There’s been a lot of talk about Artificial Intelligence (AI) in the project management space but many still remain in the dark about the actual technologies available, their capabilities and their potential. We’ll give you the realistic rundown of the five prominent AI technologies for project management and how their impact on the field. Get started on your journey of leveraging AI for project management.
What is Artificial Intelligence?
Gartner defines artificial intelligence as the application of ‘advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.’ They take many different forms with some of the most prominent being machine learning, artificial neural networks and deep learning.
Far from the dystopian all-seeing capabilities we often see on television, AI is bound to strict logic and rules-based frameworks that make processing nuanced and complex situations and data difficult. Though they have a massive potential, current forms of AI are still highly dependent on human input and can only work as well as the framework it is built upon.
Project Management and AI
Project management is a sphere that will see drastic change with the introduction and integration of AI, with reports suggesting that 80% of current project management activities will be taken over by AI by 2030. These statistics have often engendered reactions of either fear and excitement for project managers. The critical difference between the two reactions is often a matter of the degree of understanding of AI’s genuine capacity, not simply its rose-coloured projections in the future. We’ll give you a realistic and in-depth look at the prominent AI technologies and how their genuine impact on the project management space.
AI for Project Management: Top 5 AI technologies
The PMI report, ‘AI Innovators: Cracking the Code on Project Performance’ gives us valuable insight into the current and future impact of AI technology on project management.
1. Machine Learning
Machine Learning analyses data to build models by detecting patterns, yielding improved decision making with minimal human intervention. Projects generate and use a considerable volume of data, that without AI, involves human effort and judgement to sort, understand and apply. As machine learning better understands and maps the human decision-making process, this will result progressively in less human involvement, saving considerable time and money.
2. Decision Management
Decision Management technologies create intelligent processes or sets of processes, based on rules and logic to automate decision making. Again, project management is a field rich with decision making it a space where these technologies will prosper, likely beginning with many routine and more simple decisions. It is likely project management’s venture into these technologies will come from the products or services they are planning and delivering and will be dependent on other technology providing input information such as sensors, or event and time triggers.
3. Knowledge-based systems
Knowledge-based systems (KBS) emulate and mimic human intelligence, skills or behavior in a particular field, topic or skill. The majority of current systems cannot exceed the capability of humans, with early examples – such as IBM’s Deep Blue – only now, exceeding human abilities
In project management, Knowledge-Based Systems would gather It takes data, including communications from risk management, cost management and resource management processes to gain insight into the nature of the project or projects they are analysing. This insight allows for robust analysis, reduced error rates and considerable savings in labour.
4. Deep Learning
Deep Learning builds, trains and tests neural networks that predict outcomes and/or classify unstructured data based on probabilities
Chief data officer at PMI, Mark Broome says, “Advanced forecasting and deep learning models will assist in predicting work effort activities, tracking project progress and updating forecasts as the project progresses.”.
5. Robotic Process Automation
Robotic Process Automation (RPA) can be seen as the easiest gateway for AI into project management. RPA is a form of business process automation that deploys metaphorical ‘bots’ to perform specific tasks through a defined set of instructions. These instructions are typically limited to frameworks built on ‘if, when, else’ statements to complete tasks.
RPA is currently already taking over many menial administrative tasks of project managers from invoice approvals to reporting. This will significantly lift the load off project managers and allow them to focus on more important value-adding activities such as early brainstorming, communication and strategic planning. At its current state, RPA has already impacted 21% of organisations and is slated to make moderate to high future impact on 62% of organisations.
What does AI in project management look like?
All this talk about AI technologies and their capabilities, but what will it realistically look like when it is rolled out in our organisations in the near future. Like any other revolutionary change, AI will come in stages and most organisations will struggle to fully harness AI’s potential in one step. Make sure to check out our post on the evolution of AI in project management to get a realistic image of how AI will be integrated into your everyday activities.
