BACK

Core Message About Software Development and Productivity
- Software development aims to deliver meaningful value to customers by solving their problems and making processes easier or less painful.
- Productivity is often misunderstood as being busy; true productivity means creating meaningful outcomes with minimal wasted effort.
- The goal is to focus on delivering business value, not just completing tasks.

Common Pitfalls and Productivity Traps in Teams
- Mistaking new tools or technologies (frameworks, AI, etc.) as automatic improvements without focusing on real value.
- Over-fixation on processes that dictate how teams work instead of helping delivery.
- Obsession with metrics like story points and velocity, losing sight of actual value delivered.
- Fear of trying new approaches, leading to stagnation and recurring problems.
- Misapplication of Agile methodologies: following frameworks rigidly rather than adapting to produce value.
- DevOps failures by not changing culture, leading to adding bottlenecks instead of collaboration improvements.
- Blind trust in latest trends (e.g., AI), ignoring potential inconsistencies, hallucinations, security risks, and the need for human oversight.

Challenges with AI in Development
- AI can hallucinate, creating inaccurate information or code.
- Inconsistent alignment with codebase and potential security vulnerabilities.
- Critical thinking is essential to validate AI outputs; AI should be treated like a code review, not an unquestioned authority.
- Relying too much on AI can diminish critical thinking skills and create false confidence.
- Human skills and judgment remain vital in software engineering.

Best Practices and Recommendations
- Focus on engineering fundamentals: align closely with customer needs and teams.
- Prioritize automating high-value, frequent tasks rather than everything.
- Perform careful review and scrutiny of AI-generated work.
- Keep humans actively involved to maintain accountability and ownership.
- Avoid misusing sensitive data in AI tools or other systems.
- Implement zero-trust security policies and secure-by-design approaches.
- Foster a culture of accountability where teams take collective responsibility for outcomes and assist across roles pragmatically.

Key Actionable Items
- Evaluate value before adopting new technologies or frameworks; don’t chase trends blindly.
- Avoid rigid adherence to processes; adapt methodologies to support value delivery.
- Use metrics as indicators, not goals; focus on delivering meaningful business outcomes.
- Encourage experimentation balanced with cautious risk assessment.
- Treat AI assistance critically; verify and validate all AI outputs thoroughly.
- Prioritize automating tasks based on frequency and impact.
- Protect sensitive data rigorously; never input customer/user data into insecure AI models.
- Promote team accountability and collaboration to solve problems together.

Conclusion
- The hardest part of software development is understanding what to build and aligning people toward that goal.
- Tools and technology alone don’t guarantee productivity or value.
- Focus on meaningful outcomes, critical thinking, and human collaboration to truly be productive.

The Productivity Trap: Why Agile, AI, and DevOps Aren’t Saving You

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12:20 - 12:50, 27th of May (Tuesday) 2025 / DEV TRENDS STAGE

Are you tired of your company chasing the latest buzzwords without seeing real results? Are you rolling out 'transformations' that only seem to create more confusion and chaos? In this talk, we’ll cut through the hype and look at the common pitfalls companies face when adopting these practices... And ways to actually make them work.

LEVEL:
Basic Advanced Expert
TRACK:
AI/ML Cloud DevOps
TOPICS:
Agile AI DevOps IT Management SoftwareEngineering

Jade Wilson

Microsoft