AI: A Game-Changing Toolkit For Project Management?

Guest post by qualified Project Manager, Declan Foster

“AI will create jobs if it succeeds and destroy jobs if it fails”. This quote might be reassuring if it were not for the fact that it is a tweet generated by GPT-3, a massive neural network AI tool capable of generating human-like text and can even generate computer code.

This tool developed by Elon Musk’s Open AI, a San Francisco-based AI research laboratory, has created a lot of buzz lately and is indicative of the seismic shift that AI will bring to most industries and professions.

When Declan Foster started his career in project management in the late 90s, the profession came to grips with managing virtual teams due to the emerging popularity of offshoring and follow-the-sun support and software development models. Fast forward 20 years, and the profession now faces the challenge of managing non-human resources; chatbots, digital employees, and machine learning tools.

The project management profession is at an inflection point due to the challenges and opportunities that the rapid progress in AI technologies presents. The abundance of big data, developments in computer technology, and algorithms have led to giant leaps in AI technology adoption in recent years.

What is AI?

AI is defined as machines acting to mimic human cognition to solve problems. The most common components are Machine Learning (ML), Natural Language Processing (NLP), and Robotics. ML uses statistics and algorithms and large data sets to predict outcomes, whether predicting from a scan if a particular growth is cancerous or predicting if a loan application will default.

The most ubiquitous examples of NLP are arguably Alexa or those chatbots that you may have encountered when using your utility or bank website. Self-driving vehicles are the most apparent forms of robots that we will see in public spaces.

The use cases for Machine Learning in project management are placed into two areas; classification and regression. For classification, it is essentially a yes or no approach to predict whether a project will be successful or not or whether the project is likely to finish on time.

Where there are continuous values, regression algorithms, e.g. are used, based on x factors, what is the predicted cost of completing a particular task? Or what is the expected delay to completion based on the risk profile and historical data? In addition, we can use Natural Language Processing (NLP) sentiment analysis to understand how our stakeholders might be feeling about the changes brought about by a project.

Existential Threat to the Profession.

Well, is there an existential threat to the project management profession? The short answer is no, but AI will undoubtedly impact the profession. To understand the threat to any job, we can use the automation vulnerability matrix created by MIT.

Based on the degree of danger to the core skill set and the degree of threat to the delivery mechanism, we can place jobs into a deconstructed, displaced, disrupted, or durable category.

Due to the unique core skill-set of a good project manager and the current delivery mechanism being too costly or challenging to automate, the profession would be placed in the ‘Durable Jobs’ category. Some may argue that it will be a deconstructed role, much like college professors whose skills remain safe but have had to get used to online education delivery, even before Covid-19.

It can be helpful for a project manager to have insights into the durability of jobs that they may manage or come into contact with. US-based National Public Radio produced a practical tool that shows the likelihood of any role being automated in the coming 20 years, e.g., according to the tool, the role of bookkeeper has a 97.6% chance of being automated.

Should project managers be advocates for introducing AI into our projects and organisations? Professor Paul Boudreau, the author of “Applying Artificial Intelligence to Project Management,” certainly thinks so. He believes that the application of AI to project management can increase “project success rates to 95% or higher consistently”.

As part of his research into AI, Professor Boudreau developed an AI project success predictor tool based on a neural network that used 87 factors, or features, from 35 real projects to predict the success of any project. This work provides a fascinating insight into how AI, and specifically machine learning, and how it will be applied to project management soon. You can visit Stonemeadow Consulting to get more details on the tool.

Other key players in this space include UK-based SharkTower and Scopemaster. Sharktower uses machine learning models to analyse data to spot problems before they happen, showing project health, slippage, and team sentiment objectively. ScopeMaster is a requirements analyser tool that uses AI to find and fix requirements issues, saving significant rework on software projects.

Challenges for Project Management

Implementing AI into project management is not without its challenges. Not least of which are concerns about bias and data privacy. However, adequately designed machine learning tools can reduce or eliminate bias. They do not need to retain data once the model has been trained, which helps with data privacy concerns.

Colin Hammond agrees that there are many challenges to overcome, including getting project managers to accept the need for AI. “It is a bit like when I started using spellcheckers in word processing. I felt that I didn’t need them because my spelling was good, but now that I am used to them, I wouldn’t be without a spellchecker and grammar checker”, said Hammond.

Project Management Opportunities

AI will not replace the project management profession, but we should take the opportunities presented by AI to improve our profession. Nikki Horwood, from Sharktower, believes that AI can help project managers become project leaders. “AI can help the project management profession provide strategic advice and measurable value to the business?”, said Horwood.

Thought leaders, including Professor Thomas Malone of MIT, have advocated for a collective intelligence approach when using AI, where “people and computers can be connected so that collectively they act more intelligently than any one person, group, or computer has acted before”.

Top Tips to Future- proof your career as a Project Manager in the Age of AI

• Develop or improve those skills that AI cannot replace. These include collaboration, imagination, teamwork, coaching, and creativity.
• Educate yourself on Data Science and AI. While you don’t necessarily need to have a degree in AI, you will need to have sufficient education and knowledge to have informed and meaningful conversations with data scientists and machine learning specialists.
• Learn to understand and appreciate your project data. How and where is data currently captured, and what can you do to improve the quality and breadth of project data used in machine learning.
• Be proactive in preparing your organisation for AI and be seen as a champion for AI. You could start by assessing your organisational maturity. Use an AI maturity model to gauge where your organisation is now and wants to be in five years. Microsoft has created a helpful maturity model. It breaks down the maturity of AI in an organisation into four phases; Foundational, Approaching, Aspirational and Mature.

We are at an exciting time for AI. It has gone from science fiction to buzzword to being a true enabler of business transformation. The introduction of AI tools can free up time for those in project management to perform more value-adding activities, including team building and development, stakeholder engagement, and relationship building. In addition, many project managers will be responsible for implementing AI systems in the coming years.