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Partner Basecamp · Cohort 9

London

May 20-21, 2026
+38
Net Promoter Score
55
Day 1 Responses
88%
Matched Pairs
44%
Day 2 Response Rate
Programme Satisfaction
Positive NPS of +38 — promoters outweigh detractors with moderate passive presence.
+38NPS · n=24
46% Promoters46% Passives8% Detractors
95% CI: +12 → +63  ·  True NPS lies within this range with 95% confidence (n=24 respondents)
Confidence Arc
Moderate end-of-Day-1 build confidence — cohort is progressing; Day 2 has room to close remaining gaps (mean 4.0/5).
4.0end-of-D1 build / 5
4.4D2 Design / 5
4.2D2 Commercial / 5
4.2D2 Build / 5
Audience
55 participants across 12 organisations — Developer majority with applying in practice the most common AI experience level.
Top Organisations
Accenture (25) Version 1 (4) Theodo (4) Infosys (4) Netlight (4)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Well-structured curriculum with expert trainers enabled participants to gain Claude expertise and exchange ideas despite potential overwhelm for juniors.
Passives
Practical exercises and key concepts were valuable, but clearer guidance on exercise scope and pre-work would enhance learning outcomes.
Detractors
Limited novelty for experienced Claude users, and instructors lacked depth to address advanced technical questions comprehensively.
Survey & Data Quality
Survey & Data Quality
Day 1 responses55
Day 2 responses24 (44% of Day 1)
Matched pairs21 (88% of Day 2)
Orgs resolved55 of 55 respondents matched to named org
Day 2-only respondents3 (12.5%) — suppresses NPS by 5 points; no Day 1 data, excluded from persona & confidence analysis
⚠ Low Day 2 rateOnly 44% of Day 1 respondents completed Day 2. Push the survey link during or immediately after the closing session.
Organisations
All Day 1 respondents · n=55
Accenture25 (45%)
Version 14 (7%)
Theodo4 (7%)
Infosys4 (7%)
Netlight4 (7%)
Deloitte3 (5%)
Horváth3 (5%)
Capgemini2 (4%)
Ayesa2 (4%)
valantic2 (4%)
Unit81 (2%)
BCG1 (2%)
Function × Seniority
All Day 1 respondents · n=55
FunctionPractitionerSr PractitionerMgr / Sr MgrDirector+Total
Engineering5126225
Architecture365317
Business Leadership––235
Project / Engmt21418
Experience Profile
AI experience level · n=55 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Developer12%48%24%16%25
Architect18%24%24%35%17
Transformation Lead15%38%23%23%13
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Senior practitioner (5–9 years)11%58%11%21%19
Manager or Senior Manager18%24%29%29%17
Practitioner (0-4 years in role)30%30%30%10%10
Director, Senior Director, or Principal—33%33%33%9
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
8 (15%)
Applying in practice
21 (38%)
Delivering independently
13 (24%)
Operating at the frontier
13 (24%)
Prior to today, how much had you worked with Claude or the Anthropic API?
Not at all
3 (5%)
A little
21 (38%)
Regularly
31 (56%)
Did the depth land for this audience?
Technical depth perception · n=55 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Developer36%60%4%25
Architect12%88%—17
Transformation Lead—92%8%13
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Applying in practice24%71%5%21
Delivering independently31%62%8%13
Operating at the frontier15%85%—13
Learning and exploring—100%—8
Overall depth distribution
Too basic
11 (20%)
About right
42 (76%)
Too advanced
2 (4%)
Did the pace work across the room?
Session pace perception · n=55 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Developer8%88%4%25
Architect6%88%6%17
Transformation Lead—77%23%13
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Applying in practice—86%14%21
Delivering independently15%69%15%13
Operating at the frontier8%92%—13
Learning and exploring—100%—8
Overall pace distribution
Too slow — could have covered more
3 (5%)
Well paced
47 (85%)
Too fast — not enough time to apply
5 (9%)
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=55 · 1–5
Mean end-of-D1 build confidence by Persona
Developer4.24/5 · n=25
Architect4.06/5 · n=17
Transformation Lead3.54/5 · n=13
Programme mean: 3.9/5 · 15 cohorts
Mean end-of-D1 build confidence by AI Experience Level
Operating at the frontier4.54/5 · n=13
Delivering independently4.15/5 · n=13
Applying in practice3.90/5 · n=21
Learning and exploring3.25/5 · n=8
Overall end-of-D1 build confidence distribution
1
0 (0%)
2
1 (2%)
3
12 (22%)
4
27 (49%)
5
15 (27%)
How relevant was today's content to your current role?
Content relevance rating · n=55 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Architect4.47/5 · n=17
Transformation Lead4.15/5 · n=13
Developer3.92/5 · n=25
Mean relevance by AI Experience Level
Learning and exploring4.25/5 · n=8
Delivering independently4.15/5 · n=13
Operating at the frontier4.15/5 · n=13
Applying in practice4.10/5 · n=21
Overall relevance distribution
1
0 (0%)
2
2 (4%)
3
8 (15%)
4
25 (45%)
5
20 (36%)
How likely are you to recommend attending this programme to a colleague?
n=24 Day 2 respondents · 44% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 9113847
■ Detractors 0–6■ Passives 7–8■ Promoters 9–10
NPS Breakdown
46%
46%
8%
NPS +38 (n=24)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +42 · 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)
Accenture
67%
33%
0%
NPS +67 (n=6)
Version 1
25%
75%
0%
NPS +25 (n=4)
By Persona
Promoters (9–10)Passives (7–8)Detractors (0–6)
Architect
75%
25%
0%
NPS +75 (n=4)
Developer
50%
40%
10%
NPS +40 (n=10)
Transformation Lead
29%
71%
0%
NPS +29 (n=7)
Programme means · 15 cohorts: Architect +49 · Developer +47 · Transformation Lead +37
By AI Experience Level
Promoters (9–10)Passives (7–8)Detractors (0–6)
Learning and exploring
100%
0%
0%
NPS +100 (n=4)
Delivering independently
40%
60%
0%
NPS +40 (n=5)
Applying in practice
29%
71%
0%
NPS +29 (n=7)
Operating at the frontier
40%
40%
20%
NPS +20 (n=5)
Programme means · 15 cohorts: Applying in practice +45 · Delivering independently +42 · Operating at the frontier +39 · Learning and exploring +39
What is the main reason for your score?
n=21 responses · organised by NPS segment
Promoters (score 9–10)· 10 responses
Well-structured curriculum with expert trainers enabled participants to gain Claude expertise and exchange ideas despite potential overwhelm for juniors.
9“The training was full of very interesting content, a lot of cool insights from the trainers and a lot of opportunities to exchange ideas with the co-participants. The only reason I didn't give it a 10 is because for very junior Claude enthusiasts it might be a bit overwhelming in my opinion.”
Passives (score 7–8)· 9 responses
Practical exercises and key concepts were valuable, but clearer guidance on exercise scope and pre-work would enhance learning outcomes.
7“I would like to see more engineering oriented basecamp with prework that we can build on top with. During the day it was not really clear which parts of the exercises we should do with Claude and which of them without.”
Detractors (score 0–6)· 2 responses
Limited novelty for experienced Claude users, and instructors lacked depth to address advanced technical questions comprehensively.
4“Few new things but mainly existing topics you mostly know when working with claude. Instructors did not seem to be capable of answering deeper technical questions.”
End-of-Programme Confidence
n=24 respondents · 1 = not confident, 5 = very confident
How confident are you in your ability to design an evaluation for an AI solution?
12345Mean
——12%38%50%4.38
Programme mean: 4.1/5 · 15 cohorts
Leaving today, how comfortable are you advising clients on AI when questions arise?
12345Mean
——17%50%33%4.17
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
——17%50%33%4.17
Programme mean: 4.2/5 · 15 cohorts
Confidence Trajectory by Persona
D1→D2 delta · apply-AI confidence · matched pairs only · n=21
Positive delta = confidence grew · negative = dropped
Architect (n=4)
+0.50
Developer (n=10)
+0.00
Transformation Lead (n=7)
+0.00
What one takeaway will you share with a colleague or client?
n=16 responses
Participants value Claude's accessible API and discover prompt optimization directly reduces token consumption and costs.
“Claude's token hunger is all stemming for suboptimal prompting”
Participants gain confidence from learning concrete evaluation methods and seeing tool calls integrated with testing workflows.
“Tool calls and evals!”
Participants recognize tool calls as essential infrastructure for building production-ready AI applications.
“Tool calls and evals!”
Participants absorb concepts faster through concrete eval examples rather than theoretical frameworks.
“Some concrete examples of evals”
Participants signal strong engagement and retention when actively participating in workshop exercises.
“Join the workshop!”
What will you build for yourself or apply at work in the next 30 days?
n=18 responses
Participants want practical tools like eval visualisers to systematically test and measure AI system performance.
“Eval visualisers perhaps”
Multi-agent orchestration for real business problems like RFPs is immediately valuable to implement in their projects.
“Multi agent system for RFPs”
Participants need frameworks for evaluating architectural decisions, particularly when choosing between MCPs and alternative approaches.
“Definitely the Evaluation framework and the context optimisation strategies. And I need to do a deeper dive on the decision-making for MCPs versus skills.”
Consultants plan to apply Claude-specific features like caching and code execution to improve their production solutions immediately.
“Caching of prompts and evals to test/improve prompts”
Participants want to repeat exercises and directly apply learnings to their own ideas outside the workshop environment.
“Several things. Redo exercises. Apply to ideas I've had.”
Partner Basecamp Survey Report · Cohort 9 · Generated by analyze.py v2.3
NPS Reason
n=21 responses · grouped by NPS segment
Promotersscore 9–10 · 10 responses
1evals10
2Well structured content and well focused on the important Engineering requirements.10
3Diversity of topics10
4Extremely helpful and informative, boosting confidence and motivation10
5I attended the course with not much knowledge of Claude and its framework, and have ended the workshop with an abundance of knowledge. Very insightful workshop!10
6Broad exposure to all elements of Technical Claude Knowledge10
7Take some time off daily tasks to deep dive. Great support from team.9
8The program was well structured and covered important topics!9
9The possibilities are endless so always good to touch base9
10The training was full of very interesting content, a lot of cool insights from the trainers and a lot of opportunities to exchange ideas with the co-participants. The only reason I didn't give it a 10 is because for very junior Claude enthusiasts it might be a bit overwhelming in my opinion.9
Passivesscore 7–8 · 9 responses
1Great content8
2The course was technical but gave a sense that the outcomes expected are achievable8
3Great insights into building AI agents leveraging Claude API, and how to optimise those agents8
4New concepts learned and practical exercises done8
5Good coverage of key concepts. Plenty to take away and explore further.8
6It was good structured and covered all main topics.8
7It helped a lot to know what we can do with Claude and Claude Code7
8It could be split into take home section and a more focused in person session7
9I would like to see more engineering oriented basecamp with prework that we can build on top with. During the day it was not really clear which parts of the exercises we should do with Claude and which of them without.7
Detractorsscore 0–6 · 2 responses
1More for less experienced recipients5
2Few new things but mainly existing topics you mostly know when working with claude. Instructors did not seem to be capable of answering deeper technical questions.4
Most Valuable
What one takeaway will you share with a colleague or client? · n=16 responses
1how easy it is to evaluate AI outputs
2How easy Claude code is to use
3Claude’s token hunger is all stemming for suboptimal prompting
4Tool calls and evals!
5Prompt caching
6How integration with AI can deliver value in the long run
7Claude code auto mode is quite good!
8Some concrete examples of evals
9Never stop learning
10Caching
11Ground is shifting
12Join the workshop!
13Do skilljar courses
14Modifying the CLAUDE.md is affecting the result more then I expected.
15Claude features
16The Evaluation framework is my top keynote.
30-Day Intentions
What will you build for yourself or apply at work? · n=18 responses
1E2E app and deploy on Azure
2Something to help with PRs
3RFP orchestrator bot
4Multi agent orchestration for a finance product
5Multi agent system for RFPs
6To take all the learnings from this session and implement it in actual client scenario! Come up with valuable solutions
7Eval visualisers perhaps
8Evals
9Definitely play more with custom agents in the SDLC
10Financial Testing Application
11I will use Claude code for my coming project works, and try to expand my knowledge on this area
12Caching of prompts and evals to test/improve prompts
13Custom agent for PR reviews
14I am already working on a POC, however I will start using Eval
15Several things. Redo exercises. Apply to ideas I've had.
16Cover my ai solutions with more tests by using the evaluations. I will check the prompts of my projects.
17Continue to develop workshops skills and understand the provided code and theory,
18Definitely the Evaluation framework and the context optimisation strategies. And I need to do a deeper dive on the decision-making for MCPs versus skills.