GCC 9 min read

The 45-Day Problem: What Time-to-Fill Really Measures in GCC Hiring

AV
Andy Vincent, Founder - Qfyre TechLabs··9 min

Fifty-eight percent of GCCs say they take longer than 45 days to fill a critical role. Nobody says when the clock starts.

In March 2026, Ceipal and People Matters published the GCC Talentscope India 2026 report. One finding travelled further than the rest: 58 percent of GCCs in India take more than 45 days to fill critical roles. It has since been repeated in blogs, decks and vendor pitches, usually as proof that GCC hiring is slow.

Maybe it is. But look at what the number actually says. The release does not define "time to fill". It does not say whether the clock starts when a requisition is approved, when sourcing begins, or when the hiring manager first sees a profile. It does not say whether the clock stops at offer, at acceptance or at the first day of work. It does not define "critical". The survey behind it covers more than 150 responses from GCC leaders, and the fieldwork dates are not stated. Ceipal sells staffing software and issued the release itself. None of that makes the figure false. It makes it a mood, and not yet a metric.

That matters because in India the gap between the possible start and end points is enormous.

Three Clocks, One Label: Time-to-Fill vs Time-to-Hire

Most TA dashboards run a single timer. In practice there are at least three.

Requisition to slate. How long until the hiring manager sees profiles worth interviewing. This is the part a recruiter or vendor controls almost entirely.

Slate to offer. Interview scheduling, panel feedback, compensation approval, background checks on the way to an offer. This is mostly the client's own calendar, and it is where decisions go to wait.

Offer to joining. The candidate accepts, then serves a notice period. In the US that is often two weeks. In India it can be three months. HR Dive, writing about the difference between time-to-hire and time-to-fill, notes that in India notice is typically given two to three months in advance, and that the candidate may take another offer in the meantime, which restarts the clock.

Same label, three different problems, three different owners. A GCC that "takes 58 days to fill" a role could be slow at the first stage, slow at the second, or perfectly fast at both and simply waiting out a notice period. The fix for each is different. Buying faster sourcing will not shorten a notice period, and shortening interview loops will not help a vendor that sends thin slates.

How Long Are Notice Periods in Indian IT, Really

Notice periods in India are long in tech, and they are not evenly long. Analytics India Magazine's report on notice periods in Indian IT found almost one in three IT roles carries a 90-day term. IT accounts for 34.3 percent of all 90-day notice jobs across India Inc, ahead of BFSI at 11.2 percent. Among IT employees with 14 or more years of experience, 43 percent are on 90 days. Across India's organised sector, 54.1 percent of jobs carry one month or less.

Two caveats the data deserves. The report does not state its sample size or method, and it dates from December 2022, updated in December 2024. Treat it as the best public indication of the pattern and not as a precise current measure. Also, it covers the market as a whole. Product companies and GCCs often sit at the shorter end, though we have not found a published source that says so, and a TA leader should check the actual terms in their own offers.

The pattern still points one way. The more senior the profile, the longer the third clock. And the 8 to 15 year band, where much of GCC critical hiring sits, is where the 60 to 90 day terms cluster.

Which Clock Loses Candidates

This is the argument worth having in a TA review, and it is less comfortable than the stat.

Candidates do not leave a process because the total is 52 days or 38. They leave because of what happens during a specific stretch. Our own view from live mandates, which is a Qfyre read and not a published figure, is that strong data and AI profiles are off the market within three to four weeks of starting a search. If that is even roughly right, the first two clocks are where the damage happens. A candidate who is shortlisted in week one, interviewed in week two and given an offer in week four is a different case from one who waits for panel feedback in week five.

The third clock does a different kind of damage. A candidate who accepts and then sits through a 90-day notice period is exposed to counter-offers, to a competing offer, to a change of mind. Public numbers on offer dropout in India exist, but they are weak. One 2025 article cites a Communications Today survey from Q3 2024 saying about a third of candidates who accepted offers backed out before joining. The same piece quotes Taggd saying 25 to 35 percent of accepted offers drop. In both cases the article gives no sample size or method. They agree in direction and we would not put a hard number in a board paper on their strength.

The cost of a slow first two clocks is lost candidates. The cost of a long third clock is lost certainty. They need different responses, and a single "time to fill" number hides which one you have.

What the Ceipal GCC Talentscope Data Does Tell Us

The same report holds more useful material than the headline. Fifty percent of GCCs make key hiring decisions without predictive analytics. No single hiring effectiveness metric reaches 51 percent. Talent scarcity affects 49 percent. GenAI and prompt engineering is the top skill priority at 66 percent, ahead of data science at 58 percent and AI/ML engineering at 50 percent (Ceipal and People Matters).

Read together, that is a picture of GCCs hiring for scarce skills while measuring the process loosely. The 58 percent figure may be best understood as a symptom of that looseness. If half of GCCs cannot say with analytics how their hiring decisions perform, they probably also cannot say which of the three clocks is the slow one.

There is a second reason for caution. One recruitment software vendor's own benchmark page concedes that India has no independent national benchmark for time to hire by sector and level, and describes its own figures as aggregations of recruiter and platform data rather than a census. Another gives senior AI, cloud and cybersecurity hiring as 50 to 70 days without naming a dataset. We would not cite either. The honest position is that a clean, independent India benchmark for tech time-to-fill does not exist yet. A TA leader who hears "the industry average is 45 days" is hearing a number that has no owner.

How to Audit Time-to-Fill on Your Own Critical Hires

Run the same audit on your own last ten critical hires. It takes an afternoon.

For each, record four dates: requisition approved, first slate delivered, offer released, first day worked. Then split the intervals. You will probably find one stage that accounts for most of the delay, and it will probably not be the one the team assumed.

If the delay sits in requisition to slate, the question is a vendor and sourcing question. Ask how many profiles reached interview, what the interviews-to-offer ratio was, and how long a strong slate really took. If it sits in slate to offer, the question is internal: panel availability, feedback turnaround, who signs off compensation. This is the most fixable and the least discussed, because the owner is usually in the room.

If the delay sits in offer to joining, the levers are different. Some employers negotiate notice-period buyouts for critical roles, though cost and policy vary and we have no published data on how common it is. Some run structured contact during the notice period. Some simply price the risk: a shortlisted backup candidate held warm for the highest-risk roles.

Next, ask your vendors for their clocks, by role group, not one blended number. A vendor who says "we fill in 30 days" should be asked which 30 days. If the answer is requisition to submitted slate, that is a useful number. If it is requisition to joining, it is either remarkable or a figure that has been massaged. Ask for the median, not the average, because a few fast low-seniority fills will flatter any mean.

The Speed Trade-Off Nobody Puts on a Slide

Speed is not free. Compressing the first clock too hard produces thin evaluation, and thin evaluation shows up later as wasted panel time and offers that fall apart at the compensation stage. We argued in an earlier piece that a rate card hides the cost of those profiles that never convert. The same logic applies to speed. A 12-day slate that needs three rounds of replacements is slower than a 20-day slate that holds up.

So the target is a defined clock with a sensible range for each stage and each role group, and not "faster". Senior AI and data roles will legitimately run longer than mid-level backend work. A single 45-day line across all of them punishes the wrong things.

There is also the reverse risk. Once a metric has a number attached, teams manage to the number. If the dashboard says 45 days, someone will find a way to stop the clock early, by counting offer release as "filled", or reclassifying a role as non-critical. Defining the clock before setting the target protects against that.

Where This Leaves the 58 Percent

Use it, with attribution. It is a real survey from a recognisable pair of publishers, and it tells you that a majority of GCC leaders feel hiring for critical roles is slow. That is worth knowing. It should not be quoted as an industry benchmark, a standard to hit or a measure of anyone's performance, because the people who published it did not define it.

The more useful question for any TA leader is simpler. When we say a role took 45 days, which 45 days did we mean? Until that has a written answer, the number is going to keep arguing with itself in every vendor review.

Qfyre TechLabs is a precision tech talent and staffing company working with GCCs across BFSI and Hi-Tech, built around FYREScore, an AI-powered recruitment intelligence platform. Qfyre's GCC hiring briefs and the AI Hiring Playbook are free to download. To map the three clocks across your own open roles, connect with Andy Vincent on LinkedIn.

Sources

AV

About Andy Vincent

Andy Vincent has spent over 20 years bridging enterprise technology delivery and talent strategy. He started as a Java/J2EE developer building data-driven systems on Oracle SQL, then moved through onsite delivery, business development, principal consulting, and program management on multi-hundred-million-dollar engagements across Healthcare, BFSI, and Retail. He leads Tech Solutioning at Alfvo LLC and sets strategic direction at Qfyre TechLabs, working only with organizations that execute on what they promise. His view: hiring and technology delivery are the same problem, seen from opposite ends.

Connect with Andy on LinkedIn
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