AI in real estate is still too often discussed like a shiny add-on: a chatbot here, a dashboard there, maybe a pilot with one team. But the discussion at Bidwells’ Oxford Productivity Engine Conference was more ambitious: the recurring view was that AI will matter most when it reshapes the entire real estate value chain, not when it’s bolted onto a single task.
Think of AI less as a tool and more as infrastructure for better and more collaborative decision-making. It can support every phase of real estate, from research, origination, underwriting, consents, delivery, operation through to exit and ongoing evolution. The real shift happens when those AI-enabled insights flow through the chain, not when they sit in one place.
Oxford itself offers a useful lens on why AI needs to be tackled end-to-end. The Future of Real Estate Initiative at Oxford Saïd brings researchers, real estate leaders and entrepreneurs into the same room to translate AI capability into real asset decisions. That kind of ecosystem matters because end-to-end AI isn’t just a tech rollout; it’s coordination between data, people and processes – enabling quicker and more reliable decision making at the human level.
So what does “value-chain AI” look like in practice? Three shifts stood out.
1) Faster, broader, more granular decisions - especially early in the cycle
AI is already changing the front end of real estate investment because it brings machine-scale processing to human craft: pulling together wider market signals, spotting patterns in complex data, and stress-testing assumptions quickly. That creates faster, deeper intelligence, while the interpretation, context and commercial judgement remain human-led.
In due diligence, this means decisions can be made earlier and with more confidence. That doesn’t just speed up a deal team; it broadens what’s feasible to analyse in the first place. Portfolios that once felt fragmented, data-heavy and time-consuming to assess become investible as AI enables more comprehensive insight at pace - improving the quality of advice without stretching programmes or teams.
The same principle applies to smaller, everyday decision cycles too. When AI takes on the heavy lifting - drafting, summarising, structuring, comparing - professionals get time back for the judgement work. Just imagine how this could supercharge development management – allowing planners to marshal the placemaking which is so critical when talent moves globally.
It’s not about replacing jobs or reducing democracy; it’s freeing capacity for higher-value thinking, faster iteration, and better decision making in the interests of all.
2) Operational AI closes the loop between “plan” and “performance”
AI’s value doesn’t stop once a deal completes or when a project secures planning permission. In fact, some of the most powerful gains sit in operations, where assets generate constant signals about how spaces are used, what’s under-performing, and where costs and carbon are leaking.
When those operational insights are captured and interpreted well, they don’t just improve day-to-day performance; they feed back into better design choices, sharper investment strategy, and more accurate future underwriting and design.
A good way to think about this is decision-making in the moment. On complex estates, AI-enabled visualisation and digital twins help expert teams translate operational data into clear, decision-ready insight much faster.
AI can flag emerging problems earlier, prioritise response, and route the right resources - but the best outcomes come when technology strengthens, rather than replaces, the human layer. That balance matters: occupiers and investors benefit from responsiveness and reliability, while expert teams stay accountable for judgement, safety and experience.
Used this way, operational AI becomes more than efficiency tooling; it helps every next project start smarter than the last.
3) The “AI-ready” value chain depends on data foundations and joined-up adoption
AI can only create real advantage in real estate when the basics are in place. Right now, readiness is uneven along the value chain. Many organisations are still at an exploratory stage, trying to pin down what AI means for their specific workflows and risk appetite.
A common blocker is the weakest link problem: even if one part of the chain is moving fast, partners and suppliers may not be. That makes whole-chain transformation harder, because data and processes don’t flow cleanly from one stage to the next.
Which brings it back to foundations. AI is only as good as the data it’s fed - and few environments make that clearer than large, complex, legacy estates. The University of Oxford is a case in point: a historic, operationally intensive portfolio with layers of buildings, users and systems built up over time. Before AI can genuinely support whole-estate decisions, the digital groundwork has to be done. As their Head of Estates Digital explained, their information currently sits across “80 to 90 different systems,” so the priority is stitching that into consistent standards and structured models. Only then can AI be used confidently at scale, because it’s working from a reliable, connected view of the asset base.
There’s a second, quieter foundation too: governance. AI output can look polished, which tempts teams to treat it as finished work. But that’s risky in a profession built on judgement and accountability. Embed professional checks, clear rules on use, and transparency about sources and assumptions.
Get those two things right - data discipline and professional governance - and AI stops being a series of disconnected experiments. It becomes something that can genuinely run through the value chain, improving the quality and speed of decisions at every handover.
So, are you using AI to make one step better, or to make the whole chain smarter?
If it’s the former, you’ll likely get tactical productivity. If it’s the latter, you unlock compound advantage: better investment calls, better buildings, better operations, and a feedback loop that keeps improving your next move.
Or, as our panel at the Oxford Productivity Engine Conference put it, AI at every step is “what transforms the industry.” As we embrace this transformational opportunity, we can’t wait to see what this brings in 2026.
