Food for Agile Thought #390: AI Replacing Developers? OKRs & User Story Mapping, Good Goals/Bad Goals, Agile Architecture

TL; DR: AI Replacing Developers — Food for Agile Thought #390

Welcome to the 390th edition of the Food for Agile Thought newsletter, shared with 46,277 peers. This week, we ask a simple question: Is AI replacing Developers a likely future? In the meantime, we delve into the importance of (software) architecture in an agile context, suggest alleviating some of the additional cognitive load developers shouldering with DevOps, and deep-dive into lessons learned on overcoming long-standing behaviors and winning hearts and minds.

Then, we suggest that as a product leader, you must ensure that you negotiate the right aspects by going beyond debating solutions and delving deeper to reach a consensus. Moreover, Itamar Gilad cautions against using generative AI to produce product management artifacts, pointing to serious tradeoffs, and Mike Cohn dissects a familiar template, looking at the “elements, advantages, and drawbacks of the three-part story template.”

Finally, we advocate using OKRs with Story Maps to support your release strategy, list positive attributes your goals should reflect, and cherish the ProductOps Manifesto. Lastly, we applaud Matt Schlicht for creating an epic intro to the latest AI trend; we might all become managers, herding bots.

Food for Agile Thought #390: AI Replacing Developers? OKRs & User Story Mapping, Good Goals/Bad Goals, Agile Architecture — Age-of-Product.com
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Download the 60 ChatGPT Prompts for Scrum Masters & Product Owners Guide for Free

Welcome to the Download Page of the Product Owner & Product Manager Salary Report

ChatGPT can be an excellent tool for those who know how to create prompts. The simplest form of prompting ChatGPT is to feed it the task and ask for results. However, this approach is unlikely to trigger the best response from the model.

Instead, invest more time in prompt engineering, and provide ChatGPT with a better context of the situation, desired outcomes, data, constraints, etc.

Free Download 60 ChatGPT Prompts for Scrum Masters, Product Owners, Product Managers — Age-of-Product.com

The free ‘60 ChatGPT Prompts for Scrum Masters & Product Owners Guide’ will help you learn to do that:

Cannot see the form? Please click here.

Example ChatGPT Prompts for Scrum Masters

Prompt: “I want you to act as an experienced Scrum Master. Please [insert your task here.]”

Examples of tasks:

  1. Provide tips for facilitating effective Sprint Planning meetings.
  2. Summarize the outcome of the Daily Scrum with the following data: [Your data.]
  3. Design a Retrospective.
  4. Design a Retrospective with stakeholders from [Stakeholder departments.]
  5. List strategies for resolving team conflicts and promoting a healthy work environment.
  6. Recommend activities to make Sprint Retrospectives engaging and productive.
  7. Summarize the outcome of the Retrospective with the following data: [Your data.]
  8. Create tips for coaching and supporting the Product Owner in Product Backlog refinement.
  9. Explain the benefits and drawbacks of different estimation techniques.
  10. Suggest how to help the Scrum team balance technical debt and new feature development.

Finally, should everything fail, there is another nifty trick: You can use ChatGPT to create ChatGPT prompts!

Learn more by downloading the complete 60 ChatGPT Prompts Guide!

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ChatGPT Product Owner Job Interview — Will You Compete for a Position w/ an LLM Soon?

Agility and Scrum According to OpenAI’s ChatGPT — Be Surprised!

Self-Management: The Top Ten Business Reasons to Trust Your Teams

TL; DR: Self-Management

Is self-management an essential building block on an organization’s path to business agility or a nice-to-have cultural twist to, for example, keep teams happy and attract new talent?

While many people, particularly at the management level, are skeptical about the concept, I am convinced that organizations need to descale and regroup around aligned, autonomous, self-managing teams in a complex environment. Ultimately, only the people closest to the customers’ problems can solve those within the given constraints while contributing to an organization’s sustainability.

Please continue reading and delve into the reasons that support self-management.

Self-Management: The Top Ten Business Reasons to Trust Your Teams — Age-of-Product.com
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Food for Agile Thought #389: Scrum’s Failure, Staying Scrappy for Innovation, Forecasting 2.0, ProductOps Handbook

TL; DR: Scrum’s Failure — Food for Agile Thought #389

Welcome to the 389th edition of the Food for Agile Thought newsletter, shared with 46,163 peers. This week, we delve into Scrum’s failure: Jason Godesky reminds us that few have ever worked in a true Scrum team, but many have in a Scrum cargo cult environment. Moreover, we delve into “bias and noise” and how they influence forecasting, and we learn how you can use Fluid Scrum Teams to manage multiple data products over various Sprints. Also, in shameless self-promotion, I want to point to the No Nonsense Agile Podcast that Murray Robinson, Shane Gibson, and I recorded recently. (Speaking of anti-patterns: There is also a new article on Jira and its perils if misused, see below.)

Then, we analyze the fundamental misalignment between the ‘business’ — focusing on one customer at a time — and ‘product/engineering,’ focusing on the overall impact for the installed base. Additionally, we point to the importance of embracing a learning mindset as the driver of innovation while process, vetos, institutional memory, and the vilification of failure work against it, and we share a practical and actionable list of recommendations to create a ProductOps handbook.

Finally, we suggest five experiments to help foster a safe working environment, from equal speaking time during meetings to celebrating failure, plus a new tool to ease the often challenging task of communicating project issues: the Definition of Broken. More tools are from John Cutler, who points to the free template library of the complete North Star metrics framework. Lastly, Nicole Abi-Esber and Juliana Schroeder claim that people underestimate how much others want to receive feedback.

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Jira Anti-Patterns

TL; DR: Jira Anti-Patterns

If you ask people to come up with popular attributes for “Agile” or “agility,” Scrum and Jira will likely be among the top ten featured. Moreover, in any discussion about the topic, someone will mention that using Scrum running on top of Jira does not make an organization agile. However, more importantly, this notion is often only a tiny step from identifying Jira as a potential impediment to outright vilifying it. So in March 2023, I embarked on a non-representative research exercise to learn how organizations misuse Jira from a team perspective as I wanted to understand Jira anti-patterns.

Read on and learn more about how a project management tool that is reasonably usable when you use it out of the box without any modifications turns into a bureaucratic nightmare, what the reasons for this might be, and what we can do about it.

Jira Anti-Patterns —Age-of-Product.com
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Food for Agile Thought #388: Perils of Deterministic Thinking, ChatGPT for Product Work, Interruption & Context Switching, Pre-Mortems

TL; DR: The Perils of Deterministic Thinking — Food for Agile Thought #388

Welcome to the 388th edition of the Food for Agile Thought newsletter, shared with 46,012 peers. This week, we delve into the perils of deterministic thinking and reflect on what growth may mean for a Scrum team, covering the spectrum from product discovery to DevOps. Also, we suggest strategies to minimize the influence of engineering work’s two most costly factors and point to wrong ideas typically plaguing agile transformation, from installing practices to changing culture to the mystical one-time change effort.

Then, we share real-life examples, including prompts on how product managers already use ChatGPT daily and follow John Cutler, who looks beyond the usual product-market fit approach, claiming that the biggest challenge is Product-Reality Fit instead. Also, we map the problem of creating product goals onto the Cynefin Framework and detail what experimentation entails in the complex or chaotic domain. Speaking of creating goals: you want to mitigate the risk of running in the wrong direction. Ant Murphy suggests an excellent tool for that: the pre-mortem risk analysis.

Finally, Sevawise Games created a free Monte Carlo Estimation tool that supports Parabol’s fine primer on estimation, from Planning Poker to Wall Estimation to #NoEstimates. Moreover, we explore whether we can use ChatGPT-4 to create workshops for agile practitioners; for example, Scrum Masters. Lastly, Dave Gray details the development steps of the well-known Innovation Ecosystem Map.

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