The Beautiful Dance Between Artificial Intelligence and Scrum with Justin Thatil and Tarik Smajic

This week, Dan Neumann is joined by Tarik Smajic from Machine Learning Team and by Justin Thatil, an Agile colleague. Justin and Tarik are both Scrum Masters but Tarik’s work is in Artificial Intelligence or Machine learning. In this episode, they explore together with Dan, the differences and similarities between Scrum and AI as well as how they complement each other by sharing valuable case examples.   Key Takeaways What makes AI Teams different from the Scrum framework? Scrum helps to reduce complexity, and certainly, machine learning is a very complex subject. Scrum is a way to start establishing norms in AI teams. In the traditional software development life cycle, there are established phases in order to build software and this includes an exploratory aspect. It is more than data. We give the client for free only the data that we are willing to give them, but there is even more data that you can think about that in the past was considered waste data. There are patterns that can be found in data, that is why it is called predictive data. We used to want all the data available but we started to figure out that not all that data is needed, and in case it is necessary to synthesize data that has any predictive implication. The beautiful dance Scrum proposes: Scrum works by just enabling the particular accountabilities to do their thing, to be empowered to shine in their field of action. Once you stop trying to solve problems using predictive and prescriptive analytics and start understanding where the value lies and where models need to be built. Case: A Team faces a product challenge. Let the Team have the time to research (but it can’t be forever). The Team needs to go through one cycle to establish a baseline. It is better if you adopt Scrum, starting from scratch. Sprint reviews in AI: The race to the minimum viable product can look like looking at your data asset and learning from it. Tarik shares several examples. It is important to establish what the development phases look like while the ideation and intake Team handles the values assessments and figures out what use cases there are; prioritizing them is the product management Team’s work. Then the research aspects follow; you want the engineers to build the pipelines and then do the testing. Scrum of Scrums: Tarik shares how they use one Scrum of Scrums on a weekly basis that only lasts 15 minutes. A necessary question to ask during a Scrum of Scrums meeting is: Am I putting anything in anybody elses’ duties? How realistic are the expectations? The meeting produces a forecast of what can happen. Application of Scrum in the AI and ML worlds: Tarik shares his experience. Everything in Scrum is iterative. There are three phases of learning something. It takes a while to master things; patience is required. It is OK to bend the rules, you don’t have to do it all by the book.   Mentioned in this Episode: Link to a previous episode Getting Things Done: The Art of Stress-Free Productivity, by David Allen   Want to Learn More or Get in Touch? Visit the website and catch up with all the episodes on AgileThought.com! Email your thoughts or suggestions to [email protected] or Tweet @AgileThought using #AgileThoughtPodcast!