Every year approximately $43 trillion is invested in projects and as many as 65% of these projects fail. As Life Science Project Managers, these numbers should be a wake-up call for us to improve project success rates. Imagine if we moved the needle in the right direction by improving success rates by as little as 1%. Think of the positive impact this would have on the lives of patients. The good news is that it is possible and we are not alone, as we may have an intelligent Project Management Assistant powered by Generative AI in 2024.

The year 2023 was a memorable milestone in the software industry with the announcement of OpenAI ChatGPT’s general availability in the first quarter. That announcement led several other companies to invest in Generative AI, e.g., Google’s Bard and then Gemini, Amazon’s Bedrock and most recently x.ai’s Grok. While these innovations are going to supercharge problem solving, aka delivery of projects, we know as Project Managers that at super speed things can go wrong. That is why management of projects will become much trickier and the role of Project Managers will evolve and become much more important as a catalyst into these innovations with data and intelligence.

Thinking of the Life Science industry as our domain, Generative AI will play a role in various subdomains, e.g., Research & Development, Manufacturing & Supply Chain, Commercialization & Medical Affairs, and other corporate functions. Every management consulting firm from the Big 4 to other niche consulting players in the industry came up with multiple use cases. These cases bring in efficiency and efficacy to reduce waste (i.e., cost of drug development from molecules to market or cost of care for patients) via data-based reasoning and intelligence from Generative AI based models. The models are available out of the box in a safe/secured environment by cloud service providers (e.g., Amazon, GCP or Azure). The models are brought closer to the data in order to generate intelligence that in turn helps to reduce waste and bring in efficiency.

A team can create a low-fidelity prototype (a quick and easy way to translate high-level design concepts into tangible and testable artifacts) in Research & Development, as a solution to prove the concept and project-associated value creation in terms of efficiency or reduction of waste in the Research & Development function. The most difficult part of all these innovations is not development of a prototype, but rather moving from prototype to production. Project Managers are going to be some of the most important players as coaches or reliable helping hands to executives for making solutions production ready.

Thinking about this more, let’s assume that our team is following Agile Project Management practices in small teams of 6 to 8 members with multi-disciplinary skill sets, e.g., Product Manager, Molecular Biologist, Data Scientist, Python Developer, Scrum Master and Cloud Engineer. Our role as Project Manager will evolve to also include product management if we correctly upgrade our knowledge from Project Management to Product Management with necessary research and development knowledge. Once again, there are possibilities to enhance our knowledge base to manage continuous evolution of a Product with a roadmap in multiple program iterations (PI). Each PI can run in 4 to 6 sprints where each sprint can be of maximum 2 weeks. Traditional project management responsibilities, e.g., leading the team, fulfilling requirements, managing day to day project activities will still be there and a scrum master can fulfill those responsibilities. Product management responsibilities, e.g., defining market need, being voice of customer, maintaining product requirements backlog etc., are emerging qualities, as the industry adopts an agile delivery model.

Once we lay out the roadmap and empower team members with task-based progress measurement and reporting, the team will create a lot of data as epics, stories and tasks in an Agile Project Management tool. This dataset will play a big role to make ourselves intelligent (i.e., as both Project & Product Manager) in order to dig deeper into everyday progress, dependencies and constraints in order to correct inefficiencies (e.g., removing blockers, prioritizing tasks and escalating to executives) with an assistant of course powered by Generative AI. We may be lucky in 2024 to have assistants embedded as a feature within Agile Project Management tools and we all need to know how to ask the right question (i.e., Prompt Engineering – the process of structuring text or refining prompts that can be interpreted and understood by a generative AI model) to get the right answer.

My final comment is a call to action for my project management colleagues. As Project Managers, our role is becoming more important beyond scope, cost and schedule management. We are becoming an ally by reengineering ourselves to also be Product Managers to embrace Generative AI to deliver prototypes to production! Once we can show one use case from prototype to production, we can show executives that it works and multiple use cases will follow! Let’s ask for a Generative AI Based Assistant, learn Prompt Engineering and prepare for a rollercoaster ride with Generative AI in Life Sciences in 2024.

References:
How AI Will Transform Project Management (hbr.org)
Biopharma’s Path to Value with Generative AI | BCG
The road ahead reaches a turning point in 2024 | Bill Gates (gatesnotes.com)

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