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 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.
Please provide us the below details to download the playbook
ITeS, Semiconductor, CPG (Consumer Packaged Goods), Computer Hardware,
Agritech, Financial Services, Chemicals, Management Consulting, Telecom and
GCCs (Global Capability Centers), Start-ups and Service Companies
There is limited
when applied directly
to DS/AI projects
The need for
is extremely high
which makes process
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
To know more
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