Real estate has always been a slow business by design. Leases run seven to ten years. Diligence takes weeks. Underwriting assumes conditions that hold across a hold period measured in cycles rather than quarters. The pace has been a feature, giving investors time to be deliberate about commitments that are expensive to reverse.

That timeline no longer matches how the businesses occupying the space operate. JLL’s Future of Work research, which surveyed 2,200 C-suite executives and CRE leaders across 21 countries, found innovation-intensive sectors have compressed their planning horizons to somewhere between 12 and 24 months. Most lease renewals are still being evaluated against assumptions that run seven to ten years out, which means the income underwriting a deal rests on a forecast the tenant may no longer be making.

Ram Srinivasan, Managing Director of AI Adoption, AI Integration and Future of Work Advisory at JLL, describes the mismatch as a problem with the decision model rather than with the leases themselves. “Real estate decisions are long-term commitments, but businesses no longer have the luxury of thinking seven years in advance,” he said. “The decision-making model needs to evolve.”

AI is compressing the analysis side at the same time it compresses the planning side. Daniel Rooney, Managing Director at JLL Consulting, ran the same engagement both ways to see what the difference looked like. “We ran a client’s full portfolio analytics two ways,” he said. “The traditional manual process took four weeks. The AI-assisted version took minutes.”

For an asset manager evaluating acquisitions, that collapse changes which deals are even worth looking at. A multi-week analysis cycle forces triage. Opportunities get screened on surface characteristics because fully underwriting them is too expensive to do speculatively, and the ones that survive get modeled against a narrow set of scenarios. Three hours spent on a deal that fails screening is three hours an analyst could have spent on one that did not.

Deal screening has emerged as its own product category in response. Both Dealpath and Altrio launched dedicated screening tools in 2026, treating the job of scoring an inbound offering memorandum against a firm’s buy box as separate from underwriting rather than as a feature inside a pipeline tool. The workflow reads broker emails and OMs, pulls property type, pricing, and basic financials, and returns a fit score before anyone opens a spreadsheet.

Speed matters more in a market where the bid-ask spread has stayed wide and sellers are selective about counterparties. An investor able to move from teaser to credible offer in days rather than weeks is more attractive on assets where the seller has a reason to transact quickly, and some firms are winning deals on certainty of execution rather than on price.

Lending is seeing a similar compression. Credit packages have traditionally been assembled over weeks, with analysts pulling rent rolls, abstracting leases, reconciling operating statements, and building comparables by hand. A 2026 TIMVERO survey found institutions running purpose-built AI underwriting platforms reporting a 40 to 60 percent reduction in analyst time per commercial loan. That shortens the window between quote and term sheet and lets a lender reprice as conditions move during the process rather than carrying stale assumptions to closing.

Portfolio review is moving to a continuous cadence as well. Hold-sell decisions have historically run annually or semiannually because evaluating each asset against current market conditions was substantial work. Running that analysis against live data surfaces the assets that have drifted from their original thesis closer to when the drift happens.

Nearly everyone reports doing something with AI, though what they are getting from it varies widely. Dealpath’s 2026 State of AI in CRE Investing, a vendor survey of more than 100 institutional investment and technology professionals, found 97 percent of respondents with AI integrated somewhere in their investment process. Only 5 percent of firms in JLL’s survey report having achieved all their AI program goals, and Deloitte’s 2026 Commercial Real Estate Outlook found 27 percent running into real implementation problems including technical issues, lack of expertise, and internal resistance.

For the first time in fifteen years of JLL’s research, talent shortage outranked budget as the primary obstacle to creating value in corporate real estate. Teams lack people who can take AI output and convert it into an investment decision rather than a report.

Screening ten times more deals also creates an exposure that did not exist before, which is maintaining diligence depth across a much larger pipeline. The consequences of thinner underwriting show up at the investment committee rather than at the screening stage, which is late enough to be expensive.

JLL’s research found the organizations getting the most from AI share four characteristics, and none of them involve the tools themselves. Those firms are committed to growing their workforce, have built organizational resilience into how they operate, maintain a long-term strategic view, and have their HR, IT, and real estate functions working together rather than separately.

The pressure to move faster is not going to ease. Business planning horizons are unlikely to lengthen, the leases signed this year will still be in place when the next set of assumptions turns over, and the firms able to evaluate more deals in less time will keep having first conversations with sellers who want certainty. What that produces is an advantage built on organizational capability rather than on access to tools everyone can buy.

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