The British School of Outdoor Education (BSOE)

Pilot Program Review – Goal 2

The Pilot Program

Grace now wants Marcus to review the second goal of the pilot: increasing recruiter productivity by freeing up time for more strategic work such as candidate engagement, interviewing, and workforce planning.

Goal 2

Increase recruiter productivity by freeing up time for strategic tasks

Marcus gathers data from the team’s time-tracking software and recruiter feedback to understand whether AI helped shift effort away from screening and toward higher-value work.

87 hrs

Pre-AI screening time

Weekly time spent on résumé screening before AI support.

12.6 hrs

Post-AI screening time

Weekly time spent on résumé screening after TalentSync AI.

74.4 hrs

Time freed up

Hours moved away from screening and available for more strategic work.

+166.67%

Engagement growth

Increase in time spent on candidate engagement after AI implementation.

Activity Time spent (hrs/week)
Resume screening87
Candidate engagement15
Interviewing candidates10
Workforce planning5
Total117 hrs/week

Pre-AI (Manual Process) Data

Activity Time spent (hrs/week)
Resume screening12.6
Candidate engagement40
Interviewing candidates25
Workforce planning10
Total87.6 hrs/week

Post-AI (TalentSync AI Screening) Data

GPT prompt

Markdown prompt for a GPT model

Marcus wants the GPT to determine whether recruiter productivity increased, show the time shifts clearly, and highlight the main insights in a simple report.

You are a TA specialist and you are reviewing the success of implementing automated candidate screening into your ATS system with an AI tool. Analyze the following data set to determine if there was an increase in recruiter productivity by freeing up time for strategic tasks, as well as any other insights that may be of interest. Please provide a table with any numerical insights shown, as well as a short 1–3-point summary after, highlighting the main insights. The table should include percentages. Focus on highlighting the time shifts. ### Pre-AI (Manual Screening) Data: | Activity | Time Spent (hrs/week) | |————————–|———————–| | Resume Screening | 87 | | Candidate Engagement | 15 | | Interviewing Candidates | 10 | | Workforce Planning | 5 | | **Total** | **117 hrs/week** | ### Post-AI (TalentSync AI Screening) Data: | Activity | Time Spent (hrs/week) | |————————–|———————–| | Resume Screening | 12.6 | | Candidate Engagement | 40 | | Interviewing Candidates | 25 | | Workforce Planning | 10 | | **Total** | **87.6 hrs/week** |
GPT output

Table: Recruiter Productivity Analysis

The model shows a very large drop in screening time and strong increases in the time recruiters spend on strategic activities.

Activity Pre-AI time (hrs/week) Post-AI time (hrs/week) Time shift (hrs) Time shift (%)
Resume screening8712.6-74.4-85.52%
Candidate engagement1540+25.0+166.67%
Interviewing candidates1025+15.0+150.00%
Workforce planning510+5.0+100.00%
Total11787.6-29.4-25.13%
85.52%

Screening reduction

Reduction in résumé screening time after AI support.

+25 hrs

Candidate engagement gain

Extra weekly time spent engaging with candidates.

+15 hrs

Interviewing gain

Extra weekly time spent interviewing candidates.

-25.13%

Total workload shift

Total weekly recruiter workload fell from 117 to 87.6 hours.

Quiz

What are some of the key insights from this data?

Select all that apply.

Correct answers

  • AI automation reduced résumé screening time by 85.52%, freeing up 74.4 hours per week for recruiters.
  • Time spent on candidate engagement, interviewing, and workforce planning increased significantly, showing that recruiters can now focus on high-value tasks.
Conclusion

Concluding the analysis

Now that Marcus has the data analyzed, he can move on to creating a report and building the business case for expanding TalentSync AI more widely across the recruitment process.