How Instawork's new AI-powered solution, Hiring, is transforming permanent placement
January 16, 2025
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4
min
How AI Can Improve Your Hiring Process Today
March 13, 2025
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4
min
Hiring hourly workers at scale is hard. Businesses do not just need people. They need workers who show up on time, have the right skills, and behave professionally on the job. Traditional staffing models rely on resumes, availability, and first-come, first-served matching. That approach often leads to last-minute cancellations, inconsistent quality, and constant rehiring.
Instawork takes a different approach. Instead of guessing, we collect data at every stage of a worker’s journey to predict reliability and job fit. Every decision, from who can apply to a role to who sees a shift first, is informed by data gathered before and during work. By continuously learning from each shift, the system becomes smarter and more precise in matching workers to roles.
This is how it works.
Instawork does not match workers to shifts based solely on speed or availability. We evaluate how likely a worker is to succeed in a specific role at a specific business.
To do this, we combine two categories of data:
Together, these signals determine role eligibility, worker ranking, and shift access timing. The goal is simple: give the highest-fit workers access to shifts first, while still ensuring every shift is filled.
Before a worker can apply for a shift, especially for skilled roles, we determine whether they are likely to succeed in that position.
During onboarding, Instawork gathers data about a worker’s experience and skills. For skilled roles such as line cooks, bartenders, forklift drivers, event servers, or warehouse leads, this includes:
After the interview, an automated agent reviews the results to ensure the worker meets the requirements. Each role has defined skill requirements, and if a worker does not meet them, they are not eligible to apply for shifts in that role. This ensures only qualified workers can apply.
Once workers begin taking shifts, Instawork continuously gathers performance data. This is where predictions become more accurate over time.
After each shift, businesses provide feedback on:
This feedback directly affects a worker’s future access to shifts. Feedback is applied with context:
Instawork also tracks objective reliability data, including:
These signals are strong predictors of future behavior and heavily influence how and when a worker is offered new shifts. Over time, the system builds a detailed performance profile for each worker, by role, skill, and behavior.
When a business posts a shift, Instawork does not notify all available workers at once.
Instead, the system calculates a fit score for each eligible worker based on:
Workers are ranked by this fit score and grouped into tiers.
To balance quality and fill rate, shifts are released in stages:
This approach balances two risks. Waiting too long can leave shifts unfilled. Opening the shift too broadly too early can reduce quality. Tiered dispatch protects quality early while still ensuring coverage.
Every completed shift improves the system:
Because off-shift vetting and on-shift performance are connected in a single feedback loop, predictions compound. The more the system is used, the better it becomes at matching workers to shifts.
Instawork’s advantage is not a single feature or model. It is the system as a whole.
Eligibility checks, skill validation, performance feedback, reliability tracking, and dispatch timing all work together in one closed loop. This level of integration allows Instawork to deliver both reliability and quality at scale, something traditional staffing models and simple marketplaces struggle to replicate.
For businesses, the result is straightforward:
That is how Instawork consistently delivers reliable, qualified workers while still filling shifts on time.
