Every institutional CRE investor is planning to deploy AI. Almost none of them have the data foundation to do it well.
Recently, Rob Cain, Dealpath’s Senior Vice President, Product, joined Vivek Thadani, MSCI’s Executive Director & Global Head of Real Assets Product Management, for an insightful discussion on closing the AI readiness gap in commercial real estate. They discussed why AI adoption in CRE still lags behind other industries, the disconnect between firms’ AI ambitions and real-world implementation, and what it takes to bridge that gap.
Read more below for our top 5 takeaways from Closing the AI Readiness Gap in CRE: Unlocking Data Foundations & Better Comps.
1. Real Estate is Seeing Universal Adoption, As Well As Universal Barriers
The real estate industry is racing to implement AI, but the majority of institutional investors admit they aren’t fully prepared to deploy it effectively.
Rob shared more on Dealpath’s recently commissioned survey of top institutional real estate investors on their readiness and sentiments toward adopting and implementing AI. The survey found that 100% of firms are adopting or planning to adopt AI, yet 93% face significant barriers to adoption and readiness, with top barriers including:
- Lack of internal expertise (43%)
- Regulatory/compliance concerns (42%)
- Budget constraints (39%)
- Decentralized data (36%)
The panelists discussed the gap between opportunities that AI can unlock, and near-universal hurdles, such as decentralized data.
2. Every Firm Needs to Address the Data Fragmentation Problem
Most barriers to AI adoption, including budget constraints, regulatory concerns, and lack of internal expertise, will eventually be solved at an industry level. But AI tools are only as powerful as the volume and integrity of the data behind them. To leverage AI properly, firms need to address key data fragmentation-related challenges, such as unstructured data, siloed ownership, fragmented systems, and auditability concerns.
According to Dealpath’s survey, 100% of respondents cited data sprawl across multiple platforms. 98% of respondents noted that while improving data infrastructure is a top priority, decentralized data remains a persistent challenge.
As AI models grow increasingly more sophisticated every week, data has increasingly become a competitive advantage — as well as a liability. Early AI movers stand to gain a competitive edge by addressing data fragmentation challenges sooner. Building and iterating on their databases, as well as establishing and refining repeatable workflows, allows firms to reap the benefits of AI earlier.
Key milestones for successfully building an AI-ready database include:
Standardizing and Centralizing Your Data
The foundation of an AI-ready database starts with bringing fragmented data sources together. That means uniting best-in-class, third-party market data with your firm’s own proprietary transactional data — and establishing standardized, consistent naming conventions across market tracking, acquisitions, origination, and portfolio management so that data maps cleanly across systems without redundancy.
From there, firms should implement templated, standardized workflows to ensure data is captured in a repeatable, automated way, laying the groundwork to leverage AI agents as they mature. All transaction, underwriting, comp, lease, asset, valuation, and pipeline data should live in a single system of record rather than scattered across spreadsheets or legacy tools.
Building Organizational Readiness
AI transformation requires both top-down sponsorship and bottom-up buy-in. Firms that move fastest are the ones where leadership has built a case for executive sponsorship, sought out dedicated AI leadership, and developed a strategy and budget to manage change and enforce new AI-powered processes.
At the same time, adoption only sticks when employees at every level in the organization are motivated and confident enough to utilize AI in their day-to-day work. Governance matters here too — setting clear policies for data access and quality standards ensures that AI is built on trusted inputs from the start.
Strategizing by Identifying Use Cases & Goals
Consider where manual work currently drains the most resources and define clear KPIs, such as ROI goals, that turn a hypothetical exercise into a business objective. When you have a clear roadmap of concrete use cases, and build towards this, this becomes less of an exercise in hypotheticals and more of a concrete project with real outcomes.
Maintaining Control & Auditability
CRE investment is important work with millions at stake, so a rigorous approach to data management and security keeps decisions explainable, defensible, and auditable.
3. Proprietary Data + Market Intelligence: The Winning Combination
Getting your data infrastructure right is only half the equation. The firms gaining a real edge are the ones rethinking what data feeds into their models — not just how it’s organized.
Historically, firms treated proprietary data and market intelligence as separate datasets. Historical deals lived in a repository, and market intelligence lived in other places. The panelists shared how their clients have found that leveraging proprietary knowledge alongside market intelligence solidifies their competitive advantage.
Your proprietary data captures your firm’s institutional memory — every deal evaluated, every assumption tested, every opportunity passed on — while market intelligence reveals the broader landscape of activity, pricing, and opportunity you wouldn’t otherwise see. When those datasets connect, AI surfaces insights that may have otherwise been undiscoverable.
4. From Experimentation to Accountability
2023 and 2024 were years of experimentation and rapid adoption of low-stakes AI use cases. Most firms were operating without clearly defined KPIs or ROI objectives with one goal: to avoid being left behind. In 2026, though, the era of AI experimentation is over.
With the emergence of AI-native applications purpose-built for underwriting workflows, a new era of AI has dawned across the industry. The firms that will win aren’t trivially deploying AI solutions and hoping for the best. They’re targeting specific use cases that deliver measurable value such as faster screening, better-informed decisions, and insights that were previously impossible to surface.
Dealpath’s survey reflects this shift. Firms expect AI to drive faster deal evaluation and closing (61%), greater team efficiency (61%), and more accurate underwriting (50%). Today’s highest-traction use cases — document analysis (67%), portfolio monitoring (61%), and memo automation (56%) — are low-risk tasks where ROI is easiest to prove, but momentum is building toward strategic applications like AI-recommended comps, underwriting support, and deal scoring.
5. AI in Action: Bringing Dealpath and MSCI Together for Smarter Decisions
In a preview of Dealpath’s AI Studio, a robust suite of AI-powered tools to accelerate AI innovation across the real estate investment lifecycle, Rob demonstrated an AI-fueled Dealpath investment workflow leveraging MSCI data.
Dealpath’s AI-Recommended Comps accelerates underwriting by automatically identifying the most relevant comparables from your firm’s proprietary database and MSCI RCA (Real Capital Analytics). Rather than manually filtering by proximity, price, and square footage, analysts can surface comps instantly — enabling teams to evaluate more deals and make confident decisions faster, with less manual work.
MSCI data is additionally integrated directly into Dealpath Connect, the only private exchange for institutional real estate listings from leading global brokerages such as CBRE, JLL, and most recently Cushman & Wakefield, reflecting a broader industry shift toward direct, data-driven deal distribution and AI-fueled analysis. When a new listing arrives, relevant market data and comps are surfaced automatically, so acquisitions teams can decide faster whether to push an opportunity into their pipeline — significantly scaling sourcing and screening with AI-powered insights and AI-recommended comps.
Watch the Full Conversation
The firms that build their data foundations now won’t just be ready for AI — they’ll be the ones defining what AI-powered real estate investing looks like.
To watch the full conversation, including a deeper discussion of organizational AI readiness and more, watch the webinar on demand.
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