How Does AI Fit with Agile Values and Principles?

Agile Principles

How AI Fits

• Insights On-Demand to continuously improve cycle time (and all the metrics that feed it).
• Frontloads improvements by tackling the most impactful and easiest to fix issues sequentially.
• Predict delivery risks early and on the fly to proactively prevent and mitigate them.

• As reflected in values, facilitates faster, data-driven decisions arising from changes.
• Identifies programming language and technology gaps resulting from changes.

• As per notes in the value of working software over documentation.
• Reinforced by the insights that continuously improve cycle time and the many metrics that impact it. 

• Accelerates the development of cross-functional teams through the sharing of data and how each stakeholder’s efforts impact others.
• Facilitates organizational alignment by helping all teams focus on the metrics important to strategic objectives.
• Dramatically reduces research time to support reports and meetings.
• Easier to generate reports plus transparent access to the data helps keep everyone on the same page with a single version of truth. 

• Identify your best developers (and vendors) on a per language or technology basis.
• Evaluate and assess how their best practices can help lower performing developers and teams.
• Provide better matching in mentoring assignments on an organization-wide basis.

• Less time on research plus better data serves to improve the effectiveness of all meetings – one-on-ones, standups, sprint planning, retrospectives, OKRs, etc.

• AI helps to optimize cost performance by reducing decision latency and improving decision quality through factoring all of the complexities involved in software delivery team management. 

8. Sustainable development, able to maintain a constant pace

• More accurately predict and plan Sprints.
• Optimize utilization for all developers to avoid burnout.
• Objective data offsets bias in performance, pay, and promotion evaluations, along with other mechanisms, to reduce turnover and improve team stability.

• Facilitates an organization-wide effort, from CEO to individual contributor, where all stakeholders have a role in helping others be and do their best.
• Helps all stakeholders focus on the metrics critical to organizational objectives and avoids working at cross-purposes as can happen with conventional team performance metrics.
• Identify technology gaps to inform training and recruiting efforts. 
• Any improvement in velocity at the expense of quality is not really an improvement and this plays out in a variety of metrics and processes at the team and individual level in everything from mentoring, PR code reviews, commenting, and more. 

• Identifies low-ROI tasks and features so efforts can be shifted to those with a higher ROI.
• Spot (and remedy) perfectionist programming and unnecessary customized development.
• Help optimize team size and mix to minimize “administrative overhead” and issues like code complexity.

• In a preliminary respect with new projects, selecting team members with the right mix of skills and experience relative to project requirements is critical to success. 

• Ultimately, AI is a tool to help with continuously improving developer and team performance alongside changes in personnel and projects. 
• Provides an objective feedback loop to assess whether a developer, team, project, or even the entire organization is actually improving and correlate that to impact on ROI.