Partner Basecamp · Insights · London & San Francisco

Programme NPS +42 · 16 cohorts · 565 D2 respondents

16 v2 cohorts · 745 D1 respondents · Historical baseline NPS: +35

Programme NPS · all cohorts

+42

P 53% · Pa 36% · D 11%
n=565 D2 respondents

Cohorts

16 with full v2 data

Respondents

745

745 D1 · 565 D2

Cohort summary

Cohort	NPS	P%	Pa%	D%	D2 n	D1 n	Match	Conf Δ
C6 London May 18–19, 2026	+43	57.1%	28.6%	14.3%	21	39	95%	+0.24
C7 San Francisco May 19-20, 2026	+32	53.3%	25.0%	21.7%	60	65	83%	+0.09
C8 San Francisco May 21-22, 2026	+43	54.1%	35.1%	10.8%	37	48	92%	+0.32
C9 London May 20-21, 2026	+38	45.8%	45.8%	8.3%	24	55	88%	+0.22
C11 San Francisco May 26-27, 2026	+73	73.2%	26.8%	0.0%	41	49	93%	+0.49
C12 San Francisco May 28-29, 2026	+50	58.0%	34.0%	8.0%	50	67	80%	-0.11
C13 London June 2-3, 2026	+40	40.0%	60.0%	0.0%	15	19	93%	+0.34
C14 London June 4-5, 2026	+39	50.0%	38.9%	11.1%	18	17	78%	+0.06
C15 San Francisco June 16-17	+55	63.6%	27.3%	9.1%	33	69	97%	+0.22
C16 London June 15-16, 2026	+17	39.1%	39.1%	21.7%	23	36	74%	+0.35
C18 San Francisco June 22–23, 2026	+35	48.6%	37.8%	13.5%	37	45	81%	+0.31
C19 San Francisco June 24–25, 2026	+55	64.5%	25.8%	9.7%	62	67	98%	+0.43
C20 San Francisco July 14–15, 2026	+35	45.8%	43.8%	10.4%	48	46	88%	+0.26
C21 San Francisco July 16–17, 2026	+24	38.8%	46.9%	14.3%	49	54	94%	+0.22
C22 London July 20–21, 2026	+41	43.8%	53.1%	3.1%	32	55	75%	+0.39
C23 London July 22–23, 2026	+47	46.7%	53.3%	0.0%	15	14	40%	+0.53

Conf Δ = D2 apply-AI minus D1 baseline (v2 cohorts only). Match = D1/D2 linked respondents.

London · San Francisco · programme snapshot

London

+37

P 46% · Pa 45% · D 9%
n=148 D2 · 7 cohorts

C6 London May 18–19, 2026, C9 London May 20-21, 2026, C13 London June 2-3, 2026, C14 London June 4-5, 2026, C16 London June 15-16, 2026, C22 London July 20–21, 2026, C23 London July 22–23, 2026

San Francisco

+44

P 55% · Pa 33% · D 11%
n=417 D2 · 9 cohorts

C7 San Francisco May 19-20, 2026, C8 San Francisco May 21-22, 2026, C11 San Francisco May 26-27, 2026, C12 San Francisco May 28-29, 2026, C15 San Francisco June 16-17, C18 San Francisco June 22–23, 2026, C19 San Francisco June 24–25, 2026, C20 San Francisco July 14–15, 2026, C21 San Francisco July 16–17, 2026

Same content and programme design. City split reflects internal team and audience differences.

Audience composition · 745 D1 respondents pooled

Pooled across all cohorts. NPS by org aggregated from v2 cohorts only (n ≥ 4 suppressed).

Function

Engineering

35%

Project and engagement mana…

25%

Architecture

24%

Business leadership

15%

Seniority

Manager or Senior Manager

30%

Senior practitioner

25%

Practitioner

21%

Director, Senior Director, …

18%

Partner, Managing Director,…

AI Proficiency

Applying in practice

46%

Learning and exploring

21%

Delivering independently

20%

Operating at the frontier

13%

Organisation distribution · pooled

Organisation	n	%
Deloitte	143	19.2%
PwC	107	14.4%
Accenture	64	8.6%
Infosys	37	5.0%
KPMG	32	4.3%
Capgemini	27	3.6%
Cognizant	27	3.6%
McKinsey	25	3.4%
DXC Technology	20	2.7%
Fractional AI / Ode	18	2.4%
Ascendion	17	2.3%
Persistent Systems	16	2.1%
valantic	14	1.9%
Fractal Analytics	14	1.9%
UST Global	14	1.9%
NEC	13	1.7%
Lovelytics	12	1.6%
Version 1	12	1.6%
AlixPartners	11	1.5%
Reply	10	1.3%

NPS by organisation · aggregate (v2 cohorts, n ≥ 4)

Organisation	NPS	n
Persistent Systems	+83	n=12
Fractal Analytics	+78	n=9
Accenture	+76	n=33
Ascendion	+72	n=14
NEC	+67	n=12
Forgd.AI	+60	n=5
Version 1	+56	n=9
Capgemini	+54	n=13
Deloitte	+54	n=101
Zartis	+50	n=6
Lovelytics	+50	n=6
AlixPartners	+44	n=9
Cognizant	+43	n=21
UST Global	+42	n=14
Infosys	+38	n=21
Reply	+38	n=8
PwC	+29	n=77
DXC Technology	+27	n=11
b.telligent	+25	n=4
Quantium	+16	n=6
KPMG	+14	n=22
McKinsey	+14	n=15
EPAM	-12	n=8
Fractional AI / Ode	-30	n=10
valantic	+0	n=6

Day 1 calibration · confidence, depth, pace and relevance

Pooled across v2 cohorts. Confidence on 1–5 scale. Depth/pace as % of persona respondents.

D1 Confidence by Persona · mean /5

Architect
4.16/5 · n=176

83%

Developer
4.07/5 · n=261

81%

Transformation Lead
3.68/5 · n=304

74%

Bar = % of max scale (5). Pooled across v2 cohorts.

D1 Confidence by AI Proficiency · mean /5

Applying in practice
3.82/5 · n=341

76%

Delivering independently
4.32/5 · n=146

86%

Operating at the frontier
4.61/5 · n=94

92%

Learning and exploring
3.36/5 · n=154

67%

Bar = % of max scale (5). Pooled across v2 cohorts.

Content Depth by Persona · pooled

Persona	Too basic	About right	Too advanced
Architect	17% n=31	81% n=146	2% n=3
Developer	23% n=60	74% n=192	3% n=9
Transformation Lead	10% n=29	76% n=231	14% n=44

Session Pace by Persona · pooled

Persona	Too slow	Well paced	Too fast
Architect	5% n=9	87% n=156	8% n=15
Developer	10% n=27	78% n=203	12% n=31
Transformation Lead	5% n=16	79% n=239	16% n=49

Content Relevance by Persona · mean /5 · programme mean 4.09/5

Architect
4.31/5 · n=176

86%

Developer
4.10/5 · n=261

82%

Transformation Lead
3.96/5 · n=304

79%

Relevance = Day 1 session relevance rating (1–5). Pooled across v2 cohorts.

Outcomes · NPS by persona, proficiency and open text

Aggregate NPS breakouts from v2 cohorts (n ≥ 4 suppressed). 459 NPS open-text responses available.Synthesis not generated

NPS by Persona · aggregate (v2 cohorts, n ≥ 4)

Persona	NPS	n
Architect	+52	n=113
Developer	+43	n=166
Transformation Lead	+38	n=200

NPS by AI Proficiency · pooled (v2 cohorts, n ≥ 4)

Proficiency	NPS	P%	Pa%	D%	n
Applying in practice	+44	54.3%	35.4%	10.3%	n=223
Delivering independently	+43	54.3%	34.8%	10.9%	n=92
Operating at the frontier	+33	50.9%	30.9%	18.2%	n=55
Learning and exploring	+48	57.4%	33.0%	9.6%	n=94

Open text · NPS reasons · 459 responses

Synthesis not generatedRun with --synthesis to generate thematic analysis via Claude Haiku. 459 responses available.

Confidence · D1→D2 deltas across all three dimensions

Apply-AI · Design-AI · Commercial confidence. D1 baseline → D2 delta. v2 cohorts only.

D1 → D2 Confidence by Dimension · per cohort

Cohort	D1 build	D2 apply-AI	D2 design-AI	D2 commercial
C6 London May 18–19, 2026	4.00	4.24 +0.24	4.05 +0.05	4.19 +0.19
C7 San Francisco May 19-20, 2026	4.08	4.17 +0.09	4.28 +0.20	4.28 +0.20
C8 San Francisco May 21-22, 2026	4.00	4.32 +0.32	4.14 +0.14	4.19 +0.19
C9 London May 20-21, 2026	3.95	4.17 +0.22	4.38 +0.43	4.17 +0.22
C11 San Francisco May 26-27, 2026	3.71	4.20 +0.49	4.12 +0.41	4.20 +0.49
C12 San Francisco May 28-29, 2026	4.15	4.04 -0.11	4.16 +0.01	4.14 -0.01
C13 London June 2-3, 2026	3.93	4.27 +0.34	4.13 +0.20	4.33 +0.40
C14 London June 4-5, 2026	4.00	4.06 +0.06	4.00 +0.00	4.06 +0.06
C15 San Francisco June 16-17	3.84	4.06 +0.22	3.88 +0.04	4.06 +0.22
C16 London June 15-16, 2026	3.82	4.17 +0.35	3.91 +0.09	3.96 +0.14
C18 San Francisco June 22–23, 2026	3.80	4.11 +0.31	3.97 +0.17	4.14 +0.34
C19 San Francisco June 24–25, 2026	3.84	4.27 +0.43	4.15 +0.31	4.16 +0.32
C20 San Francisco July 14–15, 2026	3.76	4.02 +0.26	4.06 +0.30	4.15 +0.39
C21 San Francisco July 16–17, 2026	3.96	4.18 +0.22	4.18 +0.22	4.10 +0.14
C22 London July 20–21, 2026	3.83	4.22 +0.39	4.09 +0.26	4.34 +0.51
C23 London July 22–23, 2026	3.67	4.20 +0.53	4.07 +0.40	4.33 +0.66

D2 value shown above; delta (D2 – D1) shown below in smaller text. Scale 1–5.

Confidence Delta by Persona · pooled

Transformation Lead
n=200

+0.39

Architect
n=113

+0.27

Developer
n=166

+0.08

D1→D2 apply-AI mean delta. Positive = gained confidence. Scale 1–5.

Confidence Delta by NPS Segment · pooled

Promoter
n=261

+0.27

Passive
n=174

+0.29

Detractor
n=40

+0.05

Promoters gain more confidence than detractors — or vice versa?

4 systematic patterns · 9 flags · 7 suggested queries

Systematic patterns = same direction vs programme NPS across ≥2 cohorts. Suggested queries pre-written for ask.py.

Organisation patterns · consistent across ≥2 cohorts

Pattern	Direction	Avg Δ	Cohorts	Seen in
Accenture scored above programme NPS in all 3 cohorts analysed (avg +24 pts) Likely reflects audience fit, not delivery variance.	↑ Above programme	+24 pts	3	C7 San Francisco May 19-20, 2026, C9 London May 20-21, 2026, C11 San Francisco May 26-27, 2026
Persistent Systems scored above programme NPS in all 2 cohorts analysed (avg +36 pts) Likely reflects audience fit, not delivery variance.	↑ Above programme	+36 pts	2	C15 San Francisco June 16-17, C20 San Francisco July 14–15, 2026
KPMG scored below programme NPS in all 2 cohorts analysed (avg -28 pts) Likely reflects audience fit, not delivery variance.	↓ Below programme	-28 pts	2	C12 San Francisco May 28-29, 2026, C21 San Francisco July 16–17, 2026
Infosys scored below programme NPS in all 2 cohorts analysed (avg -27 pts) Likely reflects audience fit, not delivery variance.	↓ Below programme	-27 pts	2	C7 San Francisco May 19-20, 2026, C15 San Francisco June 16-17

Outlier flags

↑ NPS outlier — high cohortC11 San Francisco May 26-27, 2026 NPS (+73) is notably above the sample mean (+42). Check for composition effects or unusually strong delivery.

⚠ NPS outlier — low cohortC16 London June 15-16, 2026 NPS (+17) is notably below the sample mean (+42). Prioritise review of audience composition and facilitator notes.

● High passive rateHigh passive rate in C9 London May 20-21, 2026 (46%). Passives are the most likely source of churn — explore what would convert them to promoters.

● High passive rateHigh passive rate in C13 London June 2-3, 2026 (60%). Passives are the most likely source of churn — explore what would convert them to promoters.

● High passive rateHigh passive rate in C20 San Francisco July 14–15, 2026 (44%). Passives are the most likely source of churn — explore what would convert them to promoters.

● High passive rateHigh passive rate in C21 San Francisco July 16–17, 2026 (47%). Passives are the most likely source of churn — explore what would convert them to promoters.

● High passive rateHigh passive rate in C22 London July 20–21, 2026 (53%). Passives are the most likely source of churn — explore what would convert them to promoters.

● High passive rateHigh passive rate in C23 London July 22–23, 2026 (53%). Passives are the most likely source of churn — explore what would convert them to promoters.

↓ Confidence regressionC12 San Francisco May 28-29, 2026 shows a negative confidence delta (-0.11). Participants left less confident than they arrived — check whether the programme surfaced complexity without resolving it.

Suggested ask.py queries

Run these commands from the Partner Basecamp/ folder to investigate the patterns above. Add --save when you have a conclusion to commit to the insights cache.

Accenture — org pattern

Accenture scored above programme NPS in all 3 cohorts analysed (avg +24 pts)

cd "$REPO" && python3 _scripts/ask.py --question "What explains Accenture's persistent above-programme NPS score across 3 cohorts, and what would change it?" --cache "/Users/edward.whittall/Library/CloudStorage/Box-Box/Anthropic/Phase II/Partner Enablement/Partner Basecamp v2/Partner Basecamp — Survey Pipeline/findings/insights/c6-c23/insights-data-c6-c23.json" --save

Persistent Systems — org pattern

Persistent Systems scored above programme NPS in all 2 cohorts analysed (avg +36 pts)

cd "$REPO" && python3 _scripts/ask.py --question "What explains Persistent Systems's persistent above-programme NPS score across 2 cohorts, and what would change it?" --cache "/Users/edward.whittall/Library/CloudStorage/Box-Box/Anthropic/Phase II/Partner Enablement/Partner Basecamp v2/Partner Basecamp — Survey Pipeline/findings/insights/c6-c23/insights-data-c6-c23.json" --save

KPMG — org pattern

KPMG scored below programme NPS in all 2 cohorts analysed (avg -28 pts)

cd "$REPO" && python3 _scripts/ask.py --question "What explains KPMG's persistent below-programme NPS score across 2 cohorts, and what would change it?" --cache "/Users/edward.whittall/Library/CloudStorage/Box-Box/Anthropic/Phase II/Partner Enablement/Partner Basecamp v2/Partner Basecamp — Survey Pipeline/findings/insights/c6-c23/insights-data-c6-c23.json" --save

Low NPS cohort: C16 London June 15-16, 2026

NPS +17 vs sample mean

cd "$REPO" && python3 _scripts/ask.py --question "What differentiates C16 London June 15-16, 2026's below-average NPS — is this delivery, composition, or programme fit?" --cache "/Users/edward.whittall/Library/CloudStorage/Box-Box/Anthropic/Phase II/Partner Enablement/Partner Basecamp v2/Partner Basecamp — Survey Pipeline/findings/insights/c6-c23/insights-data-c6-c23.json" --save

High passives: C9 London May 20-21, 2026

Passive rate: 46%

cd "$REPO" && python3 _scripts/ask.py --question "What is driving the high passive rate in C9 London May 20-21, 2026 — what do the verbatims say and what would convert them to promoters?" --cache "/Users/edward.whittall/Library/CloudStorage/Box-Box/Anthropic/Phase II/Partner Enablement/Partner Basecamp v2/Partner Basecamp — Survey Pipeline/findings/insights/c6-c23/insights-data-c6-c23.json" --save

High passives: C13 London June 2-3, 2026

Passive rate: 60%

cd "$REPO" && python3 _scripts/ask.py --question "What is driving the high passive rate in C13 London June 2-3, 2026 — what do the verbatims say and what would convert them to promoters?" --cache "/Users/edward.whittall/Library/CloudStorage/Box-Box/Anthropic/Phase II/Partner Enablement/Partner Basecamp v2/Partner Basecamp — Survey Pipeline/findings/insights/c6-c23/insights-data-c6-c23.json" --save

High passives: C20 San Francisco July 14–15, 2026

Passive rate: 44%

cd "$REPO" && python3 _scripts/ask.py --question "What is driving the high passive rate in C20 San Francisco July 14–15, 2026 — what do the verbatims say and what would convert them to promoters?" --cache "/Users/edward.whittall/Library/CloudStorage/Box-Box/Anthropic/Phase II/Partner Enablement/Partner Basecamp v2/Partner Basecamp — Survey Pipeline/findings/insights/c6-c23/insights-data-c6-c23.json" --save

Investigation log · no conclusions saved yet

Conclusions committed via ask.py --save, open signals from Tab 06 patterns, and pre-written queries.

No conclusions saved yetRun cd "$REPO" && python3 _scripts/ask.py --question "..." --cache "$BOX/findings/insights/insights-data-<slug>.json" --save after reviewing an answer to commit it here. Conclusions build the investigation record over time.

Open signals

Systematic patterns

consistent across ≥2 cohorts

Outlier flags

anomalies flagged

Conclusions saved

from ask.py --save

Suggested queries

pre-written in Tab 06

Patterns not yet addressed by a saved conclusion:

Accenture scored above programme NPS in all 3 cohorts analysed (avg +24 pts)
Persistent Systems scored above programme NPS in all 2 cohorts analysed (avg +36 pts)
KPMG scored below programme NPS in all 2 cohorts analysed (avg -28 pts)
Infosys scored below programme NPS in all 2 cohorts analysed (avg -27 pts)

Pre-written queries → Tab 06 · Patterns

Tab 06 contains pre-written ask.py commands for each pattern and flag detected. Run them from the Partner Basecamp/ folder, review the output, then re-run with --save to commit the conclusion here.

Accenture — org pattern — Accenture scored above programme NPS in all 3 cohorts analysed (avg +24 pts)
Persistent Systems — org pattern — Persistent Systems scored above programme NPS in all 2 cohorts analysed (avg +36 pts)
KPMG — org pattern — KPMG scored below programme NPS in all 2 cohorts analysed (avg -28 pts)
Low NPS cohort: C16 London June 15-16, 2026 — NPS +17 vs sample mean
High passives: C9 London May 20-21, 2026 — Passive rate: 46%
High passives: C13 London June 2-3, 2026 — Passive rate: 60%
High passives: C20 San Francisco July 14–15, 2026 — Passive rate: 44%

Data Quality · survey compliance and completeness

Issues flagged for transparency and database migration. Assessment and remediation tracked with delivery team.

Programme Summary · Survey Compliance

Total Day 2 responses	565
Matched to Day 1	489 (86.5% of Day 2)
Day 2-only respondents	76 (13.5%) — no Day 1 data, excluded from persona & confidence analysis
NPS impact of D2-only respondents	suppresses programme NPS by 5 points

Cohorts with Data Quality Flags

1 cohort with data quality flags · tracked for delivery team review and remediation

C23 London July 22–23, 2026

Low match rate (40.0%), More D2-only (9) than matched

40.0% matched · 9/15 D2-only