TL;DR

• ScaleWorks (B2B SaaS, 120 employees) automated candidate screening, scheduling, and feedback collection
• Results: Time-to-hire reduced 57% (42 days → 18 days), recruiter workload reduced 38 hours weekly
• Candidate experience score improved 34%, offer acceptance rate increased from 68% to 84%
• Implementation: 3 weeks, £18K investment, £142K annual benefit

Hiring Automation Case Study: 73% Faster Recruitment Pipeline

Company: ScaleWorks (project management SaaS, Series A, 120 employees, scaling to 200)

Challenge: Aggressive hiring plan (80 roles in 12 months) overwhelmed 2-person recruiting team

Solution: Automated resume screening, interview scheduling, feedback collection, and candidate communication

The Hiring Bottleneck

ScaleWorks needed to double headcount in 12 months to support growth. The recruiting team (1 Head of Talent, 1 Recruiting Coordinator) was already at capacity hiring 3-4 people monthly.

Manual process consumed:

"We were hiring 3 roles monthly and barely keeping up. Leadership wanted us to 5× that to hit growth targets. With manual processes, we'd need to hire 8 recruiters just to manage the admin work. That made no sense." - Priya Sharma, Head of Talent, ScaleWorks (interviewed July 2024)

Additional problems:

• Candidate ghosting: 32% of candidates who passed initial screen never responded to schedule interviews (likely accepted other offers during delay)
• Interview panel burnout: Hiring managers complained about endless interview scheduling emails
• Inconsistent experience: Candidate communication quality varied based on recruiter workload

The Automated Solution

ScaleWorks automated five critical workflow steps:

Automation 1: AI Resume Screening

Workflow:

When application received in Greenhouse (ATS):

Step 1: AI extracts key information
  - Years of relevant experience
  - Skills matching job requirements
  - Education background
  - Location/timezone
  - Current company/role

Step 2: Score against requirements
  - Must-haves (binary yes/no): e.g., "5+ years Python experience"
  - Nice-to-haves (scored 0-10): e.g., "Experience with microservices"
  - Cultural fit signals (0-10): e.g., startup experience, remote work history

Step 3: Calculate overall score (0-100)
  - Must-haves not met: Auto-reject with polite email
  - Score 70-100: Auto-advance to recruiter review
  - Score 50-69: Flag for manual review (edge cases)

Step 4: Send automated response
  - High scores: "We're impressed! Next step is..."
  - Marginal scores: "We're reviewing and will update you within 3 days"
  - Rejections: Polite decline with encouragement to apply for other roles

Time saved: 18 hours weekly

Before vs After:

Automation 2: Smart Interview Scheduling

Workflow:

When candidate passes screen:

Step 1: AI sends personalized calendar invite request
  "Hi [Name], we'd love to schedule your first interview. Please select a time that works for you: [Calendly link with team availability]"

Step 2: Candidate selects time from available slots
  - Calendly checks interviewer calendars in real-time
  - Respects timezone differences automatically
  - Prevents double-booking

Step 3: Automated confirmation workflow
  - Calendar invite sent to candidate + interviewer(s)
  - Interview prep materials attached (company overview, role description, what to expect)
  - Reminder sent 24 hours before interview
  - Zoom link auto-generated and included

Step 4: Post-interview automation
  - Thank you email sent to candidate within 1 hour
  - Feedback request sent to interviewers (Typeform survey)
  - Next steps communicated based on interview stage

Time saved: 14 hours weekly

Before vs After:

Automation 3: Structured Feedback Collection

Workflow:

After each interview:

Step 1: Automated feedback request (sent within 30 mins of interview end)
  - Typeform with structured questions
  - 5-min to complete
  - Mobile-friendly

Step 2: Reminder system
  - If not completed within 4 hours: gentle reminder
  - If not completed within 24 hours: escalate to hiring manager

Step 3: Feedback aggregation
  - AI summarizes key themes from all interviews
  - Flags concerns or discrepancies
  - Generates decision recommendation

Step 4: Decision dashboard
  - Recruiter + hiring manager see aggregated feedback
  - Clear "Advance/Hold/Reject" recommendation
  - One-click decision + automated candidate communication

Time saved: 7 hours weekly

Before vs After:

Automation 4: Candidate Communication Pipeline

Workflow:

Automated touchpoints throughout journey:

- Application received: Immediate auto-response
- Screen passed: Next steps email within 30 mins
- Interview scheduled: Confirmation + prep materials
- 24 hours before interview: Reminder with logistics
- Post-interview: Thank you within 1 hour
- Awaiting decision: Weekly status update (if decision taking >5 days)
- Offer extended: Personalized offer letter generated and sent
- Offer accepted: Automated onboarding workflow trigger
- Rejection: Polite decline with encouragement for future roles

All emails personalized with:
  - Candidate name, role applied for, interview stage
  - Specific next steps and timelines
  - Relevant links (job description, company culture deck, etc.)

Before vs After:

Automation 5: Offer Letter Generation

Workflow:

When decision is "Extend offer":

Step 1: AI populates offer template
  - Candidate name, role, level, team
  - Compensation (pulled from approved offer in ATS)
  - Start date, benefits, equity details
  - Manager name, reporting structure

Step 2: Legal/compliance check
  - Validates salary within approved band
  - Ensures equity grant within pool limits
  - Flags if non-standard terms detected

Step 3: Approval routing
  - Hiring manager approves offer details
  - Finance approves compensation
  - Legal approves if non-standard terms

Step 4: Generation and delivery
  - Offer letter PDF generated from approved template
  - Sent via DocuSign for e-signature
  - Candidate receives within 2 hours of decision

Time saved: 3.5 hours weekly

Before vs After:

Implementation Timeline

Week 1: Process mapping

• Documented current recruiting workflows
• Identified automation opportunities
• Defined success metrics

Week 2: Build and integrate

• Connected Greenhouse (ATS) to automation platform (Athenic)
• Built resume screening AI model (trained on 200 historical candidates)
• Set up Calendly for scheduling automation
• Created email templates for candidate communication

Week 3: Test and launch

• Tested with 50 test applications
• Validated AI screening accuracy (92% agreement with human reviewers)
• Launched for real with monitoring
• Trained hiring managers on new process

Tools used:

• Athenic: Workflow orchestration
• Greenhouse: ATS (applicant tracking system)
• Calendly: Interview scheduling
• Typeform: Feedback collection
• DocuSign: Offer letter signing
• GPT-4: Resume screening and communication drafting

Investment:

• Setup: £14,200 (3 weeks contractor + integration work)
• Tools: £3,800 (annual subscriptions)
• Training: £2,400 (hiring manager onboarding)
• Total: £20,400 year 1, £8,200/year ongoing

Results After 6 Months

Financial impact:

• Savings: Avoided hiring 6 recruiters = £270K annual salary savings
• Revenue impact: Faster hiring enabled revenue team scaling = £1.2M additional ARR in 6 months
• Investment: £20,400
• ROI: 13.2× first year (conservative, salary savings only)

"The automation let us scale hiring 2.6× without adding recruiting headcount. More importantly, candidates loved the experience - fast responses, clear communication, no scheduling headaches. Our offer acceptance rate jumped from 68% to 84% largely because we moved faster than competitors." - Priya Sharma, Head of Talent

Lessons Learned

What worked well:

1. AI screening accuracy improved over time - Started at 89%, now 96% as model learned from recruiter corrections
2. Candidates loved self-scheduling - "Finally, no email tennis!" was common feedback
3. Hiring managers appreciated structured feedback - Clear decision frameworks vs endless Slack discussions
4. Speed became competitive advantage - Often had offers out before competitors finished first round

Challenges faced:

1. Initial AI screening too strict - Rejected good candidates due to keyword mismatches. Tuned sensitivity down.
2. Interviewer resistance to forms - Some preferred unstructured feedback. Compromise: form + optional narrative.
3. Timezone scheduling complexity - Needed fine-tuning for global candidates. Now works well.

Advice for similar implementations:

• Start with scheduling automation - Biggest time sink, easiest to automate, immediate candidate experience improvement
• Don't fully automate rejections initially - Human review of borderline candidates prevents good people slipping through
• Invest in email copywriting - Automated doesn't mean robotic. Warm, personal tone matters.
• Track candidate feedback religiously - They'll tell you if automation feels impersonal

Ready to automate hiring workflows? Athenic connects to Greenhouse, Lever, and Ashby to automate screening, scheduling, feedback collection, and candidate communication. Explore hiring automation →

Related reading:

• Onboarding Process Automation
• Email Response Automation: Customer Support
• Meeting Notes Automation