Six months ago, I wrote about the dragons we chase at Inospace. About building for scale, investing in people, and upgrading the engine behind a fast-growing business. What I did not write about was the conversation that was already happening behind the scenes and has since become the loudest conversation in every boardroom, WhatsApp group, and LinkedIn feed in the country.
AI. What it can do. What it cannot. Who implements it. And whether any of us are ready for it.
There is an enormous temptation, when AI enters the conversation, to reach for the spectacular. Predictive analytics. Autonomous operations. Fully automated client journeys. Boardroom presentations full of flowcharts and future-state diagrams that look incredible and mean almost nothing.
The reality is far less glamorous. Before you can bolt AI onto your business, you need to take an honest look at what sits underneath it.
At Inospace, we have come through a period of rapid growth. Multiple acquisitions, national expansion, new product lines, new teams. When you grow that fast, your tech stack does not evolve neatly alongside you. It accumulates. Systems get layered on top of systems. Workarounds become permanent. Data lives in six different places, formatted in six different ways, maintained by six different people who all believe their version is the correct one.
That is the real starting point for AI in most businesses. Not a clean runway. A construction site, which is where we know how to operate.
So before we talked about what AI could do for us, we focused on streamlining the backend. Getting the foundations right. Consolidating data sources, cleaning up integrations, standardising processes across a national portfolio. It is not exciting work. Nobody posts about it on LinkedIn. But without it, every AI implementation is a house built on sand.
One of the most important distinctions we have had to make internally is the difference between AI that adds value today and AI that sounds impressive in a strategy deck.
The low-hanging fruit is not complicated. It is the repetitive, rules-based, high-volume work that eats operational hours without requiring human judgment. Lease clause extraction. Invoice matching across entities. Compliance deadline tracking. Maintenance request categorisation. Editing property listings. These are tasks where AI does not need to be creative or contextual, it just needs to be fast and consistent.
That is where the return is immediate and measurable. You do not need a six-month pilot programme to prove it. You need a Tuesday afternoon and someone who understands both the process and the tool.
The pie in the sky is the stuff that makes for great keynote presentations but falls apart the moment it meets your actual business. Fully autonomous property management. AI that replaces your leasing team. Chatbots that handle every client interaction without human involvement. These ideas are not impossible in principle, but they assume a level of data maturity, system integration, and operational discipline that most growing businesses simply do not have yet.
The danger is not that businesses ignore AI. It is that they skip the boring stuff, chase the spectacular, and end up with an expensive proof of concept that never makes it into production.
I recently attended the ISS Inside Self Storage Expo in Las Vegas, and amid all the noise about automation and autonomous operations, the most grounded conversations kept coming back to the same point: the operators getting the best results from AI are not replacing humans with bots. They are using AI to make their people sharper and more effective.
That distinction matters more than most businesses realise. There is a strong temptation right now to hand entire workflows over to AI, chatbots that handle client queries end to end, automated call systems that remove the human from the conversation entirely. And for some use cases, that works. But in property, where the relationship between operator and client is the product, fully removing the human is not innovation. It is a risk.
The differentiator is not whether you use AI. Everyone will. The differentiator is whether your people are still in the loop, better informed, faster to respond, and freed from the admin that used to slow them down. AI that supports a person on a call is a competitive advantage. AI that replaces the call is a gamble on whether your client cares about the difference.
In our world, they do. Our clients are not interfacing with a platform. They are renting space from people. That human connection is not a legacy cost to be optimised away. It is the thing that keeps tenants in the building. AI should amplify it, not replace
This is the question that keeps coming up in our business, in the market, and in every conversation I have with other operators.
AI has created an entirely new category of consultant overnight. Scroll LinkedIn for five minutes and you will find hundreds of people positioning themselves as AI transformation specialists. Some of them are brilliant. Many of them learned prompt engineering three months ago and now charge by the hour to do what a curious operator could learn from a twenty-minute video.
I say that without judgment, because here is the truth: even this article was drafted with the help of AI. Not written by it alone, like many LinkedIn posts, but shaped, structured, and sharpened using it as a thinking partner. If that makes you uncomfortable, you might want to stop reading. If it makes you curious, that is kind of the whole point.
One of my team and I recently spent time learning key new tools within Claude from TikTok videos. Not a consultancy engagement. Not a workshop. TikTok. And the content was exceptionally accurate and immediately applicable. That is the world we are operating in now. The knowledge is not locked behind a paywall or a consulting retainer. It is everywhere.
But that raises a genuine question. If the tools are this accessible, who is the right person to implement AI in your business?
Is it the twenty-two-year-old varsity graduate who grew up on these platforms, thinks natively in prompts, and can build a prototype in an afternoon but has never managed a P&L, handled a client dispute, or navigated a legacy system?
Or is it the senior consultant with fifteen years of operational experience who understands your business inside out, but approaches new technology with caution, sometimes too much caution, and may not fully grasp what the tools can do today versus what they could do two years ago?
The answer, uncomfortably, is neither on their own. And both together.
The varsity grad without business context will build something clever that solves the wrong problem. The senior consultant without technical curiosity will write a strategy document that never gets implemented. What you need is the combination, people who understand the operational reality of the business and are willing to get their hands dirty with the tools. Or, failing that, a team structure where those two perspectives sit close enough together to challenge each other daily.
At Inospace, that is how we are approaching it. Not outsourcing AI strategy to a consultant who will disappear after the slide deck is delivered. Not handing it to the youngest person in the room because they are "digital natives." Building it into the operational team, led by people who own the outcomes and understand the problems worth solving.
Here is what I have come to believe after twelve months of working through this: the competitive advantage of AI is not the technology itself. Everyone will have access to the same models, the same tools, the same platforms. The advantage is in how quickly and how honestly you can answer three questions.
What is broken in our business right now?
Which of those problems can AI solve better, faster, or cheaper than our current approach?
And do we have the operational discipline and data maturity to make it work, not in a demo, but in production, at scale, every day?
If you cannot answer those questions clearly, no amount of AI investment will save you. If you can, you do not need a six-figure consulting engagement to get started. You need a curious team, a clean dataset, and the discipline to start with what is real before chasing what is possible.
That is where Inospace is right now. Not ahead of the curve. Not behind it. On it with our hands on the wheel and our eyes on the road, not the rearview mirror.
The engine is running. Now we are tuning it!
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