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DXC Technology

4 cohorts  ·  0 feedback entries  ·  0 open actions
Programme record · DXC Technology

Quantitative data from post-programme surveys. NPS, participation, and benchmark against programme average across all cohorts attended.

Pooled NPS
too few to report
n=11 D2 · need ≥20
Cohorts attended
4
CohortCityDatesD1 nD2 nNPSProg avgGapP/Pa/D
C20San FranciscoJuly 14–15, 202644+50+35+15
P 50%/Pa 50%/D 0%
C21San FranciscoJuly 16–17, 202611+0+24-24
P 0%/Pa 100%/D 0%
C22LondonJuly 20–21, 202672+50+41+9
P 50%/Pa 50%/D 0%
C23LondonJuly 22–23, 202684+0+47-47
P 0%/Pa 100%/D 0%
Capability Profile
Persona mix
Developer 10%Architect 20%T.Lead 70%
AI proficiency
Learning 40%Applying 35%Independent 15%Frontier 10%
Seniority mix
Sr Prac 33%Mgr 67%
Content depth
Too basic 5%About right 90%Too advanced 5%
Pace
Well paced 75%Too fast 25%
Relevance (mean)
4.2 / 5
D1 confidence (mean)
4.0 / 5
Confidence Δ (D1→D2)
n=11 · too few
Confidence growth by domain
Build Δ
n=4 · too few
Design Δ
n=4 · too few
Commercial Δ
n=4 · too few
Persona × Seniority
PersonaIndividual ContributorSr PractitionerMgr / Sr MgrDirectorSVP / C-Level
Architect22
Developer2
Transformation Lead6
Persona × AI Proficiency
PersonaLearningApplyingIndependentFrontier
Architect112
Developer2
Transformation Lead7412
Satisfaction by Persona
Transformation Lead
n=4 · too few
Strategic Insights
Team composition: 70% Leaders — partner is investing in strategic/leadership capability
Feedback log · DXC Technology

Anecdotal feedback from partner reps, stakeholders, and other sources. Each entry is sourced and benchmarked against survey data where available. Hearsay entries are flagged — weight accordingly.

No feedback logged yet. Run org_intake.py to add entries.
Action items · DXC Technology

Org-specific actions arising from survey data and feedback. Programme-wide actions are tracked separately in programme-actions.yaml.

No open action items.