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Partner Basecamp · Cohort 16

London

June 15-16, 2026
+17
Net Promoter Score
36
Day 1 Responses
74%
Matched Pairs
64%
Day 2 Response Rate
Programme Satisfaction
Mixed signal — NPS +17 with notable detractor presence.
+17NPS · n=23
39% Promoters39% Passives22% Detractors
95% CI: -14 → +48  ·  True NPS lies within this range with 95% confidence (n=23 respondents)
Confidence Arc
Moderate end-of-Day-1 build confidence — cohort is progressing; Day 2 has room to close remaining gaps (mean 3.8/5).
3.8end-of-D1 build / 5
3.9D2 Design / 5
4.0D2 Commercial / 5
4.2D2 Build / 5
Audience
36 participants across 11 organisations — Transformation Lead majority with applying in practice the most common AI experience level.
Top Organisations
Deloitte (11) Version 1 (4) Fractal Analytics (4) Capgemini (4) Quantium (3)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Hands-on exercises and tutor quality enabled deep learning of AI concepts applicable to client conversations.
Passives
Practical training was valuable but lacked reference materials and struggled to serve both beginner and experienced participants equally.
Detractors
Program focused on technical exercises rather than business-critical topics like go-to-market strategy, solution design, and POC failure prevention.
Survey & Data Quality
Survey & Data Quality
Day 1 responses36
Day 2 responses23 (64% of Day 1)
Matched pairs17 (74% of Day 2)
Orgs resolved36 of 36 respondents matched to named org
Day 2-only respondents6 (26.1%) — suppresses NPS by 12 points; no Day 1 data, excluded from persona & confidence analysis
Organisations
All Day 1 respondents · n=36
Deloitte11 (31%)
Version 14 (11%)
Fractal Analytics4 (11%)
Capgemini4 (11%)
Quantium3 (8%)
valantic3 (8%)
Infosys2 (6%)
Zartis2 (6%)
IndiciumAI1 (3%)
Firemind1 (3%)
SVA1 (3%)
Function × Seniority
All Day 1 respondents · n=36
FunctionPractitionerSr PractitionerMgr / Sr MgrDirector+Partner / MDTotal
Engineering424––10
Architecture1243–10
Business Leadership111227
Project / Engmt3222–9
Experience Profile
AI experience level · n=36 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Transformation Lead19%50%25%6%16
Developer20%40%40%—10
Architect40%40%20%—10
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Manager or Senior Manager27%55%18%—11
Practitioner (0-4 years in role)44%44%11%—9
Director, Senior Director, or Principal14%43%43%—7
Senior practitioner (5–9 years)14%43%29%14%7
Partner, Managing Director, or Executive——100%—2
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
9 (25%)
Applying in practice
16 (44%)
Delivering independently
10 (28%)
Operating at the frontier
1 (3%)
Prior to today, how much had you worked with Claude or the Anthropic API?
Not at all
5 (14%)
A little
15 (42%)
Regularly
16 (44%)
Did the depth land for this audience?
Technical depth perception · n=36 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Transformation Lead12%81%6%16
Developer20%80%—10
Architect—100%—10
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Applying in practice6%88%6%16
Delivering independently30%70%—10
Learning and exploring—100%—9
Operating at the frontier—100%—1
Overall depth distribution
Too basic
4 (11%)
About right
31 (86%)
Too advanced
1 (3%)
Did the pace work across the room?
Session pace perception · n=36 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Transformation Lead6%94%—16
Developer—90%10%10
Architect—100%—10
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Applying in practice—94%6%16
Delivering independently10%90%—10
Learning and exploring—100%—9
Operating at the frontier—100%—1
Overall pace distribution
Too slow — could have covered more
1 (3%)
Well paced
34 (94%)
Too fast — not enough time to apply
1 (3%)
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=36 · 1–5
Mean end-of-D1 build confidence by Persona
Developer4.10/5 · n=10
Architect4.10/5 · n=10
Transformation Lead3.81/5 · n=16
Programme mean: 3.9/5 · 16 cohorts
Mean end-of-D1 build confidence by AI Experience Level
Operating at the frontier5.00/5 · n=1
Delivering independently4.30/5 · n=10
Applying in practice3.88/5 · n=16
Learning and exploring3.67/5 · n=9
Overall end-of-D1 build confidence distribution
1
0 (0%)
2
1 (3%)
3
9 (25%)
4
16 (44%)
5
10 (28%)
How relevant was today's content to your current role?
Content relevance rating · n=36 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Developer4.50/5 · n=10
Architect4.30/5 · n=10
Transformation Lead3.88/5 · n=16
Mean relevance by AI Experience Level
Applying in practice4.38/5 · n=16
Learning and exploring4.22/5 · n=9
Delivering independently3.90/5 · n=10
Operating at the frontier3.00/5 · n=1
Overall relevance distribution
1
0 (0%)
2
1 (3%)
3
8 (22%)
4
11 (31%)
5
16 (44%)
How likely are you to recommend attending this programme to a colleague?
n=23 Day 2 respondents · 64% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 161223645
■ Detractors 0–6■ Passives 7–8■ Promoters 9–10
NPS Breakdown
39%
39%
22%
NPS +17 (n=23)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +44 · 16 cohorts
Promoters mean: 52%
Passives mean: 39%
Detractors mean: 9%
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
50%
50%
0%
NPS +50 (n=6)
Transformation Lead
25%
38%
38%
NPS -12 (n=8)
Programme means · 16 cohorts: Architect +51 · Developer +46 · Transformation Lead +40
By AI Experience Level
Promoters (9–10)Passives (7–8)Detractors (0–6)
Applying in practice
71%
14%
14%
NPS +57 (n=7)
Delivering independently
33%
50%
17%
NPS +17 (n=6)
Learning and exploring
25%
50%
25%
NPS 0 (n=4)
Programme means · 16 cohorts: Learning and exploring +48 · Delivering independently +44 · Applying in practice +43 · Operating at the frontier +37
What is the main reason for your score?
n=18 responses · organised by NPS segment
Promoters (score 9–10)· 7 responses
Hands-on exercises and tutor quality enabled deep learning of AI concepts applicable to client conversations.
9“The contents are very interesting and really brings an edge when talking and demoing something to clients”
Passives (score 7–8)· 6 responses
Practical training was valuable but lacked reference materials and struggled to serve both beginner and experienced participants equally.
7“Target audience feels split. I think less experienced with agentic work found it super helpful but as someone that is quite experienced with this, I felt there was a final 5-10% of the what if or even better that was missing in my perspective. Eg what are the first principles for prompt design/writing”
Detractors (score 0–6)· 5 responses
Program focused on technical exercises rather than business-critical topics like go-to-market strategy, solution design, and POC failure prevention.
2“There is no differentiator to a half day set of excercises I can do remote. It is a good tech practice but does not touch upon half of the intent. There was no go to market. No what to do to not have failed pocs”
End-of-Programme Confidence
n=23 respondents · 1 = not confident, 5 = very confident
How confident are you in your ability to design an evaluation for an AI solution?
12345Mean
—4%22%52%22%3.91
Programme mean: 4.1/5 · 16 cohorts
Leaving today, how comfortable are you advising clients on AI when questions arise?
12345Mean
——26%52%22%3.96
Programme mean: 4.2/5 · 16 cohorts
How confident are you in your ability to lead a conversation about Anthropic and Claude with a client?
12345Mean
——9%65%26%4.17
Programme mean: 4.2/5 · 16 cohorts
Confidence Trajectory by Persona
D1→D2 delta · apply-AI confidence · matched pairs only · n=17
Positive delta = confidence grew · negative = dropped
Architect (n=3)
+0.67
Developer (n=6)
-0.17
Transformation Lead (n=8)
+0.62
What one takeaway will you share with a colleague or client?
n=10 responses
Participants valued learning Claude's capabilities and pricing models, recognizing Anthropic's strong technology despite early integration challenges.
“Anthropic has a long way to go figuring out how to work with system integrators but has good tech”
Participants consistently highlighted evals as a critical learning, treating them as the systematic testing foundation for AI applications.
“Evals, it's like unit testing for AI”
Participants sought concrete understanding of Claude's commercial applications and how pricing models affect business strategy decisions.
“What Claude can do and esp about pricing strategy”
Participants recognized that optimizing prompt design and context engineering should precede decisions about switching between models.
“Prompt design and context engineering before swapping models”
What will you build for yourself or apply at work in the next 30 days?
n=14 responses
Participants want to apply evaluation frameworks to real products, signaling readiness to embed testing practices into their work.
“Apply Eval on our company website chat assistant”
Consultants see agentic systems as practical tools to automate their own workflows, not just theoretical concepts to understand.
“Agentic teams to support my day to day work”
Participants identify immediate, concrete use cases where AI tools could solve internal operational friction.
“Tool to do May Travel expenses”
Workshop-based learning formats drive higher perceived value than theoretical instruction for this audience.
“The evals workshop was very useful”
Developers want guidance on integrating Claude into existing codebases rather than building from scratch.
“Improving my existing repos to work better with Claude Code”
Partner Basecamp Survey Report · Cohort 16 · Generated by analyze.py v2.5
NPS Reason
n=18 responses · grouped by NPS segment
Promotersscore 9–10 · 7 responses
1Did a lot of exercise and learnt a lot10
2I learned a lot. Really nice Team of Tutors!10
3Lot of interesting content and discussion10
4Hands on Tech dev and understanding9
5Enjoyed the session9
6The contents are very interesting and really brings an edge when talking and demoing something to clients9
7It’s good to learn something from you guys.9
Passivesscore 7–8 · 6 responses
1Good hands on practice and introduction of important features8
2As someone who is starting to build my knowledge to support clients with AI solutions I learnt a lot! I wish I could get access to the slides to easily refer back to some concepts.8
3The hands on approach was quite helpful to understand the concepts.8
4Lots to learn and useful grounding and latest in Claude.8
5Most of the first day’s content is mandatory learning in my company7
6Target audience feels split. I think less experienced with agentic work found it super helpful but as someone that is quite experienced with this, I felt there was a final 5-10% of the what if or even better that was missing in my perspective. Eg what are the first principles for prompt design/writing7
Detractorsscore 0–6 · 5 responses
1It’s nice on a personal level to know these things but from a business perspective I would like to learn more insights and solution design (eg why do these 95% of use cases fail?)6
2Good to take out some time to learn with unlimited tokens, most of it could have been done online / remotely6
3Nice atmosphere but no specific audience, neither sales focus nor technically enabling. More of a networking and knowledge sharing event5
4Was expecting the session to be deep insights into Claude, but was more at the surface level.5
5There is no differentiator to a half day set of excercises I can do remote. It is a good tech practice but does not touch upon half of the intent. There was no go to market. No what to do to not have failed pocs2
Most Valuable
What one takeaway will you share with a colleague or client? · n=10 responses
1What Claude can do and esp about pricing strategy
2Demo instead of talk
3Evals
4Learning can be fun!
5If something is not working as expected, it’s highly likely it’s not the model.
6Evals, it's like unit testing for AI
7Anthropic has a long way to go figuring out how to work with system integrators but has good tech
8Evals
9Prompt design and context engineering before swapping models
10Maybe we don’t need software devs?
30-Day Intentions
What will you build for yourself or apply at work? · n=14 responses
1Yes. Definitely
2Tool to do May Travel expenses
3Attempt at Agentic apps
4The evals workshop was very useful
5Build an app for parents to navigate the school journey when they are new in Uk
6Improving my existing repos to work better with Claude Code
7Apply Eval on our company website chat assistant
8Evals will be something I would aim to take up more.
9Finalize two pics
10An agentic coding system
11Agentic teams to support my day to day work
12Experiment with html/js slides rather than ppt.
13Second brain
14Not sure. Evals maybe