4.1 Culture Meets Technology
Culture consists of "shared mind programming" with core values at its center (Hofstede, 2010). When these cultural values intersect with the four AI specificities you learned in Module 2, unique patterns of friction emerge.
"Different people from different backgrounds. I think it's... it provides a different perspective of the problem in hand."
— P3
This module explores five project phases where cultural differences create high friction, and two phases that remain universally consistent.
See Figure 4 in the research paper: Phases of an AI project affected by culture
4.2 Five High-Friction Project Phases
PHASE 1
Problem Definition
HIGH FRICTION
Aligning high customer expectations with disruptive technical reality.
"Sort of early on when we're kind of brainstorming, coming up with ideas, going back and forth with maybe a stakeholder trying to figure out what their requirements are."
— P16
PHASE 2
Data Analytics
HIGH FRICTION
Interpreting domain knowledge and data representation across cultures.
"The main knowledge... Domain knowledge. But there's also a kind of physical cultural challenge, and that is in how do we represent things?"
— P37
PHASE 3
Ethics & Privacy
HIGH FRICTION
Auditing stochastic risk and managing compliance across regulations.
"If you recruit someone from Europe... security, safety and GDPR comes to them. If you recruit from India or Bangladesh? They may not know this."
— P13
PHASE 4
Business Problem Solving
HIGH FRICTION
Localizing AI solutions to regional specifics.
"The difference between fintech in Kenya and Nigeria... in Kenya mobile money is obvious. In Nigeria, a different system. You have to study it."
— P25
PHASE 5
Feedback Loops
HIGH FRICTION
Cultural norms determine how direct or indirect feedback should be.
"Difficult to us to tell that way so directly."
— P1, P2 (Brazil)
"Latin America takes things very seriously. I almost got into trouble by being too blunt."
— P21
4.3 Universal Engineering Paradigms
Interestingly, two phases operate independently of cultural influence, following the universal logic of mathematics and engineering:
📊 STATISTICAL ANALYSIS
"During the analysis and planning of the project we apply statistical analysis... I don't believe it's multicultural... plays a major role in this."
— P3
💻 TECHNICAL EXECUTION
"We're talking the same language, more or less. I find that part it's definitely more equal than if it was something non-tech related."
— P9
Important Note: While execution is neutral, governance and sovereignty decisions regarding those technologies remain culturally contingent.
"From the Netherlands, there was a tendency to use the AI project to do centralization... was not welcomed by German and Belgian subdivisions because they wanted their own independence."
— P29
4.4 Data Treatment: Bias, Privacy, and Ethics
Data Preparation
🇳🇱
Netherlands: Comma for decimal separator
🇮🇳
India: Comma for thousand separator
"So take for example numeric. That is a very notorious one. Here in the Netherlands we use a comma for decimal separator. In India, we use a comma for thousand separator."
— P38
Privacy & Regulatory Gaps
The autocatalytic nature of AI leaves regulatory frameworks struggling to keep pace:
"We try to comply with GDPR in Europe. This relation is a little bit loose when dealing with customers in Asia or United States."
— P3
"Working in Nepal... they were hesitant about sharing data. Probably because they didn't have a standard for sharing data with us."
— P27
Ethics as Cultural Responsibility
"The guys that work with us were not from data science... they do not know why the model was giving them that answer. We need this responsibility of building the model. We need to remove this kind of biases."
— P2, on fixing gender bias in salary models
4.5 Knowledge Orchestration
Four Key Dimensions
1. Epistemic Depth & Uncertainty Avoidance
"Asian cultures, like Japanese cultures... that's why you provide all information."
— P21
2. Temporal Pacing
"Mexican clients need to know... how is the project in production?"
— P7
"US clients want little during, but at the end... like a PhD dissertation."
— P7
3. Tacit vs. Explicit Knowledge
"Mexican clients give definitions, but not complete... they wait for you to know."
— P7
4. Modality of Transfer
"Spanish executive level... more difficult, separate technical from business talks."
— P1, P2
4.6 The Stakeholder-Business Process Matrix
Cultural Friction by Stakeholder Pair
Legend: F=Feedback, KS=Knowledge Sharing, W=Working Methods, M=Monitoring,
P=Problem Definition, CR=Customer Relations, T=Training, WC=Work Culture, I=Inquiry
"I can act as a communication point because I can like a bridge."
— P3, on being a cultural bridge
4.7 Beyond National Culture
Cultural differences exist even within countries. Strategies must be recalibrated for specific cities and subregions.
"I'm used to Bangalore then Bombay... is it going to be exactly the same? That's simply not true. Same for UAE vs Qatar vs Saudi Arabia."
— P38
"In the US... Northerners being more direct, Southerners more roundabout. You have to read between the lines."
— P37
🇦🇪
Emirati generosity: "In Indian culture, giving something frequently might be seen as looking for hidden benefit. In Emirati culture, it's natural to feed people, to be so nice."
📌 Module 4 Key Takeaways
- Five high-friction phases: Problem Definition, Data Analytics, Ethics & Privacy, Business Problem Solving, Feedback Loops
- Two universal phases: Statistical Analysis and Technical Execution operate independently of culture
- Data treatment varies: Number formats, labeling conventions, privacy expectations differ significantly
- Knowledge sharing has four dimensions: Epistemic depth, temporal pacing, tacit vs. explicit, modality
- Subcultural granularity matters: Even within countries, regional differences require adaptation
🔗 Connect to Your Assessment
Your assessment results identify specific cultural friction points:
- Question 1.1-1.3: Organizational preparation for cross-cultural work
- Question 2.4, 2.8, 2.11: Leadership cultural awareness and monitoring
- Question 3.4: Presence of "Cultural Bridges" in your team
- Question 4.1-4.4: Personal cross-cultural capabilities
→ Review your cultural readiness scores
✍️ Cultural Audit Exercise
For your current or recent AI project:
- Which of the five high-friction phases caused the most challenges?
- What cultural differences in feedback styles did you observe?
- Do you have "cultural bridges" on your team? If not, where are the gaps?
- How do your data preparation practices account for regional variations?
Save your notes—you'll use them in Module 6 for your action plan.