Beyond the Easy Wins: How AI is finding the next layer of portfolio savings

Every portfolio review starts with the savings you can see. Underused buildings. Overlapping leases. Locations that should have closed two cycles ago. Those are the wins that move fast, and they account for most of what gets reported back to the business.

The harder question is what comes next.

Once the obvious consolidation is done and the visible inefficiencies are out, most portfolios still hold value that is harder to see: patterns that connect operational performance, lease structures, capital plans and financial signals in ways no single report will show. This is where dashboards stop being enough.

Here’s a recent example of a client project where this stage of the journey really mattered, and what we did to solve it.

The brief: a merger, and a number to hit

A global financial and professional services company had just completed a series of mergers and acquisitions that added more than 10 million sq. ft. to its portfolio. With that came a target, a specific amount of cost the combined business had committed to take out. For the real estate team, that meant reducing total cost of occupancy (TCO): the rent, operating expense and capital tied up across the portfolio.

They came to CBRE with a clear brief. Find where space could be optimized, and where occupancy costs could come down. They were equally clear on how they wanted to get there. This was a target to hit together, not a problem to hand off.

Getting the data right, together

Before any savings, the data had to be right. After a large integration, real estate information sits across facilities management, lease administration, project management, occupancy and capital planning, in different systems and formats. Bringing it into CBRE Vantage® Analytics as one connected view took close work with teams across the client’s organization.

Two questions drove that work: what data to bring in, and how to present it so it would hold up in a real business conversation. The client was in the room for those decisions. Of the occupiers we work with directly, this team was among the most engaged in the data and technology strategy and the fastest to adopt Vantage. That engagement is what made everything after it possible.

The first wave: $70 million

With the data integrated, the first year delivered roughly $70 million in realized savings. Working in Vantage with Ellis AI, we identified consolidation opportunities across the combined portfolio: downsizing in some markets, relocating in others, closing the locations that no longer earned their place. The work spanned more than 35 markets across 30 countries. We also used Ellis AI to interpret the data by business segment, which sharpened the conversation with each part of the client’s organization.

A strong result by any measure. Then the question got harder. With the obvious consolidation done, where was the next layer?

The second wave: Next Action Engine

CBRE’s Next Action Engine, part of Vantage Analytics, took the work further. Running with Ellis AI, it draws on the same integrated data and surfaces patterns no single report would show, then returns recommended actions ranked by impact and risk.

So far it has identified an additional $15 million in savings opportunities, now in flight for this client. Some are capital planning decisions that read differently when held against lease expiry data. Others are operational signals that, taken together, point to moves no single team would have flagged on its own.

The actions themselves look like work CBRE has always done with occupiers. What changed is what triggered them: patterns the team could not reasonably have found by reading each report in isolation.

Why it worked, and why it travels

This is a large portfolio, and the absolute numbers reflect that. The pattern behind them does not depend on size.

Most occupiers already know where the first wave will come from. They know which buildings are underused, which leases overlap, which locations have outlived their purpose. That knowledge is what makes the first wave move fast. The harder question is what comes after, once the visible inefficiencies are out and the portfolio looks well-shaped on paper. That is where most teams hit a flat curve. The data exists. The capacity to read it across systems does not.

That is the gap AI is closing. Tools like Next Action Engine connect data that used to sit in separate reports and turn the patterns into a recommended next step, which puts the second wave within reach for occupiers at any scale.

The technology was only half of it. What made this engagement work was the partnership: a client that believed in the data and technology strategy, put its own people alongside ours, and adopted the tools rather than watching from the sidelines. AI moved to the front of how this team runs its portfolio because they chose to take it there with us. That is the part that carries to any portfolio, whatever its size.