BUILDING BLOCKS

1. Foundational AI Knowledge

You need to understand what AI is (and isn’t), including key concepts like machine learning, neural networks, large language models, and generative AI. Importantly you need to focus on differentiating human and machine intelligence, and how AI should be treated as a function of well-structured data systems.

Literacy begins with demystifying AI—knowing what it can realistically do, and what remains human terrain.

2. Data Fluency & Data Economics

Data is an economic asset, so understanding its collection, structuring, monetisation, reuse, and governance is important. Data enables both automation, and insight.

You cannot be AI-literate without being data-literate—and understanding how data compounds value over time. I mentioned him last week but Bill Schmarzo, the so-called ‘Dean of Big Data’ is an exceptional writer and teacher on data literacy, and if you don’t already, you should follow him. His 2023 book ‘AI and Data Literacy’, tells you pretty much all a non data specialist needs to know.

3. Problem Framing & Value Alignment

Involves translating business or operational problems into questions AI can help solve, starting with value creation not technical feasibility. You must “Start with the problem, not the model”, and this applies to functional automation and innovation.

Design and Systems Thinking are excellent frameworks to help you gain the ability to break down complex problems into AI-solvable units aligned with business outcomes.

4. Use Case Fluency

It is useful to have a use-case first mindset. An understanding of where AI delivers value—identifying repeatable, high-ROI applications within CRE operations and strategy. Good use-cases are an engine of learning and scaling.

Literacy includes recognising where AI can augment real-world CRE value chains—from leasing to asset performance.

5. Prompting & Human–AI Interaction

The ability to frame prompts, iterate with AI systems, and extract value from conversational or generative interfaces is a super skill.

Prompting is the new digital fluency—knowing how to speak to machines to unlock creativity, insight, and automation.

6. Human Uniqueness & Judgment

Empathy, moral reasoning, creativity, spatial awareness, and strategic judgement—what remains uniquely human in a machine-enhanced workflow?

#HumanIsTheNewLuxury, as I repeatedly say. Knowing whether and where to put the ‘human in the loop’ is vital for developing AI system that are reliable and accountable.

It is essential that we preserve the integrity of human judgement, and maintain agency over deciding what really matters.

7. Decision Intelligence

How do you structure decisions for AI support? How do you diagnose, predict and prescribe integrating AI into CRE judgement frameworks. Are you able to decompose the decision-making logic. What will be the systemic impact on work; how will workflows change?

Knowing how to architect decisions is as important as knowing how to use tools.

8. Systemic & Strategic Thinking

You have to see AI not as a tactical feature but as a transformative force across business models, tenant expectations, and the entire CRE lifecycle. It must be a strong focus—redefining space, value, and experience. With links to data that enables economic and systemic transformation.

AI is not a productivity layer - it’s a catalyst for system-wide reinvention.

9. Ethics, Responsibility & Governance

Understanding bias, transparency, unintended consequences, and ethical design is a foundational AI skill. To maintain human agency and trust we need to develop systems that have decision integrity, and we must be responsible in our tool adoption and usage.

AI literacy includes the ability to anticipate and mitigate ethical risk—especially with tenant, community, or environmental data.

10. Organisational Enablement & Culture

And we need to create an environment where AI literacy is distributed, supported, and incentivised across our organisation. We need to advocate for citizen data scientists and AI marketplaces, and we need internal AI champions and shared tools. AI is going to lead to an enormous amount of cultural change in work and value perception, and handling this effectively is neither easy, or something that can be left to chance.

11. Curiosity, Experimentation & Learning Culture

Empowering low-risk experimentation, playful exploration, and rapid iteration, as a way to build AI muscle across an organisation, will deliver strong results. ‘Play is serious work!’

Literacy grows through doing—experimentation is the delivery vehicle of insight. As we discussed before, working with AI (in particular Generative) is more akin to working with humans than software, and that requires practice doesn’t it?

AND THAT’S IT

These are the 11 core building blocks you need to become instinctively familiar with. They are partly ‘ways of thinking’, partly about ‘mindset’ and partly about things you just have to learn. But none are rocket science. Anyone can become modestly capable in all of them quite easily. With a little application you could become highly capable in not much time at all.

AI literacy is about understanding the ‘rules of the game’, and how they interact. With that in place you’ll be much better placed to build high and wide; strong foundations, as we know, are a great enabler.

Integrating AI into your thinking, your teams, and your workflows will become natural once you’ve internalised the above. Just something you do.

Of course there is a lot more one could expand into - storytelling, personalisation and narrative, agent-based workflows and multi-agent orchestration, and metrics, ROI and measurement literacy, as well as all manner of domain specific imperatives, but these are all things one will build on top of these foundations.

For now, just nail these!

OVER TO YOU

How’s your AI Literacy? What about your friends and colleagues? Please circulate this. The real estate industry needs to be AI Literate. Let’s make it so, one person at a time.