Why Data Interpretation Matters for Smarter Real Estate Planning
Every month, I speak to workplace and real estate leaders sitting on mountains of occupancy data — badge swipes, Wi-Fi connections, desk bookings, sensor reports — and yet they still can’t answer the most basic boardroom question:
Do we need all this space?
The irony is, most organizations already have the data they need to act. What’s missing is context. The ability to interpret the signals, benchmark them, and translate them into actions that a CFO can stand behind.
That’s where our workplace experience platform comes in.
We take the feeds you already have, access control, desk bookings, Wi-Fi, sensors, HR rosters, parking analysis — and integrate them into a structured methodology, alongside our proprietary workplace engagement tools and space budgeting module. This creates a consistent, repeatable way to baseline your portfolio, make confident decisions, and track whether those decisions are delivering value.
The Problem Isn’t Data. It’s Decision Readiness.
Amazon’s attempt to bring employees back to the office in January 2025 was disrupted — not by protest, but by poor capacity planning. Several sites ran out of desks and parking, forcing local delays and damaging leadership credibility.
Meta, facing the opposite problem, is working to offload nearly 2 million square feet of office space across the Bay Area, including three entire campuses in Fremont (SFGate).
Different triggers, same root cause: translating occupancy signals into timely, confident briefings and board actions.
Turning Workplace Data into Actionable Insights
We begin with a current-state baseline using our workplace experience platform. This tells us what work settings best suit your employees’ workstyles, and what space allocation is right for each department.
Four Dimensions of Workplace Analytics
We then integrate multiple data feeds — digital and human — into the platform, producing clear insight across four key dimensions:
Utilization: What space is being used, when, and at what density?
Cost: What is your current spend per FTE or per utilized seat?
Experience: Are people able to do their best work in the space?
Flexibility: How much of your space supports flexible, collaborative, or on-demand use?
These outputs are never abstract. They lead directly to action.
For example:
If a site shows consistently low utilization, we’ll flag it for detailed review, whether that’s lease exit, subletting, or a renovation to optimize occupancy.
If collaboration space usage is growing quarter over quarter, we’ll highlight reallocation opportunities, grounded in the platform’s analytics.
From Baseline Analysis to Ongoing Portfolio Decisions
This baseline becomes your reference point for all future decisions. Then every 90 days, we refresh the live data and deliver a board-ready update with trigger-based actions and clear financial implications.
Why Real Estate Strategy Needs an Independent Interpreter
We’re not here to sell sensors or software licenses. We’re not pushing an IWMS. We’re your independent interpreter, connecting your existing data to your strategic goals, and translating insights into board-level decisions.
By blending the signals your systems already generate with what your employees and leaders are saying (via our engagement tools), our platform delivers insight that’s financially grounded and operationally credible.
No more guesswork. No more static reporting.
Our clients don’t need more complexity. They need clarity. A baseline they can trust. A quarterly rhythm that keeps the Board informed and decisions moving. And a partner who understands the pressure behind every square foot.
If your portfolio strategy is still being driven by gut feel and Excel estimates, it’s time to talk.
Let’s turn your data into direction.
Artificial intelligence has become part of almost every conversation about the future of work.
Over the past year I've been asked whether it will change workplace strategy, whether it will replace parts of the consulting process, and how organizations should begin using it when planning their workplaces. They're all worthwhile questions.
There is little doubt that AI will become another valuable tool for workplace professionals.
The ability to analyze large volumes of information, identify trends and test different scenarios far more quickly than we've been able to in the past will undoubtedly improve the way many organizations approach planning. What interests me more, however, is something that receives far less attention.
Throughout my career, I've found that the quality of a workplace strategy has never depended on the sophistication of the tools available. It has depended on the quality of the evidence used to inform the decisions. That was true twenty years ago, it remains true today, and I suspect it will still be true long after today's AI platforms have evolved into something entirely different.
Technology has always made analysis easier. The challenge has never really been analyzing information. The challenge has been making sure we're analyzing the right information in the first place.
Most organizations already possess an extraordinary amount of workplace data. They know how many people badge into the office, which meeting rooms are booked most frequently, how desks are being utilized and, increasingly, how employees move around the workplace during the day.
AI can certainly help make sense of those datasets, and in many cases it will do so far more efficiently than traditional methods. The challenge is that workplace strategy has never been driven by data alone.
Understanding how people actually work requires context. Occupancy figures don't explain why teams choose to collaborate in particular ways. Utilization reports don't reveal where knowledge sharing happens naturally or where it breaks down. Even employee surveys, while incredibly valuable, only represent one part of a much broader picture.
This is why we've always invested so much effort in speaking with employees, interviewing managers, engaging leadership teams and understanding the objectives of the business before drawing conclusions. Every one of those conversations adds context that simply isn't visible within a spreadsheet or dashboard. Over time those different perspectives begin to form a much clearer picture of how work is actually carried out and, just as importantly, how the workplace can better support it.
That process has become a fundamental part of the way we approach workplace strategy because organizations are rarely trying to solve a single problem.
A leadership team may believe they need to encourage greater office attendance, while employees are looking for more autonomy over where they work. Real estate leaders may be focused on reducing operating costs, while business unit leaders are concerned about collaboration, onboarding or innovation. None of those objectives are mutually exclusive, but they do need to be understood together if the workplace is going to support the business rather than simply respond to one isolated issue.
AI has an important role to play within that process.
Once reliable evidence has been gathered, AI can help identify patterns that may otherwise have been overlooked. It can compare scenarios, accelerate analysis and help workplace teams evaluate options more quickly than ever before. That creates real value because it allows more time to be spent discussing implications, refining recommendations and engaging stakeholders instead of manually compiling information.
What AI cannot do is decide which outcomes matter most for your organization.
Every business has its own culture, leadership style, operating model and ambitions. Those are strategic decisions that belong to the organization itself. The role of workplace strategy is to understand those ambitions, gather the evidence needed to support them and translate that understanding into practical recommendations for the workplace. In many respects, AI reinforces the importance of that discipline rather than reducing it.
The better the evidence, the better the analysis. The better the analysis, the greater the confidence leaders can have in the decisions they're making. If the underlying evidence is incomplete, inconsistent or based on assumptions, AI simply allows those assumptions to be processed more quickly.
Over the years I've seen organizations make significant workplace decisions based on surprisingly little evidence. Sometimes those decisions worked because experience and instinct happened to point in the right direction. Oftentimes they created challenges that could have been avoided with a deeper understanding of how people worked and what the business was genuinely trying to achieve.
That experience has shaped my view of workplace strategy more than any technology ever has.
AI will continue to evolve at an extraordinary pace, and I expect it will become an increasingly valuable part of every workplace strategist's toolkit. I'm looking forward to seeing how it improves our profession because there are already areas where it can save considerable time and provide meaningful insight. The principle that sits underneath it all, however, feels remarkably familiar.
Organizations still need to understand their people. They still need clarity about their business objectives. They still need evidence that reflects the reality of how work is performed, not how they assume it is performed.
When those foundations are in place, AI becomes a genuinely powerful capability.
Without them, it simply reaches the wrong conclusions more efficiently.