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Partner Basecamp · Cohort 30

San Francisco

August 27–28, 2026
+31
Net Promoter Score
90
Day 1 Responses
94%
Matched Pairs
79%
Day 2 Response Rate
Programme Satisfaction
Positive NPS of +31 — promoters outweigh detractors with moderate passive presence.
+31NPS · n=71
51% Promoters30% Passives20% Detractors
95% CI: +13 → +49  ·  True NPS lies within this range with 95% confidence (n=71 respondents)
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
4.2D2 Commercial / 5
4.3D2 Build / 5
Audience
90 participants across 18 organisations — Transformation Lead majority with applying in practice the most common AI experience level.
Top Organisations
Praxent (18) Deloitte (11) PwC (9) AWS (7) Fujitsu (7)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Hands-on Claude implementation exercises with clear fundamentals enabled non-technical consultants to immediately apply LLM capabilities to client problems.
Passives
Content quality was strong, but delivery format and audience targeting needed refinement; some preferred in-person expert interaction over virtual sessions.
Detractors
Training pitched too basic for Claude-experienced hires and lacked business-focused multi-agent solutions; pace felt rushed and scaffolded exercises limited independent capability building.
Segment Finding
Praxent (NPS +77, n=13) and Fujitsu (NPS -100, n=4) bracket the cohort. Praxent had zero detractors and 10 promoters, and four of five promoter verbatims name the same drivers: hands-on building and direct client relevance — 'the hands on experience with actual building with Claude is incredibly insightful,' and 'guided instruction on how to really leverage Claude in real world contexts... highlighted many areas where we can provide more value at less cost to our clients.' Notably, Praxent's respondents span three AI-experience levels and all three personas, so the result is thematic rather than demographic. Fujitsu, at the other end, returned four detractors from four respondents, but only two left verbatims — both Transformation Leads, and both pointing the same way: 'Most of my colleagues are already familiar with these topics' and 'Didn't learn much.' The complaint is prior knowledge, not facilitation. That direction matters: where C29's detractor signal was content pitched too technical for non-technical participants, C30's is the inverse — experienced attendees finding the material too familiar. Read together, the programme signal is audience-level calibration in mixed-skill rooms rather than a single direction of travel. Both segments are small (n=13, n=4); Fujitsu especially is anecdotal, not representative.
Analyst Note
Promoter open text (n=28) clusters on four themes, by frequency of mention: breadth and new concepts (10), hands-on building and exercise design (8), client relevance and applicability (5), and instructor quality and structure (4). Representative responses: 'Well structured and great material. The hands on experience with actual building with Claude is incredibly insightful and a great learning tool.' 'Guided instruction on how to really leverage Claude in real world contexts is extremely valuable.' And from a participant who came in without a technical background: 'This session was fast paced but in the best way possible! Even coming from a non-technical background, I felt that I was able to grasp the fundamentals of Claude's LLM and how it can be applied.' Within the Transformation Lead persona, mean build-confidence gain declines as proficiency rises — a pattern present in 8 of 12 cohorts with two or more readable proficiency bands. In this cohort: learning and exploring +0.89 (n=9), applying in practice +0.50 (n=20), delivering independently +0.14 (n=7). NPS does not track confidence gain consistently across the dataset; in 9 of 12 cohorts NPS also declines with proficiency, whereas in this cohort it rose (+11, +20, +71 across the same bands). This cohort's satisfaction gradient is therefore atypical and should not be generalised. Depth and pace responses distribute across different proficiency bands (n=90 Day-1 respondents). Depth rated "too basic" — 13 responses total: Delivering independently 6 of 13, Operating at the frontier 5 of 11, Applying in practice 2 of 52, Learning and exploring 0 of 14. Pace rated "too fast" — 15 responses total: Applying in practice 11 of 52, Learning and exploring 3 of 14, Delivering independently 1 of 13, Operating at the frontier 0 of 11. The two signals do not co-occur within bands. The two highest proficiency bands account for 11 of 13 "too basic" responses and 1 of 15 "too fast" responses. The "too fast" signal is concentrated in the "applying in practice" band, which returned 2 of 52 "too basic". Open-text responses within the Transformation Lead persona separate along the same axis. At practitioner level, responses describe an inability to reproduce the work independently rather than insufficient depth: "Understood workshop content but since so much scaffolding was already in Jupyter notebook I'm not sure I could independently do it end to end"; "should have been extended into a 4 day session... I thought the pace of hands on activities was too fast." One respondent identified language as a barrier during the hackathon. At director level and above, responses cite prior knowledge: "Didn't learn much"; "I feel this is for beginners level... that knowledge does not need 2 days of training. Was expecting more on how to build more multi agent solutions from business standpoint." These distributions do not support a uniform pace reduction: the depth and pace signals originate in separate segments and imply separate interventions. Band sizes range from 11 to 52; the depth signal rests on the two smallest bands (n=11, n=13). Seniority-level open-text segments (practitioner n=9, director+ n=13) fall below the 20-response threshold applied to two-way cuts and are reported qualitatively only, without segment-level scores.
Survey & Data Quality
Survey & Data Quality
Day 1 responses90
Day 2 responses71 (79% of Day 1)
Matched pairs67 (94% of Day 2)
Orgs resolved90 of 90 respondents matched to named org
Day 2-only respondents4 (5.6%) — no NPS data; no Day 1 data, excluded from persona & confidence analysis
Organisations
All Day 1 respondents · n=90
Praxent18 (20%)
Deloitte11 (12%)
PwC9 (10%)
AWS7 (8%)
Fujitsu7 (8%)
LTM5 (6%)
Cognizant5 (6%)
Perficient4 (4%)
Bain3 (3%)
Provectus3 (3%)
Accenture3 (3%)
DXC Technology3 (3%)
NEC3 (3%)
Lovelytics3 (3%)
Ascendion2 (2%)
Wipro2 (2%)
KPMG1 (1%)
Lazer Technologies1 (1%)
Function × Seniority
All Day 1 respondents · n=90
FunctionPractitionerSr PractitionerMgr / Sr MgrDirector+Partner / MDTotal
Engineering664218
Architecture3349120
Business Leadership2411825
Project / Engmt868527
Experience Profile
AI experience level · n=90 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Transformation Lead21%56%19%4%52
Architect5%70%10%15%20
Developer11%50%6%33%18
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Director, Senior Director, or Principal7%67%19%7%27
Manager or Senior Manager30%70%20
Practitioner (0-4 years in role)26%42%16%16%19
Senior practitioner (5–9 years)7%53%20%20%15
Partner, Managing Director, or Executive44%22%33%9
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
14 (16%)
Applying in practice
52 (58%)
Delivering independently
13 (14%)
Operating at the frontier
11 (12%)
Prior to today, how much had you worked with Claude or the Anthropic API?
Not at all
5 (6%)
A little
24 (27%)
Regularly
61 (68%)
Did the depth land for this audience?
Technical depth perception · n=90 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Transformation Lead12%75%13%52
Architect10%85%5%20
Developer28%67%6%18
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Applying in practice4%87%10%52
Learning and exploring71%29%14
Delivering independently46%54%13
Operating at the frontier45%55%11
Overall depth distribution
Too basic
13 (14%)
About right
68 (76%)
Too advanced
9 (10%)
Did the pace work across the room?
Session pace perception · n=90 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Transformation Lead13%73%13%52
Architect70%30%20
Developer89%11%18
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Applying in practice8%71%21%52
Learning and exploring14%64%21%14
Delivering independently8%85%8%13
Operating at the frontier100%11
Overall pace distribution
Too slow — could have covered more
7 (8%)
Well paced
68 (76%)
Too fast — not enough time to apply
15 (17%)
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=90 · 1–5
Mean end-of-D1 build confidence by Persona
Developer4.11/5 · n=18
Architect4.00/5 · n=20
Transformation Lead3.67/5 · n=52
Programme mean: 3.9/5 · 21 cohorts
Mean end-of-D1 build confidence by AI Experience Level
Operating at the frontier4.82/5 · n=11
Delivering independently4.62/5 · n=13
Applying in practice3.60/5 · n=52
Learning and exploring3.21/5 · n=14
Overall end-of-D1 build confidence distribution
1
1 (1%)
2
7 (8%)
3
23 (26%)
4
34 (38%)
5
25 (28%)
How relevant was today's content to your current role?
Content relevance rating · n=90 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Developer4.28/5 · n=18
Architect4.20/5 · n=20
Transformation Lead3.77/5 · n=52
Mean relevance by AI Experience Level
Delivering independently4.15/5 · n=13
Applying in practice3.96/5 · n=52
Learning and exploring3.93/5 · n=14
Operating at the frontier3.82/5 · n=11
Overall relevance distribution
1
0 (0%)
2
6 (7%)
3
17 (19%)
4
41 (46%)
5
26 (29%)
How likely are you to recommend attending this programme to a colleague?
n=71 Day 2 respondents · 79% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 3012388131224
Detractors 0–6 Passives 7–8 Promoters 9–10
NPS Breakdown
51%
30%
20%
NPS +31 (n=71)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +43 · 21 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)
Praxent
77%
23%
0%
NPS +77 (n=13)
AWS
60%
40%
0%
NPS +60 (n=5)
LTM
60%
20%
20%
NPS +40 (n=5)
PwC
43%
43%
14%
NPS +29 (n=7)
Deloitte
50%
12%
38%
NPS +12 (n=8)
Fujitsu
0%
0%
100%
NPS -100 (n=4)
By Persona
Promoters (9–10)Passives (7–8)Detractors (0–6)
Developer
54%
38%
8%
NPS +46 (n=13)
Architect
44%
39%
17%
NPS +28 (n=18)
Transformation Lead
53%
22%
25%
NPS +28 (n=36)
Programme means · 21 cohorts: Architect +51 · Developer +48 · Transformation Lead +36
By AI Experience Level
Promoters (9–10)Passives (7–8)Detractors (0–6)
Delivering independently
89%
0%
11%
NPS +78 (n=9)
Operating at the frontier
56%
22%
22%
NPS +33 (n=9)
Applying in practice
42%
40%
18%
NPS +24 (n=38)
Learning and exploring
46%
27%
27%
NPS +18 (n=11)
Programme means · 21 cohorts: Learning and exploring +46 · Applying in practice +45 · Delivering independently +41 · Operating at the frontier +38
What is the main reason for your score?
n=55 responses · organised by NPS segment
Promoters (score 9–10)· 28 responses
Hands-on Claude implementation exercises with clear fundamentals enabled non-technical consultants to immediately apply LLM capabilities to client problems.
9“Good to hear all the content and experience trying to work through the examples. Spend a little more time making three examples more polished. Scrolling up and down in vs code while trying to make changes and understand outputs really impacted my learning. Good over all though”
Passives (score 7–8)· 17 responses
Content quality was strong, but delivery format and audience targeting needed refinement; some preferred in-person expert interaction over virtual sessions.
8“I found the program very well paced and engaging! I learned a lot and really appreciate the energy and support from all the facilitators. It was also great being able to meet other people from other companies and hearing about their AI use cases. There were many permission issues PwC faced - we have escalated these concerns internally so hopefully these problems will be resolved for future cohorts.”
Detractors (score 0–6)· 10 responses
Training pitched too basic for Claude-experienced hires and lacked business-focused multi-agent solutions; pace felt rushed and scaffolded exercises limited independent capability building.
6“I feel this is for beginners level. Those who have already used Claude to some extent, this was very basic. Some information like eval and how to better prompts was useful, but that knowledge does not need 2 days of training. Was expecting more on how to build more multi agent solutions from business standpoint and not too technical.”
End-of-Programme Confidence
n=71 respondents · 1 = not confident, 5 = very confident
How confident are you in your ability to design an evaluation for an AI solution?
12345Mean
4%20%38%38%4.10
Programme mean: 4.1/5 · 21 cohorts
Leaving today, how comfortable are you advising clients on AI when questions arise?
12345Mean
1%18%38%42%4.21
Programme mean: 4.2/5 · 21 cohorts
How confident are you in your ability to lead a conversation about Anthropic and Claude with a client?
12345Mean
1%17%34%48%4.28
Programme mean: 4.2/5 · 21 cohorts
Confidence Trajectory by Persona
D1→D2 delta · apply-AI confidence · matched pairs only · n=67
Positive delta = confidence grew · negative = dropped
Architect (n=18)
+0.39
Developer (n=13)
+0.31
Transformation Lead (n=36)
+0.53
What one takeaway will you share with a colleague or client?
n=44 responses
Participants recognize that model selection matters less than understanding prompt construction and system architecture around it.
“It rarely is the model when things go wrong, it's the setup and harness around the model.”
Context engineering emerges as equally critical as underlying data when building AI systems at scale.
“Context Engineering is as important as the data that lives behind it”
Participants value structured evaluation frameworks and iterative testing as core design practices, not afterthoughts.
“When designing an AI solution, it is important to design evals and test to improve”
Participants acknowledge the programme requires substantial pre-work and setup effort to translate concepts into practice.
“The session is good but it needs lot of homework and setup”
Participants value practical guidance on cost optimization, token management, and eval best practices through working examples.
“Sharing in best practices for evals and token mangement”
What will you build for yourself or apply at work in the next 30 days?
n=48 responses
Participants want hands-on experience building sophisticated multi-agent systems and agents that actively manage complex work planning.
“Building better agents to help manage and plan work”
Participants seek practical guidance on model selection, cost optimization, and applying evals to production Claude implementations they've already built.
“Apply evals and measuring tweaks to my already available solutions I built with Claude. Like delivery pipeline and codebase qa bots”
Participants are exploring concrete applications across customer experience and internal operations, seeking validation for real work use cases.
“Work applications”
Participants prioritize learning evaluation frameworks and testing methodologies to measure and iterate on agent performance systematically.
“Focusing on evals.”
Participants want to deploy ready-made optimization tools and purpose-built solutions like RFP analyzers for immediate team productivity gains.
“RFP Analysis Tool”
NPS Reason
n=55 responses · grouped by NPS segment
Promotersscore 9–10 · 28 responses
1Innovative10
2Learnt a lot10
3This session was fast paced but in the best way possible! Even coming from a non-technical background, I felt that I was able to grasp the fundamentals of Claude’s LLM and how it can be applied to my clients’ problems.10
4Well structured and great material. The hands on experience with actual building with Claude is incredibly insightful and a great learning tool.10
5Loved how hands on it was10
6The practical examples and the thoroughness of the concepts covered by the mentors10
7This helped me break through my comfort zone and I’m ready to dive into all of Claude’s capabilities10
8Relevant content to our daily work10
9Overall, very informative. Even for. On technical people.10
10The comprehensiveness of walking through AI tools and limitations was extremely helpful10
11I scaled new heights on Mt. Claude. It was a fantastic workshop.10
12It’s great program for people getting used to building infrastructure around LLMs.10
13Great exposure to Anthroic pov on sdk, evaluate, supporting clients10
14Doing great, the only concern that I have most of the people coming here are building and shipping their products with Claude. So rather than demos and smaller tasks, try to spend some time giving tasks like day 2 end.10
15Very good training and engaging facilitators and attendees. Tech issues for my company specifically need to be figured out to make this a more effective training, still learned a lot regardless10
16This training teaches the concepts on different components that trainees can extend their knowledge in daily work life10
17Great learning experience9
18Super hand-ons bootcamp! Well executed9
19Hackathon9
20There are limited cases where it may not be useful but there was a lot of of great indo9
21Good topics presented and discussed9
22Really help to apply Claude code in best optimised way9
23Good to hear all the content and experience trying to work through the examples. Spend a little more time making three examples more polished. Scrolling up and down in vs code while trying to make changes and understand outputs really impacted my learning. Good over all though9
24Learning to start measuring things rather than just looking and saying this is good for shipping to production. The whys are answered9
25Good hands on training, practical application.9
26Hands on experience9
27Guided instruction on how to really leverage Claude in real world contexts is extremely valuable. The workshop highlighted many areas where we can provide more value at less cost to our clients.9
28Enjoyed working through the examples. Also loved the advice on tweaking Claude usage and maximizing ROI.9
Passivesscore 7–8 · 17 responses
1Good overall8
2Template and case studies8
3Productive session8
4I loved it. It was great to be around other like-minded individuals. Day 1 set the foundation and day 2 I left with something tangible. Very motivated to run with what I now know.8
5Well designed hands on exercises, but need more initial definition around intended audience up front to help refine the materials8
6Good content, nice peace.8
7Very good but technical exercises were a bit too much for non technical folks in our cohort.8
8Very informative and useful8
9I learned a lot about practices using AI code that I didn’t know prior. There was a lot of information that I will have to revisit in order to gain expertise. I don’t know that I feel comfortable talking about the intricacies of Claude to a client just yet.8
10I found the program very well paced and engaging! I learned a lot and really appreciate the energy and support from all the facilitators. It was also great being able to meet other people from other companies and hearing about their AI use cases. There were many permission issues PwC faced - we have escalated these concerns internally so hopefully these problems will be resolved for future cohorts.8
11I can join the team discussion.8
12Depends on role, maybe lessons needed7
13It should have more experts and more hands on participation7
14I would liked more interactive sessions on various features, live-in person from ANT than virtually.7
15Depends on the audience that would be attending. Might need to break up training between pillars.7
16Many known tech issues for our team and a workaround is needed on both sides7
17It has a lot of hands-on good for POCs but would have loved to hear from Anthropic experts more on scaling and getting prod ready enterprise level apps.7
Detractorsscore 0–6 · 10 responses
1Understood workshop content but since so much scaffolding was already in Jupyter notebook I’m not sure I could independently do it end to end6
2A bit basic for the new hires since they already use this. But definitely a good program for people out of touch with the technology6
3I couldn’t keep up because the conversation was too fast.6
4I feel this is for beginners level. Those who have already used Claude to some extent, this was very basic. Some information like eval and how to better prompts was useful, but that knowledge does not need 2 days of training. Was expecting more on how to build more multi agent solutions from business standpoint and not too technical.6
5I think it should have been extended into a 4 day session. This would have allowed more time to have in-depth conversations and learnings to walk away with. I thought the pace of hands on activities was too fast6
6Most of my colleagues are already familiar with these topics5
7For the person who speaking in English is not good like me had a bit hard at hackathon.5
8It’s good but we need more support staff.4
9I was expecting real business use case approach, learning and output solved by Claude4
10Didn’t learn much3
Most Valuable
What one takeaway will you share with a colleague or client? · n=44 responses
1Amazing learning experience
2N/a
3Context engineering
4Unlocked options to build new products
5Evals and checking different models for what we need to compare
6The session is good but it needs lot of homework and setup
7Never stop learning
8The possibilities are endless!!!
9It’s almost certainly not the model that’s the problem. Look at the prompt and context first.
10It rarely is the model when things go wrong, it’s the setup and harness around the model.
11How to minimize/ optimize cost through system design and claude best practices.
12Usage insights
13Good repo code that can be used as reference
14Prompting is very important
15Look at system and app before thinking to upgrade models
16How to show ROI and mitigate costs
17Work smarter with Claude
18You can own the conversation with the customer
19Context Engineering is as important as the data that lives behind it
20Prompt engineering, context engineering, and inference management all need to work together to enhance AI functionality al scale
21You can do this training no matter your skill level and you will learn a ton.
22I will recommend them to attend
23Model is usually not the problem.
24Controls and security that can be natively included
25It’s not the model. Proper architecture, testing, and iteration is critical.
26Agents are awesome! Feels like magic, but with lots of engineering behind
27This bootcamp is really for all levels. Beginner to advanced, and that claudes capabilities in all pillars is extremely impressive
28Great learning and insight into how to use Claude better,
29When designing an AI solution, it is important to design evals and test to improve
30If you are starting with Claude, this training is perfect.
31That you don’t have to be a developer to develop tools
32In multi agent systems, there are various ways to use different models for different tasks
33Awesome workshop
34Learned more on the capabilities of the models and betterment strategy for pricing
35The use of Claude code in general, beyond just prompting.
36That if you switch model in ai agents you have to update prompt, and tool evaluation etc.
37Sharing in best practices for evals and token mangement
38Demo it try to make him build something
39Increased capability with a better ROI margin
40The different model uses was the immediate most applicable thing I took away - I also found the "/context" tool helpful in being mindful of how much token usage and context you are working with.
41You can even prompt Claude with helping you write a good prompt.
42pronpt engineering review
43Experiment - you’ll learn more by doing and it’s amazing what you can do in a short amount of time. Also dedicate intentional time to your learning & experimentation
44Model Evals
30-Day Intentions
What will you build for yourself or apply at work? · n=48 responses
1CX experience use case
2Optimization tools to assist with daily chores
3Alot
4Build out the godot multiagent swarm
5Proposal stuff
6Building better agents to help manage and plan work
7Testing agent
8Automation frameworks
9I will definitely be creating my own agents both for work and personal uses
10We’re going to continue our work from the agent hackathon to help organize project documentation and status.
11Focusing on evals.
12RFP Analysis Tool
13Work applications
14I build regularly
15Rfp repsponse agent
16Will work on my rfp intake project to refine it
17Agent to resolve the issue on production automatically
18Couple of desktop accelerators
19Support work bench to assist in improving customer experience
20Personally, confidence to actually set up the API key and spend the $.
21Agent to help review and prioritize incoming bug forced and feature suggestions
22I will enhance the current “triage” project I have that manages my priorities, comms, scheduling etc. I will make it much more robust with skills and subagents
23I will try it out.
24Finish hackathon agent for our back office processes.
25Additional tooling for our PMO team.
26Extracting navigation and experience into user flow breakdown and diagram
27Agent cost/lattency optomization. How to chose the best model.
28I want to start building some finance tools for my clients regarding the issues im facing nowadays from forecasting budgeting and reporting tools.
29Apply evals and measuring tweaks to my already available solutions I built with Claude. Like delivery pipeline and codebase qa bots
30Applying context engineering more effectively when doing broad analysis work on a large distributed project
31AI accelerators for SAP transformation projects
32I want to teach everyone what is necessary to develop a system using AI.
33Something for the client
34Continue building out my deal dashboard
35Chief of staff multi agent systems
36Long term memeory + Traige support agent that can be scaked to multiple tenants
37I'm going to build everything I learnt in this workshop including multi agents
38Too much to describe. Got a lot of plans
39A way to track test status for multiple projects per release. A dashboard that calls out dependencies and risks.
40I’ll be learning to fine tune models in the near future myself. I wish workshop had more advanced topics integrated
41Told for developing and refining propsals for Claude Cowork and Code enablement sessions
42Agent workflows
43Eval harness for agentic chatbot to verify compliance to client expectations
44I will experiment with different use cases and can see Claude Code being applicable for synthesizing multiple documents, especially for requirement gathering sessions.
45Claude agent using MCP endpoints in Databricks.
46skills and pronpt engineering
47Myself - maybe a portfolio management team to analyze companies and monitor the markets Work - PowerPoint generator (I’ve used some skills but would like a better one) for client decks, rfps, thought leadership, etc
48We will plan to use Claude Code to migrate legacy code to modernization platforms.