Recent research highlights widespread AI/tool adoption, the need for workflow redesign, and enduring gaps in trust and delivery performance, all signaling a shift from “just code” to engineering with systems, collaboration, and governance in mind.

1) AI Fluency (LLMs, agents, & evaluation)

• What it means: Prompting with context, using RAG, evaluating model output, and integrating AI safely into dev workflows.

• Why now: Most developers use AI tools, but trust is mixed; fluency separates speed from rework.

• Leader signal: Can the developer improve accuracy with tests, guardrails, and human in the loop patterns?

2) Cloud Native & Platform Literacy

• What it means: CI/CD, IaC, containers, delivery metrics, and developer experience on internal platforms.

• Why now: DORA’s 2024 research emphasizes platform engineering and the role of developer experience in high performance.

3) Security by Design Habits

• What it means: OWASP style secure coding, secrets hygiene, dependency health, and threat modeling.

• Why now: Speed without security increases risk; teams need developers who embed security in code and pipelines.

4) Systems Thinking & Architecture Trade offs

• What it means: Understanding latency, cost, reliability trade offs, event driven patterns, and observability as part of design.

• Why now: High performers redesign workflows to capture AI value; good system design prevents fragile velocity.

5) Communication & Cross Functional Collaboration

• What it means: Clear RFCs, architecture docs, and the ability to translate technical risk to product impact.

• Why now: With trust in AI uneven, human review and cross team alignment are critical to quality and speed.