Playbook for Project Management in Data Science and Artificial Intelligence Projects

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Executive Summary

The ability of DS and AI to solve problems and offer answers that go beyond the limitations of the human brain has spurred business interest and investments in these technologies. They guide decisions on an astounding range of problems across industries like automating customer service, quick appraisal for loans, image recognition for better security, autonomous driving and smart irrigation. However, there is often a shortfall between the projected benefits from a DS/AI led solution and what organizations realize on the ground. Numerous studies have pointed to a high failure rate of these projects and low or minimal impact that does not justify the investments being made.

Going by preliminary data, PMI postulated the lack of tailored project management practices for DS/AI projects as a major factor behind the high failure rate. This playbook aims to fill this gap by building a “fit for purpose” project management framework that will help organizations and project practitioners improve the outcomes of their DS/AI projects.

THE PLAYBOOK PRESENTS A PROJECT MANAGEMENT FRAMEWORK THAT COVERS:

  • Resources for the capability-building of individuals and organizations to realize transformative project benefits, and
  • A best practices-based toolkit for each stage of a DS/AI project derived from our study of leading organizations

The playbook is a result of collaboration between PMI, a global leader in project management, and NASSCOM CoE, an eminent thought leader on DS/AI. It brings together best practices gleaned from interviews and surveys with DS/AI leaders from 25 organizations cutting across industries, geographies and types of organizations. The playbook offers both leaders’ perspectives of managing DS/AI projects and an appreciation of challenges and workaround solutions by practitioners on the ground, captured through case studies.

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10 INDUSTRIES COVERED

ITeS, Semiconductor, CPG (Consumer Packaged Goods), Computer Hardware, Agritech, Financial Services, Chemicals, Management Consulting, Telecom and Electrical equipment

3 KINDS OF ORGANIZATIONS

GCCs (Global Capability Centers), Start-ups and Service Companies

Three key challenges in DS/AI projects identified by the study

1

There is limited effectiveness of common project management practices when applied directly to DS/AI projects

2

The need for experimentation is extremely high which makes process adherence difficult

3

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

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