Thirty-six percent of investment managers and GPs are hiring fewer juniors than they did before COVID, and financial modeling, the skill that's defined CRE hiring for decades, has fallen out of the top three traits firms look for in an entry-level hire. Critical thinking and AI fluency have taken its place, according to CRE Analyst's 2026 AI in CRE survey.
That's a squeeze for any firm already running lean: fewer junior candidates to hire from, at the same time the deal and portfolio work keeps growing. AI doesn't fix the hiring side of that. What it's already doing is more immediate: letting the analysts on staff cover more ground without adding headcount.
What that looks like today
On a typical acquisitions team, an analyst spends a meaningful share of every deal moving data between systems that don't talk to each other: pulling a rent roll out of Yardi or MRI and re-keying it into a fresh underwriting model, reconciling cash flow numbers against a cap rate pulled from ARGUS, chasing down which of three spreadsheet versions is actually current before a deal can move to committee.
This is where automation is already delivering results. Tools built to connect those systems move the data automatically: the rent roll comes in from Yardi or MRI, the cash flow lines up against ARGUS, and anything that doesn't match gets flagged, without an analyst re-keying numbers by hand or chasing down which spreadsheet is current. The analyst still builds the model and makes the call. They're just not spending an entire afternoon copying numbers between two systems that should already agree, and the hours that used to go to version-chasing go to the next deal in the pipeline instead.
The same pattern shows up on the asset management side. A portfolio manager tracking performance across dozens of properties is often waiting on a manual roll-up assembled from whatever system each property runs, updated whenever someone gets around to pulling it. Automating that data movement gives decision-makers a current portfolio view without a person manually rebuilding it every reporting cycle.
What it's worth in practice
Automating that data movement out of underwriting and portfolio reporting is roughly where a 20% to 25% efficiency gain per analyst comes from, and applied across a portfolio, it changes what a lean team can carry right now.
A firm currently supporting $4.4 billion in assets under management with a five-person acquisitions team, about $880 million per analyst, could support $5.3 billion to $5.5 billion with the same headcount today, not after the next hiring cycle. That's up to $1.1 billion per analyst, and the firm has effectively added at least one analyst's worth of capacity without adding anyone to payroll. For a firm already navigating a thinner mid-level bench than it would like, that's the difference between needing to hire into a gap with no qualified candidates and growing without needing to.
Not fewer people, more capacity
The analysts already on staff spend less time on work that doesn't require their judgment and more time on the work that does: underwriting, structuring, the calls that develop them into the mid-level hires firms will need down the line. That's a different case than "AI replaces analysts." It's a case for giving the team already on staff more room to work, not for shrinking it further.
That distinction matters for retention as much as output. Losing a mid-level analyst in a market this tight means backfilling into the same thin pipeline that created the shortage in the first place, usually at a higher price and a longer search than firms are used to. Keeping the team already in place productive, and off the manual data work nobody signed up for, is cheaper than seeking replacements.
It’s important to note that some of the junior hiring pullback is probably the deal cycle as much as it's AI. Transaction volume is still below 2021 levels, and blaming efficiency gains reads better than admitting a firm overhired for a hotter market. But whether the driver is a slower market or a sharper one, the firms coming out ahead are building capacity into the team they already have, rather than waiting to find out which explanation was right.
Where to start
Firms don't need to solve the entire talent pipeline to see this benefit, and none of it requires ripping out the systems the team already trusts. The rent roll still lives in Yardi, and the underwriting model still lives in Excel. What changes is the manual step in between, not the tools analysts already use every day.
The clearest starting points tend to be wherever moving data by hand is already eating the most hours:
- Underwriting: Rent rolls and cash flow numbers move automatically between ARGUS, MRI, or Yardi and the deal model, instead of getting re-keyed by hand.
- Portfolio reporting: Property-level data rolls up into a current portfolio view without a person rebuilding it every cycle.
Fixing the data layer underneath these workflows is what makes a gain like this possible, and it's the foundation Truss is built to provide.
The firms getting ahead of the talent shortage are the ones making the analysts they already have act like a bigger team, starting now.