Scrum Masters: Solve real problems with AI workflows

For teams new to AI, zero-shot (no examples) prompts are unreliable for complex tasks. Few-shot learning, where you provide 2-5 examples in the prompt, is a simple, highly-effective technique to guide the AI, improve accuracy, and make powerful AI tools more accessible to all developers.

AI tools make it easy to generate thousands of lines of code in seconds. This often leads to "AI-slop" PRs that are so large they are impossible to review, hiding bugs and security flaws. This practice destroys team velocity and code quality by creating massive review bottlenecks.

Individual developers and teams will inevitably waste hours "reinventing the wheel" by creating and refining prompts for common tasks. This duplicate work is inefficient, expensive, and leads to inconsistent AI usage and outputs across the organization.

AI-generated code often looks plausible but contains subtle logic errors, security vulnerabilities, or performance bottlenecks.5 Blindly trusting and merging this code is dangerous, erodes quality, and creates massive, hidden technical debt.

Relying on AI to generate code and tests at the same time is risky. The AI can misinterpret requirements and produce code with subtle bugs. Manually reviewing large blocks of AI-generated code is slow and error-prone. Test-Driven Development (TDD) provides the perfect framework to solve this.

Implement a formal, multi-level AI literacy framework to build durable skills across the entire organization. AI literacy is a new core competency that is not limited to engineers. It emphasizes critical thinking, ethical reasoning, and the ability to evaluate AI outputs, which are essential skills to mitigate bias, reduce privacy risks, and build resilient, trust-based AI workflows.

Mandate the use of secure prompt engineering practices as the first line of defense in the AI-assisted development lifecycle. The prompt is the new "shift-left"; a vague or naive prompt will predictably generate insecure code, while an explicit, security-aware prompt will produce safer, more robust outputs. This practice is a form of proactive risk mitigation, not just an output-optimization technique.

Integrate AI into the quality assurance (QA) process to automatically generate test cases, optimize test suites, and perform "self-healing" maintenance on brittle automation scripts. This moves AI's role in testing beyond simple TDD (Recommendation 7) and uses it to solve the significant economic and time costs of test maintenance.