# AI screens and schedules hourly plant applicants around the clock, so recruiters stop living on the phone (Twin City Fan, General Motors, Nestlé)

> AI screening and interview scheduling for plant jobs: Twin City Fan cut time to hire from 60 to 20 days; GM from 5-7 days to 29 min to schedule.

- Canonical URL: https://aiagentsformanufacturing.com/use-cases/ai-hourly-hiring-screening-interview-scheduling-twin-city-fan-gm/
- Department: Office & management (https://aiagentsformanufacturing.com/departments/office/)
- Evidence level: Verified. Real deployment at a named or precisely described company, with a measured result from a primary source.
- Added: 2026-10-23
- Last updated: 2026-10-26
- Publisher: AI Agents for Manufacturing (https://aiagentsformanufacturing.com)
- Manufacturers named: General Motors (https://aiagentsformanufacturing.com/companies/#general-motors), Nestlé (https://aiagentsformanufacturing.com/companies/#nestle), Twin City Fan (https://aiagentsformanufacturing.com/companies/#twin-city-fan)
- AI tools deployed: Blucor (https://aiagentsformanufacturing.com/tools/#blucor), Paradox (https://aiagentsformanufacturing.com/tools/#paradox)

## Role

Office & management (HR and recruiting: screening and interview scheduling for plant roles). Typical users: recruiter, HR generalist or HR manager, plant manager, production supervisor or hiring manager, recruiting coordinator

## The problem

Plants need a steady flow of operators, assemblers, welders and machine operators, and many applicants apply from a phone. Recruiters spend their days calling people back, asking the same screening questions and juggling supervisors' calendars, and good candidates take another job while they wait. Twin City Fan, a fan maker with about 1,000 employees and plants in Elkton, Aberdeen and Mitchell, South Dakota, received about 200 applications a month for hourly roles and phone-screened 90% of them: about 250 hours of calls a month for one full-time and one part-time recruiter, on top of $20,000 a month in job advertising, and 60% of hourly hires left within a year. At General Motors, which receives 1 to 2 million applications a year, more than 55 contracted recruiting coordinators were scheduling interviews and it still took 5 to 7 days to schedule one.

## How AI helps

A conversational AI assistant talks to every applicant by phone, text, chat or WhatsApp, at any hour. At Twin City Fan an AI recruiter runs the first phone screen with the same full set of questions for every applicant (shift availability, physical requirements, certifications, distance and pay expectations), and the recruiting team reads the results instead of making the first calls. At GM the assistant books interviews directly in the hiring managers' calendars, integrated with Workday; the 2022 Josh Bersin report describes scheduling for candidates who had already been screened, so GM is a scheduling example, not a screening one. At Nestlé the Paradox page says the assistant automates screening and interview scheduling, integrated with SAP SuccessFactors. Recruiters and hiring managers still interview, decide and make every offer.

## Step by step

1. Measure the baseline per hourly role: applications a month, recruiter hours on calls and scheduling, days from application to interview and to start, no-shows, and how many hires leave in the first 90 days and the first year.
2. Write down the real must-haves for each role with the supervisor (shifts, physical requirements, certifications, distance, pay range) and turn them into one screening script used for every applicant.
3. Have HR and legal review the questions and the process for local employment and anti-discrimination law, and decide how candidates are told they are talking to an AI.
4. Start with one or two openings, as Twin City Fan did, and read the transcripts to check the screening before extending it.
5. Connect scheduling to the supervisors' calendars and the applicant tracking system, with reminders to cut no-shows.
6. Keep every hiring decision with recruiters and supervisors, and review time to hire, no-shows and first-year retention every month.

## Tools

- Blucor (AI phone screening recruiter for hourly roles): used at Twin City Fan
- Paradox conversational recruiting assistant (screening and interview scheduling, with Workday or SAP SuccessFactors): used at General Motors and Nestlé, and by Medtronic for high-volume manufacturing roles
- Other tools to evaluate: the scheduling and chat-to-apply features of your applicant tracking system, and manufacturing hiring platforms such as FactoryFix
- Any LLM (ChatGPT, Claude, Copilot, Gemini) with the prompt below, to write a consistent screening script and scorecard from your job descriptions, with no candidate data

## Result

Twin City Fan (fan manufacturer, six facilities), Blucor AI recruiter, started in October 2024 with two openings and extended to the whole hourly funnel: recruiter time on first phone calls from 200 to 250 hours a month to zero (the time to read the AI's results and handle exceptions is not counted); time to hire from 60 to 20 days; screening cost from about $150,000 a year (1.5 recruiters) to $60,000 a year (the subscription); 978 screenings (platform data as of 23 July 2026) across 68 openings and 17 role types. The company also reports company-wide turnover from about 50% to 38% and the number of people hired each year to hold headcount from 427 (2023) to 253 (2025), which the vendor links to more consistent screening (vendor case study, with the before figures, turnover and hiring reported by the company's HR director). General Motors, Paradox conversational assistant 'EV-e' with Workday: time to schedule interviews from 5 to 7 days to 29 minutes, nearly 50,000 interviews scheduled automatically a year, and more than $2 million saved in the first year in direct recruiting costs (2022 report; these gains are about scheduling, not screening), while recruiting coordinators moved to more strategic work (Josh Bersin Company case study and vendor case study). Nestlé, Paradox assistant 'Olivia' with SAP SuccessFactors: interviews scheduled up 600% year over year and about 8,000 hours of recruiting work automated a year, with no starting number, scope or period given for the 600% (vendor case study).

As reported by the source; most figures are vendor or self-reported and not independently verified.

## Human review: cautions

- Hiring decisions stay with people: the AI screens and schedules, recruiters and supervisors decide, and borderline answers go to a recruiter rather than to a rejection.
- Screening questions must comply with employment and anti-discrimination law where you hire, and some jurisdictions regulate automated hiring tools; have HR and legal review them and tell candidates when they are talking to an AI.
- Applicant data is personal information: use an approved tool with a clear retention policy, and never paste candidate data into a public chatbot.
- Twin City Fan's figures are published by the vendor, with the turnover and hiring figures reported by the company for the whole company, so they are not due to the AI alone; the GM figures come from a Josh Bersin Company case study and the vendor; the Nestlé figures are published by the vendor, whose page also mentions 8,000 hours a month spent on scheduling before the project.
- The Twin City Fan result is for a company that receives about 200 hourly applications a month and pays $60,000 a year for the service; the cost and the volume that make it pay off for a small plant that hires a few people a year are not published.

## Try it yourself (prompt)

```text
You are an experienced HR recruiter for a manufacturing plant.

I will paste: (1) the job description for one hourly role [title, shift pattern, location, pay range, physical requirements, certifications, start date]; (2) the supervisor's notes on what makes people succeed or leave in this role; (3) our standard recruiting rules.

Do this:
1. Separate the true must-haves (legal or safety requirements, shifts, certifications) from the nice-to-haves, quoting my text for each.
2. Write a short screening script of 8 to 12 questions, the same for every applicant, in plain language that works by text message or phone, with no questions about age, family, health, religion, origin or other protected characteristics.
3. For each question, define what answer meets the requirement and what should go to a recruiter for a conversation instead of a rejection.
4. Write a realistic job preview message (shift, physical work, pay range, location) so applicants can decide early.
5. Draft the interview confirmation and reminder messages.
6. List anything in my job description that is unclear or could be discriminatory, as questions for HR.

The AI never rejects a candidate: recruiters and supervisors make every decision.
Use only the data I paste in; if something is missing, write 'not found' or list it as a question instead of guessing. Do not send, order, post or change anything in any system: produce drafts for my review and approval.
```

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## Sources

- https://www.blucor.ai/case-studies/twin-city-fan
- https://info.paradox.ai/hubfs/Content%20Marketing%20Assets/GM%20+%20Bersin%20-%20Case%20Study%202023.pdf
- https://www.paradox.ai/case-studies/general-motors
- https://www.paradox.ai/case-studies/nestle

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