Artificial intelligence is impossible to ignore in real estate right now but knowing how to approach it strategically is another matter entirely. At FlexSA\u2019s Annual Conference, Yardi\u2019s Mandy Jhamat sat down with Zoe Webster, AI and innovation consultant at Authentic Innovation, to discuss how to meaningfully embed AI in flex operations.<\/p>\n\n\n\n
Start with strategy. Yours, not AI\u2019s<\/strong><\/p>\n\n\n\n The most common mistake organisations make, according to Webster, is letting the technology lead. \u201cOften in many organisations it becomes, \u2018we must do AI and here is what we\u2019re going to do\u2019,\u201d she said. \u201cBut let\u2019s stop. What is it that is really important to the organisation?\u201d<\/p>\n\n\n\n Her framework is deliberate and sequential \u2013 begin with your corporate strategy, understand what you are genuinely trying to achieve over the next three to five years, and only then ask which elements of that ambition could be meaningfully enabled by AI. From there, a data strategy and an AI strategy should follow, not precede, that clarity of purpose.<\/p>\n\n\n\n She was quick to acknowledge this is tidier in theory than practice. \u201cIt is a little bit messy,\u201d she admitted. \u201cIt is not one size fits all.\u201d The three strategies \u2013 corporate, data and AI \u2013 are in constant conversation with each other, each informing and updating the others as technology and business conditions evolve.<\/p>\n\n\n\n \u201cAI is nothing without data. But a data strategy alone won\u2019t save you if you don\u2019t know what business problem you\u2019re trying to solve.\u201d<\/em><\/p>\n\n\n\n Zoe Webster, Authentic Innovation<\/p>\n<\/blockquote>\n\n\n\n Data: the foundation most organisations are still building<\/strong><\/p>\n\n\n\n The survey statistic that drew the most visible reaction in the room was the 86% who cited data fragmentation as their main AI blocker. Webster was unsurprised. Even organisations that know where their data lives often struggle with access, ownership, storage costs and quality. \u201cWho owns that data, who has responsibility for it, whose budget is covering that storage? It gets quite involved quite quickly,\u201d she said.<\/p>\n\n\n\n For organisations earlier in their data maturity journey, she recommended starting with an honest audit \u2013 what do you have, where is it, what quality is it and can you actually access it? First-party data you hold alongside relevant third-party data are both worth assessing. The findings will shape which AI ambitions are realistic now and which need to wait.<\/p>\n\n\n\n AI literacy is a people strategy, not a training programme<\/strong><\/p>\n\n\n\n Webster is clear that AI adoption succeeds or fails on culture, not tooling. Her goal when working with organisations is not to produce uniform enthusiasm for AI, but to give people enough understanding that they can engage with it critically and contribute their own ideas from the ground up.<\/p>\n\n\n\n \u201cOften you see a top-down approach \u2013 you will use this new AI tool and you will be more productive,\u201d she said. \u201cWhat really works, for the long term, is getting people in the organisation comfortable enough that they can come up with their own ideas and take those to leadership.\u201d<\/p>\n\n\n\n What good AI literacy looks like in practice:<\/strong><\/p>\n\n\n\n On the question of whether everyone in an organisation should reach the same view of AI, Webster pushed back: \u201cI think it\u2019s healthy that people have different perspectives. The magic happens when you bring those people together.\u201d She noted that the most sceptical voices often do the most valuable work, asking the questions about risk and bias that the enthusiasts overlook.<\/p>\n\n\n\n Governance: accountability needs a name attached to it<\/strong><\/p>\n\n\n\n When it comes to governance, Webster\u2019s advice was unambiguous \u2013 someone specific must own it. \u201cOften you get, everyone is accountable \u2013 and in that case, no one is,\u201d she said. \u201cWhen something goes wrong, who is responsible? Unless someone\u2019s name is down, nothing really happens.\u201d<\/p>\n\n\n\n She recommended that organisations review existing IT, information security and acceptable use policies to assess whether they already address AI and to supplement them with an explicit AI policy that sets out both what is permitted and the principles guiding every decision. The UK government\u2019s five AI principles offer a useful starting framework, particularly around accountability, transparency and fairness.<\/p>\n\n\n\n Fairness, she was careful to clarify, cuts two ways \u2013 it means testing AI systems for bias and discriminatory outputs, but it also means ensuring equitable access to AI tools and literacy across the whole organisation, not just for those already closest to the technology.<\/p>\n\n\n\n \u201cIn the absence of policy, people don\u2019t stop. They just do it in the shadows. Either way, that\u2019s a problem.\u201d<\/em><\/p>\n\n\n\n Zoe Webster, Authentic Innovation<\/p>\n<\/blockquote>\n\n\n\n Build or buy? The honest answer<\/strong><\/p>\n\n\n\n The question of whether to build bespoke AI solutions or procure existing ones came up from the floor, and Webster gave a characteristically practical answer. Building your own large language model or end-to-end AI management system rarely makes sense for most organisations, but it might, under specific conditions. For example, if your data is too sensitive to share with any vendor, if you see a genuine path to commercialising what you build, or if your need is genuinely too bespoke for anything available off the shelf.<\/p>\n\n\n\n The part most organisations underestimate, she cautioned, is what comes after the prototype. \u201cBuilding it is almost the easy bit. The harder part is the ongoing operations and maintenance and the opportunity cost of the time and energy you have taken away from your core business.\u201d Anyone considering a build route needs to be honest about whether they can sustain it.<\/p>\n\n\n\n Sustainability: a risk hiding in plain sight<\/strong><\/p>\n\n\n\n When the conversation opened up for Q&A, an audience member raised the question of sustainability. Webster acknowledged directly that AI is computationally intensive and energy hungry, and that at present, the transparency needed for organisations to accurately measure the carbon cost of their AI use simply does not exist. \u201cThere is a real risk that what you\u2019re doing with AI runs counter to any kind of corporate sustainability objectives.\u201d. She advised organisations to stay aware of the tension and be open with their people about it and expressed hope that regulation will eventually force greater disclosure from providers.<\/p>\n\n\n\n\n
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