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Programme snapshot
Programme · all cohorts
25
Cohorts run
83
Organisations
1402
D1 sessions · all cohorts
Respondents
Programme NPS
+43
weighted aggregate
NPS
53%
Promoters
37%
Passives
10%
Detractors
D2 Confidence means /5
4.17
all cohorts
Apply / Lead
4.16
v1 + v2
Advise clients
4.08
v2 only
Design eval
Respondent function
EngineeringArchitectureBusiness LeadershipProject / Engmt
33%
24%
16%
26%
San Francisco
16 cohorts · C7, C8, C11, C12, C15, C18, C19, C20, C21, C24, C25, C29, C30, C31, C32, C33
+44NPS→ Latest -4 vs avg
n=887 D2 respondents
London
9 cohorts · C6, C9, C13, C14, C16, C22, C23, C26, C27
+41NPS→ Latest ++2 vs avg
n=274 D2 respondents
Cohort detail
C6 London · v2
May 18–19, 2026
+43
NPS
Avg score 8.3/10
57% Prom · 29% Pass · 14% Det
n=21 · D2 rate 54%
C7 San Francisco · v2
May 19-20, 2026
+32
NPS
Avg score 8.3/10
53% Prom · 25% Pass · 22% Det
n=60 · D2 rate 92%
C8 San Francisco · v2
May 21-22, 2026
+43
NPS
Avg score 8.6/10
54% Prom · 35% Pass · 11% Det
n=37 · D2 rate 77%
C9 London · v2
May 20-21, 2026
+38
NPS
Avg score 8.3/10
46% Prom · 46% Pass · 8% Det
n=24 · D2 rate 44%
C11 San Francisco · v2
May 26-27, 2026
+73
NPS
Avg score 9.1/10
73% Prom · 27% Pass · 0% Det
n=41 · D2 rate 84%
C12 San Francisco · v2
May 28-29, 2026
+50
NPS
Avg score 8.5/10
58% Prom · 34% Pass · 8% Det
n=50 · D2 rate 75%
C13 London · v2
June 2-3, 2026
+40
NPS
Avg score 8.5/10
40% Prom · 60% Pass · 0% Det
n=15 · D2 rate 79%
C14 London · v2
June 4-5, 2026
+39
NPS
Avg score 8.5/10
50% Prom · 39% Pass · 11% Det
n=18 · D2 rate 106%
C15 San Francisco · v2
June 16-17
+55
NPS
Avg score 8.8/10
64% Prom · 27% Pass · 9% Det
n=33 · D2 rate 48%
C16 London · v2
June 15-16, 2026
+17
NPS
Avg score 7.8/10
39% Prom · 39% Pass · 22% Det
n=23 · D2 rate 64%
C18 San Francisco · v2
June 22–23, 2026
+35
NPS
Avg score 8.1/10
49% Prom · 38% Pass · 14% Det
n=37 · D2 rate 82%
C19 San Francisco · v2
June 24–25, 2026
+55
NPS
Avg score 8.6/10
64% Prom · 26% Pass · 10% Det
n=62 · D2 rate 92%
C20 San Francisco · v2
July 14–15, 2026
+35
NPS
Avg score 8.4/10
46% Prom · 44% Pass · 10% Det
n=48 · D2 rate 104%
C21 San Francisco · v2
July 16–17, 2026
+24
NPS
Avg score 7.9/10
39% Prom · 47% Pass · 14% Det
n=49 · D2 rate 91%
C22 London · v2
July 20–21, 2026
+41
NPS
Avg score 8.3/10
44% Prom · 53% Pass · 3% Det
n=32 · D2 rate 58%
C23 London · v2
July 22–23, 2026
+47
NPS
Avg score 8.7/10
47% Prom · 53% Pass · 0% Det
n=15 · D2 rate 107%
C24 San Francisco · v2
July 27–28, 2026
+45
NPS
Avg score 8.5/10
55% Prom · 36% Pass · 9% Det
n=64 · D2 rate 110%
C25 San Francisco · v2
July 29–30, 2026
+44
NPS
Avg score 8.4/10
50% Prom · 44% Pass · 6% Det
n=54 · D2 rate 93%
C26 London · v2
July 27–28, 2026
+47
NPS
Avg score 8.5/10
51% Prom · 44% Pass · 4% Det
n=70 · D2 rate —
C27 London · v2
July 29–30, 2026
+43
NPS
Avg score 8.4/10
50% Prom · 43% Pass · 7% Det
n=56 · D2 rate 78%
C29 San Francisco · v2
August 24–25, 2026
+48
NPS
Avg score 8.3/10
58% Prom · 31% Pass · 11% Det
n=84 · D2 rate 87%
C30 San Francisco · v2
August 27–28, 2026
+31
NPS
Avg score 8.2/10
51% Prom · 30% Pass · 20% Det
n=71 · D2 rate 79%
C31 San Francisco · v2
September 9–10, 2026
+36
NPS
Avg score 8.2/10
53% Prom · 30% Pass · 17% Det
n=64 · D2 rate 83%
C32 San Francisco · v2
September 15–16, 2026
+56
NPS
Avg score 8.8/10
61% Prom · 35% Pass · 4% Det
n=71 · D2 rate 104%
C33 San Francisco · v2
September 17–18, 2026
+40
NPS
Avg score 8.5/10
50% Prom · 40% Pass · 10% Det
n=62 · D2 rate 92%
NPS direction
Trend over time · dot position shows direction · hover for score and CI
Dashed line = programme average · error bars show 95% confidence interval · bar height reflects cohort size — shorter = more respondents · hover for detail

Scores have remained broadly stable across 25 cohorts. The most recent cohort (C33 S.F.) scored NPS +40 — 3 points below the programme average (+43).

0 +30 +50 Avg +43 C6 LDN: NPS +43 · n=2195% CI: +12 → +74 C6 LDN May 18–19, 2026 C7 S.F.: NPS +32 · n=6095% CI: +11 → +52 C7 S.F. May 19-20, 2026 C8 S.F.: NPS +43 · n=3795% CI: +21 → +65 C8 S.F. May 21-22, 2026 C9 LDN: NPS +38 · n=2495% CI: +12 → +63 C9 LDN May 20-21, 2026 C11 S.F.: NPS +73 · n=4195% CI: +60 → +87 C11 S.F. May 26-27, 2026 C12 S.F.: NPS +50 · n=5095% CI: +32 → +68 C12 S.F. May 28-29, 2026 C13 LDN: NPS +40 · n=1595% CI: +15 → +65 C13 LDN June 2-3, 2026 C14 LDN: NPS +39 · n=1895% CI: +8 → +70 C14 LDN June 4-5, 2026 C15 S.F.: NPS +55 · n=3395% CI: +32 → +77 C15 S.F. June 16-17 C16 LDN: NPS +17 · n=2395% CI: -14 → +48 C16 LDN June 15-16, 2026 C18 S.F.: NPS +35 · n=3795% CI: +12 → +58 C18 S.F. June 22–23, 2026 C19 S.F.: NPS +55 · n=6295% CI: +38 → +71 C19 S.F. June 24–25, 2026 C20 S.F.: NPS +35 · n=4895% CI: +17 → +54 C20 S.F. July 14–15, 2026 C21 S.F.: NPS +24 · n=4995% CI: +5 → +44 C21 S.F. July 16–17, 2026 C22 LDN: NPS +41 · n=3295% CI: +22 → +60 C22 LDN July 20–21, 2026 C23 LDN: NPS +47 · n=1595% CI: +22 → +72 C23 LDN July 22–23, 2026 C24 S.F.: NPS +45 · n=6495% CI: +29 → +62 C24 S.F. July 27–28, 2026 C25 S.F.: NPS +44 · n=5495% CI: +28 → +60 C25 S.F. July 29–30, 2026 C26 LDN: NPS +47 · n=7095% CI: +34 → +61 C26 LDN July 27–28, 2026 C27 LDN: NPS +43 · n=5695% CI: +27 → +59 C27 LDN July 29–30, 2026 C29 S.F.: NPS +48 · n=8495% CI: +33 → +62 C29 S.F. August 24–25, 2026 C30 S.F.: NPS +31 · n=7195% CI: +13 → +49 C30 S.F. August 27–28, 2026 C31 S.F.: NPS +36 · n=6495% CI: +17 → +54 C31 S.F. September 9–10, 2026 C32 S.F.: NPS +56 · n=7195% CI: +43 → +70 C32 S.F. September 15–16, 2026 C33 S.F.: NPS +40 · n=6295% CI: +24 → +57 C33 S.F. September 17–18, 2026

Confidence intervals acknowledge that each cohort NPS is a measure of respondents, not all participants. The bar at each cohort shows the range within which the true NPS most likely sits — shorter bars reflect larger cohorts, where more responses produce a tighter estimate.

Function: v1 cohorts estimated from role type · marked (est.) · seniority and experience from v2 cohorts only

Function & seniority
Primary function · all cohorts + recent
Top bar = all cohorts aggregate · v1 estimated from role type
EngineeringArchitectureBusiness LeadershipProject / Engmt
All cohorts (includes est. v1)n=1402
33%
24%
16%
26%
C30 San Franciscon=90
20%
22%
28%
30%
C31 San Franciscon=77
29%
21%
16%
35%
C32 San Franciscon=68
38%
34%
12%
16%
C33 San Franciscon=67
31%
27%
34%
Level / seniority · v2 cohorts only
Sr PractitionerPractitionerManager / Sr ManagerDirector / Sr DirectorPartner / MD / Exec
All cohortsn=1402
22%
25%
28%
20%
C30 San Franciscon=90
21%
17%
22%
30%
10%
C31 San Franciscon=77
31%
32%
18%
17%
C32 San Franciscon=68
25%
26%
25%
19%
C33 San Franciscon=67
16%
24%
28%
31%
Experience profile · v2 cohorts only
AI tools experience prior to programme
Learning & exploringApplying in practiceDelivering independentlyOperating at the frontier
All cohortsn=1402
21%
47%
18%
13%
C30 San Franciscon=90
16%
58%
14%
12%
C31 San Franciscon=77
21%
56%
13%
10%
C32 San Franciscon=68
16%
47%
22%
15%
C33 San Franciscon=67
19%
58%
15%
Prior exposure to Claude or Anthropic API
Not at allA littleRegularly
All cohortsn=1402
8%
43%
49%
C30 San Franciscon=90
27%
68%
C31 San Franciscon=77
12%
39%
49%
C32 San Franciscon=68
44%
51%
C33 San Franciscon=67
49%
48%
Composition over time · v2 cohorts
Proficiency and persona mix per cohort
Descriptive only — shifts reflect partner nomination decisions

% of D1 respondents per cohort · v2 cohorts only · descriptive — shifts reflect partner nomination decisions

Proficiency level over time
Frontier
20%C66%C719%C824%C98%C1119%C125%C1312%C149%C153%C162%C1819%C1911%C2018%C2116%C220%C2310%C2412%C250%C2624%C2713%C2912%C3010%C3115%C328%C3360%
Independent
20%C622%C723%C824%C914%C1122%C1221%C1341%C1417%C1528%C1618%C1815%C1920%C2024%C219%C2221%C2321%C2412%C250%C2619%C2716%C2914%C3013%C3122%C3215%C3360%
Applying
41%C658%C738%C838%C951%C1133%C1253%C1335%C1456%C1544%C1651%C1848%C1946%C2035%C2154%C2236%C2340%C2450%C250%C2629%C2752%C2958%C3056%C3147%C3258%C3360%
Learning
18%C614%C721%C814%C926%C1125%C1221%C1312%C1417%C1525%C1629%C1818%C1924%C2022%C2120%C2243%C2329%C2426%C250%C2628%C2719%C2916%C3021%C3116%C3219%C3360%
Persona mix over time
Architect
33%C618%C721%C831%C918%C1121%C125%C1335%C1423%C1528%C1622%C1827%C1930%C2022%C2127%C2221%C2322%C2426%C250%C2629%C2720%C2922%C3021%C3134%C3227%C3380%
Developer
36%C632%C725%C846%C920%C1146%C1274%C1335%C1433%C1528%C1620%C1839%C1926%C2044%C2136%C2229%C2329%C2424%C250%C2644%C2734%C2920%C3029%C3138%C3231%C3380%
Transformation Lead
31%C649%C754%C824%C961%C1133%C1221%C1329%C1444%C1544%C1658%C1834%C1944%C2033%C2136%C2250%C2348%C2450%C250%C2626%C2746%C2958%C3051%C3128%C3242%C3380%

Transformation Lead aggregates the survey's Business Leadership and Project / Engagement Management function categories.

Relevance & confidence baseline
How relevant was today's content to your role? (mean /5)
Top bar = programme average · recent cohorts below
Programme avg
4.08/5
C33 San Francisco
4.13/5
C32 San Francisco
4.24/5
C31 San Francisco
3.91/5
C30 San Francisco
3.97/5
Day 1 confidence baseline (mean /5)
Top bar = programme average · recent cohorts below
Programme avg
3.91/5
C33 San Francisco
4.11/5
C32 San Francisco
4.07/5
C31 San Francisco
3.86/5
C30 San Francisco
3.85/5
Session quality
Technical depth · all cohorts
Top row = all cohorts · recent cohorts below
Too basicAbout rightToo advanced
CohortToo basicAbout rightToo advanced
All cohorts15%75%10%
C30 San Francisco14%76%10%
C31 San Francisco14%66%19%
C32 San Francisco9%82%9%
C33 San Francisco9%78%13%
Pace · all cohorts
Top row = all cohorts · recent cohorts below
Too slowWell pacedToo fast
CohortToo slowWell pacedToo fast
All cohorts6%80%14%
C30 San Francisco8%76%17%
C31 San Francisco3%78%19%
C32 San Francisco6%79%15%
C33 San Francisco87%13%
NPS direction
Score trend · dot position shows direction · hover for score and CI
Dashed line = programme average · error bars show 95% confidence interval · bar height reflects cohort size — shorter = more respondents · hover for detail

Scores have remained broadly stable across 25 cohorts. The most recent cohort (C33 S.F.) scored NPS +40 — 3 points below the programme average (+43).

0 +30 +50 Avg +43 C6 LDN: NPS +43 · n=2195% CI: +12 → +74 C6 LDN May 18–19, 2026 C7 S.F.: NPS +32 · n=6095% CI: +11 → +52 C7 S.F. May 19-20, 2026 C8 S.F.: NPS +43 · n=3795% CI: +21 → +65 C8 S.F. May 21-22, 2026 C9 LDN: NPS +38 · n=2495% CI: +12 → +63 C9 LDN May 20-21, 2026 C11 S.F.: NPS +73 · n=4195% CI: +60 → +87 C11 S.F. May 26-27, 2026 C12 S.F.: NPS +50 · n=5095% CI: +32 → +68 C12 S.F. May 28-29, 2026 C13 LDN: NPS +40 · n=1595% CI: +15 → +65 C13 LDN June 2-3, 2026 C14 LDN: NPS +39 · n=1895% CI: +8 → +70 C14 LDN June 4-5, 2026 C15 S.F.: NPS +55 · n=3395% CI: +32 → +77 C15 S.F. June 16-17 C16 LDN: NPS +17 · n=2395% CI: -14 → +48 C16 LDN June 15-16, 2026 C18 S.F.: NPS +35 · n=3795% CI: +12 → +58 C18 S.F. June 22–23, 2026 C19 S.F.: NPS +55 · n=6295% CI: +38 → +71 C19 S.F. June 24–25, 2026 C20 S.F.: NPS +35 · n=4895% CI: +17 → +54 C20 S.F. July 14–15, 2026 C21 S.F.: NPS +24 · n=4995% CI: +5 → +44 C21 S.F. July 16–17, 2026 C22 LDN: NPS +41 · n=3295% CI: +22 → +60 C22 LDN July 20–21, 2026 C23 LDN: NPS +47 · n=1595% CI: +22 → +72 C23 LDN July 22–23, 2026 C24 S.F.: NPS +45 · n=6495% CI: +29 → +62 C24 S.F. July 27–28, 2026 C25 S.F.: NPS +44 · n=5495% CI: +28 → +60 C25 S.F. July 29–30, 2026 C26 LDN: NPS +47 · n=7095% CI: +34 → +61 C26 LDN July 27–28, 2026 C27 LDN: NPS +43 · n=5695% CI: +27 → +59 C27 LDN July 29–30, 2026 C29 S.F.: NPS +48 · n=8495% CI: +33 → +62 C29 S.F. August 24–25, 2026 C30 S.F.: NPS +31 · n=7195% CI: +13 → +49 C30 S.F. August 27–28, 2026 C31 S.F.: NPS +36 · n=6495% CI: +17 → +54 C31 S.F. September 9–10, 2026 C32 S.F.: NPS +56 · n=7195% CI: +43 → +70 C32 S.F. September 15–16, 2026 C33 S.F.: NPS +40 · n=6295% CI: +24 → +57 C33 S.F. September 17–18, 2026

Confidence intervals acknowledge that each cohort NPS is a measure of respondents, not all participants. The bar at each cohort shows the range within which the true NPS most likely sits — shorter bars reflect larger cohorts, where more responses produce a tighter estimate.

Segment composition · Promoter / Passive / Detractor by cohort
Shows whether passives or detractors are shifting over time
PromotersPassivesDetractors
Programme avgn=1161
53%
37%
10%
C6 London NPS +43n=21
57%
29%
14%
C7 San Francisco NPS +32n=60
53%
25%
22%
C8 San Francisco NPS +43n=37
54%
35%
11%
C9 London NPS +38n=24
46%
46%
8%
C11 San Francisco NPS +73n=41
73%
27%
C12 San Francisco NPS +50n=50
58%
34%
8%
C13 London NPS +40n=15
40%
60%
C14 London NPS +39n=18
50%
39%
11%
C15 San Francisco NPS +55n=33
64%
27%
9%
C16 London NPS +17n=23
39%
39%
22%
C18 San Francisco NPS +35n=37
49%
38%
14%
C19 San Francisco NPS +55n=62
64%
26%
10%
C20 San Francisco NPS +35n=48
46%
44%
10%
C21 San Francisco NPS +24n=49
39%
47%
14%
C22 London NPS +41n=32
44%
53%
C23 London NPS +47n=15
47%
53%
C24 San Francisco NPS +45n=64
55%
36%
9%
C25 San Francisco NPS +44n=54
50%
44%
C26 London NPS +47n=70
51%
44%
C27 London NPS +43n=56
50%
43%
7%
C29 San Francisco NPS +48n=84
58%
31%
11%
C30 San Francisco NPS +31n=71
51%
30%
20%
C31 San Francisco NPS +36n=64
53%
30%
17%
C32 San Francisco NPS +56n=71
61%
35%
C33 San Francisco NPS +40n=62
50%
40%
10%
NPS by persona
Developer · Architect · Transformation Lead / Business
PersonaAppearancesPooled NPSP / Pa / D distributionBy cohort (v1 uses role-group proxy)
Developer23+49
56%P 36%Pa 7%D n=319
C11 San Francisco: +78C12 San Francisco: +44C13 London: +56C14 London: +80C15 San Francisco: +67C16 London: +50C18 San Francisco: +40C19 San Francisco: +56C20 San Francisco: +70C21 San Francisco: -6C22 London: +25C24 San Francisco: +62C25 San Francisco: +50C27 London: +55C29 San Francisco: +52C30 San Francisco: +46C31 San Francisco: +53C32 San Francisco: +70C33 San Francisco: +53C6 London: +22C7 San Francisco: +17C8 San Francisco: +62C9 London: +40
Architect21+50
57%P 36%Pa 7%D n=237
C11 San Francisco: +71C12 San Francisco: +67C14 London: +0C15 San Francisco: +14C18 San Francisco: +56C19 San Francisco: +59C20 San Francisco: +43C21 San Francisco: +50C22 London: +38C24 San Francisco: +50C25 San Francisco: +54C27 London: +35C29 San Francisco: +65C30 San Francisco: +28C31 San Francisco: +54C32 San Francisco: +55C33 San Francisco: +44C6 London: +67C7 San Francisco: +38C8 San Francisco: +88C9 London: +75
Transformation Lead / Business23+34
49%P 35%Pa 16%D n=389
C11 San Francisco: +73C12 San Francisco: +40C13 London: +0C14 London: +40C15 San Francisco: +69C16 London: -12C18 San Francisco: +19C19 San Francisco: +58C20 San Francisco: +17C21 San Francisco: +39C22 London: +50C24 San Francisco: +33C25 San Francisco: +41C27 London: +29C29 San Francisco: +36C30 San Francisco: +28C31 San Francisco: +7C32 San Francisco: +38C33 San Francisco: +30C6 London: +80C7 San Francisco: +25C8 San Francisco: +17C9 London: +29
NPS score distribution · v2 cohorts
Score breakdown 0–10 (cells show count · grows with each v2 cohort)
Cohort012345678910
C6 London123384
C7 San Francisco2563121022
C8 San Francisco476416
C9 London113847
C11 San Francisco56921
C12 San Francisco11298920
C13 London4515
C14 London21645
C15 San Francisco327912
C16 London1223645
C18 San Francisco111268513
C19 San Francisco246102020
C20 San Francisco14912715
C21 San Francisco11321310811
C22 London110768
C23 London2616
C24 San Francisco1239141520
C25 San Francisco11111131116
C26 London1213181422
C27 London1111915721
C29 San Francisco1121411151831
C30 San Francisco12388131224
C31 San Francisco13161181024
C32 San Francisco1111015835
C33 San Francisco1122520823
NPS by organisation · recurring
Organisations in ≥2 cohorts — aggregate and per-cohort NPS
OrganisationAppearancesAvg NPSBy cohort
Deloitte16+41C11 San Francisco: +82C12 San Francisco: +83C14 London: +0C16 London: +0C18 San Francisco: -20C19 San Francisco: +83C20 San Francisco: +44C24 San Francisco: +67C25 San Francisco: +33C27 London: +50C29 San Francisco: +50C30 San Francisco: +12C31 San Francisco: +10C33 San Francisco: +50C7 San Francisco: +62C8 San Francisco: +47
PwC14+16C11 San Francisco: +62C15 San Francisco: +86C18 San Francisco: +60C19 San Francisco: +54C20 San Francisco: -11C21 San Francisco: +33C24 San Francisco: +60C25 San Francisco: +0C29 San Francisco: -71C30 San Francisco: +29C31 San Francisco: -38C33 San Francisco: +11C7 San Francisco: -45C8 San Francisco: +0
DXC Technology7+38C20 San Francisco: +50C23 London: +0C24 San Francisco: +69C25 San Francisco: +70C27 London: +25C29 San Francisco: +71C31 San Francisco: -20
Accenture5+70C11 San Francisco: +75C27 London: +50C29 San Francisco: +85C7 San Francisco: +73C9 London: +67
Cognizant5+53C18 San Francisco: +50C19 San Francisco: +89C25 San Francisco: +75C29 San Francisco: +67C7 San Francisco: -14
NEC5+56C21 San Francisco: +50C29 San Francisco: +40C31 San Francisco: +67C32 San Francisco: +65C33 San Francisco: +60
Ascendion4+79C19 San Francisco: +75C25 San Francisco: +75C29 San Francisco: +100C31 San Francisco: +67
KPMG3+16C12 San Francisco: +17C21 San Francisco: +0C24 San Francisco: +30
LTM3+68C30 San Francisco: +40C32 San Francisco: +86C33 San Francisco: +77
Infosys2+16C15 San Francisco: +33C7 San Francisco: +0
Persistent Systems2+82C15 San Francisco: +83C20 San Francisco: +80
Fractional AI / Ode2-25C21 San Francisco: -30C24 San Francisco: -20
Capgemini2+61C22 London: +57C27 London: +65
What respondents said
NPS verbatim responses by segment · v2 cohorts
Up to 3 most recent per segment · thematic synthesis in insights.py

930 total responses · showing up to 3 most recent per segment · thematic synthesis in insights.py

Promoter · 494 responses
"Very detailed thorough presentation and exercises"Score 10 · C33 San Francisco
"Hands on. - build along exercises"Score 10 · C33 San Francisco
"Learning"Score 10 · C33 San Francisco
Passive · 330 responses
"Very interactive"Score 8 · C33 San Francisco
"Get insights on Claude concepts"Score 8 · C33 San Francisco
"Hands on was great but more guided hands on would have made it more meaningful"Score 7 · C33 San Francisco
Detractor · 106 responses
"No advanced concepts covered; differentiation with other frontier models from different organizations not explained. The clients want to know “why Claude or Anthropic”. Don’t have an answer to that. Basic concepts covered; nothing much in scope for an AI architect already working in the field.. maybe the course is designed like this but proper expectations should be set to allow the partner organizations to identify the right candidate/cohort."Score 5 · C33 San Francisco
"I think the trainings should be a little bit more guided vs just having people gather in a room a say he is the zip file and go figure it out. It also didn’t help that PwC was constantly having access problems which is PwC’s fault for not helping us prepare ahead of time. Patricio was tremendous in helping us. I would say the most fun was the hackathon. But the main thing is the trainings should be more guided, because the way it was ran, that could probably be done online. If it’s in person, it should be more interactive with the instructors."Score 2 · C33 San Francisco
"Good"Score 6 · C33 San Francisco

v1 cohorts include apply and client confidence metrics · three-way split and distributions from v2 only

D2 confidence means
Apply / Design / Commercial (mean /5 · Apply shows Δ vs D1 · top row = programme avg)
Apply / Lead conversation · v1 + v2
Programme avg
4.17
C33 San Francisco
4.29 (+0.18)
C32 San Francisco
4.23 (+0.16)
C31 San Francisco
4.06 (+0.20)
C30 San Francisco
4.28 (+0.43)
Advising clients on AI · v1 + v2
Programme avg
4.16
C33 San Francisco
4.26
C32 San Francisco
4.21
C31 San Francisco
4.09
C30 San Francisco
4.21
Design an evaluation · v2 only
Programme avg
4.08
C33 San Francisco
4.21
C32 San Francisco
4.11
C31 San Francisco
3.97
C30 San Francisco
4.10
Confidence trajectory D1 → D2 · v2 cohorts
Mean delta in build confidence by matched pairs
Developer
Programme avg
+0.08n=321
C33 San Francisco
+0.35n=17
C32 San Francisco
-0.13n=23
C31 San Francisco
+0.06n=17
C30 San Francisco
+0.31n=13
C29 San Francisco
+0.16n=31
C27 London
-0.23n=22
C25 San Francisco
+0.21n=14
C24 San Francisco
+0.19n=16
C23 London
+0.00n=2
C22 London
+0.38n=8
C21 San Francisco
-0.25n=16
C20 San Francisco
+0.20n=10
C19 San Francisco
+0.36n=25
C18 San Francisco
+0.60n=5
C16 London
-0.17n=6
C15 San Francisco
-0.08n=12
C14 London
+0.20n=5
C13 London
+0.11n=9
C12 San Francisco
+0.00n=16
C11 San Francisco
+0.00n=9
C9 London
+0.00n=10
C8 San Francisco
+0.12n=8
C7 San Francisco
-0.17n=18
C6 London
+0.22n=9
Architect
Programme avg
+0.29n=242
C33 San Francisco
+0.00n=16
C32 San Francisco
+0.30n=20
C31 San Francisco
+0.23n=13
C30 San Francisco
+0.39n=18
C29 San Francisco
+0.53n=17
C27 London
+0.18n=17
C25 San Francisco
+0.46n=13
C24 San Francisco
+0.20n=10
C23 London
+1.00n=1
C22 London
+0.50n=8
C21 San Francisco
-0.08n=12
C20 San Francisco
+0.14n=14
C19 San Francisco
+0.65n=17
C18 San Francisco
+0.22n=9
C16 London
+0.67n=3
C15 San Francisco
+0.00n=7
C14 London
+0.00n=4
C13 London
+0.00n=1
C12 San Francisco
+0.22n=9
C11 San Francisco
+0.14n=7
C9 London
+0.50n=4
C8 San Francisco
+0.50n=8
C7 San Francisco
+0.12n=8
C6 London
+0.33n=6
Transformation Lead
Programme avg
+0.39n=392
C33 San Francisco
+0.30n=20
C32 San Francisco
+0.12n=16
C31 San Francisco
+0.26n=27
C30 San Francisco
+0.53n=36
C29 San Francisco
+0.39n=33
C27 London
+0.21n=14
C25 San Francisco
+0.59n=22
C24 San Francisco
+0.52n=21
C23 London
+0.67n=3
C22 London
+0.25n=8
C21 San Francisco
+0.83n=18
C20 San Francisco
+0.22n=18
C19 San Francisco
+0.42n=19
C18 San Francisco
+0.25n=16
C16 London
+0.62n=8
C15 San Francisco
+0.62n=13
C14 London
+0.40n=5
C13 London
+0.75n=4
C12 San Francisco
-0.13n=15
C11 San Francisco
+0.77n=22
C9 London
+0.00n=7
C8 San Francisco
+0.33n=18
C7 San Francisco
+0.21n=24
C6 London
+0.20n=5
D2 confidence score distributions · v2 cohorts
1–5 distribution · cells show % of respondents
Row intensity indicates response concentration at each score.
Design an evaluation for an AI solution
Cohort12345Mean
All cohorts1%3%18%43%35%4.09
C6 London24%48%29%4.05
C7 San Francisco18%35%47%4.28
C8 San Francisco3%11%57%30%4.14
C9 London12%38%50%4.38
C11 San Francisco2%17%46%34%4.12
C12 San Francisco4%20%32%44%4.16
C13 London20%47%33%4.13
C14 London6%17%50%28%4.00
C15 San Francisco39%33%27%3.88
C16 London4%22%52%22%3.91
C18 San Francisco11%16%38%35%3.97
C19 San Francisco21%44%35%4.15
C20 San Francisco23%48%29%4.06
C21 San Francisco2%20%35%43%4.18
C22 London19%53%28%4.09
C23 London27%40%33%4.07
C24 San Francisco2%3%16%41%39%4.12
C25 San Francisco2%4%19%43%33%4.02
C26 London3%1%20%53%23%3.91
C27 London5%20%43%32%4.02
C29 San Francisco2%5%14%43%36%4.05
C30 San Francisco4%20%38%38%4.10
C31 San Francisco6%22%41%31%3.97
C32 San Francisco6%10%52%32%4.11
C33 San Francisco3%13%44%40%4.21
Advising clients on AI when questions arise
Cohort12345Mean
All cohorts0%1%16%46%36%4.16
C6 London14%52%33%4.19
C7 San Francisco15%42%43%4.28
C8 San Francisco16%49%35%4.19
C9 London17%50%33%4.17
C11 San Francisco2%15%44%39%4.20
C12 San Francisco2%2%20%32%44%4.14
C13 London7%53%40%4.33
C14 London6%11%56%28%4.06
C15 San Francisco21%52%27%4.06
C16 London26%52%22%3.96
C18 San Francisco22%43%35%4.14
C19 San Francisco2%13%53%32%4.16
C20 San Francisco21%44%35%4.15
C21 San Francisco2%24%35%39%4.10
C22 London9%47%44%4.34
C23 London7%53%40%4.33
C24 San Francisco2%2%9%50%38%4.20
C25 San Francisco11%56%33%4.22
C26 London1%20%60%19%3.94
C27 London2%2%18%46%32%4.05
C29 San Francisco1%2%15%44%37%4.13
C30 San Francisco1%18%38%42%4.21
C31 San Francisco2%22%42%34%4.09
C32 San Francisco3%13%45%39%4.21
C33 San Francisco3%11%42%44%4.26
Lead a conversation about Anthropic and Claude
Cohort12345Mean
All cohorts0%3%15%45%38%4.17
C6 London24%29%48%4.24
C7 San Francisco3%13%47%37%4.17
C8 San Francisco14%41%46%4.32
C9 London17%50%33%4.17
C11 San Francisco15%51%34%4.20
C12 San Francisco2%2%20%42%34%4.04
C13 London7%53%40%4.27
C14 London6%11%56%28%4.06
C15 San Francisco18%58%24%4.06
C16 London9%65%26%4.17
C18 San Francisco22%46%32%4.11
C19 San Francisco13%47%40%4.27
C20 San Francisco6%15%50%29%4.02
C21 San Francisco2%20%35%43%4.18
C22 London16%47%38%4.22
C23 London13%53%33%4.20
C24 San Francisco3%9%50%38%4.22
C25 San Francisco2%7%46%44%4.33
C26 London1%3%11%59%26%4.04
C27 London2%5%20%46%27%3.91
C29 San Francisco1%5%12%40%42%4.17
C30 San Francisco1%17%34%48%4.28
C31 San Francisco5%22%36%38%4.06
C32 San Francisco6%11%38%45%4.23
C33 San Francisco2%15%37%47%4.29
Programme health
Flag detection matrix · hover ● for detail
Trend column compares first-half vs second-half cohorts · meaningful from 4+ cohorts
FlagC6 LondonC7 San FranciscoC8 San FranciscoC9 LondonC11 San FranciscoC12 San FranciscoC13 LondonC14 LondonC15 San FranciscoC16 LondonC18 San FranciscoC19 San FranciscoC20 San FranciscoC21 San FranciscoC22 LondonC23 LondonC24 San FranciscoC25 San FranciscoC26 LondonC27 LondonC29 San FranciscoC30 San FranciscoC31 San FranciscoC32 San FranciscoC33 San FranciscoTotalTrend
Low satisfaction2/25→ stable
High passive rate10/25↑ more
Low D2 response rate3/25↓ fewer
Confidence regression1/25↓ fewer
Persona NPS split9/25↓ fewer
Org concentration5/25↓ fewer
● WARNING● INSIGHT● INFOHover dots for detail · Trend compares first half vs second half of cohorts
Field notes · analyst annotations
Cohort-level observations · written after human Tier-2 review
Add entries via: analyze.py --outlier '…' --flag '…' then re-run trends.py

7 cohorts with analyst annotations · written via analyze.py --outlier / --flag after human review

C24 San FranciscoJuly 27–28, 2026NPS +45
Segment outlier
Cross-org theme in the segment split: Fractional AI / Ode (NPS -20, n=5) and DXC Technology (NPS +69, n=13) look like opposite outliers, but their passive/detractor verbatims share the same root cause. Fractional AI / Ode's more experienced attendees found the content too foundational and asked for an engineer vs non-engineer track split. DXC's own passives (its more senior, frontier-level attendees) echoed the same ask — more depth, less pre-built exercise structure. DXC's high NPS is carried by promoters at multiple experience levels plus zero detractors, not uniform enthusiasm. Read together: this cohort's more advanced practitioners, regardless of org, wanted more advanced/less scripted content — an audience-calibration signal, not a content-quality one. n=5 for Fractional AI / Ode is directional only.
C25 San FranciscoJuly 29–30, 2026NPS +44
Segment outlier
PwC's NPS 0 (n=9) was driven almost entirely by corporate laptop / IT access blocking the hands-on exercises — all three detractors cite it directly, not the content. The same environment/setup friction recurs across the 44% passive segment. Highest-leverage fix: a pre-event laptop and access check. Secondary signal: the most experienced 'Applying in practice' attendees wanted more technical depth.
C26 LondonJuly 27–28, 2026NPS +47
Programme note
Day 1 was fielded with the wrong instrument: the one-day Basecamp template (format v3-1day) was copied and used as the Day 1 Pulse. Its column set is identical to the India one-day workbooks — 23 columns against 16 for two-day v2 — and it omits the three fields the two-day analysis depends on: work email, organisation, and the Day 1 confidence item. Consequently 0/70 D2 respondents could be matched to Day 1 (programme mean 84%), so this cohort carries no persona, org or proficiency segmentation and no confidence-delta or trajectory findings. No ask.py investigation is possible — without demographic identifiers nothing traces to a segment, so the Tier-2 suggested passive-rate query should not be run. The 86 Day 1 responses are not lost: they form a complete, valid one-day dataset readable by formats/one-day/india_analyze.py, though they cannot be joined to Day 2. D2 NPS (+47, n=70) and the qualitative data are unaffected and valid on their own, and are retained in the pooled all-respondent programme NPS — including this cohort moves that figure by +0.38 points (+42.76 to +43.14, n=809).
C27 LondonJuly 29–30, 2026NPS +43
Segment outlier
Capgemini's NPS (+65, n=20, 0 detractors) was driven by a genuinely foundational-level room - 41% early/foundational AI experience. Promoters cite the hands-on format as what landed for them. Mirror image of cohorts where advanced practitioners find material too basic.
Programme note
Passive rate ran +4pts above programme average (43%). No single driver - mix of advanced practitioners wanting more depth, some feeling hands-on time was rushed, and light corporate-environment friction. Not a satisfaction problem: 0% detractors cohort-wide.
C29 San FranciscoAugust 24–25, 2026NPS +48
Segment outlier
Ascendion (NPS +100, n=5) and PwC (NPS -71, n=7) bracket the cohort. Ascendion was carried by the depth of the agentic-development content and Claude's tool-integration story for client POCs — both verbatim 10s came from Architects at opposite ends of the experience range, one 'delivering independently' and one still 'learning and exploring.' A third Ascendion respondent scored 9, calling the programme 'a great start for a consultant in agentic solutions' but not next-level for someone already building — a visible ceiling effect even inside the top-scoring org. PwC, at the other end, had 5 detractors of 7, and four of those five verbatims name the same cause: not enough structured instruction, upfront exercise framing, or explicit key takeaways for participants who aren't deeply technical. One detractor separately cites unresolved firm IT issues that blocked participation; one is a frontier-level Developer for whom the material was simply too familiar. Both PwC passives raise the same framing gap, so the signal is content-fit and scaffolding rather than content quality — the same gap that drove C28's NPS drag, now visible in a second cohort. Both segments are small (n=5, n=7); directional only.
Programme note
Promoter open text (n=37) clusters on five themes, by frequency of mention: learning gain and new capability (13), hands-on exercise design (10), breadth of Claude and agentic coverage (8), instructor quality and engagement (5), and applicability to client work (5). Representative responses: 'Immersive and breadth covered on agentic based development and diagnosis.' 'It was interactive and the instructors were very helpful.' 'The basecamp didn't feel boring for a single sec. The way the program was designed was so interactive and hands-on, making it like a game we are playing.' From a self-described beginner: 'I was nervous coming in as a beginner. But now I feel much more confident in my abilities. Learned a lot of techniques that I will have to expand on when I get back.'
C30 San FranciscoAugust 27–28, 2026NPS +31
Segment outlier
Praxent (NPS +77, n=13) and Fujitsu (NPS -100, n=4) bracket the cohort. Praxent had zero detractors and 10 promoters, and four of five promoter verbatims name the same drivers: hands-on building and direct client relevance — 'the hands on experience with actual building with Claude is incredibly insightful,' and 'guided instruction on how to really leverage Claude in real world contexts... highlighted many areas where we can provide more value at less cost to our clients.' Notably, Praxent's respondents span three AI-experience levels and all three personas, so the result is thematic rather than demographic. Fujitsu, at the other end, returned four detractors from four respondents, but only two left verbatims — both Transformation Leads, and both pointing the same way: 'Most of my colleagues are already familiar with these topics' and 'Didn't learn much.' The complaint is prior knowledge, not facilitation. That direction matters: where C29's detractor signal was content pitched too technical for non-technical participants, C30's is the inverse — experienced attendees finding the material too familiar. Read together, the programme signal is audience-level calibration in mixed-skill rooms rather than a single direction of travel. Both segments are small (n=13, n=4); Fujitsu especially is anecdotal, not representative.
Programme note
Promoter open text (n=28) clusters on four themes, by frequency of mention: breadth and new concepts (10), hands-on building and exercise design (8), client relevance and applicability (5), and instructor quality and structure (4). Representative responses: 'Well structured and great material. The hands on experience with actual building with Claude is incredibly insightful and a great learning tool.' 'Guided instruction on how to really leverage Claude in real world contexts is extremely valuable.' And from a participant who came in without a technical background: 'This session was fast paced but in the best way possible! Even coming from a non-technical background, I felt that I was able to grasp the fundamentals of Claude's LLM and how it can be applied.' Within the Transformation Lead persona, mean build-confidence gain declines as proficiency rises — a pattern present in 8 of 12 cohorts with two or more readable proficiency bands. In this cohort: learning and exploring +0.89 (n=9), applying in practice +0.50 (n=20), delivering independently +0.14 (n=7). NPS does not track confidence gain consistently across the dataset; in 9 of 12 cohorts NPS also declines with proficiency, whereas in this cohort it rose (+11, +20, +71 across the same bands). This cohort's satisfaction gradient is therefore atypical and should not be generalised. Depth and pace responses distribute across different proficiency bands (n=90 Day-1 respondents). Depth rated "too basic" — 13 responses total: Delivering independently 6 of 13, Operating at the frontier 5 of 11, Applying in practice 2 of 52, Learning and exploring 0 of 14. Pace rated "too fast" — 15 responses total: Applying in practice 11 of 52, Learning and exploring 3 of 14, Delivering independently 1 of 13, Operating at the frontier 0 of 11. The two signals do not co-occur within bands. The two highest proficiency bands account for 11 of 13 "too basic" responses and 1 of 15 "too fast" responses. The "too fast" signal is concentrated in the "applying in practice" band, which returned 2 of 52 "too basic". Open-text responses within the Transformation Lead persona separate along the same axis. At practitioner level, responses describe an inability to reproduce the work independently rather than insufficient depth: "Understood workshop content but since so much scaffolding was already in Jupyter notebook I'm not sure I could independently do it end to end"; "should have been extended into a 4 day session... I thought the pace of hands on activities was too fast." One respondent identified language as a barrier during the hackathon. At director level and above, responses cite prior knowledge: "Didn't learn much"; "I feel this is for beginners level... that knowledge does not need 2 days of training. Was expecting more on how to build more multi agent solutions from business standpoint." These distributions do not support a uniform pace reduction: the depth and pace signals originate in separate segments and imply separate interventions. Band sizes range from 11 to 52; the depth signal rests on the two smallest bands (n=11, n=13). Seniority-level open-text segments (practitioner n=9, director+ n=13) fall below the 20-response threshold applied to two-way cuts and are reported qualitatively only, without segment-level scores.
C31 San FranciscoSeptember 9–10, 2026NPS +36
Segment outlier
PwC (n=8, NPS -38) detractor verbatims cluster around facilitation format, not content: they wanted more instructor-led walkthroughs and demos versus independent workbook time, and a clearer functional/technical agenda split. Ascendion (n=6, NPS +67) left with concrete applied takeaways (cost optimization, custom agents, prompt engineering).
Data quality · survey compliance and completeness
Cohorts with flagged data quality issues
Issues are flagged for transparency and database migration. Assessment and remediation tracked with delivery team.

2 cohorts with data quality flags · tracked for delivery team review and remediation

C23 LondonLow match rate (40.0%), More D2-only (9) than matched40.0% matched · 9/15 D2-only
C26 LondonLow match rate (0.0%), More D2-only (70) than matched, small cohort n00.0% matched · 70/70 D2-only
Org Landscape · organisations across all cohorts
Organisation reach — appearances, people, recency
People count from D1 org distribution · Avg NPS from D2 respondents meeting minimum n · Capability profile from v2 cohorts only
83
Organisations reached
1332
D1 headcount
Total respondents
25
Cohorts
48%
40 orgs in 2+ cohorts
Returning orgs
OrganisationAppearancesPeopleRecencyAvg NPS
Returning organisations
Deloitte24211current+41
PwC17180current+16
Accenture1494current+70
Ascendion1447current+79
DXC Technology10772 cohorts ago+38
NEC1063current+56
Capgemini958current+61
Cognizant946current+53
Lovelytics918current
Infosys83710 cohorts ago+16
McKinsey832current+38
AWS720current+60
Bain7203 cohorts ago+25
IBM718current+60
Fractal Analytics7172 cohorts ago
EPAM7154 cohorts ago
Forgd.AI7101 cohort ago
valantic6165 cohorts ago
KPMG5443 cohorts ago+16
LTM427current+68
AlixPartners4127 cohorts ago+50
IndiciumAI465 cohorts ago
Fractional AI / Ode3241 cohort ago-25
Persistent Systems31611 cohorts ago+82
Version 131215 cohorts ago+25
Slalom385 cohorts ago
Zartis3715 cohorts ago
Bounteous3419 cohorts ago
Wipro34current
UST Global21413 cohorts ago+38
Reply21010 cohorts ago+50
Altimetrik29current+50
Provectus281 cohort ago+40
NTT Data275 cohorts ago
Quantium2712 cohorts ago
The Agile Monkeys265 cohorts ago
Hellman & Friedman232 cohorts ago
ABeam Consulting231 cohort ago
BCG2218 cohorts ago
Lazer Technologies221 cohort ago
First appearance
Praxent1183 cohorts ago+77
Fujitsu173 cohorts ago-100
b.telligent1618 cohorts ago+25
Infomotion1524 cohorts ago
MHP155 cohorts ago
Aimpoint Digital154 cohorts ago+75
SFEIR1424 cohorts ago
Sia1422 cohorts ago
Netlight1421 cohorts ago
Theodo1421 cohorts ago
Nimble Gravity1416 cohorts ago
Perficient143 cohorts ago
Grant Thornton14current-50
Praecipio1322 cohorts ago
Horváth1321 cohorts ago

+ 7 new orgs with <3 respondents in the last 3 cohorts — included in total reach count above.

Capability Profile

Orgs with ≥3 respondents who appeared within the last 3 cohorts. v2 data only.

Deloitte211 people · 24 cohorts · current
AI Proficiency
Frontier 14Independent 32Applying 106Learning 59
Personas
Architect 29Developer 65T.Lead 117
PwC180 people · 17 cohorts · current
AI Proficiency
Frontier 15Independent 19Applying 111Learning 35
Personas
Architect 16Developer 31T.Lead 133
Accenture94 people · 14 cohorts · current
AI Proficiency
Frontier 11Independent 17Applying 46Learning 20
Personas
Architect 27Developer 28T.Lead 39
DXC Technology77 people · 10 cohorts · 2 cohorts ago
AI Proficiency
Frontier 11Independent 9Applying 31Learning 26
Personas
Architect 18Developer 21T.Lead 38
NEC63 people · 10 cohorts · current
AI Proficiency
Frontier 6Independent 6Applying 32Learning 19
Personas
Architect 14Developer 37T.Lead 12
Capgemini58 people · 9 cohorts · current
AI Proficiency
Frontier 6Independent 9Applying 24Learning 19
Personas
Architect 22Developer 14T.Lead 22
Ascendion47 people · 14 cohorts · current
AI Proficiency
Frontier 4Independent 10Applying 21Learning 12
Personas
Architect 13Developer 21T.Lead 13
Cognizant46 people · 9 cohorts · current
AI Proficiency
Frontier 7Independent 5Applying 20Learning 14
Personas
Architect 12Developer 11T.Lead 23
McKinsey32 people · 8 cohorts · current
AI Proficiency
Frontier 2Independent 14Applying 14Learning 2
Personas
Architect 3Developer 13T.Lead 16
LTM27 people · 4 cohorts · current
AI Proficiency
Frontier 1Independent 3Applying 18Learning 5
Personas
Architect 13Developer 7T.Lead 7
Fractional AI / Ode24 people · 3 cohorts · 1 cohort ago
AI Proficiency
Frontier 7Independent 4Applying 11Learning 2
Personas
Developer 19T.Lead 5
AWS20 people · 7 cohorts · current
AI Proficiency
Frontier 8Independent 9Applying 3
Personas
Architect 16Developer 1T.Lead 3
Lovelytics18 people · 9 cohorts · current
AI Proficiency
Frontier 1Independent 4Applying 8Learning 5
Personas
Architect 8Developer 3T.Lead 7
IBM18 people · 7 cohorts · current
AI Proficiency
Frontier 1Independent 3Applying 11Learning 3
Personas
Architect 5Developer 9T.Lead 4
Fractal Analytics17 people · 7 cohorts · 2 cohorts ago
AI Proficiency
Frontier 1Independent 4Applying 8Learning 4
Personas
Architect 5Developer 8T.Lead 4
Forgd.AI10 people · 7 cohorts · 1 cohort ago
AI Proficiency
Frontier 5Independent 4Applying 1
Personas
Architect 8Developer 1T.Lead 1
Altimetrik9 people · 2 cohorts · current
AI Proficiency
Frontier 1Independent 3Applying 5
Personas
Architect 5Developer 4
Provectus8 people · 2 cohorts · 1 cohort ago
AI Proficiency
Frontier 3Independent 2Applying 3
Personas
Architect 1Developer 7
Wipro4 people · 3 cohorts · current
AI Proficiency
Applying 2Learning 2
Personas
Architect 3Developer 1
Grant Thornton4 people · 1 cohort · current
AI Proficiency
Frontier 1Independent 1Applying 1Learning 1
Personas
Architect 1T.Lead 3
Hellman & Friedman3 people · 2 cohorts · 2 cohorts ago
AI Proficiency
Frontier 1Applying 2
Personas
Architect 2T.Lead 1
ABeam Consulting3 people · 2 cohorts · 1 cohort ago
AI Proficiency
Frontier 1Independent 1Applying 1
Personas
Architect 2T.Lead 1
Regional divergence · audience profile and outcomes by city
London
9 cohorts · n=274 D2
+41
NPS
4.11
D2 conf /5
San Francisco
16 cohorts · n=887 D2
+44
NPS
4.19
D2 conf /5
Primary function
London
EngineeringArchitectureBusiness LeadershipProject / Engmt
All cohorts (includes est. v1)n=377
41%
28%
14%
17%
C22 Londonn=55
36%
27%
16%
20%
C23 Londonn=14
29%
21%
29%
21%
C27 Londonn=72
44%
29%
11%
15%
San Francisco
EngineeringArchitectureBusiness LeadershipProject / Engmt
All cohorts (includes est. v1)n=1025
31%
23%
16%
29%
C30 San Franciscon=90
20%
22%
28%
30%
C31 San Franciscon=77
29%
21%
16%
35%
C32 San Franciscon=68
38%
34%
12%
16%
C33 San Franciscon=67
31%
27%
34%
AI experience · v2 cohorts
London
Learning & exploringApplying in practiceDelivering independentlyOperating at the frontier
All cohorts (includes est. v1)n=377
22%
41%
21%
17%
C22 Londonn=55
20%
55%
9%
16%
C23 Londonn=14
43%
36%
21%
C27 Londonn=72
28%
29%
19%
24%
San Francisco
Learning & exploringApplying in practiceDelivering independentlyOperating at the frontier
All cohorts (includes est. v1)n=1025
21%
49%
18%
12%
C30 San Franciscon=90
16%
58%
14%
12%
C31 San Franciscon=77
21%
56%
13%
10%
C32 San Franciscon=68
16%
47%
22%
15%
C33 San Franciscon=67
19%
58%
15%
Seniority · v2 cohorts
London
Sr PractitionerPractitionerManager / Sr ManagerDirector / Sr DirectorPartner / MD / Exec
All cohorts (includes est. v1)n=377
19%
30%
31%
17%
C22 Londonn=55
15%
25%
44%
15%
C23 Londonn=14
29%
50%
21%
C27 Londonn=72
19%
36%
24%
18%
San Francisco
Sr PractitionerPractitionerManager / Sr ManagerDirector / Sr DirectorPartner / MD / Exec
All cohorts (includes est. v1)n=1025
23%
23%
27%
21%
C30 San Franciscon=90
21%
17%
22%
30%
10%
C31 San Franciscon=77
31%
32%
18%
17%
C32 San Franciscon=68
25%
26%
25%
19%
C33 San Franciscon=67
16%
24%
28%
31%
Technical depth
London
Too basicAbout rightToo advanced
CohortToo basicAbout rightToo advanced
All cohorts16%79%5%
C22 London7%87%5%
C23 London93%7%
C27 London15%76%8%
San Francisco
Too basicAbout rightToo advanced
CohortToo basicAbout rightToo advanced
All cohorts14%75%11%
C30 San Francisco14%76%10%
C31 San Francisco14%66%19%
C32 San Francisco9%82%9%
C33 San Francisco9%78%13%
Pace
London
Too slowWell pacedToo fast
CohortToo slowWell pacedToo fast
All cohorts5%82%14%
C22 London5%62%33%
C23 London86%14%
C27 London4%89%7%
San Francisco
Too slowWell pacedToo fast
CohortToo slowWell pacedToo fast
All cohorts6%80%14%
C30 San Francisco8%76%17%
C31 San Francisco3%78%19%
C32 San Francisco6%79%15%
C33 San Francisco87%13%
D2 confidence means /5
MetricLondonSan Francisco
Apply / Lead conversation4.114.19
Advising clients on AI4.124.17
Design an evaluation4.064.09