# Zed Truong

Zed Truong works on applying AI in commercial real estate. Their posts focus on helping property firms use their data, workflows, and institutional knowledge across teams and software tools.

- Profile: https://b2binfluencer.marketing/creators/zed-truong/
- LinkedIn: https://www.linkedin.com/in/zed-truong-
- LinkedIn followers: 21,294
- Topics: artificial-intelligence, commercial-real-estate
- Based on 9 dated posts, latest 2026-10-08

## Summary

Zed Truong is most useful for explaining how commercial real estate firms can apply AI to data, workflows, and relationships.

- Best for: Fits enterprise CRE data governance or AI platform vendors seeking thought leadership with owners, operators, and brokerage leaders.
- Not for: Does not fit a property software vendor seeking lock-in, high-volume promotion, paid demos, or performance claims.
- Check first: Confirm current post analytics, publication cadence, and whether they accept collaborations and on what terms.

### Takeaways

- AI makes polished pitches cheap, putting market knowledge and relationships at an advantage.
- Access to data is the starting point, not the goal; firms need a Company Brain.
- Owning data on paper does not mean you can use it.

## Main ideas

- **AI makes polished pitches cheap, putting market knowledge and relationships at an advantage**: After a week of meetings in Dallas with teams at Savills, Transwestern and NAI Global about AI adoption heading into 2027, Truong's first takeaway is that AI raises the bar for winning a pitch. As polished presentations become easier to produce, proprietary market knowledge and relationships gain value rather than lose it — "the future of valuable CRE work will not be at a desk." AI does not replace the work; it shifts the advantage to information and relationships a competitor cannot easily reproduce.
- **Access to data is the starting point, not the goal: firms need a "Company Brain"**: Truong argues that having data available is only the beginning: AI also needs to know which comparables to exclude, whose buyer criteria have changed, and who owns a relationship. Truong calls the system that turns raw context into meaning a "Company Brain," built around a firm's data, operating rules and institutional knowledge. A general model cannot hold a firm's case-by-case judgment; the firm has to encode it.
- **Individual AI adoption outruns organizational support**: An individual broker can build a useful tool in a weekend, Truong observes, but keeping it current and making it work across a team or office takes clear ownership, structured data and governance—things most companies are not ready for. The Dallas post lays out the sequence: structure the firm's data and build a team or "company brain" first, then pair training with live workflows, measure the impact and expand what works. Truong also argues that firm-wide intelligence sharing depends on making knowledge-sharing useful to brokers and preserving credit for their relationships, making adoption an incentive problem as much as a technical one.
- **Owning data on paper does not mean you can use it**: Truong's sharpest argument is about vendor lock-in. Thesis Driven reported the dispute between EliseAI and RealPage, which paused new RealPage integrations for EliseAI's leasing, maintenance and resident communication tools; Truong cites the outlet as the source. The operator pays for both tools, spends time creating the records and owns the buildings, yet expanding an AI rollout depends on a relationship between two vendors they do not control. Truong asks what the operator really owns and argues that any intelligence a firm builds should be exportable across different AI tools, so each workflow makes the firm smarter without making it harder to leave a vendor.
- **Choose AI projects that win business and close deals, not ones that build dashboards**: Firms that connect their intelligence early act, learn and compound knowledge faster, Truong argues, and the window to build that advantage is shrinking. Their test for any project is commercial: does it help win business, close deals or improve decisions, rather than produce dashboards and administrative workflows? If the advantage comes from firm-specific context, projects should be judged by whether they put that context to work.

## Work to read

- [AI adoption in commercial real estate](https://www.linkedin.com/posts/zed-truong-_were-grateful-to-wrap-up-a-productive-week-activity-7511462337732571136-li0d)
- [Data ownership and AI vendor disputes](https://www.linkedin.com/posts/zed-truong-_2027-could-be-the-year-cre-operators-realize-activity-7514092605966839808-r1ss)
- [Declaration as CRE AI expert](https://www.linkedin.com/posts/zed-truong-_after-labor-day-weekend-im-officially-declaring-activity-7503131456576204800-YUHj)

## Brand relationships

- **PSV** (disclosed_partnership)

## Partnerships

Zed Truong specializes in artificial intelligence and commercial real estate (CRE), developing concepts such as the “Company Brain” to structure institutional knowledge and data governance within real estate firms. They engage with major industry players, including Savills, Transwestern, and NAI Global, focusing on how AI integration can help win business and improve decision-making. Their public posts emphasize thought leadership and industry analysis rather than specific commercial partnership opportunities. Public materials don’t specify partnership formats or an inquiry route; ask directly about both.

## Alternatives

- Laura L Kreuger- MBA: Choose Kreuger for a campaign about ownership decisions rather than CRE data and AI adoption. In one post, they argue that owners unsure whether to hold, sell, or improve a property need to understand its operating picture before choosing a path, and discuss options such as a 1031 exchange, DST, or cost-segregation study (ownership decisions). In another, they make the case for more entry points to commercial real estate ownership, including smaller LP positions and co-investments, as ways to learn how deals and investor relationships work (building paths to ownership). Their work is a stronger fit for campaigns about asset strategy, capital, or access to ownership.
- Lauren Niziol: Choose Niziol for a campaign about the people and conversations that shape property management. They describe a cohort-based class on handling conflict through real scenarios and practice, including resident complaints, owner expectations, and team disagreements (tough conversations and conflict resolution). Another post traces how two employees moved from receptionist roles into department leadership, crediting support, mentorship, trust, and exposure to company finances (growing into leadership). Their work is a better fit for training, staff development, or workplace culture than for a campaign centered on CRE data and AI.
- Mitch Fox: Choose Fox for a campaign that needs concrete examples of AI applied to property-management workflows. They describe invoice checks that progressed from shadow-mode comparisons with human decisions, to guidance for property managers, and then to automation that routes exceptions for review (how invoice automation was introduced). They also list applications including turn updates, video-to-punch-list inspections, resident and prospect questions, and service-request triage (AI in property management). These operational examples make Fox a closer match for campaigns about workflow automation or implementation than Truong's focus on data context and vendor access.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/