Databricks发布Cushman & Wakefield企业AI与数据治理客户案例

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Financial Services
July 16, 2026
Your AI is ready. Your data foundation probably isn’t
Cushman & Wakefield’s CIO describes unifying AI at scale, building trust across 53,000 colleagues, and why the right partnership is the accelerant, not the brake.

by CIO.com

Summary

Cushman & Wakefield built its enterprise AI core over four years by embedding technologists directly into business units under a product operating model — prioritizing trust and human behavior over the "AI pilot craze."

A capital investment model that requires technology initiatives to be co-created with a business leader has kept the company's 53,000+ employees aligned around one enterprise set of outcomes, maturing the operating model three times in four years.

Using Databricks — including Genie for natural-language data governance and quality checks — Cushman & Wakefield has cut idea-to-outcome timelines from months to days.

For most large enterprises, AI transformation is a story of silos — business units running competing experiments, data sitting in disconnected systems, and outcomes staying out of reach.
Cushman & Wakefield has taken a different path. Sal Companieh, Chief Digital and Information Officer for the global commercial real estate services firm, has spent the past four years building an enterprise AI core designed for trustworthy, durable, and scalable impact across 53,000 colleagues worldwide.
Sal sat down with CIO.com to discuss how the core is built on a deliberate operating model, unified data strategy, and a Databricks partnership that goes far beyond features.
CIO.com: How did you transition from fragmented AI efforts to a centralized enterprise AI core, and what were the biggest challenges?
Sal Companieh: When I took this role four years ago, I instituted a product operating model that embedded technologists in every business unit. The goals were to rebuild connectivity, trust, and business-forward thinking on both the data and experiential sides, and to give us the ability to co-create. I ensured that technologists had accountability for revenue and EBITDA; they brought the creativity, the ingenuity, the agitation.
While many organizations were going through an “AI pilot craze,” we intentionally maintained a top-down focus, which helped us continue to garner trust while also augmenting our data. Most companies were running pilots. We were building the foundation that would make every pilot worth something.
When AI adoption surged, we were intentionally top-down — what we call the “Cushman Way.” We identified the largest go-to-market or employee experience transformations and attacked them first, helping us build trust and strengthen our data foundation. The biggest challenge was managing the maturity differences across the organization. While everyone focused on technology, we were anchored on human behavior and generating trust.
CIO.com: What macro trends are shaping Cushman & Wakefield’s digital and AI strategies today?
Sal: Decision-making on the investor side is maturing and becoming highly data-centric. Across services organizations, we’re seeing a pivot from relationship-based buying to intelligence- and insights-based buying. Our ability to differentiate through partnership and authenticity remains a constant client demand.
For us, the AI surge happening in the market was a pace accelerator and not a pivot in strategy. We have maintained the same strategy and operating model from the start; it’s just that now the technology has finally caught up to our ambition. That distinction matters because it means we haven’t been chasing a trend. We have been executing on a plan we’d already committed to.
About three and a half years ago, we shifted how we approached capital investment for technology. Outside of cyber and infrastructure, everything had to be co-created and co-presented with a business leader. That kept us fully aligned with business priorities. There is one enterprise set of priority outcomes for the company; technology is one component of delivering on it. Every technologist can draw a direct line from what we say on earnings calls to the work they do every day.
CIO.com: What strategies have you implemented to build shared standards and a common platform while still enabling business-unit flexibility?
Sal Companieh: Three things drive our approach:
the operating model, which prevents duplication and separation from the common platform;
the financial investment model, which aligns capital to build firmwide capabilities regardless of which P&L benefits;
enterprise standards for ways of working, where architecture teams ensure technology is approached as uniformly as possible, with flexibility at the business-unit level.
We’ve successfully matured our operating model three times. Flexibility itself is a skill we’re continuously building.
CIO.com: How does Databricks fit into your strategy?
Sal: Our ability to build Lego bricks of different capabilities and fit them together for every business unit was our foundational strategy. In this way, Databricks fits into three areas. First, I believe the word “partner” should mean something, and in terms of our partnership with Databricks, it means leadership, culture, and the ability to genuinely co-create with us, as well as their understanding of our maturity level and the pace at which we want to transform.
Second, we were impressed with their product roadmap. We weren’t buying just a product suite for today’s needs; we wanted to align their investment strategy to the capabilities we needed to move forward. Third, actual feature functionality. Our intelligence layer is critical to our growth and differentiation.
Databricks lets us pull different levers depending on the business unit we’re supporting. We are using Genie to put trusted data directly into the hands of the business by simplifying data quality and data governance workflows. Employees can use natural language queries to identify missing or inconsistent records, validate data quality across systems, review governance policies, and monitor compliance metrics without needing deep technical expertise. This enables business users to quickly explore complex datasets, improve confidence in enterprise data, and drive faster, more informed decisions.
CIO.com: How has the Databricks architecture specifically shaped the way you unify and govern data at enterprise scale?
Sal: Our fundamental hypothesis is to tether our organization’s knowledge on a global scale. We know this is materially beneficial to ourselves and our clients’ needs, so we need the best platforms to protect and enable this knowledge. That’s where the Databricks platform comes in.
We’re a 108-year-old company whose history would suggest data moves through a human supply chain across the organization. Databricks has allowed us to digitize the movement of insights, piece by piece. Our intelligence layer is critical to our growth and differentiation. Having the ability to build up capabilities and fit them together differently for every business unit — while keeping the common platform intact — is our foundational strategy.
The enthusiasm for AI hasn’t subsided, but it has been wellrounded with a growing recognition that healthy, governed, scalable data is what actually accelerates outcomes. Databricks is central to how we enforce that uniformly across the organization, and we can flex where needed at the business-unit level.
CIO.com: Can you share examples of measurable outcomes from this transformation?
Sal: The time from idea to outcome has gone from months to days. We have a meeting, ideate, and within the following week, we are delivering value to the business. For example, the ability to onboard, integrate, and activate new clients and acquisitions has materially reduced. But the most important outcome is the shift in human behavior. We’re not fighting the change continuum anymore. A question that historically would have required five phone calls, three emai