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Partner Basecamp · Cohort 18

San Francisco

June 22–23, 2026
+35
Net Promoter Score
45
Day 1 Responses
81%
Matched Pairs
82%
Day 2 Response Rate
Programme Satisfaction
Positive NPS of +35 — promoters outweigh detractors with moderate passive presence.
+35NPS · n=37
49% Promoters38% Passives14% Detractors
95% CI: +12 → +58  ·  True NPS lies within this range with 95% confidence (n=37 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
4.0D2 Design / 5
4.1D2 Commercial / 5
4.1D2 Build / 5
Audience
45 participants across 9 organisations — Transformation Lead majority with applying in practice the most common AI experience level.
Top Organisations
PwC (16) Deloitte (8) McKinsey (7) Cognizant (4) Lovelytics (3)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Hands-on, interactive delivery with practical Claude capabilities unlocked new technical possibilities participants hadn't previously explored.
Passives
Content delivery succeeded, but gaps in tooling depth and final exercise clarity prevented transition from satisfied to enthusiastic.
Detractors
Trainer expertise insufficient and content too basic for advanced internal teams; exercises lacked proper context and clear instructions.
Survey & Data Quality
Survey & Data Quality
Day 1 responses45
Day 2 responses37 (82% of Day 1)
Matched pairs30 (81% of Day 2)
Orgs resolved45 of 45 respondents matched to named org
Day 2-only respondents7 (18.9%) — suppresses NPS by 2 points; no Day 1 data, excluded from persona & confidence analysis
Organisations
All Day 1 respondents · n=45
PwC16 (36%)
Deloitte8 (18%)
McKinsey7 (16%)
Cognizant4 (9%)
Lovelytics3 (7%)
AlixPartners3 (7%)
EPAM2 (4%)
Ascendion1 (2%)
UST Global1 (2%)
Function × Seniority
All Day 1 respondents · n=45
FunctionPractitionerSr PractitionerMgr / Sr MgrDirector+Partner / MDTotal
Engineering4221–9
Architecture–343–10
Business Leadership1–1215
Project / Engmt4593–21
Experience Profile
AI experience level · n=45 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Transformation Lead42%38%19%—26
Architect—70%20%10%10
Developer22%67%11%—9
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Manager or Senior Manager25%56%19%—16
Senior practitioner (5–9 years)20%60%20%—10
Practitioner (0-4 years in role)33%56%11%—9
Director, Senior Director, or Principal44%33%11%11%9
Partner, Managing Director, or Executive——100%—1
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
13 (29%)
Applying in practice
23 (51%)
Delivering independently
8 (18%)
Operating at the frontier
1 (2%)
Prior to today, how much had you worked with Claude or the Anthropic API?
Not at all
3 (7%)
A little
23 (51%)
Regularly
19 (42%)
Did the depth land for this audience?
Technical depth perception · n=45 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Transformation Lead23%62%15%26
Architect20%70%10%10
Developer44%44%11%9
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Applying in practice30%61%9%23
Learning and exploring—69%31%13
Delivering independently62%38%—8
Operating at the frontier—100%—1
Overall depth distribution
Too basic
12 (27%)
About right
27 (60%)
Too advanced
6 (13%)
Did the pace work across the room?
Session pace perception · n=45 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Transformation Lead8%81%12%26
Architect—90%10%10
Developer33%67%—9
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Applying in practice9%83%9%23
Learning and exploring—85%15%13
Delivering independently38%62%—8
Operating at the frontier—100%—1
Overall pace distribution
Too slow — could have covered more
5 (11%)
Well paced
36 (80%)
Too fast — not enough time to apply
4 (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=45 · 1–5
Mean end-of-D1 build confidence by Persona
Architect4.20/5 · n=10
Developer4.00/5 · n=9
Transformation Lead3.69/5 · n=26
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 independently4.38/5 · n=8
Applying in practice4.00/5 · n=23
Learning and exploring3.23/5 · n=13
Overall end-of-D1 build confidence distribution
1
1 (2%)
2
1 (2%)
3
14 (31%)
4
16 (36%)
5
13 (29%)
How relevant was today's content to your current role?
Content relevance rating · n=45 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Architect4.10/5 · n=10
Transformation Lead3.81/5 · n=26
Developer3.67/5 · n=9
Mean relevance by AI Experience Level
Applying in practice4.04/5 · n=23
Operating at the frontier4.00/5 · n=1
Delivering independently3.62/5 · n=8
Learning and exploring3.62/5 · n=13
Overall relevance distribution
1
0 (0%)
2
3 (7%)
3
10 (22%)
4
23 (51%)
5
9 (20%)
How likely are you to recommend attending this programme to a colleague?
n=37 Day 2 respondents · 82% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 18111268513
■ Detractors 0–6■ Passives 7–8■ Promoters 9–10
NPS Breakdown
49%
38%
14%
NPS +35 (n=37)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +42 · 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)
PwC
60%
40%
0%
NPS +60 (n=10)
Cognizant
50%
50%
0%
NPS +50 (n=4)
Deloitte
20%
40%
40%
NPS -20 (n=5)
By Persona
Promoters (9–10)Passives (7–8)Detractors (0–6)
Architect
56%
44%
0%
NPS +56 (n=9)
Developer
60%
20%
20%
NPS +40 (n=5)
Transformation Lead
38%
44%
19%
NPS +19 (n=16)
Programme means · 16 cohorts: Architect +51 · Developer +47 · Transformation Lead +38
By AI Experience Level
Promoters (9–10)Passives (7–8)Detractors (0–6)
Delivering independently
67%
17%
17%
NPS +50 (n=6)
Learning and exploring
43%
43%
14%
NPS +29 (n=7)
Applying in practice
38%
50%
12%
NPS +25 (n=16)
Programme means · 16 cohorts: Applying in practice +46 · Learning and exploring +46 · Delivering independently +41 · Operating at the frontier +37
What is the main reason for your score?
n=32 responses · organised by NPS segment
Promoters (score 9–10)· 15 responses
Hands-on, interactive delivery with practical Claude capabilities unlocked new technical possibilities participants hadn't previously explored.
9“Great info and learning that keeps changing day to day and help stay up to speed and what to expect.”
Passives (score 7–8)· 12 responses
Content delivery succeeded, but gaps in tooling depth and final exercise clarity prevented transition from satisfied to enthusiastic.
8“The program very much expanded my learning and understanding of how the tools work and how to build. I felt the final exercise lacked the proper instructions and impact but otherwise was a great program.”
Detractors (score 0–6)· 5 responses
Trainer expertise insufficient and content too basic for advanced internal teams; exercises lacked proper context and clear instructions.
4“I really liked the little pro tips here and there to enhance my knowledge of Claude and agentic development. The prework and the slides and exercises themselves were great. However I think some of the content was slightly technical for my particular team (more AI education focused than product building). I also found the instructors to have varying levels of knowledge about Claude and its products and the level of depth of their knowledge was only okay.”
End-of-Programme Confidence
n=37 respondents · 1 = not confident, 5 = very confident
How confident are you in your ability to design an evaluation for an AI solution?
12345Mean
—11%16%38%35%3.97
Programme mean: 4.1/5 · 16 cohorts
Leaving today, how comfortable are you advising clients on AI when questions arise?
12345Mean
——22%43%35%4.14
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
——22%46%32%4.11
Programme mean: 4.2/5 · 16 cohorts
Confidence Trajectory by Persona
D1→D2 delta · apply-AI confidence · matched pairs only · n=30
Positive delta = confidence grew · negative = dropped
Architect (n=9)
+0.22
Developer (n=5)
+0.60
Transformation Lead (n=16)
+0.25
What one takeaway will you share with a colleague or client?
n=27 responses
Participants recognize Claude as a superior AI option, emphasizing that implementation approach matters more than model selection.
“Claude is the better AI option”
Participants need to shift from end-to-end testing toward designing evaluation frameworks upfront and integrating them into CI/CD pipelines.
“Evals. We need to enhance our QA cicd flow. Previously more e2e testing for ai.”
Participants learn best through doing, not instruction; practical exercises drive skill development more than classroom content.
“Knowledge is not unequal to practice. Try and create things. The more you do the better you become.”
Participants recognize context engineering as a critical capability for building effective AI agent systems.
“That context engineering is critical”
Participants need guidance on combining optimal prompting strategies with practical evaluation implementation for production deployment.
“Optimal prompting strategy and eval implementation”
What will you build for yourself or apply at work in the next 30 days?
n=25 responses
Participants want hands-on experience building agentic systems, particularly code conversion harnesses that demonstrate agent capabilities.
“Fully agentic code conversion harness”
Participants plan to integrate Claude into their organizations, leveraging desktop features and shareable custom skills for client work.
“Enhanced prompting as well as custom skills that can be shared across my organization.”
Participants want to operationalize evaluation frameworks and caching strategies learned, integrating them into actual client engagements.
“Try to use more claude desktop features that i wasn't aware of and test out all the things that i've learnt like the eval and prompt caching strategies in the client engagement.”
Participants seek to advise clients on concrete AI applications for process optimization across operational and business objectives.
“Advise cognizant team and clients on ways to leverage AI for efficient process improvements, whether they are for day to day activities or for business oriented goals.”
Participants identify persistent memory and agent teams as critical topics requiring deeper technical exploration beyond programme scope.
“I want to dive deeper into a few topics including persistent memory and agent teams”
Partner Basecamp Survey Report · Cohort 18 · Generated by analyze.py v2.5
NPS Reason
n=32 responses · grouped by NPS segment
Promotersscore 9–10 · 15 responses
1Learned about new possibilities in Claude that I had no idea about10
2Good information10
3Great learning!10
4Great content. Very useful and applicable content10
5Training was hands on and well paced10
6The session was very interactive and technical intensive.10
7It was really great and good learning how to build quick app with all Claude resources10
8Highly interactive and informative!10
9Very good for your intended audience :)10
10Hands on exercises10
11Excellent and interesting content9
12Leaning9
13Great info and learning that keeps changing day to day and help stay up to speed and what to expect.9
14The overall content and learning was very helpful and engaging with guiding development9
15Good blend of hands-on experience with best practices and real world solutions9
Passivesscore 7–8 · 12 responses
1Useful for learning to build, but would have liked more time spent within Claude desktop and Claude cowork8
2Hands on project build at the end of the session.8
3The program very much expanded my learning and understanding of how the tools work and how to build. I felt the final exercise lacked the proper instructions and impact but otherwise was a great program.8
4I love the session it's handson8
5Great atmosphere, good materials. One unexpected value for me was getting to connect and share experience with my remote colleagues.8
6Good topics, well spekears and a good step zero for get into vibe coding8
7Makes sense in the current context8
8Good practical knowledge7
9Enjoyable, well paced. Covered a lot of topics7
10Sorry but already known a lot/used in our team. Good to know for me was evals.7
11Fun and knowledgeable in person experience. Would be more likely to recommend with better pre-work / tech setup instructions as well as business use cases.7
12Workshop is helpful for developers. I would have appreciated more conversations on walkthrough of tasks and purpose of the way the steps are setup.7
Detractorsscore 0–6 · 5 responses
1Not very technical6
2We already do most of these things internally so it felt like review rather than learning. Day 2 was more new information than day 1.6
3I really liked the little pro tips here and there to enhance my knowledge of Claude and agentic development. The prework and the slides and exercises themselves were great. However I think some of the content was slightly technical for my particular team (more AI education focused than product building). I also found the instructors to have varying levels of knowledge about Claude and its products and the level of depth of their knowledge was only okay.4
4Trainers do not know enough. Their response is fluffy. The context setting for exercises and sessions need heavy uplifting2
5Too basic. Inadequate facilitators to students ratio.0
Most Valuable
What one takeaway will you share with a colleague or client? · n=27 responses
1Model is not a problem, they way we handle it is what.....
2Claude code usage
3Claude is the better AI option
4Full capabilities
5How to be more efficient and cost less
6That context engineering is critical
7Build system around got strong.
8Importance of designing Evals from the start
9Use claude code
10Knowledge is not unequal to practice. Try and create things. The more you do the better you become.
11Got a complete knowledge on most of Claude’s products.
12App Building activities was best part of the training
13/compact can be tailored to the users need - nice pro tip
14How to use Claude in our daily work life
15Evals. We need to enhance our QA cicd flow. Previously more e2e testing for ai.
16Good to start working on IA
17Hard to list one.. pretty much all activities learnt.
18Tradeoffs between models and cost especially where a better model could be cheaper when able to complete in a one shot
19Skills and plugins best practices.
20Institutional memory
21Skip. Don't come. Just clone repo and do the exercises at home.
22Focus on Ideating than developing
23Optimal prompting strategy and eval implementation
24There’s a lot to win if you are rational about it
25Understanding claude platform
26Do not be over prescriptive on the how, but be very prescriptive on the eval's the goals and the overall objective
27This training is only good if you know 90% of what they will cover.
30-Day Intentions
What will you build for yourself or apply at work? · n=25 responses
1Personal project management
2Calendar automation app
3Agents
4Automated messages and tasks on my computer that I do daily now
5Fully agentic code conversion harness
6An agent to stay updated on ai concepts and news
7Automated executive narrative commentary solution with context management
8Multi modal AI and multi agentic solutions
9Need some agents to help manage financial planning m.
10For myself I will stock analysis agent
11Finish financial advisory system and put it to use
12Bringing Claude into Client’s eval framework.
13Some client usecases
14web 3 personal assistant systems
15I already built a kotlin app for a personal stuff
16Advise cognizant team and clients on ways to leverage AI for efficient process improvements, whether they are for day to day activities or for business oriented goals.
17I want to dive deeper into a few topics including persistent memory and agent teams
18Enhanced prompting as well as custom skills that can be shared across my organization.
19All of it
20Data assessment agent
21Try to use more claude desktop features that i wasn't aware of and test out all the things that i’ve learnt like the eval and prompt caching strategies in the client engagement.
22Data connections between platforms
23Daily/weekly routines.
24Yes today
25I have to figure out on my own. The training did not give me a good base