KPI Framework
Four KPIs. No buzzwords. Measurable.
Most companies invest in AI training without knowing whether it works. Our framework delivers four concrete metrics — objective, transparent, auditable. Not gut feeling, but data.
Adoption Rate
Before vs. after. Typical: 10% → 70%+.
What we measure
Share of employees who actively and regularly use AI tools in their daily work. Not: installed, but: productively applied. What matters is not the licence count but actual weekly usage.
How we measure
Licence activation rate + tool usage logs (weekly active users). Before/after comparison against pre-training baseline. Monthly trend reports to leadership.
Benchmark
Before structured training: typically 10–15%. After 90-day programme: 65–75%+. 72% of companies have deployed AI in at least one function.
— McKinsey Global Survey on AI, 2024
Real-world example
A 200-engineer IT firm raised active Copilot usage from 12% to 71% in 10 weeks — the key was structured prompting training, not just tool provisioning.
Productivity Gain
Ticket velocity per engineer. Research-backed: up to 1.5× uplift.
What we measure
Measurable output uplift per employee: ticket velocity, cycle time, code output or comparable role-specific metrics. The productivity gain is the hardest ROI proof point for the C-suite.
How we measure
Jira / Azure DevOps velocity (story points / sprint), mean lead time per ticket, code-quality metrics (review rounds). Measured before and after training over 2–3 sprints. Control group for >50 employees.
Benchmark
Peer-reviewed research shows up to 1.5× uplift for experienced developers with Copilot. Field average: 20–40% productivity improvement. Junior developers often benefit even more.
— Peng et al. 2023, GitHub/Microsoft Research 2024
Real-world example
Sprint velocity of an 8-person team rose from avg. 34 to avg. 48 story points (+41%) after 6 weeks of structured prompting training — with consistent code quality.
Ability & Willingness
Individual score per employee. Basis for targeted development.
What we measure
Individual score per employee: how competent is the person with AI tools (ability) and how willing are they to adopt AI (willingness)? The combination of both dimensions shows where training has the greatest leverage.
How we measure
Structured assessment: 12 questions (self-report + hands-on task). Result: 2D score matrix (ability 0–100, willingness 0–100). Baseline pre-training + re-assessment in week 13.
Benchmark
Pre-training: ability avg. 28, willingness avg. 62. Post-training: ability avg. 71, willingness avg. 84. The 'high willingness / low ability' group (38% of workforce) delivered the highest ROI.
— BCG × Harvard, AI at Work 2024
Real-world example
Targeting motivated but inexperienced employees (38% of workforce) instead of blanket training: 3× better results at half the training cost.
Workforce Segmentation
High performers | adopters | non-adopters. Data basis for HR decisions.
What we measure
Segmentation of the workforce into three clusters: high performers, adopters, non-adopters. Data-driven basis for targeted development, mentoring programmes, and team composition. Personnel decisions stay 100% with you.
How we measure
Combination of adoption rate + ability score + productivity data. Automatic cluster assignment via score thresholds, manually validated by management. Quarterly refresh.
Benchmark
Typical distribution after 90 days: 25% high performers, 55% adopters, 20% non-adopters. Goal: non-adopter rate below 10% after 6 months.
— Sentient Dynamics, Kundendaten 2025–2026
Real-world example
Leaders use segmentation for: targeted upskilling of adopters, mentoring programmes (high performers → non-adopters), data-driven team composition for new projects.
Methodology: how we collect the data.
No additional tooling overhead. We use your existing infrastructure and deliver auditable results.
Step 1 — Baseline assessment (week 0)
12-question assessment per employee (ability & willingness). Tool-usage audit across all active AI licences. Productivity snapshot from Jira / Azure DevOps (last 4 sprints).
Step 2 — Continuous tracking (weeks 1–12)
Weekly usage metrics (active users, session duration, feature usage). Sprint-velocity tracking. Monthly micro-assessments (5 minutes, automated). Trends via dashboard for leadership.
Step 3 — Final evaluation (week 13)
Full re-assessment. Before/after comparison of all 4 KPIs. Workforce segmentation with cluster assignment. Management report with concrete recommendations and 12-month roadmap.
Step 4 — Cadence & tools
Data drawn from existing tools (Jira, Azure DevOps, GitHub, Copilot dashboard) — no additional tooling overhead. All data collection GDPR-compliant, no personal data shared externally.
The outcome: clear identification of your high performers, a measurable productivity boost, data-based recommendations for workforce optimisation. Personnel decisions stay 100% with you.
Frequently asked questions.
How long until we have reliable KPI data?
After the baseline assessment (week 0) and one full sprint cycle (2–3 weeks) you have initial before/after data for adoption and productivity. The complete picture with workforce segmentation is available after 13 weeks.
#kpi-faq-1Do we need special tools for tracking?
No. We pull data from your existing tools: Jira, Azure DevOps, GitHub, Copilot dashboard. No additional tooling overhead, no extra licences.
#kpi-faq-2Is the data GDPR-compliant?
Yes. All data collection is GDPR-compliant. Individual scores are processed internally only and never shared with third parties. Works council coordination is supported throughout the process.
#kpi-faq-3What does the KPI framework cost?
The KPI framework is part of our success-based pricing model. You pay only when measurable savings materialise — 60% of identified annual savings, the rest stays with you.
#kpi-faq-4Download the KPI framework.
Four KPIs, benchmarks, measurement methodology — all on 4 pages. Enter your details and we'll email you the framework.
KPI framework one-pager (PDF)
4 pages. 4 KPIs. Concrete.
- Adoption rate, productivity, ability & willingness, segmentation
- Benchmarks (McKinsey, BCG/Harvard, GitHub Research)
- Delivered as a PDF by email — ready to share.
Discuss KPIs — in 30 minutes.
We show you what these four KPIs look like for your organisation — with benchmark context and a roadmap. No obligation, no sales pitch.