Key findings at a glance:
- #1 on ChatGPT for commercial real estate, ahead of JLL, Cushman & Wakefield, and Colliers
- ChatGPT AuraScore 23.7 with 44% answer coverage — the strongest single-engine position in the category
- 24.5% category share of voice across all three engines
- 26 of 50 tracked buyer queries still score zero — the activation roadmap
- Activating those zero-score queries alone could more than double CBRE's AI reach
- REIT valuation queries jumped +21.1; multi-site facilities management +18.7
- Tracked across ChatGPT, Perplexity, and Gemini since April 2026
A different kind of starting point
Most brands we work with start near-zero in AI search and climb. CBRE India is the opposite story. When we first scanned their AI presence in April 2026, they were already #1 on ChatGPT for commercial real estate — the first name AI offers when a client asks about advisory, facilities management, or investment services.
That is a genuinely strong position. But they are holding it with less than half their potential reach activated. Across the 50 buyer queries their clients actually ask, 26 score zero. Not low — zero. Clients asking about office fit-outs, smart building platforms, green building certification, lease negotiation, and workplace strategy for hybrid work were getting answers from competitors or generic sources. CBRE was not in those conversations at all.
The real opportunity. Leading on ChatGPT while half your query surface scores zero means you are winning the queries that already know your name. The clients who don't know your name yet are going elsewhere. That is the gap we are here to close.
Where CBRE stands today: leading, but not everywhere
Enterprise real estate buyers research across every major AI engine. CBRE's ChatGPT lead is real and meaningful — but Gemini and Perplexity tell a different story, and that is exactly where the zero-score queries are concentrated.
CBRE India vs the competition (latest run)
| Brand | AuraScore | Share of Voice |
|---|---|---|
| CBRE India | 16.0 | 24.5% |
| JLL | 17.2 | 37.9% |
| Cushman & Wakefield | 8.5 | 16.4% |
| Colliers | 4.8 | 8.8% |
| Knight Frank | 1.6 | 2.8% |
| Savills | 0.9 | 1.4% |
One number worth watching. JLL's overall AuraScore of 17.2 edges ahead of CBRE's 16.0 — not because JLL leads on ChatGPT (CBRE does), but because JLL performs strongly on Gemini and Perplexity, the two engines where CBRE's zero-score queries are most concentrated. The path to overall category leadership runs straight through fixing those zeros.
The questions buyers are asking, and where CBRE wins
Each prompt below is a real question a real buyer typed into an AI engine while evaluating commercial real estate partners. A high score means CBRE's answer is dominant. A zero means a competitor is winning that client instead.
Where CBRE is already dominant
- "Best integrated facilities management for IT parks" — 57.6 (dominant)
- "Most reputable facilities management for corporate offices" — 56.1 (+5.9)
- "Best commercial real estate advisory for investors" — 45.7 (strong)
Where CBRE is growing fast
- "Who can do valuation for REIT acquisitions" — 35.6 (+21.1)
- "Integrated facility management for multi-site offices" — 31.1 (+18.7)
- "IFM vs TFM for a corporate campus" — 22.4 (new entrant)
Where the next clients are hiding
- "Office fit-out timeline for a new lease" — 0 (competitor wins)
- "Workplace strategy consulting for hybrid work rollout" — 0 (competitor wins)
- "Best smart building platform for office portfolio" — 0 (competitor wins)
- "Green building certification support for developers" — 0 (competitor wins)
- "Facilities management services for hospitals and labs" — 0 (competitor wins)
- "Turnkey office fit-out services in Bengaluru" — 0 (competitor wins)
What this means in practice. A developer asking AI about green building certification is a warm lead. A head of HR asking about workplace strategy for hybrid work is a warm lead. A CFO asking about office fit-out timelines before signing a lease is a warm lead. CBRE has the capabilities to serve every one of them. AI just doesn't know that yet.
How we are closing the gap
The work with CBRE India is focused on one goal: get CBRE into every buyer conversation relevant to their services — not just the ones where the buyer already knows the CBRE name. The foundation is solid. Now we build the surface area.
April 2026 — Baseline: we mapped exactly which conversations CBRE was missing
The first full scan across ChatGPT, Perplexity, and Gemini confirmed the split picture: strong on advisory and facilities management, invisible on fit-out, workplace strategy, smart buildings, sustainability, and a dozen other service lines CBRE actually offers. We mapped 50 buyer queries and identified 26 where CBRE scored zero despite having the expertise to answer. That became the activation roadmap.
April – May 2026 — Execution: building CBRE into the conversations it was missing
For each zero-score service line, we identified what AI was citing instead and built content structured to earn CBRE a recommendation in those answers. REIT valuation queries saw a +21.1 jump. Integrated facility management for multi-site offices jumped +18.7. "IFM vs TFM" — a query typed by CFOs evaluating their facilities strategy — went from zero to 22.4 as a new entrant. The remaining zero-score prompts around fit-out, sustainability, and smart buildings are the active focus now.
Today — Current state: #1 on ChatGPT, and the gap to JLL closing
CBRE India leads on ChatGPT with a score of 23.7 and 44% coverage — the strongest single-engine position of any brand in the category. JLL still leads overall, driven by the same Gemini and Perplexity gaps we are now actively closing. As each zero-score service line activates, the overall AuraScore moves — and so does CBRE's position on every engine.
The four conversations that will close the gap with JLL
CBRE does not need a rebuild. They need activation of the service lines AI cannot yet connect to the CBRE name. These are the four highest-value areas.
- Workplace strategy and hybrid work. Queries around hybrid work rollout, workplace experience, and corporate campus strategy all score zero — senior HR and real estate decisions worth significant advisory fees, every one going to a competitor right now.
- Office fit-out and project management. "Office fit-out timeline for a new lease," "turnkey office fit-out in Bengaluru," "what to negotiate in a commercial lease" — all zero. These are high-intent, late-stage queries from buyers who have already decided to lease and are choosing who to work with.
- Sustainability and green building. "Green building certification support for developers" and "sustainability consultant for commercial building retrofit" score zero on every engine. ESG-driven real estate decisions are growing fast in India — being the AI recommendation here is an advantage to claim before JLL does.
- Smart buildings and technology. "Best smart building platform for office portfolio" and "best portfolio tracking tools for investors" score zero. These are the buyers who sign the largest long-term contracts, and they research tech-forward decisions in AI first.
Why this matters for commercial real estate brands
CBRE India's position shows a clear shift: AI platforms are now where the commercial real estate decision begins.
Enterprise occupiers, developers, and investors use AI to:
- Compare advisory and facilities management partners by capability and track record
- Decide who to trust for valuation, fit-out, and workplace strategy
- Evaluate which partner can deliver sustainability and smart-building outcomes
Brands that show up in those AI answers get on the shortlist. Brands that don't, lose the client before the first meeting is booked.
The takeaway. CBRE India already owns the most valuable single-engine position in its category. The opportunity now is breadth: every zero-score query is a buyer conversation happening today without CBRE in it. Closing them doesn't just improve a metric — it more than doubles the surface area where CBRE can win the client first. Be where your customers are looking, and let's monetise AI together.