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Partner Basecamp · Cohort 13

London

June 2-3, 2026
+40
Net Promoter Score
19
Day 1 Responses
93%
Matched Pairs
79%
Day 2 Response Rate
Programme Satisfaction
NPS +40 with 60% passives — score is held up by low detractor count, not promoter strength. Passive signal is weak: neither endorsing nor rejecting the programme.
+40NPS · n=15
40% Promoters60% Passives0% Detractors
95% CI: +15 → +65  ·  True NPS lies within this range with 95% confidence (n=15 respondents)
Flag — 60% 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 3.9/5).
3.9end-of-D1 build / 5
4.1D2 Design / 5
4.3D2 Commercial / 5
4.3D2 Build / 5
Audience
19 participants across 7 organisations — Developer majority with applying in practice the most common AI experience level.
Top Organisations
b.telligent (6) Deloitte (4) Version 1 (4) Zartis (2) BCG (1)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Hands-on Claude experience and peer knowledge exchange transformed participants' AI perspectives and built confidence.
Passives
Strong foundational program, but advanced users and non-technical attendees needed differentiated content depth.
Survey & Data Quality
Survey & Data Quality
Day 1 responses19
Day 2 responses15 (79% of Day 1)
Matched pairs14 (93% of Day 2)
Orgs resolved19 of 19 respondents matched to named org
Day 2-only respondents1 (6.7%) — suppresses NPS by 4 points; no Day 1 data, excluded from persona & confidence analysis
Organisations
All Day 1 respondents · n=19
b.telligent6 (32%)
Deloitte4 (21%)
Version 14 (21%)
Zartis2 (11%)
BCG1 (5%)
Infosys1 (5%)
Slalom1 (5%)
Function × Seniority
All Day 1 respondents · n=19
FunctionPractitionerSr PractitionerMgr / Sr MgrDirector+Total
Engineering842–14
Architecture–––11
Business Leadership––1–1
Project / Engmt––213
Experience Profile
AI experience level · n=19 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Developer21%50%21%7%14
Transformation Lead25%75%——4
Architect——100%—1
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Practitioner (0-4 years in role)25%38%25%12%8
Manager or Senior Manager40%60%——5
Senior practitioner (5–9 years)—75%25%—4
Director, Senior Director, or Principal—50%50%—2
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
4 (21%)
Applying in practice
10 (53%)
Delivering independently
4 (21%)
Operating at the frontier
1 (5%)
Prior to today, how much had you worked with Claude or the Anthropic API?
Not at all
2 (11%)
A little
8 (42%)
Regularly
9 (47%)
Did the depth land for this audience?
Technical depth perception · n=19 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Developer14%86%—14
Transformation Lead—100%—4
Architect—100%—1
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Applying in practice10%90%—10
Learning and exploring—100%—4
Delivering independently—100%—4
Operating at the frontier100%——1
Overall depth distribution
Too basic
2 (11%)
About right
17 (89%)
Did the pace work across the room?
Session pace perception · n=19 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Developer—93%7%14
Transformation Lead—75%25%4
Architect—100%—1
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Applying in practice—90%10%10
Learning and exploring—75%25%4
Delivering independently—100%—4
Operating at the frontier—100%—1
Overall pace distribution
Well paced
17 (89%)
Too fast — not enough time to apply
2 (11%)
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=19 · 1–5
Mean end-of-D1 build confidence by Persona
Architect5.00/5 · n=1
Developer3.86/5 · n=14
Transformation Lead3.25/5 · n=4
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 independently4.25/5 · n=4
Applying in practice3.70/5 · n=10
Learning and exploring3.25/5 · n=4
Overall end-of-D1 build confidence distribution
1
0 (0%)
2
1 (5%)
3
6 (32%)
4
8 (42%)
5
4 (21%)
How relevant was today's content to your current role?
Content relevance rating · n=19 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Developer4.14/5 · n=14
Architect4.00/5 · n=1
Transformation Lead3.75/5 · n=4
Mean relevance by AI Experience Level
Delivering independently4.50/5 · n=4
Learning and exploring4.25/5 · n=4
Operating at the frontier4.00/5 · n=1
Applying in practice3.80/5 · n=10
Overall relevance distribution
1
0 (0%)
2
0 (0%)
3
6 (32%)
4
6 (32%)
5
7 (37%)
How likely are you to recommend attending this programme to a colleague?
n=15 Day 2 respondents · 79% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 134515
■ Detractors 0–6■ Passives 7–8■ Promoters 9–10
NPS Breakdown
40%
60%
0%
NPS +40 (n=15)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +42 · 15 cohorts
Promoters mean: 52%
Passives mean: 37%
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)
b.telligent
25%
75%
0%
NPS +25 (n=4)
By Persona
Promoters (9–10)Passives (7–8)Detractors (0–6)
Developer
56%
44%
0%
NPS +56 (n=9)
Transformation Lead
0%
100%
0%
NPS 0 (n=4)
Programme means · 15 cohorts: Architect +51 · Developer +46 · Transformation Lead +39
By AI Experience Level
Promoters (9–10)Passives (7–8)Detractors (0–6)
Applying in practice
44%
56%
0%
NPS +44 (n=9)
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)· 4 responses
Hands-on Claude experience and peer knowledge exchange transformed participants' AI perspectives and built confidence.
10“Really great to establish Claude knowledge and a great base to build on”
Passives (score 7–8)· 8 responses
Strong foundational program, but advanced users and non-technical attendees needed differentiated content depth.
7“For a practitioner that works with Claude Code on a daily basis there was not a lot of new topics. Still, good to understand the Anthropic frameworks which is going to help talking to costumers.”
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
——20%47%33%4.13
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
—7%—53%40%4.27
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
Developer (n=9)
+0.11
Transformation Lead (n=4)
+0.75
What one takeaway will you share with a colleague or client?
n=9 responses
Participants recognized that evaluations transform abstract AI concepts into concrete, measurable quality assurance practices.
“Evals make it real”
Participants moved from uncertainty about AI tool mechanics to confident understanding of how to implement them.
“No longer a fear of how these tools work”
What will you build for yourself or apply at work in the next 30 days?
n=10 responses
Participants want to build autonomous, self-improving agents and automate manual workflows using advanced techniques like evals and context engineering.
“I will try to create an agent to automatize a manual and respetable task I do monthly at work.”
Participants plan to translate learning into client-facing applications and extend hackathon prototypes into production use cases.
“Will start working with the team to work out how we can apply the learnings with our clients”
Participants value practical application and want to immediately implement techniques learned rather than absorb theory alone.
“Put into practice everything we learnt”
Participants see evals and context engineering as foundational techniques for building workshops and validating agent performance.
“Leveraging evals and context engineering more to develop some workshops”
Participants intend to migrate existing systems and projects to Claude as their preferred AI model.
“Plan to move things to Claude”
Partner Basecamp Survey Report · Cohort 13 · Generated by analyze.py v2.3
NPS Reason
n=12 responses · grouped by NPS segment
Promotersscore 9–10 · 4 responses
1Really engaging, great instructors10
2My perspective towards AI has changed, got to play around Claude10
3Really great to establish Claude knowledge and a great base to build on10
4Evals, able to exchange ideas with other people about Claude feature9
Passivesscore 7–8 · 8 responses
1Anthropic wasn't there8
2Very helpful across many areas. Would be great to have additional deep dives here and there.8
3Really enjoyed it but would recommend it only to technical colleagues as opposed to more design/delivery oriented peers8
4I found it very useful.8
5- Great overview over Claude Produkts - Really good hands on practice - A bit more time on practice and hackathon would be nice8
6Great program! Have more questions than when I came. But know how to answer them!7
7Enable someone non technical to use Claude7
8For a practitioner that works with Claude Code on a daily basis there was not a lot of new topics. Still, good to understand the Anthropic frameworks which is going to help talking to costumers.7
Most Valuable
What one takeaway will you share with a colleague or client? · n=9 responses
1Explore more!
2Evals make it real
3Within one hour what you can build for a client is seriously cool
4AI is here to stay, now is the time to start using
5No longer a fear of how these tools work
6A lot
7I recommend it. Speakers are fantastic.
8Evals, their importance and necessity to qa.
9The mindset change to adopt
30-Day Intentions
What will you build for yourself or apply at work? · n=10 responses
1A lot. Autonomous self improving agents
2Extend the hackathon use case
3A memory cartology
4Plugins and skills
5Put into practice everything we learnt
6Better utilisation of Ai
7Leveraging evals and context engineering more to develop some workshops
8Plan to move things to Claude
9I will try to create an agent to automatize a manual and respetable task I do monthly at work.
10Will start working with the team to work out how we can apply the learnings with our clients