The Real Cost of Occupancy Planning Consulting (And What Replaces It)

Here's a question worth sitting with: Are you satisfied with what you're spending on occupancy planning projects, and how much do you trust the recommendations you're getting back?

If the honest answer brings up some hesitation, you're not alone. Corporate real estate teams at Fortune 2000 companies routinely spend between $500,000 and $1.5 million a year on occupancy consulting: portfolio studies, utilization audits, right-sizing analyses, and design validation engagements. These projects take three to six months, produce a PDF, and by the time the final report lands on your desk, the assumptions it was built on have already shifted.

Headcount changed. A return-to-office policy was updated. A business unit reorganized. The consultant's recommendation, frozen in time from the data they collected months ago, no longer reflects reality.

This isn't a criticism of the people doing the work, as occupancy consultants are often talented professionals solving hard problems. The issue is the model: episodic, project-based analysis applied to a continuous, fast-changing problem.

What you're actually paying for

We know that when our customers engage consultants for real estate or workplace challenges, the engagements typically covers some combination of three exercises: figuring out how much space the organization needs (portfolio right-sizing), determining what type of space it should be (workplace design validation), and deciding which teams should sit where (stack planning).

Each of these exercises follows a similar pattern. The consultant collects data: badge swipes, booking system exports, manual observations, sometimes sensors deployed temporarily. They reconcile that data in spreadsheets, build models, draft scenarios, and present findings. The whole cycle can run 12 to 26 weeks depending on scope and portfolio size.

The price tag reflects the labor intensity. Industry benchmarks put a consultant-led occupancy study at roughly $0.25 per square foot. For a one-million-square-foot portfolio, that's $250,000 for a single engagement, and most large enterprises run multiple cycles per year across different regions or business units.

Add it up across portfolio right-sizing ($500K to $1.5M in annual decision spend), workplace design ($300K to $1M), and stack planning ($300K to $750K), and you're looking at $1.5 million to $3 million a year in total decision-making costs for a Fortune 2000 company with 50 or more sites. And that's before you account for the internal hours your own team spends managing the engagement: chasing business units for headcount data, reconciling numbers across four-plus systems, and sitting through rounds of stakeholder review meetings.

The real cost isn't the invoice. It's the lag.

Money aside, the deeper problem is time. Three to six months is a long time in a world where attendance patterns shift weekly, hybrid policies evolve quarterly, and reorgs can happen with a single leadership decision.

Consider what happens during a typical consultant engagement: the consultant collects occupancy data over a two-to-four-week observation period. They analyze it for another four to eight weeks. They draft recommendations, circulate for review, revise, and deliver a final report. By the time that report reaches the decision-maker, the data it was built on is three to five months old.

In the meantime, your organization may have announced a new return-to-office policy, hired 200 people in one region, or consolidated a business unit. The consultant's model doesn't know about any of that. It can't, because it was built on a snapshot, not a living dataset.

This is why so many leaders describe a familiar frustration: the consulting engagement confirms what they already suspected, arrives too late to be fully actionable, and leaves them needing another engagement six months later to update the analysis.

What the alternative looks like

The alternative isn't "do it yourself in Excel." That's how most teams try to bridge the gap today, and it creates its own problems. Research suggests 88% of spreadsheets contain errors, and manual scenario modeling is time-consuming enough that teams only run a handful of scenarios when they should be running hundreds.

The alternative is continuous intelligence, baked into our Predictive Planning product: a tool that does what the consultant does (model scenarios, assess capacity, recommend design changes) but does it continuously, on a self-serve basis, and in minutes instead of months.

This is what Predictive Planning was built for. VergeSense's Large Spatial Model, trained on more than 200 million square feet of real workplace behavior data, runs over 1,000 behavioral simulations per floor to predict how your workforce will actually interact with your space. It doesn't require months of observation data or data from sensors. It works with a floor plan and basic workforce metadata, and it updates instantly when your inputs change.

Here’s how Predictive Planning can speed up your decision making process:

• When a CRE leader needs to evaluate giving back a floor, the capacity and experience impact is available in seconds.
• When a team is considering a shift from three to five days in office, the model recalculates across the full portfolio as soon as the policy input changes.
• When a data-backed recommendation needs to be in front of the CFO by Friday, the platform generates an AI executive summary ready for leadership without weeks of slide-building.

The cost difference between the two approaches is stark. Where a consultant engagement delivers a static answer once for $250,000 or more, Predictive Planning delivers dynamic answers continuously, at a fraction of the annual consulting spend, and with no lead time.

The goal: a new approach to planning

To be direct: the goal of this new approach to planning is to combine the best of both worlds to improve outcomes for critical real estate and workplace decisions. There are engagements where deep human expertise (change management, organizational design, executive facilitation) adds value that no software replicates. But the time-intensive data-gathering, scenario-modeling, and utilization-analysis work that takes up many occupancy consulting engagements? That work is better done by a continuously updated AI model than by a team billing $400 an hour to build the same Excel model they built for the last client.

Leading CRE teams are already making this shift. They're using Predictive Planning for the analytical heavy lifting (scenario modeling, capacity forecasting, design validation) and reserving consultant budgets for the strategic and organizational work that truly requires human judgment.

The result: faster answers, better data, lower cost, and a planning capability that doesn't go stale the moment the engagement ends.

The question to ask yourself

If your CEO asked you today how much space you could give back, and what the impact on employee experience would be, could you answer in under an hour?

If not, the issue probably isn't your team's competence. It's the approach you're using to get answers. And that approach is worth rethinking.

See Predictive Planning in action →