Over the coming months, Wigwam is going to be sharing a lot more about artificial intelligence — specifically, how it applies to self-storage, what it can actually do for operators, and where the real commercial opportunities are sitting right now.
We’ll cover a lot of ground, looking at areas like demand forecasting, pricing, site selection, customer behaviour and operational efficiency. To explain why any of that matters, we’ll start with a problem that almost every storage operator runs into, usually without realising it’s a problem at all.
Most storage businesses are run on lag.
Pull up the occupancy report, scan the revenue figures, check the enquiry log, and everything feels OK.
Alas, the problem is that every single one of those numbers is already old.
Occupancy moves after demand appears. Enquiries land after someone has already decided they need storage. Revenue reflects choices customers made weeks, sometimes months, before you see them on a spreadsheet. By the time the picture looks clean and organised, the moment it was describing has already passed.
Experienced operators will develop a feel for this over time. Spend enough time inside a market and you start picking up signals the dashboard can’t show you. Like a subtle shift in the type of enquiries coming through, or more people mentioning house moves than usual, or local agent remarking that viewings have picked up in a particular area and renovation skips appearing on streets you drive past regularly.
None of those things look significant on their own, but put them together and they’re often pointing at a demand wave that hasn’t reached your enquiry volume yet, but will.
That instinct is genuinely valuable. The operators who’ve developed it have a real edge, meaning they can feel the market shifting before it announces itself.
The trouble with this is that instinct doesn’t scale.
It works well when you’re close to one site like we once were, and you’re deeply embedded in one local market, absorbing signals almost unconsciously from years of proximity. But try to extend that across seventeen sites and the bandwidth just isn’t there. You can’t feel fifteen markets simultaneously the way you can feel one.
Which is where AI comes into play in the self storage industry.
Those same signals that experienced operators learn to read aren’t actually invisible, but they do exist in data. Search behaviour patterns. Property transaction volumes shifting in specific postcodes. Competitor pricing moving in ways that suggest they’re seeing something. Changes in customer enquiry types, even before volumes climb. Seasonal patterns that have historically preceded demand spikes by weeks.
The signals are there, but are buried inside thousands of small data points that no human could realistically process fast enough to act on them.
Other industries like airlines and hotels worked this out a long time ago. Airlines don’t wait to see empty seats before adjusting prices, they’re constantly modelling demand against behaviour patterns that precede actual bookings. Hotels do the same. Retailers have built entire supply chains around anticipating demand before it appears at the till. The logic is that if you can see what’s coming before it arrives, you can position for it rather than scramble in response to it.
Self-storage has been slow here because the sector has been so operationally focused that the question of what lies ahead has always taken a back seat to the question of what’s happening today.
We think that gap is now genuinely worth closing.
At Wigwam, we spend most of our time doing the practical work like managing sites, talking to customers, watching how different locations behave across seasons and economic conditions. That operational grounding remains important because you can’t run storage facilities from a dashboard, and we’ve never believed otherwise.
But we’ve been quietly building a view on how AI can make self-storage businesses better in ways that actually show up in occupancy, margin, and the quality of decisions operators make every day. We’ve been testing it, arguing about it internally, and getting much clearer on where it genuinely delivers and where it’s just noise.
So this is the start of that conversation. The question now is which operators will learn to use these tools — and which ones will still be watching everyone else in the rear-view mirror.
Stay tuned, or follow us on LinkedIn.
