathes of industry workflows become possible to execute in an entirely different way to today.

Even more so when the ‘intelligence’ of that autonomous agent matches or surpasses that of a human expert. OpenAI recently released research (GDPval) showing that today, across many tasks, 47.6% of deliverables by Claude Opus 4.1 were graded as better than or as good as the human deliverable. Fast forward 15 months and you get to ‘match the performance of human experts across many industries’. And one more year and ‘frequently outperform experts’ becomes commonplace.

Given the above you need to plan ahead. Whoever redesigns their operations for this world will have an inordinate advantage. And this advantage will compound, as these systems have a flywheel effect where each completed workflow acts as learning material for the next. In contrast those who don’t will find their knowledge degrades in value increasingly fast. Being a great saddle maker when cars arrived was not a great place to be.

Unlocking the Future - Data

To unlock the future, two foundational shifts are necessary:

First, the leap to expert-level, autonomous AI is impossible with the fragmented data infrastructure that plagues the CRE industry today. Professionals spend up to 80% of their time just gathering and cleaning data, a massive bottleneck to high-value work.

The vast majority of crucial information, leases, legal contracts, property photos, is unstructured and remains largely untapped. By thinking of data as a critical asset future ready companies will be creating a data spine that pulls all of this together and makes it possible to be effectively managed.

One often hears real estate people proclaim that proprietary data is their ‘gold mine’, but this will not last. More and more information is becoming open in one way or another, and AI makes the scraping, aggregating and synthesising of disparate data sources increasingly easy. Being able to orchestrate all of these data sources is where competitive advantage will lie. One doesn’t need to own data to extract value from it.

Unlocking the Future - Agents

Secondly, the enormous value of AI that can work autonomously for hours is that one can start to orchestrate whole swarms of customised, bespoke ‘Agents’. By unbundling and rebundling workflows (which we’ve covered multiple times in this newsletter) it becomes possible to chain any number of tasks together to achieve a goal.

Autonomous Agents in Action

Here are four examples of autonomous workflows that will be common by December 2026.

Investment & Acquisitions: Predictive Underwriting

Today: AI-assisted valuation models use historical data. Due diligence is a manual, weeks-long process involving expensive experts.

December 2026: AI agents will perform autonomous due diligence. Fed an offering memorandum, an orchestrated team of agents will extract financials, abstract lease terms, and check planning/zoning laws, producing a comprehensive risk report in hours, not weeks. Valuation will become dynamic, with AI continuously analysing news, social sentiment, and satellite imagery to identify mis-priced assets before the market does.

Development & Construction: Proactive Project Orchestration

Today: AI assists with design optimisation. Project management is manual and reactive.

December 2026: Generative design will produce near-complete schematic designs and BIM models based on a set of constraints (budget, codes, energy targets). On-site, AI agents will act as a central intelligence hub, integrating live data from IoT sensors and supply chain APIs to proactively orchestrate schedules, predicting delays and recommending solutions before they disrupt the project.

Leasing & Tenant Management: Autonomous Leasing

Today: Simple AI chatbots handle basic tenant inquiries. Negotiations are fully human-led.

December 2026: End-to-end autonomous leasing agents will manage the entire workflow 24/7 - from engaging prospective tenants with hyper-personalised conversations to conducting virtual tours, running automated screening, and generating customised lease documents. For complex negotiations, a generative AI "copilot" will assist humans in real-time by redlining contracts, flagging risks, and suggesting legally compliant alternative clauses based on the firm's playbook.

Asset & Portfolio Management: Real-Time Risk Orchestration

Today: Predictive maintenance alerts are common. Portfolio analysis is a periodic, backward-looking activity.

December 2026: AI will enable continuous, real-time portfolio risk orchestration. Agents will work 24/7, monitoring tenant credit risk, tracking loan covenant compliance, and identifying ESG compliance gaps. Crucially, they will move to prescriptive intelligence, not just flagging a risk but autonomously modelling "what-if" scenarios and recommending quantified, data-driven solutions for human asset managers to approve.

Humans Must Maintain Agency

In each of these cases designing the autonomous workflow is a super-skill. It is up to you what to automate, and where to insert a ‘human in the loop’. You’ll probably design different processes for different circumstances. But the point is to balance autonomy with agency.

Much of the process for which you used to charge will become commoditised, so you need to recreate value elsewhere.

The focus will move from being a "doer" of tasks to a "strategic overseer" who can:

Provide Strategic Judgment: AI lacks common sense and an understanding of local nuance; human experts will provide the critical context and make the final judgment call.

Master Negotiation and Relationships: AI cannot replicate the empathy, rapport, and emotional intelligence required for high-stakes deal-making.

Build Trust: In a world of automation, trust becomes the ultimate currency. Authenticity and integrity remain fundamentally human differentiators.

Recommendations for 2026 Readiness

The transition to an autonomous, AI-driven operational model is not optional; it will trigger a period of "digital Darwinism" where technologically advanced firms gain insurmountable advantages.

1. Make Data Your Core Strategy: Immediately begin the work of breaking down data silos and building a unified "data spine." This is a C-suite level business initiative, not an IT project.

2. Domain-Specific Reasoning: Autonomous Agents need exceptional ‘context, clarity and constraints’, so assembling detailed instructions as to what inputs are required, what processes need to be executed, and what outputs are desired is essential. You can feed all the domain-specific information you have access to into the system, but you have to be very clear as to what the Agent can access, where it is, and how to get to it.

3. Invest in Your People: Proactively manage the cultural shift by framing AI as a tool for augmentation, not replacement. Invest heavily in upskilling programs to equip your team for higher-value strategic roles.

4. Establish Robust AI Governance: The regulatory landscape is evolving rapidly. Build frameworks now to ensure data privacy, prevent algorithmic bias, and maintain transparency. This will become a source of competitive advantage.

Conclusion

The above may seem like science fiction and a world away from what you think AI is capable of. But it is not. This is short term prediction based on what we know for sure today. By the end of next year we will all have access to models that can perform autonomously (but based on our supervision) for a full working day, and they will be ‘expert’ in many of the tasks you have to do.

To make use of these capabilities you will have to have to have developed a unified data spine, and thought hard about what value you can generate now that many of your workflows will be commoditised by automation. I’ve no doubt this exists in spades: we all spend so much time processing data rather than thinking hard about it. In that thinking there is surely much value?

And, of course, much of the industry won’t have done any of this, or even thought much about it, so short term at least, they’ll be huge value to be had from being an early adopter.

OVER TO YOU

How ready are you? What’s the state of your data? Do you have a decent grasp of the principles of data science? Have you built any Agents yet? Are you working for a future ready firm? If not, is it time to move on?

PS *

Some will argue this timeline is too aggressive, that implementation will lag capability, that I'm underestimating organisational inertia. They might be right. But here's the crucial asymmetry: if you prepare for rapid transformation and it takes 36 months instead of 15, you've merely invested in data infrastructure and AI literacy earlier than necessary. If you wait for certainty and transformation happens on the aggressive schedule, you've lost your business. In an environment of exponential change, the rational strategy is to prepare for the aggressive scenario.