Agile and AI Development: Bridging Complexity with Flexibility - Amit Sinha

Why Agile with AI is Important Today?

The world of technology is changing as always. Many feel threatened today with the impact of Artificial Intelligence (AI) on their jobs and career. Agile is not spared either. However, building AI products is complex and integrating Agile ways of working with AI development is crucial for organizations to stay competitive. Agile mindset of flexibility and iterative development matches up perfectly for handling complexities and uncertainties inherent to AI development. AI projects often involve a lot of experiments, ongoing learning, frequent changes or adjustments based on emerging data and insights.

Agile methodologies promote adaptability, collaboration, and continuous improvement. They provide a lightweight structured framework and are well suited to accommodate these dynamic requirements. By adopting Agile principles and truly understanding their application, organizations can effectively manage the iterative cycles of AI product development. This will help with timely delivery of innovative and valuable AI solutions while staying adaptable to new information and changing markets. This teaming up of Agile and AI will boost the success of AI projects by driving innovation, efficiency, time-to-market and ultimately impacting business value.

Why is AI Development Challenging?

Developing AI products brings unique challenges to the table; challenges that set them apart from traditional software projects. Here are some common challenges that I see often with AI

1. There’s an inherent uncertainty in planning and delivering AI solutions.Unlike routine software development where initial requirements and outcomes can be defined upfront, AI projects are way more unpredictable.The unpredictability is due to the experimental nature of AI, where continual testing and refinement of initial assumptions (hypothesis) is needed to achieve a viable product. This often spans numerous iterations.
2. Secondly, AI development calls for a high level of technical expertise, coordination and collaboration across different domains such as but not limited to Data Science, Data Engineering, Machine Learning (ML). These domain experts must work together to analyze huge amounts of data, develop algorithms, build, test and fine-tune models. Such an effort between various disciplines can be challenging to coordinate, and becomes even more intimidating with shifting goals and requirements that are typical to AI projects.
3. ?Thirdly, the circular nature of AI development means that progress is not always linear. Initial stages will involve extensive research and experimentation and may not lead to immediate tangible results.This makes it difficult to break work, estimate stories, identify people and resources accurately, leading to potential project delays and budget overruns.

Despite the above challenges, AI projects have immense potential for success and will bring in high return on investment. They already are, and will continue to shape the future of technological innovation and human evolution.Organizations that effectively tackle these challenges will harness the transformative power of AI to create solutions that will drive innovation, speed-up decision making, and offer competitive advantages.

However, to achieve success will need a flexible, adaptive and collaborative mindset that translates to behaviors and practices that can effectively manage the complexities and uncertainties inherent to AI product development. This is where Agile comes into play with its empiricism, inspect and adapt, customer focus, systems thinking on one hand and servant leadership, collaboration, swarming, attention to results, commitment, team ownership (accountability), healthy conflict and foundation of trust on the other (hand). These two sides of Agile can provide a structured, adaptable and safe ground for making AI projects thrive!

How Can We Help You?

Hernan Tocuyo , my peer Coach and I, worked for a year at a client where we were onboarded with bringing their DATA ART (Agile Release Train) to become effective and high performing. Multiple teams in that ART are engaged in high complexity work, including AI, Machine Language, Data Science, Data Engineering etc. They develop algorithms, test hypotheses, and build models. Their work includes enablers, spikes, and needs cross coordination between domain experts and across ARTs. They use all industry standard data management tools such as Teradata, Apache, Databricks, Hadoop, Informatica, Tableau etc.

Using Agile values and Agile and SAFe principles we brought all teams to a high level of effectiveness in a very short time. They figured out better ways to write stories, have conversations, refine work, collaborate and make their PI planning effective. Some data scientists paired with us and brought out an effective way to write their Data Science stories and created and continuously polished templates that everyone could use. Inspired by our work with them and the collaboration between Agile Coaches and Data Scientists, we wrote a blog on this

Conclusion:

Agile done wisely and sensibly does work and works well enough. The teams from this above client have members across 4 time zones with very little overlapping hours. Despite this and other challenges, they have developed into effective, highly collaborative, high performing teams.

The current AI wave is creating a lot of fear about “Agile is dead” and all the noise of Scrum Master jobs being taken away. I would call out to all the passionate Scrum Masters that AI will never be able to replace the need of human beings who inspire greatness in others, create safe environments to work and are true Servant Leaders serving their teams and organizations. Strive to be a powerful Servant Leader Scrum Master; truly imbibe the universal values and principles of Agile and Scrum and you will never find yourself out of job. There will always be a need for humans who can bring positive behaviors out in others through their caring attitude and inspire change for the betterment of an organization.

We have done successful end-end organic Agile Transformations for multiple clients using two of the most popular scaling frameworks, Scaled Agile (SAFe) and Scrum@Scale. Contact us to learn how we did it for them . Due to huge success with onsite and client training, we started to offer both SAFe training and certifications and will be starting with Scrum Alliance training and certifications soon. In addition, Agilonomics’ own training workshops with certification are also available. Each of these is taught by an expert coach with many 100s of hours of training and hands-on coaching. Check them all out https://agilonomics.com/safe-training-and-certification/