The headline is not good: time-to-hire is going in the wrong direction. After years of conversation about optimizing hiring velocity, the actual numbers are heading up. Josh Bersin Company's 2024 Global Hiring Benchmark, drawing on data through early 2023, shows median global time-to-hire reaching 44 days — the highest level in over a decade. In the U.S., SHRM's Human Capital Benchmarking puts the average time-to-fill at 41 days as of mid-2023, up from 36 days in 2022. Both numbers are trending the wrong way. But the aggregate masks significant variation that is more useful to understand than the average.
The Role-Type Breakdown: Not All Hires Are Created Equal
The aggregate time-to-hire figure conflates wildly different hiring environments. At one end, hospitality and retail roles fill in 14–18 days. At the other, engineering and executive searches routinely run 55–90 days or longer. The meaningful comparison is not "are we above or below 44 days?" — it is "are we faster or slower than the benchmark for our specific role types?"
LinkedIn Talent Solutions and SHRM data compiled by HRRef for 2024 shows the following median time-to-hire by role category: Software Engineering 55 days, Data/Analytics 50 days, Product Management 45 days, Sales (individual contributor) 24 days, Sales (leadership) 58 days, Marketing 32 days, HR/People Ops 25 days, Operations 22 days, Customer Support 12 days, Executive (VP+) 90 days. These numbers establish the real benchmarks — the ones a talent function should be measuring against, not the 36-44 day national average that gets cited in every conference presentation.
Five Drivers of Increasing Time-to-Hire
1. Interview process inflation: The number of interviews per hire has increased significantly over the past three years. Gem's 2025 Recruiting Benchmarks, reporting on 2024 data, show 20 interviews per hire in 2024 versus 14 in 2021 — a 42 percent increase across 140 million applications analyzed. More interviews equal more scheduling cycles, more handoffs, and more time. The increase is not explained by improved decision-making outcomes; offer acceptance rates and quality-of-hire scores have not improved commensurately. It represents process overhead that has accumulated without intentional design.
2. Candidate scarcity in specialized roles: The structural skills gap in technical and specialized roles means that qualified candidate pools are genuinely smaller than they were five years ago. For software engineers, data scientists, AI/ML specialists, and healthcare professionals, the time-to-hire increase is partly an authentic reflection of supply constraints, not just process inefficiency.
3. Remote-work candidate geography: Remote and hybrid hiring has expanded candidate geographies dramatically, which is a benefit — but it has also increased the coordination complexity of interview scheduling across time zones, lengthening individual stage durations even when the number of stages hasn't changed.
4. Recruiter workload concentration: The wave of tech layoffs in 2022–2023 concentrated recruiting capacity reductions in the exact organizations that historically had the most disciplined hiring processes. Many companies that reduced their talent teams aggressively are now operating with the same or higher req loads against smaller headcount — which produces slower velocity even with the same process quality.
5. Slow hiring manager responsiveness: Multiple industry surveys identify hiring manager feedback speed as the single largest controllable source of time-to-hire delay. When hiring managers take three to five business days to provide feedback after each interview stage, a four-stage process adds two to three weeks of delay before a single offer decision is made.
"Time-to-hire is not one problem — it's five. The organizations making progress are the ones who know which of the five is driving their specific number and are targeting that lever specifically."
The Cost Attached to Every Additional Day
Time-to-hire is not just an operational metric — it is a cost metric. Every additional day a role sits open carries a vacancy cost that compounds against salary, productivity, and team load. For a revenue-generating role at a $120,000 base salary, the standard vacancy cost formula — salary × impact multiplier (2x for IC revenue roles) ÷ 260 working days — produces approximately $923 per day of vacancy cost. Over a 44-day fill cycle, that is $40,600 before a single recruiting fee is counted. Cutting 10 days off that cycle saves over $9,000 per hire. Across 100 hires per year at that salary level, the math becomes $900,000 in recovered productivity value.
What the Fast Movers Are Doing Differently
Organizations in the fastest quartile — filling software engineers in 14–21 days versus the 35–45 day median — share three consistent practices. First, they run parallel rather than sequential interview stages, eliminating the scheduling lag between rounds. Second, they use structured interviews with scoring rubrics, which produce sufficient data for decision-making in fewer rounds. Third, they have pre-built offer authority: the compensation range, offer parameters, and approval process are defined before the candidate reaches offer stage, not during it. The combination of these three practices accounts for most of the velocity gap between best-in-class and median performers.
The takeaway: The 44-day national average is a benchmark to understand, not accept. The five drivers of time-to-hire growth are all partially addressable through process design. The fastest-filling organizations have not found new channels or hired more recruiters — they have eliminated the delay that accumulates between stages through better process architecture.