CBRE’s decision to create a chief knowledge officer role was driven by the growing interdependence of its corporate strategy, research, data and technology work. The company elevated Sandeep Davé from chief digital and technology officer (CDTO) to lead those areas together, arguing that its scale and AI ambitions made their separation harder to sustain. The change is an operating-model bet—not evidence that AI alone prompted a restructure or that the new structure has already delivered measurable returns.
What changed at CBRE?
In a case study published October 9, 2025, CIO reported that CBRE created the chief knowledge officer role for Sandeep Davé, previously its CDTO. The remit brings corporate strategy, global research, data and overall technology direction under one executive.
This is more than a title change: it is an attempt to connect functions that shape what the company learns, what it prioritizes and how it acts on information. The available account does not provide a full organizational chart or establish that every technology or business team now reports to Davé. It also does not describe the role as a replacement for a CIO.
Why did CBRE combine the functions?
Scale makes coordination harder—and more valuable
CBRE serves clients in more than 100 countries and operates across commercial real estate services that include capital markets, leasing advisory, investment management, project management and facilities management, according to the CIO account. A broad footprint generates information across markets, services and clients. That information becomes useful only when teams can connect it to research, strategic priorities, systems and decisions made in the business.
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Separate functions can have distinct tools, priorities and definitions of the same information. That can mean duplicated effort or AI projects that work technically but do not fit frontline processes. A unified remit is intended to reduce those gaps, although the source does not measure whether the restructure has done so.
AI depends on the information underneath it
CBRE’s rationale was not simply that it needed more AI. Its leadership emphasized that data quality, governance and strategic alignment determine how much value AI can provide. A capable model cannot compensate for unreliable inputs, unclear ownership or a use case disconnected from business needs. Research and business context help interpret outputs; strategy helps decide where they matter; technology makes useful capabilities repeatable.
CBRE believed it had reached a different level of maturity
The CIO article describes organizational maturity as another factor: CBRE had spent years building platforms, infrastructure and AI capabilities and believed it was ready to bring previously more independent functions closer together. The account does not set out a formal maturity model or specify which governance or adoption thresholds triggered the change.
What does “knowledge” mean in this model?
Here, knowledge is best understood as the result of connecting enterprise data with research expertise, business context, strategic choices and technology. Data by itself is not insight; research can interpret it, strategy can direct attention, and platforms can help turn decisions into repeatable work. AI may accelerate parts of that flow, but the label “chief knowledge officer” should not be mistaken for a formally published CBRE definition of knowledge management.
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Research matters because it transforms raw information into market intelligence and client-facing insight. Davé said CBRE was streamlining research processes and using AI and automation to improve efficiency and output quality, as reported by CIO. That supports a claim about the company’s stated aims—not an independently verified finding that research is now more accurate or commercially effective. Automating repetitive preparation or handling large content sets also does not establish that research judgment is being replaced.
How does the approach connect to operating work?
The reported examples show the kinds of work CBRE aims to connect to data and AI, but they do not prove that the executive restructure caused each deployment.
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- Facilities management: CBRE has described predictive analytics that inform repair-versus-replace decisions, help reduce duplicate work orders and optimize service delivery. The available account gives no quantified savings or independently verified outcome.
- Employee access: CIO reported that more than 65,000 CBRE employees use its generative-AI platform, Ellis AI, to access trusted data, generate insights and automate routine tasks. That is a reported usage figure, not a measure of productivity, business impact or adoption quality.
- Workflows: CBRE’s AI overview describes Ellis AI applications involving sales leads, lease-data abstraction, research-content processing, supply-chain contract analysis, search and summarization, valuation quote ingestion and review, lease-document extraction, and assistance with summarization, translation, content creation and information retrieval.
CBRE’s technology overview says its enterprise data platform draws on more than 300 global sources. That illustrates the scale of the integration challenge; a large number of sources alone does not demonstrate consistent definitions, reliable data or successful business outcomes.
Why does organizational design matter to AI?
The CIO article invokes Conway’s Law: organizations tend to build systems that reflect the way their teams communicate. If research, data, technology and business strategy operate in separate lanes, their tools and workflows can reproduce those boundaries. Bringing the functions under one leadership remit may make shared platforms and connected processes easier to pursue, but it is not proof that fragmentation has been eliminated.
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What the available evidence does—and does not—show
The public account establishes the stated rationale and offers examples of activity. It does not establish the following:
- Return on investment, revenue impact, cost savings, productivity gains or accuracy improvements.
- Complete reporting lines, budget authority, decision rights, the number of teams affected or whether every research group is included.
- That the new structure caused the facilities-management or Ellis AI examples, or that usage translates into business value.
- Whether the model has improved research quality through independent assessment.
Nor does the coverage establish that the change was primarily a head-count reduction. Efficiency is among the stated aims and examples, but the evidence describes an integration and operating-model initiative, not a confirmed workforce-reduction program.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What trade-offs should other companies consider?
Combining functions can make shared priorities, data practices and platforms easier to coordinate. It can also concentrate authority in a role with a wide span of responsibility. The available account does not report a conflict at CBRE, but placing research closer to corporate strategy and technology raises a governance question for any company: how will researchers retain analytical credibility and the freedom to report findings that challenge leadership assumptions?
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Centralization can also overlook differences across business units, regions, clients or regulatory environments. Conversely, a new executive title without changes to objectives, systems, funding and accountability risks becoming symbolic while teams continue working in silos. AI outputs still require sound data, appropriate controls and human judgment; CBRE’s AI overview describes trusted-data positioning and use cases but does not publish error rates or independent ROI evidence.
When might a similar structure make sense?
A single executive remit is one option, not a universal prescription. It may be worth considering where several conditions coincide:
- Large or fragmented data assets feed recurring cross-functional use cases.
- AI initiatives compete for funding or duplicate platforms and effort.
- Research and strategic insight need tighter links to operational systems.
- Technology infrastructure and governance are mature enough to support reuse.
- Leaders can define decision rights and preserve business-unit input.
A different arrangement—such as a cross-functional AI council, a data-and-AI center of excellence, or federated governance—may be more suitable if business units have sharply different needs, central teams are already bottlenecks, data quality is immature or leadership has not settled accountability. The key question is whether the structure closes real gaps between data, insight and action, not whether it gives those functions one leader.
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