artificial intelligence and project management: beyond ......chatbot assistants chatbot assistants...

Artificial Intelligence and Project Management: Beyond Human Imagination!

Marc Lahmann, Director, PwC Switzerland

Manuel Probst, Senior Project Manager, PwC Switzerland

www.pwc.ch/ta

PMDay 2018

Friday, November 239.20 – 10.20 am

PwC

The future of project management…

November 2018Project Management and Artificial Intelligence – Beyond human imagination!

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PwC

The future of Project Management is going to be AI…


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PwC

Phone: +41 58 792 27 62Mobile: +41 79 573 05 95E-mail: [email protected]

ManuelProbst

The project management evolution2

AI in project management3

Lessons Learned from a real life case4

The project manager of the future5


MarcLahmann

AI – A brief introduction1

Today’s session


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PwC

AI – A brief introduction1

PwC

AI – Expectation and Reality


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PwC

Artificial Intelligence – a brief introduction


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Artificial Intelligence Applications

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.

Machines perform repetitive, monotonous tasks

Enables a more efficient, cost-effective business and a more productive workforce

Automatization

Each node is a function or decision point. Performs multiple layers of calculations, and classifies based on final output

Deep Learning(Neural Networks)

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

Natural Language Processing Machine Learning

Takes high dimensional data and classifies it based on a hyperplane

PwC

The project management evolution

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PwC

A glimpse into modern project management’s history


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1950

Birth of Project Management

1950-1985

Institutionalization ofProject Management

1985-2010

Modern ProjectManagement

2020

Disruptive ProjectManagement

2010-2020

Project Managementin the digital age

http://www.google.ch/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&ved=2ahUKEwjno967iaDaAhULZ1AKHff8BZ4QjRx6BAgAEAU&url=http://www.freelogovectors.net/pmi-logo-project-management-institute/&psig=AOvVaw14us__f0XZlIM8nLzQ-pqh&ust=1522912539142834

PwC

How AI fits into project management


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PwC

How AI fits into project management


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How do you think, AI will (could)support you as a project manager

PwC

AI in project management3

PwC

Anticipated evolution of AI in project management


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Integration &Automation

Reduce operational costs and enhance the quality of standardized project management processes

ChatbotAssistants

You need to review a newly raised project risk

Interaction into the automated and integrated project management practice

Machine learning-based project management

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

Autonomous project management

AI performs necessary day-to-day operations in the project

PwC

Anticipated evolution of AI in project managementIntegration & Automation


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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

PwC

Anticipated evolution of AI in project managementChatbot Assistants


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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

PwC

Anticipated evolution of AI in project managementMachine learning-based project management


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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

PwC-internal project assessment tool

PwC

Anticipated evolution of AI in project managementAutonomous project management


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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

#PMICON18

“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 WolframCreator of Wolfram|Alpha

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PwC

Lessons Learned from a real life case

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PwC

Challenges and prerequisites for a successful AI implementation


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For the implementation of AI

CreativityAI is created to carry out specific tasks and learn to become better and better at it

CostsThe implementation and research on AI comes with high costs

ChallengesWithin the project environment

Social dynamicsThe project environment is a complex social system including people with different characteristics, backgrounds, ideas, emotions and hidden agendas

Keep recordTools for Project Management merely serve as records of what happened

Project complexity A project is a temporary endeavour undertaken to create a unique product, service, or result. (PMI)

Prerequisites

Data

Producing analytical models and results requires a

massive amount of data

Time and Financial Resources

R&D of AI takes timeand financial

resources

PM Processes

A certain degree of PM maturity is required

for successful automation

Skills and Capability

Specialised and skilled resources need

to closelycollabo-

rate

Computer Processing Power

AI requires a huge number of calculations to be processed

very quickly

PwC

PwC pilot to predict success rates of internal projects


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Prediction on

▪ Expected Net Promotor Score

▪ Expected client satisfaction

▪ Expected write-off

110.000 projects

2.07 GB of data

70.000 project write-offs

3.400 NPS and Client

Satisfaction scores

3.5 billion invoiced

Project budgetsProject invoicing dataProject write-off dataProject timesheetsProject master data

Machine learning based project analytics engine

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

Goal Predict expected client satisfaction, Net Promotor Score, and write-off for our projects.

SolutionPowerful project analytics engine combining AI and machine learning to analyse data in depth and to find new success rules and patterns using PwC’s Data Science Machine (DSM).

PwC

PwC pilot to predict success rates of internal projects


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Six 1 week sprints from zero to MvP

> 1.5 years for idea generation, business case, and acceptance

Rollout planned since autumn 2017

Timeline

Team

Data Analysts

SW & UI EngineerProject ManagementExperts

Machine Learning & Neural Network Experts

Prerequisites

Data

Producing analytical models and results requires a

massive amount of data

Time and Financial Resources

R&D of AI takes timeand financial

resources

PM Processes

A certain degree of PM maturity is required

for successful automation

Skills and Capability

Specialised and skilled resources need

to closelycollabo-

rate

Computer Processing Power

AI requires a huge number of calculations to be processed

very quickly

Key Success Factors

Change & Stakeholder Management

Transparency of deficiencies

PwC

The project manager of the future

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PwC

Anticipated evolution of AI in project management


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Key elements

Outlook

Integration & Automation

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.

Streamlining and automating tasks through integration and process automation.

You need to review

a newly raised

project risk

Chatbot Assistants

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.

Integration and automation with additional human-computer interaction, mainly based on speech or text recognition.

Machine learning-based project management

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.

Enabling predictive analy-tics and advice to the project manager based on what worked in past projects.

Autonomous project management

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.

Combining the previous phases, autonomous project management leads to little-to-no human interaction in a project.

PwC



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Strategic &Business

Management

Leadership

PMITalent

Triangle

TechnicalProject

Management

TechnicalProjectManagement

✓ ✓ ✓ ✓

Strategic &BusinessManagement

(✓) (✓)

Leadership (✓)

PwC



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PwC

Questions?

PwC


ManuelProbst


MarcLahmann

Thank you!


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AI willtransform

projectmanagement

Are youready?

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.

© 2018 PwC. All rights reserved. In this document, “PwC” refers to PricewaterhouseCoopers

AG which is a member firm of PricewaterhouseCoopers International Limited, each member

firm of which is a separate legal entity.

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