Your AI is ready. Your data foundation probably isn’t

2026-07-16 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

Cushman & Wakefield, a global commercial real estate services firm with 53,000 colleagues, has spent four years building an enterprise AI core to ensure trustworthy, durable, and scalable impact. Instead of fragmented AI efforts, Chief Digital and Information Officer Sal Companieh instituted a product operating model embedding technologists in business units, focusing on revenue and EBITDA accountability. This top-down "Cushman Way" prioritized large go-to-market or employee experience transformations, building trust and augmenting data foundationally, rather than chasing AI pilots. The firm shifted its capital investment model three and a half years ago, requiring co-creation with business leaders to align technology with enterprise priority outcomes. Their strategy, which predates the recent AI surge, leverages Databricks for its leadership, product roadmap alignment, and feature functionality, enabling a common platform with business-unit flexibility. This approach has reduced time from idea to outcome from months to days and significantly improved human behavior regarding change.

Key takeaway

For AI Architects or IT Leaders building an enterprise AI core, prioritize a top-down, human-centric strategy over fragmented pilot programs. Your success hinges on embedding technologists within business units with clear accountability and aligning capital investments directly with enterprise outcomes. Don't underestimate the human change management required; genuinely educate your teams on both the opportunities and the foundational work. This approach will accelerate time-to-value and foster organizational trust, making change an inherent, everyday activity.

Key insights

A top-down, human-centric approach to AI foundation building ensures scalable, trustworthy enterprise transformation.

Principles

• Embed technologists with business unit accountability.
• Align capital investment with firmwide capabilities.
• Prioritize human behavior and trust over technology.

Method

Institute a product operating model with embedded technologists, align capital investment with business leaders, and establish enterprise standards for technology architecture.

In practice

• Use natural language queries for data quality and governance.
• Digitize insight movement across the organization.

Topics

• Enterprise AI Core
• Data Foundation
• Databricks Platform
• Digital Transformation
• Operating Model
• Data Governance

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, AI Architect, IT Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.