AI'S DIVERGENT IMPACT ON KEY CRE ROLES: RISE, FALL, TRANSFORM

Whether AI automates the inexpert, or the expert, directly correlates to whether CRE roles will ‘Rise, Fall, or Transform’. So AI’s impact is much more complex and role specific than generally allowed for. Here are some:

Roles Set to "Rise" or “Transform”: These are roles where AI is likely to ‘augment’ the human by automating the inexpert.

Investment Analyst: AI can automate data collection, aggregation, and initial financial model population. This frees analysts to focus on strategic thinking, critical analysis, designing complex models, and interpreting data. Their value and wages are set to rise, though relative employment may contract as each analyst becomes more productive.

Acquisitions Officer: AI can automate lead generation and initial deal screening. This allows officers to focus entirely on negotiation, relationship cultivation, sourcing off-market deals, and strategic judgement. Their value and wage potential will rise, and relative employment may slightly contract.

Asset Manager: AI can handle data aggregation, reporting, and predictive forecasts. The role shifts to a higher-value, purely strategic function, focused on business planning, investor relations, and value creation. Wages are poised to rise, with likely employment contraction as managers oversee larger portfolios.

Broker (Tenant & Landlord Representative): AI can automate market analysis, listing summaries, and initial communications. This enables brokers to dedicate more time to client consultation, strategic advisory, and complex negotiation. Their value and commissions will rise, potentially leading to market consolidation and a "flight to quality” (this time of people, rather than buildings!)

Roles Set to “Fall”: These are roles where AI is likely to ‘automate’ away the value of the human by automating the expert tasks they traditionally perform.

Valuer/Appraiser: Sophisticated Automated Valuation Models (AVMs) can directly target the valuer/appraiser’s core expert task of applying valuation methodologies to standard properties. This erodes the scarcity of human valuation expertise, leading to a significant fall in wages. However, the role will likely transform and narrow, with a new elite tier of valuers/appraisers focusing on highly complex, unique properties, or high-stakes litigation/advisory work where nuanced human judgement is still critical. This group will retain ‘expert’ level incomes, whilst relative employment may expand for less-qualified users of AVMs, at lower rates.

This presents a stark trade-off. For the roles where AI automates the inexpert, people are likely to earn more as they can concentrate more of their time on high-value activities. But we will need less of them. Nice work if you’re one of the in-crowd, less so if you’re not.

And then, for the roles where AI automates the expert, we are likely to see currently highly paid people suffer a significant contraction in their earning potential, but then the chance for many less expert people, working with the AI tools, to probably raise their incomes. We can do a lot more appraisals, valuations as they become cheaper to do, but those doing them no longer need to be rare ‘experts’.

STRATEGIC CHOICES FOR FIRMS: AUTOMATION VS. COLLABORATION TOOLS

Autor and Thompson make a critical distinction between two types of AI tools, which represents a strategic choice for firms with profound consequences for work organisation and careers.

Automation Tools:

Purpose: Designed to fully replace a human task by codifying specialised knowledge into software. Primary goal: efficiency and cost reduction.

Examples: Automated lease abstraction, automated rent roll processing, tenant communication chatbots, automated financial tools.

Impact: Over-reliance on these tools can lead to a "hollowed-out" organisation and the "ladder problem", where thinning junior ranks create a pipeline gap for future senior leaders who historically learned fundamentals through these tasks.

Collaboration Tools:

Purpose: Designed to augment and amplify human professional skills, acting as a "force multiplier". Primary goal: enhance human capability and judgement.

Examples: AI-powered underwriting and investment analysis which provide a starting point for human analysis, advanced AVMs used by expert valuers/appraisers to layer nuanced market expertise, predictive analytics for brokerage to identify leads, AI-augmented CRMs for relationship management.

Impact: Strategic adoption can strengthen firms by empowering professionals, democratising expertise, and potentially creating new "middle-skill" roles (e.g., "Deal Analytics Specialist," "Asset Performance Analyst") that leverage AI for sophisticated analysis. Career progression shifts to valuing an individual's ability to effectively partner with AI and perform "judgement work”. Note: Research generally shows that AI has a strong potential to raise the capabilities of lower skilled people more than highly skilled ones. Both do gain but the highest uptick is from those in the lower quartiles of competence. See ‘​The Jagged Edge​’ study for more on this.

STRATEGIC RECOMMENDATIONS FOR CRE FIRMS

We recommend a two-pronged strategy that addresses both talent and technology.

Talent Development:

Shift from Training to Continuous Learning: Develop a culture of "constant adaptation" through continuous, integrated learning, exploring experiential methods.

Cultivate "Judgement Work": Redesign curricula to teach professionals how to effectively work with AI – asking the right questions, spotting anomalies/biases, and applying contextual understanding to AI outputs.

Autor and Thompson emphasise the objective should be to help people “acquire judgement faster”. Which might grate with a certain old school ‘learn by doing’ type, but ‘learning judgement’ is something AI can enable by exposing individuals to countless simulations they can learn from. Role playing ‘games’ can be enormously effective.

Double Down on Inherently Human Skills: Invest aggressively in capabilities AI cannot easily codify: complex, multi-party negotiation; strategic relationship management and trust-building; persuasion; and creative, "out-of-the-box" problem-solving. These will be a firm's most durable competitive advantage. And again, AI can help develop these skills. For instance our own ‘​The TDH Daily CRE Critical Thinking Challenge​’ can be used to role play endless domain specific problems or tricky circumstances.

Technology Adoption:

"Collaboration First" Procurement Policy: Leadership must shift focus from "how many headcount can this tool replace?" to "how does this tool make our best people better?" Prioritise augmentation tools, especially for core, revenue-generating functions. This is a talent retention strategy.

Integrate, Don't Silo Data: Break down internal data silos to create a unified data environment. This fuels more powerful and accurate AI-driven insights, providing a significant competitive advantage.

Manage AI Risk with "Human-in-the-Loop" Governance: Implement a robust governance model that mandates human oversight for all critical decisions, positioning AI as a powerful advisor, but ensuring final judgement and accountability rest with a human professional. This mitigates risks like opaque decision logic, data privacy concerns, and AI "hallucinations".

CONCLUSION: THRIVING IN THE AGE OF AI

AI's impact is a nuanced story of task redistribution and expertise revaluation. The simplistic narrative of 'robots taking jobs' misses the point entirely. We need to think at a much more granular level, understanding where value will fall and where it will rise. Clayton Christensen’s "Law of Conservation of Attractive Profits” describes how the ability to earn attractive profits shifts within a value chain as products or processes become commoditised’. Profits don’t disappear, they move.And they will in real estate, so we must work out in advance …. where to?

So real estate companies must pivot from a cost-cutting mindset (not everyone but often this is the default way of thinking) to one of value creation by strategically deploying AI as a collaboration tool, redesigning career paths for new middle-skill roles, and doubling down on investment in uniquely human skills that will remain scarce and valuable.

NOTE: Future Factors to WatchThe generally good news in this newsletter - higher value roles even if less of them - ‘might’ be cast aside if any of these three developments, or a combination of them, come to pass.

1. Data becomes ubiquitous and more open: Much of the ‘human’ edge remaining in an AI world revolves around knowing things that others do not. If this changes the edge diminishes. I’d rate this as likely to very likely, but over a decade rather than imminently.
2. Negotiating complicated Leases is something humans can do, face to face, far better than AI. So whilst this remains the norm, humans have a valuable edge. But if asymmetric negotiations become more commonplace an AI negotiator might well win the day. Again, I have this in the likely camp but over time.
3. And thirdly, as distributed working really starts to bed in more and more companies will be procuring their space on shorter terms, with more boilerplate agreements. The Leasing process will become simpler. This again would diminish the humans edge over the AI. I’d say this is highly likely, but again, over a decade rather than imminently.

But all this would mean is us humans will have to work where the profits are moving to again!

OVER TO YOU

Take a look at your own daily task list. Which bucket does most of your work fall into: 'expert' or 'inexpert'? Which tasks could an AI collaborator augment today? The answers will point to your future. If you're building a strategy to navigate this, I can help. Drop me a line.