Agile for Dynamic Commercialization of AI R&D

We often think about Agile as a methodology for commercial-level product development. Yet Agile organizational principles perfectly suit R&D activities as well, including for AI.

After reviewing the Agile core process model, we’ll look at a real-life project example involving scientific research combined with commercial product development – all contributing to the same product. I will share my experience as Software Engineering Manager at IBM’s Watson-Health leading that project.

The project work that will be discussed in this session deals with health data and thus is subject to health industry regulations. We will discuss how this has been handled by the product development team.

 
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Outline/Structure of the Talk

I. INTRODUCTION

  • Describing the project goal and its unique dual tracks of ongoing research with product commercialization

  • Reviewing with the attendees the background of reasons why Agile was introduced as an alternative to traditional way of managing projects and relating this back to the project under discussion

II. IMPLEMENTATION APPROACH: HOW DID WE MAKE IT WORK?

  • Introducing the team to the Agile principles and conducting initial training. Ongoing team coaching on the Agile principles

  • Team agreement on the Agile operational process adoption and rollout plan. Ongoing review and tuning of processes by the team

  • Developing and implementing technical processes in support of Agile principles that have to factor in ongoing research team AI model changes

  • Creating Agile friendly environment: adopting Agile mindset and enabling team self-organization

  • Productive, effective and open interactions: enabling collaboration and environment where learning new technologies and not afraid of asking questions

  • Operational model: co-located research team working together with distributed software engineering team

  • Celebrating team accomplishments in achieving product milestones

  • Challenges and lessons learned

III. Q&A

Learning Outcome

The evolution of AI technologies that are still exploratory in their nature—while being the same time rapidly adopted for use in commercial applications—introduces an additional level of complexity and thus challenges managing such efforts.

Product development teams and their leadership have at their disposal a range of project management techniques and tools to help maximizing the chances of such efforts being successful.

Adopting Agile methods with their open-minded, flexible approaches to managing product development efforts, in the context of the fast paced and very competitive AI market, is a key factor for enabling successful outcomes.

Target Audience

For those who are interested in real-life, Agile applications to recent AI projects.

Prerequisites for Attendees

Knowledge of Agile Manifesto

schedule Submitted 4 months ago

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