ConfSync

CONFERENCE

HR &
Operations
#16

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Case study

🧩 Scenario: The Cost of Constant Measurement

Your company has implemented an AI-powered productivity monitoring system. It tracks:

  • Sentiment from communications
  • Time “active” vs “idle” during working hours
  • Time spent in meetings
  • Response rates to emails and chats
  • Keyboard activity and screen usage

Leaders say it helps optimize workloads, detect burnout, and increase efficiency. But employees report feeling surveilled, dehumanized, and constantly under pressure. Some are overworking to stay in the green zone. Others are quiet quitting — disengaging under the radar.

HR is now tasked with re-evaluating how AI impacts employee mental health, work-life boundaries, and psychological safety — across diverse roles and cultural contexts.

🙇 Meet with a team

1. Fatima (29, Lebanese-Canadian, Neurodivergent – Product Manager)
  • Has an asynchronous work style, avoids unnecessary meetings.
  • AI flags her as “under-engaged” because of low meeting hours and camera usage.
  • She now joins more calls but feels drained and less productive.
  • Feels punished for being different, not ineffective.
2. Daniel (42, White Canadian, Gen X – Senior Sales Leader)
  • Uses the AI dashboard to “motivate” his team.
  • Encourages faster response times and calendar visibility.
  • Sees burnout indicators in the data but has sales targets to hit.
  • Believes the tool is helpful — but feels complicit in pushing people too far.
3. Ayesha (25, Indian, Gen Z – Junior Analyst)
  • Just joined the company; still learning and shadowing.
  • Not generating much measurable “output” yet — flagged as low performing.
  • She’s anxious, working late nights to stay above red zones.
4. Leticia (37, Afro-Brazilian, Millennial – People & Culture Manager)
  • Advocated for the tool to identify burnout early.
  • Realizes now it reinforces hustle culture.
  • Hears complaints from women and racialized employees about unfair signals.
  • Struggles to make the system more equitable.

đź’¬ Dive into the discussion

🟣 Round 1: Step Into Their Shoes
  • How is the employee being affected by AI-powered tracking?
  • What stress or mental health risks are emerging?
  • How do age, culture, or work style affect how burnout shows up for them?
  • What would “fair treatment” look like in their case?
🔵 Round 2: Systemic Burnout Risks
  • What kinds of behavior does the system reward or penalize?
  • Who might be disproportionately harmed (e.g., neurodivergent, early career, caregivers)?
  • What assumptions are built into the metrics?
  • How does this impact psychological safety?
🟢 Round 3: Rethinking Measurement
  • What should be measured — and what shouldn’t?
  • How can AI support mental health instead of undermining it?
  • What “humane metrics” could replace or balance out screen time and output?
  • Where should human judgment override AI insights?
đźź  Round 4: HR Strategy & Safeguards
  • How do you monitor for burnout without surveillance?
  • How do you train managers to interpret AI data ethically?
  • What policies protect employees from pressure to overperform?
  • How do you involve employees in designing fairer systems?

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