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Partner Basecamp · Cohort 15

San Francisco

June 16-17
+55
Net Promoter Score
69
Day 1 Responses
97%
Matched Pairs
48%
Day 2 Response Rate
Programme Satisfaction
Strong promoter majority — NPS +55 reflects broad programme endorsement.
+55NPS · n=33
64% Promoters27% Passives9% Detractors
95% CI: +32 → +77  ·  True NPS lies within this range with 95% confidence (n=33 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
4.1D2 Commercial / 5
4.1D2 Build / 5
Audience
69 participants across 13 organisations — Transformation Lead majority with applying in practice the most common AI experience level.
Top Organisations
Infosys (19) PwC (13) Persistent Systems (9) McKinsey (7) Cognizant (5)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Hands-on learning with Claude's practical capabilities—caching, agents, code—delivered rapid skill advancement from zero to building functional applications.
Passives
Technical depth and interactivity were strong, but pre-workshop preparation and agent-building walkthroughs would unlock greater value.
Detractors
Advanced optimization techniques—token efficiency, application integration patterns, performance tricks—were absent despite expectations for experienced practitioners.
Survey & Data Quality
Survey & Data Quality
Day 1 responses69
Day 2 responses33 (48% of Day 1)
Matched pairs32 (97% of Day 2)
Orgs resolved69 of 69 respondents matched to named org
Day 2-only respondents1 (3.0%) — suppresses NPS by 1 points; no Day 1 data, excluded from persona & confidence analysis
⚠ Low Day 2 rateOnly 48% of Day 1 respondents completed Day 2. Push the survey link during or immediately after the closing session.
Organisations
All Day 1 respondents · n=69
Infosys19 (28%)
PwC13 (19%)
Persistent Systems9 (13%)
McKinsey7 (10%)
Cognizant5 (7%)
Nimble Gravity4 (6%)
NEC3 (4%)
Fractal Analytics2 (3%)
Lovelytics2 (3%)
Deloitte2 (3%)
Ascendion1 (1%)
Persistent Systems ltd1 (1%)
Percepta1 (1%)
Function × Seniority
All Day 1 respondents · n=69
FunctionPractitionerSr PractitionerMgr / Sr MgrDirector+Partner / MDTotal
Engineering41342–23
Architecture2482–16
Business Leadership2135617
Project / Engmt7–5–113
Experience Profile
AI experience level · n=69 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Transformation Lead23%70%3%3%30
Developer13%52%26%9%23
Architect12%38%31%19%16
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Manager or Senior Manager35%40%20%5%20
Senior practitioner (5–9 years)6%56%33%6%18
Practitioner (0-4 years in role)13%67%7%13%15
Director, Senior Director, or Principal11%67%11%11%9
Partner, Managing Director, or Executive14%71%—14%7
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
12 (17%)
Applying in practice
39 (57%)
Delivering independently
12 (17%)
Operating at the frontier
6 (9%)
Prior to today, how much had you worked with Claude or the Anthropic API?
Not at all
11 (16%)
A little
29 (42%)
Regularly
29 (42%)
Did the depth land for this audience?
Technical depth perception · n=69 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Transformation Lead13%73%13%30
Developer13%83%4%23
Architect6%94%—16
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Applying in practice10%77%13%39
Learning and exploring—100%—12
Delivering independently17%83%—12
Operating at the frontier33%67%—6
Overall depth distribution
Too basic
8 (12%)
About right
56 (81%)
Too advanced
5 (7%)
Did the pace work across the room?
Session pace perception · n=69 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Transformation Lead17%73%10%30
Developer4%91%4%23
Architect—100%—16
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Applying in practice10%82%8%39
Learning and exploring—92%8%12
Delivering independently17%83%—12
Operating at the frontier—100%—6
Overall pace distribution
Too slow — could have covered more
6 (9%)
Well paced
59 (86%)
Too fast — not enough time to apply
4 (6%)
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=69 · 1–5
Mean end-of-D1 build confidence by Persona
Developer4.35/5 · n=23
Architect4.12/5 · n=16
Transformation Lead3.57/5 · n=30
Programme mean: 3.9/5 · 15 cohorts
Mean end-of-D1 build confidence by AI Experience Level
Operating at the frontier4.50/5 · n=6
Delivering independently4.25/5 · n=12
Applying in practice3.90/5 · n=39
Learning and exploring3.58/5 · n=12
Overall end-of-D1 build confidence distribution
1
0 (0%)
2
3 (4%)
3
19 (28%)
4
25 (36%)
5
22 (32%)
How relevant was today's content to your current role?
Content relevance rating · n=69 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Architect4.25/5 · n=16
Developer4.09/5 · n=23
Transformation Lead3.73/5 · n=30
Mean relevance by AI Experience Level
Learning and exploring4.08/5 · n=12
Operating at the frontier4.00/5 · n=6
Applying in practice3.95/5 · n=39
Delivering independently3.92/5 · n=12
Overall relevance distribution
1
0 (0%)
2
5 (7%)
3
13 (19%)
4
30 (43%)
5
21 (30%)
How likely are you to recommend attending this programme to a colleague?
n=33 Day 2 respondents · 48% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 15327912
■ Detractors 0–6■ Passives 7–8■ Promoters 9–10
NPS Breakdown
64%
27%
9%
NPS +55 (n=33)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +41 · 15 cohorts
Promoters mean: 51%
Passives mean: 40%
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)
PwC
86%
14%
0%
NPS +86 (n=7)
Persistent Systems
83%
17%
0%
NPS +83 (n=6)
Infosys
33%
67%
0%
NPS +33 (n=6)
By Persona
Promoters (9–10)Passives (7–8)Detractors (0–6)
Transformation Lead
69%
31%
0%
NPS +69 (n=13)
Developer
75%
17%
8%
NPS +67 (n=12)
Architect
43%
29%
29%
NPS +14 (n=7)
Programme means · 15 cohorts: Architect +54 · Developer +45 · Transformation Lead +34
By AI Experience Level
Promoters (9–10)Passives (7–8)Detractors (0–6)
Applying in practice
78%
22%
0%
NPS +78 (n=18)
Learning and exploring
50%
50%
0%
NPS +50 (n=6)
Delivering independently
50%
0%
50%
NPS 0 (n=6)
Programme means · 15 cohorts: Delivering independently +45 · Learning and exploring +44 · Applying in practice +42 · Operating at the frontier +37
What is the main reason for your score?
n=20 responses · organised by NPS segment
Promoters (score 9–10)· 13 responses
Hands-on learning with Claude's practical capabilities—caching, agents, code—delivered rapid skill advancement from zero to building functional applications.
10“There is an incredible amount you can learn in a short amount of time, regardless of what background you have. You will come out of this inspired to learn more and apply.”
Passives (score 7–8)· 6 responses
Technical depth and interactivity were strong, but pre-workshop preparation and agent-building walkthroughs would unlock greater value.
8“This was a really interactive session. But we can have a session set out for building an agent from scratch showing relevant files and its connections. Just a a video will help.”
Detractors (score 0–6)· 1 response
Advanced optimization techniques—token efficiency, application integration patterns, performance tricks—were absent despite expectations for experienced practitioners.
6“Was expecting more advanced learning related to using Claude into applications, usage of tokens and tricks to use”
End-of-Programme Confidence
n=33 respondents · 1 = not confident, 5 = very confident
How confident are you in your ability to design an evaluation for an AI solution?
12345Mean
——39%33%27%3.88
Programme mean: 4.1/5 · 15 cohorts
Leaving today, how comfortable are you advising clients on AI when questions arise?
12345Mean
——21%52%27%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
——18%58%24%4.06
Programme mean: 4.2/5 · 15 cohorts
Confidence Trajectory by Persona
D1→D2 delta · apply-AI confidence · matched pairs only · n=32
Positive delta = confidence grew · negative = dropped
Architect (n=7)
+0.00
Developer (n=12)
-0.08
Transformation Lead (n=13)
+0.62
What one takeaway will you share with a colleague or client?
n=14 responses
Participants need to understand model selection and configuration levers to unlock Claude's full potential for their projects.
“Unlock claude potential…then you have immense power at your hands.”
Participants recognize evaluation frameworks as critical for validating AI outputs before deploying to production.
“importance of evals”
Participants sought practical guidance on architecting AI agents, moving beyond theory to implementation.
“How to build an agent”
Participants want to control the application layer to differentiate value rather than relying on generic model capabilities.
“Own the application layer”
Participants benefit from pre-programme preparation and technical setup to maximize learning retention and hands-on practice.
“Come prepared and setup with everything mentioned. Be a sponge”
What will you build for yourself or apply at work in the next 30 days?
n=17 responses
Participants want hands-on capability to build custom agents and multi-agent systems rather than theory alone.
“Custom agents”
Participants are actively applying evaluation frameworks to validate agent performance in their own testing workflows.
“in making agents for testing”
Participants plan to implement Claude tools and prompt engineering directly into their client work and personal projects.
“I am going to take cowork for personal and claude code for work diligently.”
Participants see immediate value in building data integration tools that connect enterprise systems to AI agents.
“A data aggregation tool pulling from client software (SAP, Coupa, TMS, etc.)”
Partner Basecamp Survey Report · Cohort 15 · Generated by analyze.py v2.3
NPS Reason
n=20 responses · grouped by NPS segment
Promotersscore 9–10 · 13 responses
1The session covered a lot into caching and sub agents which I was looking for10
2The whole cadence of the workshop and the the planned topics.10
3Best10
4From not knowing Claude code to building an agent…more than happy with what I learned.10
5It's a fun experience and you get to learn about Claude10
6There is an incredible amount you can learn in a short amount of time, regardless of what background you have. You will come out of this inspired to learn more and apply.10
7Because I could learn the know-how for building agents systematically.10
8Enjoyed the hands on exploration of Claude code10
9the learning sessions and hackathon helped well9
10showed how we can efficiently use claude and what different capabilities it has with lot of hands on9
11Improving my current knowledge9
12Hands on hackathon9
13Was less technical depth and more into usage.9
Passivesscore 7–8 · 6 responses
1Really packed with technical pieces and a really good deep dive into the foundation8
2Its great program for hand on. Better if prep is given beforehand.8
3This was a really interactive session. But we can have a session set out for building an agent from scratch showing relevant files and its connections. Just a a video will help.8
4Would only recommend to colleagues with a technical background.8
5Presenters were very engaged! I feel I got most value from asking the presenters questions rather than doing the trainings.7
6New world on the Ai to explore7
Detractorsscore 0–6 · 1 response
1Was expecting more advanced learning related to using Claude into applications, usage of tokens and tricks to use6
Most Valuable
What one takeaway will you share with a colleague or client? · n=14 responses
1There are many ways to optimize for cost and latency.
2Smartly using models when needed - model type, levers to pull, etc.
3Yes
4prompt engineering and evals
5Unlock claude potential…then you have immense power at your hands.
6Its great program to know claude in action
7Own the application layer
8Get into the world of AI sooner than later.
9Come prepared and setup with everything mentioned. Be a sponge
10importance of evals
11Non technical people can do technical builds
12Great hackathon
13Cost optimization and token usage
14How to build an agent
30-Day Intentions
What will you build for yourself or apply at work? · n=17 responses
1A data aggregation tool pulling from client software (SAP, Coupa, TMS, etc.)
2Custom agents
3Yesy
4Trying to use Claude code and Eval.
5Multi agent systems and evaluations
6build a new agent
7I will build a travel planner.
8in making agents for testing
9I am going to take cowork for personal and claude code for work diligently.
10Agent evals
11Agents and more agents
12Build my first agent on my own.
13personal and organzations agents
14Build PowerPoint generator
15Implement prompt and context engineering in my work
16Plugins and agents
17Agentic workflows