Institutional Capital and AI: How CRE's Biggest Buyers Are Adapting
A 2024 survey by the Pension Real Estate Association found that 85% of institutional real estate investors expect AI-powered analysis to become standard practice in CRE due diligence within two years. Yet when asked about their own AI capabilities, fewer than 15% reported having production-level AI systems integrated into investment workflows.
This gap — between expectation and execution — defines the competitive landscape for institutional CRE capital in 2025.
The Institutional Imperative
Institutional investors — pension funds, sovereign wealth funds, insurance companies, and endowments — collectively manage over $2 trillion in commercial real estate assets globally. Their investment processes are characterized by rigorous due diligence requirements, standardized reporting frameworks, and governance structures that demand consistency and auditability.
These characteristics make institutional CRE both the most obvious beneficiary of AI and the most challenging environment for adoption.
The benefits are clear: AI can process more data, evaluate more opportunities, and produce more consistent analysis than human teams alone. For a pension fund evaluating dozens of potential investments quarterly, the ability to screen opportunities faster and with greater analytical depth directly impacts portfolio returns.
But the challenges are equally real: regulatory requirements around fiduciary responsibility create caution about delegating analysis to AI systems, legacy technology stacks resist integration, and institutional cultures prioritize consensus over speed.
Where Early Adopters Are Investing
Deal screening and pipeline management. The highest-adoption use case among institutional investors is AI-powered deal screening. Given that a typical institutional investor evaluates 50–100 opportunities for every one that closes, the ability to rapidly screen and prioritize the pipeline creates immediate ROI.
AI screening tools evaluate incoming opportunities against the institution's investment criteria — geography, asset class, size, return requirements, risk parameters — and produce ranked shortlists with preliminary analysis. What takes an investment analyst a full day per opportunity, AI completes in minutes.
Market intelligence and forecasting. Institutional investors are deploying AI to synthesize market data from multiple sources into forward-looking intelligence. Rather than relying on quarterly reports from brokerages (which are backward-looking by definition), AI systems produce near-real-time market views that inform allocation decisions.
Due diligence acceleration. For opportunities that pass initial screening, AI is compressing the due diligence timeline. Environmental screening, zoning analysis, market comparable analysis, and preliminary financial modeling can run in parallel within AI systems, producing a comprehensive initial assessment in days rather than weeks.
Portfolio monitoring. AI-powered analytics track portfolio performance metrics, identify emerging risks (lease rollover concentration, market deterioration signals, tenant credit changes), and generate alerts that allow proactive management rather than reactive response.
The Build vs. Buy Decision
Institutional investors face a fundamental choice: build internal AI capabilities or partner with AI-native service providers.
Building internally offers control and customization but requires significant investment in talent (data scientists, ML engineers), infrastructure (data pipelines, compute resources), and time (12–24 months to reach production). Few institutional investors have made this investment successfully.
Partnering externally provides immediate capability with lower upfront investment but requires trust in third-party systems and creates dependency on external providers. The partnership model is gaining traction, particularly for specialized workflows like site selection and due diligence where domain-specific AI delivers immediate value.
The emerging consensus: build internal capability for core portfolio analytics, partner for specialized workflow execution.
Governance and Compliance
Institutional adoption of AI requires governance frameworks that address fiduciary responsibility (ensuring AI-assisted decisions meet the same standard of care as purely human decisions), auditability (documenting AI inputs, processes, and outputs for board-level review), data security (protecting sensitive investment data within AI systems), and model risk management (understanding and monitoring AI model limitations and biases).
Leading institutions are developing AI governance frameworks modeled on their existing risk management structures — not treating AI as a separate domain but integrating it into established oversight processes.
Build's Institutional Model
At Build, we've designed our agentic AI platform specifically for institutional requirements. Every AI-generated output includes source documentation and methodology transparency. Our domain experts review all deliverables before they reach the client. And our security infrastructure meets the SOC 2 Type II standard that institutional partners require.
The model is straightforward: Build provides the AI-native workflow execution capability. The institution provides the investment judgment. Together, the combination evaluates more opportunities, moves faster on attractive deals, and produces analysis that satisfies the most rigorous institutional standards.
The 85% of institutional investors who expect AI to become standard practice are right. The question is which firms will operationalize that expectation first.