Emerging project management practices in AI

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Emerging project management practices in AI

AI is creating possibly the biggest technological wave of our lifetime. The geopolitical implications of AI are being seen beyond economic prosperity. In the race to establish AI supremacy, nations are competing to formulate their own AI strategies. Fully distributed innovation driven by decentralized development, coupled with low barriers to entry, is fuelling the pursuit of “AI for all”. Thus, AI clearly is no longer a “secret sauce “but one that continues to provide significant advantages – with the right focus on people, processes, and technology.

As more balanced views emerge on the utopian vs 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, start-ups and GCCs) and use cases.

Two established thought leaders, the Project Management Institute and NASSCOM’s Centre of Excellence for Data Science and AI have come together to bring to you a unique perspective on emerging project management practices in the DS and AI space. The recommendations are the outcome of expert consultations across a range of organizations with established DS and AI practices on specific stages of managing such projects.

As DS & AI projects become mainstream, our study confirms certain theories –

The need for experimentation is extremely high in DS/AI projects which makes process adherence very difficult. A significant proportion of organizations studied (~88%) reported gaps in their practices for AI projects
Organizations are exploring a range of project management practices to find the best fit. 76% of organizations use their own customized methodologies for DS/AI projects. However, there is limited effectiveness of common project management practices when applied directly to DS/AI projects
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. Better project management practices have a direct bearing on improving the outcomes. ~21% of the total wastage in AI projects can be saved with effective project management practices

The playbook chronicles best practices from across a range of organizations on each stage of the workflow – from business understanding, data preparation, modeling, implementation, and project closure. It examines the suitability of approaches such as agile, waterfall, Kanban to the DS & AI world and how CRISP-DM is today interspersed with these approaches. It also emphasizes the need to develop a robust data strategy and promote inter disciplinary team collaboration by organizations looking for sustained benefits.

We hope that the practitioners find this playbook to be a handy guide and the insights offered facilitate the delivery of the 3Es – efficiency, effectiveness, and experience.

Read the summary of the playbook here: Playbook for Project Management in Data Science and Artificial Intelligence Projects

Reach out to us at coe-dsai@nasscom.in if you have an interesting AI article to share. We would love to feature them here.

Snehanshu Mitra

Head – CoE, Data Science & AI, NASSCOM Snehanshu leads the AI initiatives at NASSCOM. He heads the CoE for Data Science & AI – in partnership with Govt of Karnataka and the Telangana AI Mission in partnership with GoTS. He is responsible for creating, nurturing, and scaling up a vibrant AI ecosystem that involves driving AI adoption, accelerating AI startups, leveraging AI for societal good, work with enterprises to co-innovate and promote applied research and AI skilling.