PMI South Asia Launches Playbook for Project Management in Data Science and Artificial Intelligence Projects in Collaboration with NASSCOM

PMI South Asia unveiled a one of a kind industry report, Playbook for Project Management in Data Science and Artificial Intelligence Projects, at the recently held Common Ground Symposium. The playbook is the result of a collaborative effort between PMI South Asia and the Center of Excellence (COE), Data Science and Artificial Intelligence (DS/AI), of the National Association of Software and Services Companies (NASSCOM), a premier industry association in India.

The playbook presents a framework with recommendations on resources that organizations can use to build capability for DS/AI projects and a best practices toolkit to apply to different project stages. It seeks to fill a gap in the way DS/AI projects are currently being implemented by laying down a ‘fit for purpose’ framework that is practitioner-centric and utilizes the collective experience of DS/AI leaders in eminent organizations globally. By utilizing this framework, organizations can derive transformative and accelerated outcomes from their DS/AI projects.

“Leveraging AI has moved well beyond simply automating routine tasks – organizations and project teams are now utilizing AI to help them navigate unfamiliar environments and make tough decisions based on adaptive algorithms,” Sunil Prashara, President and CEO, PMI, said at the virtual launch. “As DS/AI projects continue to infiltrate different industries, this playbook will be an important tool for the change-makers managing these projects as they look to maximize the technology’s potential.”

The recommendations and best practices identified in the playbook are gleaned from surveys and interviews with leaders from 25 organizations (global capability centers, startups, and service companies) across 10 industries and around the world, a study of relevant case studies, and secondary research.

Srini Srinivasan, Managing Director, PMI South Asia, added, “The playbook is an attempt to peer behind the veil of romantic mysticism often associated with AI projects to see how they work, and what makes them tick. We believe the playbook will help project managers in DS/AI environments embrace a ‘fit for purpose’ framework to achieve value. This framework will serve as a practical guide to those who aspire to work in, and manage, AI initiatives.”

Numerous studies have pointed to a high failure rate of DS/AI projects and the low or minimal impact that does not justify the big investments that organizations are setting aside for AI implementations. PMI South Asia expects the study findings to help project professionals around the world to unravel the reasons behind poor project performance and deliver transformative benefits for their organizations.

Commenting on the report Snehanshu Mitra, CEO, Center of Excellence, Data Science & AI, National Association of Software and Services Companies (NASSCOM) said, “As more balanced views emerge on the utopian versus dystopian future of AI, the focus is now shifting to ‘how’ to develop AI solutions. AI projects are significantly cost and time-intensive, and traditional project management frameworks may not be capable of negotiating the complexities of the workflow. Additionally, we need to explore if a uniform framework can guide the development of DS and AI solutions across organizations (service companies, startups, and GCCs) and use cases. This playbook brings to you a unique perspective on emerging project management practices in the DS and AI space.”

Some of the key findings:

  • 88% of organizations studied reported gaps in their practices for AI projects
  • ~21% of the total wastage in AI projects in 2023 can be saved with effective project management practices
  • 76% of organizations use their own customized methodologies for DS/AI projects

The playbook has identified three key challenges that DS/AI project managers face and how to solve for those challenges:

  • There is limited effectiveness of common project management practices when applied directly to DS/AI projects
  • The need for experimentation is extremely high in DS/AI projects which makes process adherence very difficult
  • Defining and measuring success is difficult as setting KPIs and pegging them to a business value depends on the availability of data, model behavior, and other factors