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Kanav Dhir, VP of Product & Marketing, VergeSense
Editor’s Note: This article is part of an exclusive series featuring insights from our ebook, The smarter office: Your guide to space management in the hybrid era. Read the full guide for more insights on actionable space intelligence, making the office a destination, AI’s role in space management, and more.
Most organizations say they want data-driven workplace decisions. But few actually have the infrastructure to make them.
Ask many teams to show the data behind their real estate and workplace decisions, and you’ll often get a utilization study from last quarter, a dashboard that only gets checked when there’s a complaint, or a consultancy report that was stale by the time it was delivered.
That’s the gap I see most often. Companies are not truly data-driven. They’re data-adjacent. And in a hybrid workplace, that gap matters.
Your office is no longer a fixed asset that can be managed with a few static assumptions. It’s a live environment where demand changes by day, by team, by floor, and by work style. To make better decisions, you need a live model of how your spaces are performing, how demand is evolving, and what will happen under different scenarios.
That’s the thread connecting the biggest workplace decisions companies are making right now: portfolio planning, floor design, restacking, sustainability, and employee experience. They all depend on the same thing: better intelligence about how space is actually being used.
Historical reporting still matters. Last quarter’s report tells you what already happened. But the most valuable workplace questions are no longer limited to what happened last quarter. They’re about what will happen next.
If we cut two floors, how much will we save, and how many employees won’t be able to find a conference room on a peak day? If we change attendance policy, what impact does that have on peak demand? Will conference rooms hit capacity before desks do?
That is where forward-looking scenario modeling changes the conversation.
The C-suite wants to quantify trade-offs. What options do we have? What happens with each one? What are we giving up?
When you make the case based on consultant advice or industry heuristics, it’s a hard sell. But when you back it with a data-driven model, you can start presenting scenarios instead of defending assumptions. That scenario modeling helps you decide where to go next.
AI is the key enabler for proactive space management at scale.
In a human-only environment, teams may have the data, but acting on it with precision and timeliness is not practical. Too much time is spent pulling reports, consolidating information across systems, and building narratives around decisions that are still based on heuristics.
AI changes that across three categories.
First, it moves teams from reactive to proactive intelligence. Instead of spending hours pulling utilization reports across multiple systems, teams can ask questions in plain language and receive answers immediately. Which floors are underperforming? Which space types are consistently over capacity? Where is demand shifting?
Second, it supports prediction. How many people will come in? How will they behave when they get there? What happens if attendance policy changes? AI helps quantify those questions before the business makes a move.
Third, it helps automate time-intensive work. Non-strategic tasks can be handled by the system, which lets teams focus on decisions that actually require judgment.
That shift matters because space decisions have consequences that outlast the decision itself. Changes to layouts, building systems, and workplace technology all have lead times. By the time a problem is visible enough to act on, the window to address it gracefully may already be gone.
A live model gives teams time to act before the issue becomes expensive.
Portfolio right-sizing answers one fundamental question: do you have the right amount of space?
Traditionally, that might mean a $500K to $1.5M consulting exercise that delivers a static report. With AI capabilities, teams can run that analysis internally and walk into a CFO conversation with a live, defensible recommendation instead of a 90-day-old PDF.
But the amount of space is only part of the equation.
Most companies also need to ask whether they have the right types of space. The problem is often the mix. AI-powered behavioral analysis can pinpoint where a floor breaks down and suggest specific changes based on how people actually behave, rather than relying on a designer’s assumptions.
If a floor has plenty of desks but too few small collaboration spaces, the answer may not be more square footage. If conference rooms are constantly booked but often sit empty, the issue may be no-shows and booking behavior. If teams struggle to sit near each other, the problem may be the neighborhood model or booking layer.
These are different questions, but they all come back to the same operating model: understand current behavior, model future demand, and make the trade-offs visible before making the change.
Restacking is a good example. It is often handled manually across disconnected systems, usually when the situation has already become urgent. Spatial intelligence lets teams see constraints coming weeks in advance and model placement scenarios side by side. Work that used to take weeks of stakeholder meetings can happen in hours.
Sustainability can sound like a separate workstream from space management. In practice, it depends on the same data.
The two biggest sustainability levers in real estate are how much space you have and how much energy it consumes. Usage analytics can help you understand both.
On the footprint side, right-sizing is one of the most direct sustainability actions an organization can take. Running a portfolio with excess square footage has cost and waste consequences. When teams have a clear, data-backed view of where they’re operating in surplus, consolidating that space reduces emissions alongside the real estate bill.
On the energy side, there are two meaningful applications.
The first is feeding utilization data directly into building management systems to create intelligent schedules. That might mean pre-cooling spaces when people are coming in and scaling back when they are not, instead of running fixed timers with no relationship to occupancy.
The second is predictive mothballing: using predictive data to identify which floors can be fully powered down on low-attendance days.
A building that knows Mondays and Fridays will be quiet can proactively turn off two of its five floors. That’s only possible when you can forecast demand in advance.
This is the bigger shift for workplace and facilities teams.
The data that makes space decisions visible is also the data that supports sustainability goals, employee experience, and leadership planning. Teams can get more out of the work they were already doing because the intelligence is connected.
That changes the leadership conversation. Instead of showing a quarterly utilization report, teams can show the trade-offs behind a decision before it is made. They can model savings, employee impact, room availability, energy use, and future demand. They can also show where AI can reduce the manual work that has kept teams stuck in reporting mode.
For employees on the receiving end of space decisions, this matters too. AI modeling can preview the impact before changes go into effect. When the impact is quantified upfront, their experience is being factored in, not guessed at.
That is the future of space management: fewer stale reports, fewer assumptions, and more live intelligence that helps teams make better calls before the stakes get expensive.
Get more expert insights in our ebook, The smarter office: Your guide to space management in the hybrid era. You can also check out recent webinar “Turn Space Usage Blind Spots into Real-Time Savings”.
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