Anonymised example

Task-level AI exposure
for five roles.

Illustrative sample only. No company identifiers. CHF figures use mid-market Swiss rates.

Sample totals

CHF 412,400 illustrative time value across exposed tasks

Average task exposure 64 / 100. Full assessment maps your real task inventory, systems, and controls.

5

roles

Finance, Support, Engineering, Marketing, HR.

25

tasks scored

Five representative tasks per role.

0–100

exposure

Higher means more automatable today.

3

actions / role

Pilot, measure, govern.

Per-role detail

What the sample shows

Role A — Finance Analyst

Exposure 70 · CHF time-value estimate 111,300

  • 78
    Compile recurring management packsHigh exposure to generative drafting with human review.
  • 62
    Reconcile ledger exceptionsPartial assist; judgment remains with the controller.
  • 71
    Draft variance commentaryStrong draft support once charts are prepared.
  • 84
    Build ad-hoc Excel modelsFormula scaffolds and scenario tables accelerate work.
  • 55
    Answer stakeholder data requestsRetrieval helps; trust and context stay human.
  1. Pilot generative drafting on the two highest-exposure tasks with human review.
  2. Instrument time saved for 30 days before expanding automation scope.
  3. Rewrite SOPs so AI-assisted steps keep an accountable human owner.

Role B — Customer Support Agent

Exposure 68 · CHF time-value estimate 64,300

  • 81
    Triage inbound ticketsClassification models route routine queues quickly.
  • 88
    Draft first-response repliesHighest exposure task in the sample set.
  • 74
    Update knowledge-base articlesRewrite and summarise from resolved tickets.
  • 28
    Escalate complex casesLow exposure; empathy and authority dominate.
  • 69
    Log CRM notesStructured summarisation reduces after-call work.
  1. Bundle similar low-judgment tasks into one assisted workflow queue.
  2. Protect relationship-critical and judgment-heavy tasks from full automation.
  3. Set an audit sample rate for AI outputs before customer exposure.

Role C — Software Engineer

Exposure 56 · CHF time-value estimate 107,500

  • 76
    Write boilerplate CRUD endpointsScaffolding accelerates delivery with review gates.
  • 72
    Generate unit-test scaffoldsCoverage templates free senior review time.
  • 64
    Summarise incident postmortemsNarrative drafts from logs and timelines.
  • 48
    Review pull-request diffsAssistive only; ownership stays with reviewers.
  • 18
    Pair on novel architectureLow exposure; design judgment leads.
  1. Pilot generative drafting on the two highest-exposure tasks with human review.
  2. Train the team on prompt libraries tied to existing templates.
  3. Map data-classification rules before connecting internal systems to models.

Role D — Marketing Manager

Exposure 66 · CHF time-value estimate 91,900

  • 86
    Draft campaign copy variantsHigh throughput when brand rules are encoded.
  • 82
    Repurpose webinar transcriptsStrong multi-format rewrite support.
  • 58
    Segment email audiencesPartial assist; CRM truth remains human-owned.
  • 34
    Brief agencies on brand voiceLower exposure; taste and accountability lead.
  • 70
    Report weekly funnel metricsNarrative packs from dashboards speed reviews.
  1. Instrument time saved for 30 days before expanding automation scope.
  2. Rewrite SOPs so AI-assisted steps keep an accountable human owner.
  3. Set an audit sample rate for AI outputs before customer exposure.

Role E — People Operations Partner

Exposure 60 · CHF time-value estimate 75,800

  • 79
    Screen CV keyword matchesHigh exposure with bias controls required.
  • 73
    Draft offer-letter templatesTemplate filling with legal review.
  • 67
    Summarise engagement surveysTheme extraction accelerates leadership packs.
  • 61
    Schedule interview panelsCoordination assist; calendar ownership stays human.
  • 22
    Advise on sensitive employee casesLow exposure; confidentiality and judgment dominate.
  1. Protect relationship-critical and judgment-heavy tasks from full automation.
  2. Map data-classification rules before connecting internal systems to models.
  3. Train the team on prompt libraries tied to existing templates.

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