Review AI and the Project Manager

AI Definitions

The book starts with some definitions:

• AI is the designing and building of intelligent agents that receives percepts from the environment and takes actions that affect that environment.
• Artificial narrow intelligence (ANI) or ‘Weak’ AI is AI that exists today (e.g., play chess, analysing data to produce reports, Siri, Google Assistant, et cetera).
• Artificial general intelligence (AGI) or ‘Strong’ AI refers to machines that exhibit human or adaptable intelligence. As such there are no real-world examples of ‘Strong’ AI.
• Besides these you get definitions of Machine Learning, applied intelligence, deep learning, responsible AI, predictive analysis, computer vision, natural language processing, intelligent automation and dark data.

AI Categories

The author distinguish four main categories of AI related to project management:

• Project management process automation (auto-scheduling, automatically tracking progress and status of tasks, exception based management)
• Project assistant style chatbots (organising meeting, progress management tracking, team reminders, …)
• Project intelligence through machine learning (supervised, unsupervised, reinforcement and rules-based learning)
• The future state of the autonomous project manager (no real life examples)

Data

Having the data is key in AI. Unfortunately projects are complex to model, but typically they have poor historical data and factors that are hard to understand (i.e. behavioural aspects of various actors). Any historical data is heavily biased, subjective, and unreliable as more often than not it did not reflect real delivery detail or accurate completion rates. 

Some AI use cases

In the book many use cases for using AI. To mention a few:

• The ultimate goal, of a supplier of AI driven PM software, with machine learning should be to automate data insights on project delivery, help project teams spot problems early, and identify where best to focus project management efforts to continually reduce the uncertainty and increase the probability of delivering project outcomes.
• Another supplier wanted to use technology and data in order to understand what made projects succeed or fail and prioritise leveraging the knowledge of the people behind them (people data) and not simply the traditional project data (when the task is complete) most people used. Or can machine learning support the ability to ‘match’ people on he projects they would be most passionate about working on.

Project are about people

Project managers, in the ‘new AI normal’ world is still about people and will:

• use all of that AI insights and predictable capability
• really have time to get to grips with complexities of people and teams
• build and lead incredible project teams into a single purpose-driven powerhouse.

The book ends with some thoughts on the future project manager (can anyone do project management, will certification be valueless, will we abandon methodology, are professional bodies irrelevant and will our skills be devalued) and AI in project management survey response details.

Conclusion. 

This book shows that AI is not the silver bullet to solve all our project related problems. The book gives many AI examples from ideas to realised AI solutions. For those who aren’t familiar yet with AI and project management you get some first insights, definitions, and AI use cases. It will help to start a discussion where you can benefit from AI. for sure, project managers can definitely benefit from AI and use their scarce time to focus on people! But I am not sure yet, if in 2030 80% of the work of today’s project managers will be eliminated.

To order AI and the project manager: managementboek.nl, bol.com