Artificial Intelligence and Project Management: Beyond Human Imagination!

Transcription

1 Artificial Intelligence and Project Management: Beyond Human Imagination! PMDay 2018 Marc Lahmann, Director, Switzerland Manuel Probst, Senior Project Manager, Switzerland Friday, November am

2 The future of project management 2

3 The future of Project Management is going to be AI 3

4 Today s session AI A brief introduction The project management evolution AI in project management Lessons Learned from a real life case The project manager of the future Manuel Probst Marc Lahmann Phone: Mobile: marc.lahmann@ch.pwc.com Phone: Mobile: manuel.probst@ch.pwc.com 4

5 1 AI A brief introduction

6 AI Expectation and Reality 6

7 Artificial Intelligence a brief introduction Artificial Intelligence is the ability of a system to perform tasks through intelligent deduction, when provided with an abstract set of information. the designing and building of intelligent agents that receive percepts from the environment and take actions that affect that environment. Artificial Intelligence Applications Machines perform repetitive, monotonous tasks Enables a more efficient, costeffective business and a more productive workforce Breaks a body of text into words, sentences/phrases and paragraphs Identifies key words in the text and how they relate to each other to determine the message Takes high dimensional data and classifies it based on a hyperplane Each node is a function or decision point. Performs multiple layers of calculations, and classifies based on final output Automatization Natural Language Processing Machine Learning Deep Learning (Neural Networks) 7

8 2 The project management evolution

9 A glimpse into modern project management s history Institutionalization of Project Management Project Management in the digital age 1950 Birth of Project Management Modern Project Management 2020 Disruptive Project Management 9

10 How AI fits into project management 10

11 How AI fits into project management How do you think, AI will (could) support you as a project manager 11

12 3 AI in project management

13 Anticipated evolution of AI in project management You need to review a newly raised project risk Autonomous project management Machine learning-based project management AI performs necessary day-today operations in the project Integration & Automation Reduce operational costs and enhance the quality of standardized project management processes Chatbot Assistants Interaction into the automated and integrated project management practice AI provides insight into the current project based on what worked in past projects Management gets predictive insights on the project schedule and the expected cash out/write offs from the beginning 13

14 Anticipated evolution of AI in project management Integration & Automation Potential uses Enhance robustness of project planning by implementing auto-scheduling with pre-programmed logic & rules Integrate issue tracking tool into project planning to identify delays in streams based on number of issues and many more! Current uses Incorporation of MS Project Online into Wunderlist for task creation and scheduling via Wicresoft Use online templates in Slack or MS Sharepoint to produce project documentation 14

15 Anticipated evolution of AI in project management Chatbot Assistants Potential uses Take over basic project management tasks, like reminding team members of pending status updates Provide basic insight into available data, by answering questions like «what is my team currently working on?» Current uses Intelligent bots for Slack can process conversations and recognize and recommend task assignments Chatbots that send reminders to teams and tracks their performance 15

16 Anticipated evolution of AI in project management Machine learning-based project management Potential uses Convert mind maps into semantic networks and derive tasks and their relationships from it Assess proposed project plans based on historical data and past team performances and highlight potential scheduling conflicts Current uses Identify and connect team members based on their skills, availability, capacity and location to setup the best team for a work package incl. prediction on performance / outcome, i.e. Polydome -internal project assessment tool 16

17 Anticipated evolution of AI in project management Autonomous project management Potential uses Assess all the given data points during the project in real-time and derive the best possible actions/decisions Sentimental analysis to crawl through stakeholder communication to understand satisfaction at any given point in time and react accordingly Current uses No real-life use cases supporting fully autonomous project management exist UNDER CONSTRUCTION 17

18 What will AI allow us to automate? We will be able to automate everything that we can describe. The problem is: it s not clear what we can describe. Stephen Wolfram Creator of Wolfram Alpha #PMICON18 18

19 4 Lessons Learned from a real life case

20 Challenges and prerequisites for a successful AI implementation Challenges Within the project environment Project complexity A project is a temporary endeavour undertaken to create a unique product, service, or result. (PMI) Social dynamics The project environment is a complex social system including people with different characteristics, backgrounds, ideas, emotions and hidden agendas Time and Financial Resources R&D of AI takes time and financial resources Skills and Capability Specialised and skilled resources need to closely collaborate Keep record Tools for Project Management merely serve as records of what happened For the implementation of AI Computer Processing Power AI requires a huge number of calculations to be processed very quickly Creativity AI is created to carry out specific tasks and learn to become better and better at it Costs The implementation and research on AI comes with high costs PM Processes A certain degree of PM maturity is required for successful automation Data Producing analytical models and results requires a massive amount of data Prerequisites 20

21 pilot to predict success rates of internal projects Goal Predict expected client satisfaction, Net Promotor Score, and write-off for our projects. Solution Powerful project analytics engine combining AI and machine learning to analyse data in depth and to find new success rules and patterns using s Data Science Machine (DSM). DSM can use algorithms from The scikit-learn python library Azure machine learning R (Programming language) Any other language Algorithms used for our pilot included Decision tree classifier Logistic regression Random forest classifier projects project write-offs Project budgets Project invoicing data Project write-off data Project timesheets Project master data NPS and Client Satisfaction scores 2.07 GB of data 3.5 billion invoiced Machine learning based project analytics engine Prediction on Expected Net Promotor Score Expected client satisfaction Expected write-off 21

22 pilot to predict success rates of internal projects Team Project Management Experts Timeline Data Analysts Machine Learning & Neural Network Experts SW & UI Engineer Time and Financial Resources R&D of AI takes time and financial resources PM Processes A certain degree of PM maturity is required for successful automation Computer Processing Power AI requires a huge number of calculations to be processed very quickly Skills and Capability Specialised and skilled resources need to closely collaborate Data Producing analytical models and results requires a massive amount of data Prerequisites > 1.5 years for idea generation, business case, and acceptance Six 1 week sprints from zero to MvP Rollout planned since autumn 2017 Key Success Factors Change & Stakeholder Management Transparency of deficiencies 22

23 5 The project manager of the future

24 Anticipated evolution of AI in project management You need to review a newly raised project risk Integration & Automation Chatbot Assistants Machine learning-based project management Autonomous project management Key elements Streamlining and automating tasks through integration and process automation. Integration and automation with additional human-computer interaction, mainly based on speech or text recognition. Enabling predictive analytics and advice to the project manager based on what worked in past projects. Combining the previous phases, autonomous project management leads to little-to-no human interaction in a project. Outlook Sophisticated project management tools will enhance the quality of project management processes and reduce the effort and labor costs. Project managers can focus more on complex project activities creating value for the project. Chatbot assistants will take over basic project management tasks, relieve project teams of repetitive tasks and provide more interactive automation capabilities. The classic project manager leading a PMO will be increasingly replaced by project assistants. Predictive project analytics will give project managers better visibility into the project s future and enhance the quality of decision making. Machine learning-based project management agents will give intelligent advice and may take action on key PM activities, i.e. scheduling and project risks. Implementation of autonomous project management for smaller, standardised projects involving relatively little human/ stakeholder interaction. Purely autonomous project managers seem unlikely within the next 10 to 20 years. 24

25 The future of Project Management is going to be AI Technical Project Management Technical Project Management Leadership PMI Talent Triangle Strategic & Business Management Strategic & Business Management ( ) ( ) Leadership ( ) 25

26 The future of Project Management is going to be AI 26

27 Questions?

28 Thank you! AI will transform project management Are you ready? Marc Lahmann Phone: Mobile: This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, PricewaterhouseCoopers AG, its members, employees and agents do not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it. Manuel Probst Phone: Mobile: All rights reserved. In this document, refers to PricewaterhouseCoopers AG which is a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity. 28