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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 combined with expert instruction delivered practical AI knowledge participants could immediately apply with clients.
Passives
Practical learning value recognized, but experienced practitioners wanted deeper conceptual frameworks and better resource access for ongoing reference.
Detractors
Technical training lacked business context—missing go-to-market strategy, failure case analysis, and solution design guidance needed for real client engagement.
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 · 15 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: +43 · 15 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 · 15 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 · 15 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 combined with expert instruction delivered practical AI knowledge participants could immediately apply with clients.
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 learning value recognized, but experienced practitioners wanted deeper conceptual frameworks and better resource access for ongoing reference.
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
Technical training lacked business context—missing go-to-market strategy, failure case analysis, and solution design guidance needed for real client engagement.
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 · 15 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 · 15 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 · 15 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 value understanding Claude's capabilities and pricing model to advise clients effectively on model selection.
“What Claude can do and esp about pricing strategy”
Participants recognize evals as a critical practice for validating AI outputs, comparable to unit testing in software development.
“Evals, it's like unit testing for AI”
Participants need practical knowledge of model capabilities and pricing to shape client AI strategies and business cases.
“What Claude can do and esp about pricing strategy”
Participants learned that optimizing prompts and context before switching models prevents unnecessary model changes and costs.
“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 their own production systems, signaling readiness to implement evals beyond workshop exercises.
“Apply Eval on our company website chat assistant”
Consultants see agentic systems as tools to augment their own workflows, not just client deliverables they might recommend.
“Agentic teams to support my day to day work”
Participants are exploring AI tools to solve immediate internal business problems like expense management.
“Tool to do May Travel expenses”
Workshop-based learning, particularly evals training, resonates most when it offers practical, applicable techniques.
“The evals workshop was very useful”
Developers are actively refactoring existing codebases to leverage Claude's capabilities for improved performance.
“Improving my existing repos to work better with Claude Code”
Partner Basecamp Survey Report · Cohort 16 · Generated by analyze.py v2.3
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