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Partner Basecamp · Cohort 14

London

June 4-5, 2026
+39
Net Promoter Score
17
Day 1 Responses
78%
Matched Pairs
106%
Day 2 Response Rate
Programme Satisfaction
Positive NPS of +39 — promoters outweigh detractors with moderate passive presence.
+39NPS · n=18
50% Promoters39% Passives11% Detractors
95% CI: +8 → +70  ·  True NPS lies within this range with 95% confidence (n=18 respondents)
Confidence Arc
Strong end-of-Day-1 build confidence — cohort leaves Day 1 ready to apply Claude in client work (mean 4.0/5).
4.0end-of-D1 build / 5
4.0D2 Design / 5
4.1D2 Commercial / 5
4.1D2 Build / 5
Audience
17 participants across 7 organisations — Developer majority with delivering independently the most common AI experience level.
Top Organisations
Deloitte (7) Zartis (3) Capgemini (2) Genioo (2) Infosys (1)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Instructors' expertise and attentiveness, hands-on learning with unlimited tokens, and interactive format drove strong satisfaction across skill levels.
Passives
Content pacing and depth suited newcomers well but felt introductory for experienced Claude users seeking advanced technical knowledge.
Detractors
Insufficient dedicated time for independent exploration of topics prevented deep technical engagement beyond high-level overview.
Survey & Data Quality
Survey & Data Quality
Day 1 responses17
Day 2 responses18 (106% of Day 1)
Matched pairs14 (78% of Day 2)
Orgs resolved17 of 17 respondents matched to named org
Day 2-only respondents4 (22.2%) — suppresses NPS by 4 points; no Day 1 data, excluded from persona & confidence analysis
Organisations
All Day 1 respondents · n=17
Deloitte7 (41%)
Zartis3 (18%)
Capgemini2 (12%)
Genioo2 (12%)
Infosys1 (6%)
valantic1 (6%)
VENZO/valantic1 (6%)
Function × Seniority
All Day 1 respondents · n=17
FunctionPractitionerSr PractitionerMgr / Sr MgrDirector+Partner / MDTotal
Engineering–411–6
Architecture1212–6
Business Leadership–––112
Project / Engmt1–2––3
Experience Profile
AI experience level · n=17 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Architect—33%50%17%6
Developer17%50%33%—6
Transformation Lead20%20%40%20%5
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Senior practitioner (5–9 years)17%33%50%—6
Director, Senior Director, or Principal—50%—50%4
Manager or Senior Manager25%—75%—4
Practitioner (0-4 years in role)—50%50%—2
Partner, Managing Director, or Executive—100%——1
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
2 (12%)
Applying in practice
6 (35%)
Delivering independently
7 (41%)
Operating at the frontier
2 (12%)
Prior to today, how much had you worked with Claude or the Anthropic API?
A little
6 (35%)
Regularly
11 (65%)
Did the depth land for this audience?
Technical depth perception · n=17 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Architect50%50%—6
Developer17%83%—6
Transformation Lead40%60%—5
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Delivering independently43%57%—7
Applying in practice17%83%—6
Operating at the frontier100%——2
Learning and exploring—100%—2
Overall depth distribution
Too basic
6 (35%)
About right
11 (65%)
Did the pace work across the room?
Session pace perception · n=17 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Architect—83%17%6
Developer—67%33%6
Transformation Lead20%80%—5
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Delivering independently—86%14%7
Applying in practice—83%17%6
Operating at the frontier50%50%—2
Learning and exploring—50%50%2
Overall pace distribution
Too slow — could have covered more
1 (6%)
Well paced
13 (76%)
Too fast — not enough time to apply
3 (18%)
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=17 · 1–5
Mean end-of-D1 build confidence by Persona
Architect4.50/5 · n=6
Transformation Lead4.00/5 · n=5
Developer3.83/5 · n=6
Programme mean: 3.9/5 · 15 cohorts
Mean end-of-D1 build confidence by AI Experience Level
Operating at the frontier4.50/5 · n=2
Delivering independently4.43/5 · n=7
Applying in practice3.83/5 · n=6
Learning and exploring3.50/5 · n=2
Overall end-of-D1 build confidence distribution
1
0 (0%)
2
0 (0%)
3
3 (18%)
4
9 (53%)
5
5 (29%)
How relevant was today's content to your current role?
Content relevance rating · n=17 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Developer4.50/5 · n=6
Architect3.83/5 · n=6
Transformation Lead3.60/5 · n=5
Mean relevance by AI Experience Level
Applying in practice4.33/5 · n=6
Delivering independently4.14/5 · n=7
Learning and exploring4.00/5 · n=2
Operating at the frontier2.50/5 · n=2
Overall relevance distribution
1
0 (0%)
2
2 (12%)
3
3 (18%)
4
5 (29%)
5
7 (41%)
How likely are you to recommend attending this programme to a colleague?
n=18 Day 2 respondents · 106% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 1421645
■ Detractors 0–6■ Passives 7–8■ Promoters 9–10
NPS Breakdown
50%
39%
11%
NPS +39 (n=18)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +42 · 15 cohorts
Promoters mean: 51%
Passives mean: 39%
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)
Deloitte
33%
33%
33%
NPS 0 (n=6)
By Persona
Promoters (9–10)Passives (7–8)Detractors (0–6)
Developer
80%
20%
0%
NPS +80 (n=5)
Transformation Lead
60%
20%
20%
NPS +40 (n=5)
Architect
25%
50%
25%
NPS 0 (n=4)
Programme means · 15 cohorts: Architect +56 · Developer +44 · Transformation Lead +36
By AI Experience Level
Promoters (9–10)Passives (7–8)Detractors (0–6)
Applying in practice
60%
40%
0%
NPS +60 (n=5)
Delivering independently
60%
0%
40%
NPS +20 (n=5)
Programme means · 15 cohorts: Learning and exploring +44 · Delivering independently +44 · Applying in practice +43 · Operating at the frontier +37
What is the main reason for your score?
n=13 responses · organised by NPS segment
Promoters (score 9–10)· 7 responses
Instructors' expertise and attentiveness, hands-on learning with unlimited tokens, and interactive format drove strong satisfaction across skill levels.
9“Very interactive and helpful to get hands on and access to unlimited tokens for the workshop. On day 1, the AI engineer q and a session would have been better with someone who had worked at Anthropic for a bit longer”
Passives (score 7–8)· 5 responses
Content pacing and depth suited newcomers well but felt introductory for experienced Claude users seeking advanced technical knowledge.
7“Depends on their previous experience. For people who are new to a lot of the concepts it is a fun introduction. For those familiar with more depth then there won’t be loads to learn”
Detractors (score 0–6)· 1 response
Insufficient dedicated time for independent exploration of topics prevented deep technical engagement beyond high-level overview.
6“Too high level, to less time to just deep dive on your own into topics.”
End-of-Programme Confidence
n=18 respondents · 1 = not confident, 5 = very confident
How confident are you in your ability to design an evaluation for an AI solution?
12345Mean
—6%17%50%28%4.00
Programme mean: 4.1/5 · 15 cohorts
Leaving today, how comfortable are you advising clients on AI when questions arise?
12345Mean
—6%11%56%28%4.06
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
—6%11%56%28%4.06
Programme mean: 4.2/5 · 15 cohorts
Confidence Trajectory by Persona
D1→D2 delta · apply-AI confidence · matched pairs only · n=14
Positive delta = confidence grew · negative = dropped
Architect (n=4)
+0.00
Developer (n=5)
+0.20
Transformation Lead (n=5)
+0.40
What one takeaway will you share with a colleague or client?
n=12 responses
Participants valued concrete, accessible examples demonstrating agent architecture including hooks, plugins, and subagents.
“Easy to understand examples of agents”
Participants recognized that AI systems require rigorous evaluation practices equivalent to traditional software engineering standards.
“Extensive AI tooling requires eval just as product source code does.”
Participants emphasized completing prerequisite tool installation and preparation work before training delivery.
“Install all tools upfront and do the pre-work.”
Participants found practical, easy-to-understand examples essential for translating agent concepts to their own projects.
“Easy to understand examples of agents”
Participants wanted detailed technical coverage of Claude-specific capabilities like hooks, plugins, and subagent orchestration.
“Hooks, plugins, subagents in claude.”
What will you build for yourself or apply at work in the next 30 days?
n=12 responses
Participants want to build concrete agentic workflows and institutional knowledge agents, seeking guidance on implementation approaches.
“I will refine an institutional knowledge agent that creates an internal wiki from various sources. There are many ways to fix.”
Participants need clarity on token access and require more hands-on exploration of available tools and skill-building resources.
“Don't know if I have token access”
Participants recognize evaluation frameworks as critical but lack confidence, committing to deeper investigation of evals and graders.
“I don't know but I'll certainly investigate and play more about Evals and Graders.”
Participants have decided Claude is their primary tool for future development work based on programme exposure.
“I will now always be on Claude to build stuff”
Partner Basecamp Survey Report · Cohort 14 · Generated by analyze.py v2.3
NPS Reason
n=13 responses · grouped by NPS segment
Promotersscore 9–10 · 7 responses
1The instructors are amazing and have solid skills. They take the time to ask questions and help you out anytime they you're stuck. Btw, the food is excellent 👌10
2Friendly, supportive team, claude tooling and great facilities10
3Great mix of theory and hands on experience10
4Great engaging session, freedom to experiment, learning encouraged.10
5Relevance10
6Great for a variety of skill levels and profiles.9
7Very interactive and helpful to get hands on and access to unlimited tokens for the workshop. On day 1, the AI engineer q and a session would have been better with someone who had worked at Anthropic for a bit longer9
Passivesscore 7–8 · 5 responses
1Well prepared exercises8
2It is ok .. lot more there8
3Really good course think is a bit slow for people who have been using Claude a lot but would be great as a first exposure8
4As a developer that already had experience on Claude and Claude code the learnings are less. However, I see very positive the comments, talks and thoughts of all attendees.8
5Depends on their previous experience. For people who are new to a lot of the concepts it is a fun introduction. For those familiar with more depth then there won’t be loads to learn7
Detractorsscore 0–6 · 1 response
1Too high level, to less time to just deep dive on your own into topics.6
Most Valuable
What one takeaway will you share with a colleague or client? · n=12 responses
1Install all tools upfront and do the pre-work.
2Time to market is going to shrink.
3Good luck
4Hooks, plugins, inference and caches for efficient usage
5Extensive AI tooling requires eval just as product source code does.
6Never build a PowerPoint ever again
7Make sure pally eval to check the output of your agents
8Caching for reducing token spend
9Easy to understand examples of agents
10Hooks, plugins, subagents in claude.
11I’ll share about evals. This was one of the important takeaways for me.
12Agent swarms
30-Day Intentions
What will you build for yourself or apply at work? · n=12 responses
1I don't know but I'll certainly investigate and play more about Evals and Graders.
2Don’t know if I have token access
3I will now always be on Claude to build stuff
4The certification
5Further development of strong evals, further tooling and take swam agent concepts further.
6I will refine an institutional knowledge agent that creates an internal wiki from various sources. There are many ways to fix.
7Future of underwriting demo
8A couple of concerte process improvement with agentic workflow
9Create some skills files. Look into tools more.
10I will now try to build an agent. This course has been v helpful for me to carve out some time to learn the main concepts and decrease the scariness of starting
11More custom pro cide agents.
12Proposal buildee