Agile framework for Gen AI based applications

What comes to mind when we think of “agile ways of working”?

Agility, as a mindset and methodology, empowers us to navigate this complex and dynamic landscape with resilience and adaptability. It enables us to embrace change, iterate quickly, and pivot when necessary, all while maintaining a steadfast focus on delivering value and driving meaningful outcomes.

Let us look at the synergy between agility and innovation to envision what lies ahead of us in the coming times. A future where Generative AI harnesses the power of agile methodologies to unlock new possibilities, drive unprecedented growth, and tackle some of the most pressing challenges facing humanity.

Why using agile for GenAI applications is so important?

• Dynamic Nature of Projects
  • GenAI projects often deal with uncertainty, whether it’s uncertainty about the data, the problem domain, or the technology itself.
  • As GenAI is also in its early stages and is fast evolving, agile’s iterative approach allows teams to adapt to uncertainty by breaking down complex problems into smaller, manageable chunks and validating assumptions through experimentation.“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change.” – Charles Darwin
• MVP vs MVAi
  Drawing parallels with MVP (Minimum Viable Product), when it comes to GenAI applications what would be most essential is MVAi (Minimum Valuable AI)
  • Minimal – allowing teams to deliver a solution quickly
  • Valuable – stakeholders have a core need addressed
  • Artificial Intelligence – mimics human intelligence

In what ways does Agile support the timely delivery of AI solutions to meet market demands?

Agile methodology is particularly well-suited for the timely delivery of AI solutions to meet market demands due to several key factors:

• Iterative Development – Build Small, Delivery Early, Fail-Fast approach
  • Agile encourages iterative development, where AI solutions are built incrementally and delivered in small, manageable increments known as sprints. This allows for continuous feedback from stakeholders, enabling teams to adjust the direction of development based on market demands and changing requirements.
    

    Iterative Development

     

  • Flexibility – Embrace the change as per evolving market needs
    Agile methodologies emphasize adaptability and flexibility, enabling teams to respond quickly to changes in market demands, technological advancements, or shifts in priorities. This ensures that AI solutions can be refined and adjusted rapidly to meet evolving market needs.
    

    Flexibility

  • Collaboration: Unified Task Force
    Agile promotes collaboration among cross-functional teams, including data scientists, developers, product owners, and business stakeholders. This collaboration fosters a shared understanding of market demands and enables teams to work closely together to deliver AI solutions that align with market needs.
    

    Collaboration

     

  • Customer-Centric Approach: Using CSAT and NPS – To Better Understand Customer Sentiments
    Agile methodologies prioritize customer satisfaction and value delivery. By continuously engaging with customers and stakeholders throughout the development process, Agile teams can ensure that AI solutions address specific market demands and deliver tangible value to end-users.
    

    Customer Feedback

     

  • Continuous Improvement: Identify, Plan, Execute, Review – Cyclic loop
    The adoption of agile builds a culture of continuous improvement, where teams regularly reflect on their processes and outcomes to identify areas for enhancement. This iterative feedback loop enables teams to optimize their approach to AI development, leading to more efficient delivery of solutions that meet market demands.
    

    Continuous Improvement

     

Examples of how Agile practices facilitate experimentation and exploration in the development of GenAI applications

Agile practices can facilitate experimentation and exploration in the development of generative AI applications in several ways:

• Sprint Planning – Agile methodologies typically involve breaking down development tasks into short iterations called sprints. During sprint planning, development teams can allocate time for experimentation and exploration of new ideas or techniques related to generative AI.
  • For example, a team working on an image generation using prompt engineering (genAI) project might dedicate a portion of each sprint to exploring different neural network architectures or experimenting with novel training techniques to improve image quality.
• Prototyping – Agile methodologies advocate for building prototypes or proof-of-concepts early in the development process to validate ideas and gather feedback. In the context of generative AI applications, teams can create prototypes to experiment with different approaches and evaluate their feasibility and effectiveness.
  • For instance, a team developing a text generation model might create a prototype to test various natural language processing techniques or to explore different ways of generating coherent and contextually relevant text.

As GenAI builds more traction in the times to come, agile methodologies could fuel the development of GenAI-based application development.