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Partner Basecamp · Cohort 23

London

July 22–23, 2026
+47
Net Promoter Score
14
Day 1 Responses
40%
Matched Pairs
107%
Day 2 Response Rate
Programme Satisfaction
NPS +47 with 53% passives — score is held up by low detractor count, not promoter strength. Passive signal is weak: neither endorsing nor rejecting the programme.
+47NPS · n=15
47% Promoters53% Passives0% Detractors
95% CI: +21 → +72  ·  True NPS lies within this range with 95% confidence (n=15 respondents)
Flag — 53% passive rate is above the programme mean. What in the programme experience left people undecided?
Confidence Arc
Moderate end-of-Day-1 build confidence — cohort is progressing; Day 2 has room to close remaining gaps (mean 3.7/5).
3.7end-of-D1 build / 5
4.1D2 Design / 5
4.3D2 Commercial / 5
4.2D2 Build / 5
Audience
14 participants across 5 organisations.
Top Organisations
DXC Technology (8) Bain (2) Accenture (2) Capgemini (1) Deloitte (1)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Hands-on learning with knowledgeable facilitators and practical tools created an immersive, well-balanced experience.
Passives
Pacing and time constraints prevented deep technical exploration, with effectiveness varying by participant's baseline knowledge.
Survey & Data Quality
Survey & Data Quality
Day 1 responses14
Day 2 responses15 (107% of Day 1)
Matched pairs6 (40% of Day 2)
Orgs resolved14 of 14 respondents matched to named org
Day 2-only respondents9 (60.0%) — suppresses NPS by 14 points; no Day 1 data, excluded from persona & confidence analysis
Organisations
All Day 1 respondents · n=14
DXC Technology8 (57%)
Bain2 (14%)
Accenture2 (14%)
Capgemini1 (7%)
Deloitte1 (7%)
Function × Seniority
All Day 1 respondents · n=14
FunctionSr PractitionerMgr / Sr MgrDirector+Total
Engineering31–4
Architecture12–3
Business Leadership–134
Project / Engmt–3–3
Experience Profile
AI experience level · n=14 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Transformation Lead71%29%——7
Developer—75%25%—4
Architect33%—67%—3
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Manager or Senior Manager29%43%29%—7
Senior practitioner (5–9 years)25%50%25%—4
Director, Senior Director, or Principal100%———3
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
6 (43%)
Applying in practice
5 (36%)
Delivering independently
3 (21%)
Prior to today, how much had you worked with Claude or the Anthropic API?
Not at all
4 (29%)
A little
6 (43%)
Regularly
4 (29%)
Did the depth land for this audience?
Technical depth perception · n=14 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Transformation Lead—86%14%7
Developer—100%—4
Architect—100%—3
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Learning and exploring—83%17%6
Applying in practice—100%—5
Delivering independently—100%—3
Overall depth distribution
About right
13 (93%)
Too advanced
1 (7%)
Did the pace work across the room?
Session pace perception · n=14 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Transformation Lead—86%14%7
Developer—100%—4
Architect—67%33%3
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Learning and exploring—67%33%6
Applying in practice—100%—5
Delivering independently—100%—3
Overall pace distribution
Well paced
12 (86%)
Too fast — not enough time to apply
2 (14%)
How confident were participants to build with Claude after Day 1?
End-of-Day-1 confidence · "How confident are you in your ability to build a client solution using Claude?" · n=14 · 1–5
Mean end-of-D1 build confidence by Persona
Developer4.00/5 · n=4
Architect3.67/5 · n=3
Transformation Lead3.43/5 · n=7
Programme mean: 3.9/5 · 15 cohorts
Mean end-of-D1 build confidence by AI Experience Level
Applying in practice4.40/5 · n=5
Delivering independently4.00/5 · n=3
Learning and exploring2.83/5 · n=6
Overall end-of-D1 build confidence distribution
1
0 (0%)
2
2 (14%)
3
5 (36%)
4
3 (21%)
5
4 (29%)
How relevant was today's content to your current role?
Content relevance rating · n=14 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Architect5.00/5 · n=3
Developer4.75/5 · n=4
Transformation Lead3.86/5 · n=7
Mean relevance by AI Experience Level
Delivering independently5.00/5 · n=3
Applying in practice4.60/5 · n=5
Learning and exploring3.83/5 · n=6
Overall relevance distribution
1
0 (0%)
2
0 (0%)
3
3 (21%)
4
3 (21%)
5
8 (57%)
How likely are you to recommend attending this programme to a colleague?
n=15 Day 2 respondents · 107% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 232616
■ Detractors 0–6■ Passives 7–8■ Promoters 9–10
NPS Breakdown
47%
53%
0%
NPS +47 (n=15)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +41 · 15 cohorts
Promoters mean: 52%
Passives mean: 38%
Detractors mean: 10%
NPS by Segment
Organisations, personas, and experience levels · ≥4 respondents shown
By Organisation
Promoters (9–10)Passives (7–8)Detractors (0–6)
DXC Technology
0%
100%
0%
NPS 0 (n=4)
By Persona
No segments with ≥4 responses.
Programme means · 15 cohorts: Architect +51 · Developer +47 · Transformation Lead +36
By AI Experience Level
No segments with ≥4 responses.
Programme means · 15 cohorts: Applying in practice +44 · Learning and exploring +44 · Delivering independently +42 · Operating at the frontier +37
What is the main reason for your score?
n=12 responses · organised by NPS segment
Promoters (score 9–10)· 6 responses
Hands-on learning with knowledgeable facilitators and practical tools created an immersive, well-balanced experience.
9“Good hands on experience with knowledgable facilitators and relevant examples”
Passives (score 7–8)· 6 responses
Pacing and time constraints prevented deep technical exploration, with effectiveness varying by participant's baseline knowledge.
7“Environment issues meant that a number of objectives could not be achieved because we ran out of time.”
End-of-Programme Confidence
n=15 respondents · 1 = not confident, 5 = very confident
How confident are you in your ability to design an evaluation for an AI solution?
12345Mean
——27%40%33%4.07
Programme mean: 4.1/5 · 15 cohorts
Leaving today, how comfortable are you advising clients on AI when questions arise?
12345Mean
——7%53%40%4.33
Programme mean: 4.2/5 · 15 cohorts
How confident are you in your ability to lead a conversation about Anthropic and Claude with a client?
12345Mean
——13%53%33%4.20
Programme mean: 4.2/5 · 15 cohorts
Confidence Trajectory by Persona
D1→D2 delta · apply-AI confidence · matched pairs only · n=6
Positive delta = confidence grew · negative = dropped
Transformation Lead (n=3)
+0.67
What one takeaway will you share with a colleague or client?
n=6 responses
Participants recognized evaluation frameworks as critical for validating AI model performance in their projects.
“Importance of evals”
Prompt engineering and clear requirement specification unlock Claude's capabilities more effectively than model selection alone.
“It is your prompt most likely, not the model!”
Investing time to adapt solutions to specific client scenarios reduces costs, delivery time, and ensures solution robustness.
“If you want to get the most of it, you need to invest in adapting to each scenario. Will save time, money and will make it robust”
What will you build for yourself or apply at work in the next 30 days?
n=7 responses
Participants want to understand how to coordinate multiple agents effectively, moving beyond single-agent implementations.
“Swarm of Agents”
Participants are building custom productivity tools and iterating on outputs to solve immediate practical problems.
“Try to improve the tools output”
Participants are applying Claude Code to build domain-specific knowledge graphs, seeing it as infrastructure for client work.
“Use Claude Code to help build and sustain a complex knowledge graph for a key customer domain”
Partner Basecamp Survey Report · Cohort 23 · Generated by analyze.py v2.3
NPS Reason
n=12 responses · grouped by NPS segment
Promotersscore 9–10 · 6 responses
1Perfect balance during all training10
2Great content10
3Really immersive experience, challenging, good learning curve10
4Getting to know more about models and tools10
5Covers a lot of useful things10
6Good hands on experience with knowledgable facilitators and relevant examples9
Passivesscore 7–8 · 6 responses
1Depends on the technical level of the person8
2Learn a lot8
3Good prep of the hosts and good venue8
4It had a faster pace then expected and not enough deep dive into high level.8
5Content8
6Environment issues meant that a number of objectives could not be achieved because we ran out of time.7
Most Valuable
What one takeaway will you share with a colleague or client? · n=6 responses
1Take time to implement
2Importance of evals
3It is your prompt most likely, not the model!
4Use Evals
5If you want to get the most of it, you need to invest in adapting to each scenario. Will save time, money and will make it robust
6Preparation is key. Think through the problem and specify clear requirements. Let Claude help more than you might think.
30-Day Intentions
What will you build for yourself or apply at work? · n=7 responses
1A lot, agent orchestration
2Create some productivity tools for myself
3Try to improve the tools output
4Swarm of Agents
5Second brain and apps that will improve my effectiveness
6Not sure
7Use Claude Code to help build and sustain a complex knowledge graph for a key customer domain