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Partner Basecamp · Cohort 6

London

May 18–19, 2026
+43
Net Promoter Score
39
Day 1 Responses
95%
Matched Pairs
54%
Day 2 Response Rate
Programme Satisfaction
Positive NPS of +43 — promoters outweigh detractors with moderate passive presence.
+43NPS · n=21
57% Promoters29% Passives14% Detractors
95% CI: +12 → +74  ·  True NPS lies within this range with 95% confidence (n=21 respondents)
Confidence Arc
Strong end-of-Day-1 build confidence — cohort leaves Day 1 ready to apply Claude in client work (mean 4.0/5).
4.0end-of-D1 build / 5
4.0D2 Design / 5
4.2D2 Commercial / 5
4.2D2 Build / 5
Audience
39 participants across 12 organisations — Developer majority with applying in practice the most common AI experience level.
Top Organisations
Reply (8) Infomotion (5) Capgemini (5) SFEIR (4) valantic (4)
Voice of Participant
What participants said — dominant theme from each NPS segment.
Promoters
Participants valued hands-on technical training on Claude and AI engineering fundamentals as practical foundation for company initiatives.
Passives
Mixed experience due to varied expertise levels; theory-practice balance appreciated but advanced users found insufficient novel content.
Detractors
Advanced practitioners found training too introductory; outdated hands-on tasks and inconsistent quality didn't justify the effort and cost invested.
Survey & Data Quality
Survey & Data Quality
Day 1 responses39
Day 2 responses21 (54% of Day 1)
Matched pairs20 (95% of Day 2)
Orgs resolved39 of 39 respondents matched to named org
Day 2-only respondents1 (4.8%) — suppresses NPS by 7 points; no Day 1 data, excluded from persona & confidence analysis
⚠ Low Day 2 rateOnly 54% of Day 1 respondents completed Day 2. Push the survey link during or immediately after the closing session.
Organisations
All Day 1 respondents · n=39
Reply8 (21%)
Infomotion5 (13%)
Capgemini5 (13%)
SFEIR4 (10%)
valantic4 (10%)
NTT Data3 (8%)
Deloitte2 (5%)
Diverger2 (5%)
Atlas Cloud2 (5%)
Infosys2 (5%)
Accenture1 (3%)
IndiciumAI1 (3%)
Function × Seniority
All Day 1 respondents · n=39
FunctionPractitionerSr PractitionerMgr / Sr MgrDirector+Partner / MDTotal
Engineering842––14
Architecture–634–13
Business Leadership–13138
Project / Engmt–13––4
Experience Profile
AI experience level · n=39 · % of row
By Persona
SegmentExploringApplyingDeliveringFrontiern
Developer21%57%14%7%14
Architect15%15%38%31%13
Transformation Lead17%50%8%25%12
By Seniority
SegmentExploringApplyingDeliveringFrontiern
Senior practitioner (5–9 years)17%25%33%25%12
Manager or Senior Manager18%55%9%18%11
Practitioner (0-4 years in role)25%62%12%—8
Director, Senior Director, or Principal20%20%20%40%5
Partner, Managing Director, or Executive—33%33%33%3
How would you describe your current experience with AI tools prior to Basecamp?
Learning and exploring
7 (18%)
Applying in practice
16 (41%)
Delivering independently
8 (21%)
Operating at the frontier
8 (21%)
Prior to today, how much had you worked with Claude or the Anthropic API?
Not at all
3 (8%)
A little
19 (49%)
Regularly
17 (44%)
Did the depth land for this audience?
Technical depth perception · n=39 respondents · % of row
By Persona
SegmentToo basicAbout rightToo advancedn
Developer36%57%7%14
Architect31%69%—13
Transformation Lead25%58%17%12
By AI Experience Level
SegmentToo basicAbout rightToo advancedn
Applying in practice25%56%19%16
Delivering independently50%50%—8
Operating at the frontier38%62%—8
Learning and exploring14%86%—7
Overall depth distribution
Too basic
12 (31%)
About right
24 (62%)
Too advanced
3 (8%)
Did the pace work across the room?
Session pace perception · n=39 respondents · % of row
By Persona
SegmentToo slowWell pacedToo fastn
Developer—86%14%14
Architect15%69%15%13
Transformation Lead8%75%17%12
By AI Experience Level
SegmentToo slowWell pacedToo fastn
Applying in practice—81%19%16
Delivering independently25%75%—8
Operating at the frontier12%75%12%8
Learning and exploring—71%29%7
Overall pace distribution
Too slow — could have covered more
3 (8%)
Well paced
30 (77%)
Too fast — not enough time to apply
6 (15%)
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=39 · 1–5
Mean end-of-D1 build confidence by Persona
Architect4.31/5 · n=13
Transformation Lead4.00/5 · n=12
Developer3.93/5 · n=14
Programme mean: 3.9/5 · 15 cohorts
Mean end-of-D1 build confidence by AI Experience Level
Delivering independently4.50/5 · n=8
Operating at the frontier4.50/5 · n=8
Applying in practice3.88/5 · n=16
Learning and exploring3.57/5 · n=7
Overall end-of-D1 build confidence distribution
1
0 (0%)
2
2 (5%)
3
7 (18%)
4
16 (41%)
5
14 (36%)
How relevant was today's content to your current role?
Content relevance rating · n=39 respondents · 1 = not relevant, 5 = highly relevant
Mean relevance by Persona
Transformation Lead4.08/5 · n=12
Architect3.92/5 · n=13
Developer3.86/5 · n=14
Mean relevance by AI Experience Level
Applying in practice4.06/5 · n=16
Delivering independently4.00/5 · n=8
Operating at the frontier4.00/5 · n=8
Learning and exploring3.57/5 · n=7
Overall relevance distribution
1
0 (0%)
2
1 (3%)
3
11 (28%)
4
16 (41%)
5
11 (28%)
How likely are you to recommend attending this programme to a colleague?
n=21 Day 2 respondents · 54% of Day 1
Score distribution · 0 = not at all likely · 10 = extremely likely
Cohort012345678910
Cohort 6123384
■ Detractors 0–6■ Passives 7–8■ Promoters 9–10
NPS Breakdown
57%
29%
14%
NPS +43 (n=21)
Promoters (9–10)Passives (7–8)Detractors (0–6)
Programme mean: +42 · 15 cohorts
Promoters mean: 51%
Passives mean: 40%
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)
Reply
50%
50%
0%
NPS +50 (n=6)
By Persona
Promoters (9–10)Passives (7–8)Detractors (0–6)
Transformation Lead
80%
20%
0%
NPS +80 (n=5)
Architect
83%
0%
17%
NPS +67 (n=6)
Developer
33%
56%
11%
NPS +22 (n=9)
Programme means · 15 cohorts: Architect +50 · Developer +48 · Transformation Lead +33
By AI Experience Level
Promoters (9–10)Passives (7–8)Detractors (0–6)
Applying in practice
50%
50%
0%
NPS +50 (n=10)
Delivering independently
75%
0%
25%
NPS +50 (n=4)
Operating at the frontier
60%
20%
20%
NPS +40 (n=5)
Programme means · 15 cohorts: Learning and exploring +44 · Applying in practice +44 · Delivering independently +41 · Operating at the frontier +37
What is the main reason for your score?
n=17 responses · organised by NPS segment
Promoters (score 9–10)· 10 responses
Participants valued hands-on technical training on Claude and AI engineering fundamentals as practical foundation for company initiatives.
9“There are many resources to understand and test the knowledge shown during the session. This help to start with new company initiatives.”
Passives (score 7–8)· 4 responses
Mixed experience due to varied expertise levels; theory-practice balance appreciated but advanced users found insufficient novel content.
7“The content was addressed to too many level of expertise. I feel some people will benefit more than others depending on their level. I had some experience and most of the content was already kind of known. But great to catchup with people (other partners and you guys).”
Detractors (score 0–6)· 3 responses
Advanced practitioners found training too introductory; outdated hands-on tasks and inconsistent quality didn't justify the effort and cost invested.
6“The general idea behind the workshop is quite nice - the quality of task preparation varied and got a bit worse towards the end it was obvious that at least some of the hands on tasks were outdated since coming here was quite an effort and costly i would have expected a higher quality I don‘t want to sound too negative but i think just passing a nice rating does not help improving”
End-of-Programme Confidence
n=21 respondents · 1 = not confident, 5 = very confident
How confident are you in your ability to design an evaluation for an AI solution?
12345Mean
——24%48%29%4.05
Programme mean: 4.1/5 · 15 cohorts
Leaving today, how comfortable are you advising clients on AI when questions arise?
12345Mean
——14%52%33%4.19
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
——24%29%48%4.24
Programme mean: 4.2/5 · 15 cohorts
Confidence Trajectory by Persona
D1→D2 delta · apply-AI confidence · matched pairs only · n=20
Positive delta = confidence grew · negative = dropped
Architect (n=6)
+0.33
Developer (n=9)
+0.22
Transformation Lead (n=5)
+0.20
What one takeaway will you share with a colleague or client?
n=15 responses
Participants value understanding Claude's capabilities and learning responsible practices for applying its power effectively.
“The unlocking of power Claude brings but still needs responsibility”
Participants recognize the industry shift from traditional coding to AI engineering as a critical professional skill transition.
“The industry is shifting from coding to AI engineering”
Participants highlight evaluation sophistication and diagnostic capabilities as essential for building measurable trust in AI systems.
“The criticality of design before build and use of Deval's to build trust”
Participants emphasize prerequisite setup and basic coding knowledge as critical foundations for maximizing course value.
“The prerequisites and setup are critical to get the most of of the course. It would help if you have a basic coding background.”
Participants value practical, applied learning that teaches smart tool usage and Claude techniques through direct experience.
“Practice and how to use Claude and tools skills smartly”
What will you build for yourself or apply at work in the next 30 days?
n=15 responses
AI agents & engineering
“Building my own agents”
Evals & testing
“Probably something around evals”
Claude / Anthropic content
“I will look at integrating anthropic models into my poc as I was impressed”
Hands-on practice
“Smart practice”
AI strategy & use cases
“Apply all the principles in internal project”
Partner Basecamp Survey Report · Cohort 6 · Generated by analyze.py v2.3
NPS Reason
n=17 responses · grouped by NPS segment
Promotersscore 9–10 · 10 responses
1Very useful technical dive into Claude10
2The principals learned are fundamental AI engineering which is the future of the industry10
3Interesting course10
4How to use Claude efficiently9
5Overall good content and would be good to have more advanced concepts.9
6Great overall coverage of topics. Maybe first day was too basic.9
7Opportunity to get a solid introduction to Anthropic tools and processes9
8Good coverage of ai topics and good hands on9
9There are many resources to understand and test the knowledge shown during the session. This help to start with new company initiatives.9
10top material!9
Passivesscore 7–8 · 4 responses
1Great content all around, day one could have been "more foundational"8
2It was very good. The combination of theory and practice is nice.8
3The content was addressed to too many level of expertise. I feel some people will benefit more than others depending on their level. I had some experience and most of the content was already kind of known. But great to catchup with people (other partners and you guys).7
4Getting an overview on the possibilities which currently can be done in combination with the direct exchange with anthropic staff. The actual working examples might be designed more robust and get a more focussed outcome7
Detractorsscore 0–6 · 3 responses
1The general idea behind the workshop is quite nice - the quality of task preparation varied and got a bit worse towards the end it was obvious that at least some of the hands on tasks were outdated since coming here was quite an effort and costly i would have expected a higher quality I don‘t want to sound too negative but i think just passing a nice rating does not help improving6
2The deep dives on the second day were more interesting. It’s a great entry point , for seasoned engineers a bit too less information/input6
3I think it would be nice for some colleagues, but for the already advanced users, I think they would not receive enough new input. They know how to build with Claude already, AI Engineering practices, model selection criteria, etc.5
Most Valuable
What one takeaway will you share with a colleague or client? · n=15 responses
1Eval sophistication
2The unlocking of power Claude brings but still needs responsibility
3Practice and how to use Claude and tools skills smartly
4we should keep doing what we are doing. Maybe a lot of the points were new to me here but it confirms that we are on the right way in how we use Claude and advice in using it
5The industry is shifting from coding to AI engineering
6Diagnostics of agents and evals
7The prerequisites and setup are critical to get the most of of the course. It would help if you have a basic coding background. Also - the criticality of design before build and use of Deval’s to build trust
8Sdk simple accelerator for acheiving complex capabilities
9Writing evals should be a priority for building trust
10How to create an ai assistented e2e workflow
11For me the key is, to get optimize system, it´s really important to improve and iterate about the prompt, not only about the models or resources
12Advisor tool and Agents SDK
13Caching!
1410% vs 90% model vs application layer
15That the problem is mostly not in the model but what revolves around it such as prompt, architecture…
30-Day Intentions
What will you build for yourself or apply at work? · n=15 responses
1Building my own agents
2Probably something around evals
3Smart practice
4Will build more évaluations!
5we have some internal processes where we should “taste the medicine” and make them pre agentic at nature
6Apply all the principles in internal project
7Diagnostics of agents, evals, inference optimisation, context engineering
8I want to decompose an existing app back to engineering basics : requirements-stories-design- LLD and then refactor
9More experimentation of the sdk
10I will look at integrating anthropic models into my poc as I was impressed
11For the moment, all AI system built for my team was in a local enviroment to accelerate the development work, but after these two days, i will build automatic process powered by Claude
12Agents migration from Bedrock Agents to AgentCore using Claude Code
13Platform blueprint to structurally capture and measure ai investment impacts for capgemini
14Eval framework for my important tasks. Boil information down for clients
15I will automate my day to day pipeline.