An exploratory investigation revealing how AI tools are reshaping team dynamics,
challenging global narratives, and creating unique adaptation patterns
This groundbreaking study challenges the prevailing global narratives about AI adoption in software development, revealing unique patterns specific to the South African context.
| Role Category | Avg. Years | Range |
|---|---|---|
| Leadership | 25.0 | 23-27 |
| HR/People Ops | 10.0 | N/A |
| Project Managers | 7.7 | 5-10 |
| Developers | 6.8 | 1-18 |
| Quality Assurance | 6.3 | 2-13 |
Both junior and senior developers show identical 75% daily AI usage, directly contradicting global reports that seniors resist AI adoption. Experience level is not the determining factor we thought it was.
While 67.9% report gains, 10.7% experienced productivity decreases, particularly in management roles. This challenges the universal productivity narrative and reveals role-specific complexities.
Developers don't blindly trust AI. They apply context-dependent scrutiny: 73.7% normal review for personal projects but 68.4% heavy review for financial logic.
Critical concern about losing fundamental programming skills due to over-reliance on AI. Teams actively developing strategies to preserve core competencies while leveraging AI benefits.
The J-Curve illustrates initial performance dips during AI adoption, followed by significant improvements as teams adapt to new workflows.
| Role Category | Increase | No Change/Variable | Decrease |
|---|---|---|---|
| Technical Roles | 71.4% | 19.0% | 9.5% |
| Management Roles | 50.0% | 16.7% | 33.3% |
How developers adjust their review intensity based on code criticality
| Code Context | Normal Review | Heavy Review | Never Trust |
|---|---|---|---|
| Personal Projects | 73.7% | 26.3% | 0% |
| Development Env | 57.9% | 42.1% | 0% |
| Production Code | 42.1% | 57.9% | 0% |
| Financial Logic | 10.5% | 68.4% | 21.1% |
Constraints that shape distinctive AI adoption patterns
A context-aware model for AI transformation in resource-constrained environments
Honestly evaluate readiness and constraints. Recognize both opportunities and risks without assumptions.
Build AI literacy while protecting core competencies. Prevent "implementation amnesia" through deliberate practice.
Select tools matching local context. Build infrastructure resilience and ensure regulatory compliance.
Implement graduated trust protocols. Apply risk-based review procedures for different code contexts.
Monitor both productivity AND skill metrics. Iterate based on evidence, not assumptions.
AI transformation in South African software teams is neither the universal success story nor the dystopian narrative often portrayed. It's a nuanced journey of adaptation, innovation, and resilience in the face of unique constraints.
This research reveals that successful AI integration requires context-aware strategies, balanced approaches that preserve human expertise, and continuous adaptation based on evidence.
Graham Kenneth Katana (2025)
Bachelor of Science Honours in Information Technology
Supervisor: Dr Stephen Akandwanaho