How do you manage AI projects with Scrum?

Managing AI projects with Scrum involves adapting Scrum principles to the unique challenges of AI development. Start with a well-defined problem statement and create user stories for AI features. Plan sprints based on data collection, model training, and evaluation phases. Conduct daily stand-up meetings to track progress. Regularly review and adjust the project's AI models and data pipelines. Involve stakeholders in sprint reviews for feedback. Emphasize flexibility and iterative development, making adjustments as AI models evolve to meet project goals.

For early stage AI companies, IMO, sprints don't work. A new paper releasing at 4:30 PM, a new funding announcement, a new model being released have the potential to completely disrupt your short-term and long-term goals. You need to adapt Scrum principles in a way that allows you to keep realigning.

This is a reasonable explanation of SCRUM, I suppose. I think a better description would be "orienting team progress around small, incremental, demo-able deliverables". That's what I think of SCRUM doing.

The goal is to learn from each iteration and adapt the process based on the insights gained. The process adaptation is crucial for fine-tuning and prompt engineering aspects of AI projects.

Managing AI projects with Scrum hinges on a blend of Scrum rituals and the flexible adaptation required in the unpredictable AI terrain. It's not just about following Scrum's ceremonies, but morphing them to cater to the iterative nature of AI projects. The essence lies in small, demonstrable progress, allowing for continual learning and adjustments. Each sprint becomes a learning curve, each review a step closer to the project's evolving objectives, and each retrospective a catalyst for fine-tuning the blend of Scrum and AI methodologies.

Scrum adaptation for AI projects necessitates a nuanced strategy that recognizes the unique difficulties and possibilities given by artificial intelligence. To effectively manage AI project development, it is critical to incorporate data management, ethical issues, and flexibility into the Scrum framework.

In my experience, adapting Scrum for AI necessitates integrating AI-specific roles such as Data Quality Analysts to ensure data adequacy. You should also consider infusing AI-centric metrics into sprint reviews, evaluating model accuracy, data bias, and computational resources alongside traditional metrics. Unlike standard projects, AI endeavors may progress non-linearly, challenging traditional Scrum metrics. Incorporating a 'Data Readiness' stage pre-sprint ensures data availability and quality. ?? Ultimately, blending AI-specific roles, metrics, and pre-sprint stages, while preserving Scrum's agile core, enables effective management and fruitful delivery of AI projects.

AI projects suffer from uncertainty in the early stages, which is not a good match with Scrum's way of working. During the early stages (exploration), it's extremely hard to anticipate and estimate tasks. We want an environment that supports quick experimentation, prototyping, and reacting to new discoveries. We want to validate our hypothesis by looking at the data. Scrum's way of working is not always good for those cases. Once we validate assumptions, we should move to a tested, reliable solution, that can be used by the business. We know the tasks we need to accomplish, and the time it'll take to complete them. At this point, the project will benefit more from Scrum's way of working.

Adapting Scrum for AI depends greatly on the size of your team and your operational needs. If you have a large team of DS/ML Engineers, and a stable product then I think a data backlog makes sense. But if you add all of these processes for a small AI startup, you're going be quickly become overburdened by process, and no longer be able to work with agility. In this scenario, I would consider keeping a standard software scrum approach, but with an understanding and acceptance that DS projects often progress non-monotonically. As such, the metrics collected to measure success may need to change.

Adapting Scrum to AI projects requires nuanced modifications. Besides standard roles, AI projects involve data scientists and machine learning engineers. Instead of just a product backlog, a data backlog becomes essential, listing data sources and preprocessing steps. The unpredictability of AI development suggests sprint reviews might focus on model improvements rather than finished features. "Completion" in AI often relates to model accuracy benchmarks. Given AI's iterative nature, the fixed-length sprint might not always align with model training phases. Data quality and validation steps become more central. Finally, seamlessly integrating AI into continuous delivery can be challenging due to model unpredictability.

In my experience, Scrum offers valuable benefits for AI projects. However, if applied rigidly, it has the potential to hinder innovation due to fixed timelines, an emphasis on short-term goals, and a tendency toward risk aversion. To maintain a balance between structured project management and fostering innovation in AI projects, teams can adapt Scrum by allowing for flexible scheduling, dedicating specific innovation-focused Sprints, involving cross-functional teams, and carefully managing the balance between customer feedback and research and development needs.

Adopting Scrum for AI projects brings multiple advantages. It fosters enhanced collaboration between data scientists, engineers, and stakeholders, promoting harmonious goal alignment and fostering innovative solutions. Scrum's iterative nature ensures stakeholders see regular progress, amplifying their trust and satisfaction. By validating results in shorter cycles, it identifies potential pitfalls early, reducing risks and curtailing escalated costs in later stages. Constant feedback mechanisms in Scrum encourage teams to adapt and refine strategies, enhancing both the project's efficiency and quality. Overall, Scrum's agility accelerates AI project timelines while ensuring high-quality, timely results.

From my experience biggest benefits I've seen in using Scrum for AI projects are the collaboration it creates amongst team members as well as bridging the communication gap with stakeholders. The iterative approach of Scrum ensures continuous delivery and the cycle of feedback and adaptations with each sprint creates a culture of continual learning and improvement, which enhances the overall efficiency and delivery of the AI projects.

Improved communication & collaboration: Vital for AI projects with diverse skill sets. Increased transparency: Keeps everyone on the same page and ensures project progress. Faster delivery: AI benefits from quicker feedback through incremental delivery. Reduced risk: Regular delivery and feedback lower the risk of mismatched solutions.

Tiene varios beneficios como la automatización de actividades, análisis de datos, mejora la estimación de esfuerzo o sugerir cambios en las actividades como retrospectivas, reuniones diarios para que el equipo no se aburra en realizar estas actividades.

To adapt Scrum for AI, we can refine roles tailored to AI-centric projects and introduce data backlogs. While Scrum can enhance communication and expedite project delivery, we must also be mindful of the unique challenges it can present, especially concerning data constraints and ethical considerations.

Utilizing Scrum methodologies in AI projects presents a unique set of challenges. Data dependencies and availability become critical factors to manage, requiring a nuanced approach to sprint planning. Striking an equilibrium between exploration of new algorithms and exploitation of proven methods adds another layer of complexity. Ethical considerations also come into play, introducing questions of societal impact and responsibility that can affect the project timeline. Lastly, effectively measuring and communicating the value generated by AI initiatives is often elusive, complicating stakeholder buy-in. These challenges necessitate a highly adaptive Scrum approach to navigate the intricate landscape of AI development.

A culture of continuous learning is essential when trying to get ahead the complexities of AI projects using Scrum. Redefining traditional notions of 'Done' to embrace incremental progress and valuing experimentation sprints that may not yield deployable code but are crucial for learning. Prioritising tasks by aligning them with business objectives and dedicating sprints to robust data management can mitigate data-related challenges. Additionally, adopting ethical guidelines ensures responsible AI development, while a clear value metrics framework helps in articulating the impact of AI initiatives to stakeholders, aiding in aligning AI endeavors with the overarching product vision.

The complexity of AI projects: AI projects can be complex and challenging, which can make it difficult to estimate the work and create realistic sprint plans. The uncertainty of AI projects: AI projects can be uncertain, as it is not always clear how long it will take to train and evaluate the AI model. This can make it difficult to stick to the sprint plan. The need for flexibility: AI projects can be unpredictable, so it is important to be flexible and to be prepared to adapt the plan as needed. This can be challenging for teams that are used to working in a more structured environment.

Ayuda al equipo a obtener nuevas prácticas, herramientas, análisis de datos y recomendaciones. Esto puede generar dependencia de la IA haciendo que en cierta manera la IA sea un límite de información para el Equipo. En la IA puede realizar recomendaciones que pueden tener consecuencias éticas por lo que será un desafío el que el usuario pueda tener el criterio para decidir si la respuesta es correcta o no.

AI projects generate tactical value. This is where the true value of AI projects exists. So, it is extremely important to identify what can and can not be done. Sometimes, more iterations and more data is needed for more accuracy which means more time is required. So, all stakeholders especially the downstream users need to understand that by rigidly defining scrums and timelines, the deliverable accuracy might fall short and we may need to supplement using manual or other deterministic technique. Many a time leaders think the AI team is not competent because they are delivering high accuracy within a defined timeframe. This will cause erosion of trust and heartburn and team will lose morale. Leaders need to make sure to avoid such outcomes

* Redefine 'Done': In AI, 'perfect' rarely happens. Focus on incremental improvements. Define success by measurable metrics, not just task completion. * Experimentation Sprints: Accept that not all sprints will produce deployable code. Some are for experimentation and learning, which is vital in AI. * Data Management: Ensure a dedicated sprint for data collection, cleansing, and validation. Good data is the backbone of AI. * Skillful Backlog Prioritization: Regularly reassess the product backlog. AI's unpredictable nature requires constant realignment with business goals. * Embrace Uncertainty: AI is exploration-heavy. Celebrate learnings from 'failures' as they offer direction.

Don't be afraid to use timeboxing for items with big uncertainty, as this is inherent to many AI development tasks. After each time box, we should be a little bit clearer on what "done" look like, and how (soon) we'll get there.

Some tips for overcoming the challenges of using Scrum for AI projects: Break down the work into small, manageable tasks: This will help to make it easier to estimate the work and create realistic sprint plans. Use a risk management process: This will help to identify and mitigate risks, such as the uncertainty of AI projects. Be prepared to adjust the sprint plan as needed: This will help to ensure that the team is able to deliver working increments of the product even if the project is unpredictable. Provide the team with the training and support they need: This will help the team to be successful in using Scrum for AI projects.

In my experience working with data and using scrum requires a lot of discipline settings the right outcomes and expectations. With data one deals with errors missing data dependency from other teams, architecture. With these one need to set up the scalable architecture runway, cloud based data platform and related technology. The stakeholders should be provided an overview of what is expected in each iteration during PI planning sessions. The teams and the value trains have to be diligent how and what pieces of AI solutions can be tackled in each sprint and how the work will be handled across teams.

Effective documentation, continuous monitoring, and maintenance processes ensure long-term model performance and compliance with evolving needs and regulations, all within an agile framework designed to maximize AI project efficiency and effectiveness.

Embrace a #lean, #iterative approach for #AI project management without #scrum by setting clear #goals, breaking projects into #manageable phases, and assembling a #cross-functional team. Use daily standups to tackle progress and challenges, and #frequent demos for feedback. #Prioritize quick iterations based on #feedback, maintain #open communication, and #celebrate milestones. Focus on flexibility, team #empowerment, and valuing #outcomes over rigid processes. This #strategy promotes #innovation, #adaptability, and #continuous improvement.