AI Adoption Metrics: Usage, Satisfaction, ROI in 30 Days

yaml
version: 1.3
owners:
  product_analytics: mia.lee@company.com
  chief_of_staff: ops-cos@company.com
  security_approver: ciso-office@company.com
regions:
  - us-east-1
  - eu-central-1
residency:
  us-east-1: VPC
  eu-central-1: VPC
rbac:
  roles:
    - name: exec_view
      rows: [org_level, region]
      columns: [kpi, period, value]
    - name: manager_view
      rows: [team_id, user_hash]
      columns: [kpi, period, value, theme]
    - name: analyst_admin
      rows: [*]
      columns: [*]
privacy:
  redaction:
    pii_fields: [user_email, free_text]
    method: sha256_salt
    salt_kms_key: arn:aws:kms:us-east-1:123456789:key/abcd-1234
slo:
  event_freshness_minutes: 15
  max_drop_rate_percent: 0.5
sources:
  events:
    - name: slack_copilot
      topic: ai.slack.sessions
      schema: session_id, user_email, team_id, event_ts
    - name: zendesk_assist
      topic: ai.zendesk.task
      schema: ticket_id, user_email, task_completed_by_ai, human_edit_seconds, fallback_used, handle_time_ms, event_ts
    - name: salesforce_enablement
      topic: ai.sf.email_assist
      schema: case_id, user_email, task_type, handle_time_ms, event_ts
  surveys:
    - name: teams_pulse
      transport: webhook
      fields: user_email, team_id, sat_1_to_5, trust_1_to_5, perceived_time_saved_bucket, free_text, survey_ts
transforms:
  - name: user_hash
    sql: |
      select sha2(concat(user_email,'::',team_id),256) as user_hash, * from input
  - name: join_events_surveys
    sql: |
      select e.session_id, coalesce(e.user_hash, s.user_hash) as user_hash,
             date_trunc('day', coalesce(e.event_ts, s.survey_ts)) as dt,
             e.task_completed_by_ai, e.human_edit_seconds, e.fallback_used,
             s.sat_1_to_5, s.trust_1_to_5, s.perceived_time_saved_bucket, s.free_text
      from events e full outer join surveys s on e.user_hash = s.user_hash
  - name: kpi_calc
    sql: |
      with daily as (
        select dt,
               count(distinct case when e_flag then user_hash end) as dau,
               count(distinct user_hash) filter (where l2_flag) as l2_users,
               count(distinct user_hash) filter (where l3_flag) as l3_users,
               avg(sat_1_to_5) as avg_sat,
               avg(trust_1_to_5) as avg_trust,
               avg(human_edit_seconds) as avg_edit_s
        from (
          select *,
            (task_completed_by_ai is not null) as e_flag,
            (task_completed_by_ai = true and human_edit_seconds between 5 and 120) as l2_flag,
            (task_completed_by_ai = true and human_edit_seconds < 5 and fallback_used = false) as l3_flag
          from join_events_surveys
        ) x group by dt
      )
      select * from daily;
roi_assumptions:
  baseline_handle_time_ms:
    zendesk_triage: 360000
    sf_email_draft: 240000
  fully_loaded_rate_per_hour_usd: 82
  holdout:
    method: agent_holdout
    percent: 10
    approval: ops-cos@company.com
kpis:
  - name: WAU
    query: select count(distinct user_hash) from events where dt between :start and :end
    threshold: 0.6  # 60% of eligible users weekly
  - name: depth_l3_ratio
    query: select l3_users::float / nullif(dau,0) from kpi_calc where dt between :start and :end
    threshold: 0.25
  - name: avg_sat
    query: select avg(avg_sat) from kpi_calc where dt between :start and :end
    threshold: 4.2
  - name: hours_returned
    query: |
      with post as (
        select sum(case when task='zendesk_triage' then (baseline_handle_time_ms.zendesk_triage - handle_time_ms)
                        when task='sf_email_draft' then (baseline_handle_time_ms.sf_email_draft - handle_time_ms)
                   end)/1000/60/60 as hours
        from events where dt between :start and :end and task_completed_by_ai = true
      )
      select hours from post
approvals:
  - step: security_review
    owner: ciso-office@company.com
  - step: enablement_signoff
    owner: enablement@company.com
  - step: finance_visibility
    owner: fpna@company.com
deliverables:
  - dashboard: powerbi_executive_adoption
  - brief_channel: slack://#ai-adoption-weekly
  - export_table: snowflake.analytics.ai_adoption_kpi_daily

{
  "title": "AI Adoption Metrics: Usage, Satisfaction, ROI in 30 Days",
  "published_date": "2025-11-14",
  "author": {
    "name": "David Kim",
    "role": "Enablement Director",
    "entity": "DeepSpeed AI"
  },
  "core_concept": "AI Adoption and Enablement",
  "key_takeaways": [
    "Adoption is a product problem: instrument DAU/WAU/MAU, depth of use, and cohort retention across teams.",
    "Pair usage with 2-minute satisfaction pulses to learn why teams use the AI—and why they don’t.",
    "Translate usage into hours returned and license efficiency; ship a CFO-grade ROI dashboard in 30 days.",
    "Governance is a feature: prompt logs, RBAC, and data residency make adoption metrics audit-ready.",
    "Run a weekly enablement loop: insights → training → product tweaks → new baseline, with one owner."
  ],
  "faq": [
    {
      "question": "How do we avoid “vanity usage” that looks good but isn’t real work?",
      "answer": "Instrument task-level events (task_completed_by_ai, human_edit_seconds) and classify depth (L1–L3). Tie to handle time and quality signals (fallback/rework). WAU alone doesn’t pass CFO scrutiny."
    },
    {
      "question": "Can we do this without moving sensitive data off our cloud?",
      "answer": "Yes. We deploy on AWS/Azure/GCP VPC or on-prem, honor residency, and log prompts/outputs with redaction. No training on your data. Snowflake/BigQuery hold telemetry with RBAC."
    },
    {
      "question": "What’s the minimum pilot size for credible ROI?",
      "answer": "We recommend 30–50 active users per use case with a 10% holdout or a pre/post baseline window. That’s enough volume to stabilize confidence intervals without slowing the pilot."
    }
  ],
  "business_impact_evidence": {
    "organization_profile": "700-employee B2B SaaS, NA/EU operations; Slack, Zendesk, Salesforce, Snowflake on AWS",
    "before_state": "No unified adoption view; 22% WAU in Support; zero structured satisfaction data; ROI estimates debated and untrusted.",
    "after_state": "Governed usage + pulse surveys + ROI dashboard live in 23 days; managers receive a weekly brief with actions; license rationalization play running.",
    "metrics": [
      "35% analyst hours returned in Support triage within 30 days",
      "WAU increased from 22% to 68% in Support; depth L3 reached 21%",
      "14% dormant licenses recycled; $380k annualized savings",
      "Fallback rate held at 7% with human-in-the-loop for edge cases"
    ],
    "governance": "Legal and Security approved due to prompt logging with redaction, strict RBAC by org/region, data residency in customer VPC, and policy that models are never trained on client data."
  },
  "summary": "Chiefs of Staff: in 30 days, wire usage analytics, pulse surveys, and ROI dashboards to prove AI adoption and scale what works—governed, auditable, and real."
}