Artificial Intelligence in Project Management
Divya Patil, Srushti Naik, Mrunmay Paratwar.
Table of Contents:
1. What is Artificial Intelligence in Project Management?
a. AI can Automate
b. AI can assist intelligently
c. AI can be used for predictive analysis
- Risk Management
- Cost Management
2. Expected partnership between AI and Project Managers.
3. Artificial Intelligence applications in Project Management.
a. Knowledge Based Expert System (KBES)
b. Artificial Neural Networks (ANN).
c. AI Chatbots
4. Conclusion.
5. References.
Artificial Intelligence in Project Management
“The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”— Stephen Hawking told the BBC
The ability of AI to analyze data can provide quick insights into project parameters. It might make it possible for project managers to base choices on past information. To improve resource deployment in projects utilizing IBM Watson’s cognitive computing technology, Cap Gemini, for instance, makes excellent use of resource planning.
Artificial Intelligence:
Since it was first used in 1956, the term “artificial intelligence” has sparked debate. All industries, including those in IT and finance, are being significantly impacted by AI. While some people are sceptical of AI’s effects, others believe it will change the world. It is also projected to have a big impact on project management ideas in the near future.
Project management:
Project management deals with arranging and putting into practice particular procedures and concepts to guarantee project success. The first of the project management process’ five steps is
- Initiation.
- Planning.
- Execution.
- Control and monitoring.
- Closing.
What is Artificial Intelligence in Project Management?
Project management To assist with human work, artificial intelligence (AI) has been included into the project management cycle. It derives meaning and supports project-related decisions using the limitless processing capacity of machine learning.
In project management, adding value throughout the project is AI’s main goal. The most competent applicants for your project can be found using AI, which can also help with resource allocation and job distribution, matching the right tasks and responsibilities to the right people, and the hiring process.
- AI can Automate:
Think of AI as a tool. It exists to relieve you of arduous, data-driven, and dull tasks.
Project managers must perform a significant amount of repetitive manual work, which should be automated. What are some significant but not particularly vital tasks? To reduce administrative work in project management, find them and then let AI handle them.By tackling labor-intensive activities, AI frees up project managers’ time to focus on something more creative, pertinent, and intellectually stimulating. The basic competencies of project managers, such as team building, network development, people management, and project vision, are suddenly given greater time. The same is true of our teams.
For instance, chatbots can automatically answer to customers, saving customer service workers’ time.
2. AI can assist intelligently.
What are my top five high-risk initiatives at the moment? Which of those should I be concerned about? You don’t have to sift through dozens of lists and spreadsheets to discover the answers to questions. All you need to do is ask, and you’ll receive an immediate response.
Consider the following scenario.
Your project has been delayed. You should begin it next month rather than this month. AI-driven algorithms will shuffle and rearrange your resources for the upcoming month. And the new tactic is ready with just one command! Now, procedures can be consolidated and streamlined using AI. Plan your team’s schedule carefully to ensure that tasks are assigned correctly and the project is unaffected.
3. AI in analysis(Predictive)
Predictive analytics is the use of AI in project management to make future predictions. As a result of all the investment being made in the AI sector, data lakes and pools will certainly start giving increasingly precise insights, enabling us to create forecasts by removing human bias and error.
AI-derived insights have the possibility of becoming both valuable and practical. By deploying your robot army, you might be able to get automatic notifications and insights that are extraordinarily precise. For instance, you might plan in advance if you think there will be a higher likelihood of a network disruption during particular activities.
- Risk management :
Artificial intelligence might be helpful in two areas: understanding danger thresholds and risk modelling. AI can also notify users of potential dangers as well as opportunities. It’s a great tool for anticipating the actions of stakeholders and adjusting your management tactics accordingly. As a result, adopting AI allows you to seize chances and lower risk.
2. Cost Management:
It’s difficult to underestimate how important cost control is to a project. AI can help your team create a number of cost models, assess them using a range of criteria, create a dynamic pricing strategy, and anticipate project costs.
Once the project has been approved and is in progress, it is possible to track cost indicators in real-time and draw the project manager’s attention to the most crucial details. AI-enabled machines can take over administrative activities like invoice entry. More monitoring, quality control, and compliance can help keep project costs down overall.
You should be able to obtain more sophisticated analysis and have greater observability than your competitors if you wish to get a competitive edge.
Expected partnership between AI and Project Managers.
It is distressing to hear statements like “AI will take our jobs.” However, it will be a while before AI replaces our need for project managers. It already is, despite the fact that we rarely give it any thought. Thanks to AI in Microsoft Word and the add-on Grammatically, you were greatly spared from the majority of my spelling and grammar issues while I was writing this essay. AI will help us in this way by handling the smaller, more error-prone tasks first before tackling the bigger ones.
More than half of respondents cited risk and change management as one of the top five internal challenges affecting their business in the “Middle East Capital Projects and Infrastructure Survey,” a PwC poll conducted from February to April 2020.
A total of 67% of respondents believed that the infrastructure industry would be disrupted or considerably changed within the next two years due to digital innovation.
Meanwhile, 66%, or two-thirds, of the respondents surveyed in the post-3 March 2020 period named machine learning and artificial intelligence (AI) as two of the top five emerging technologies that will have the most disruptive effects on the industry in the next two years.
As AI should improve the quality of project management, particularly in terms of risk and change management, a relationship between AI and project managers is anticipated.
Artificial Intelligence applications in Project Management.
- Knowledge Based Expert System (KBES):
A knowledge-based expert system is a piece of computer software that replicates the expertise and problem-solving skills of one or more human experts. The system captures the human expert’s knowledge and encodes it such that any user can comprehend it.
The knowledge engineer or human expert provides information to the KBES. The expert would frequently add specific facts, laws, or relationships to the body of existing knowledge because this knowledge is frequently declarative. The inference engine would then use the knowledge base as a data file to determine the knowledge and provide the output.
Professionals typically enter knowledge using “IF-THEN rules.” The format of the if-then rule is as follows:
The solution will be more precise and distinct depending on the rules (if) incorporated into the system. For example,
Using the example, “If the mammal stands on two feet and wears clothes, then it is a human being.”
In this instance, the KBES can recognize the solution as a “human being” because the requirements are “two feet” and “wears garments.”
Knowledge-based systems are applicable to many different applications. The applications for KBES are listed below:
- Classification: Using the aforementioned properties, the network classifies an object.
- Monitoring: In order to identify patterns, the system compares data from previous systems.
- Scheduling & Planning: Depending on the project, the KBES creates or adjusts a plan.
A KBES, for instance, aids clinicians in making more accurate disease diagnoses in the medical field. A KBES is also employed in finance management, avalanche route analysis, and industrial equipment problem diagnosis.
2. Fuzzy Logic
Fuzzy logic is a sort of many-valued logic that is defined as having truth values that could be between 0 and 1. It was developed to enable computers to discriminate between information that is neither true nor false. This logic concept, which is loosely based on true or false Boolean logic, establishes partial truth. For instance, there may be situations where it’s not evident whether a statement is true or incorrect. The formulation of arguments to deal with the circumstance is then aided by fuzzy logic. After considering all pertinent data, the fuzzy logic algorithm chooses a solution. The best decision is then taken based on the situation. It mimics how a person would make a choice.
The membership degrees, which range from 0 (no membership) to 1 (full membership), are used in fuzzy logic to describe the range of possible values. By using this technique, statements’ “degree of truth” is evaluated. Numerous fields use fuzzy logic. Its typical uses include:
- The aerospace sector, where it controls the height of satellites and spacecraft
- Larger organisations, where fuzzy logic enables decision-making support systems and employee evaluation
- Knowledge-based expert systems, among other intensive uses of AI, include natural language processing.
3. Artificial Neural Networks (ANN).
An artificial neural network (ANN) is a type of computer system that analyses data similarly to the human brain. Similar to the human brain, an ANN has neurons referred to as processing units that are connected by nodes. These neurons have both input and output units. The neural network learns about the information as soon as the input units receive it and produces one output report.
AI processes the ideal output using a technique known as backpropagation, which stands for backward propagation of error. The ANN records the data and compares the fed data’s initial solution to the known real-world answer. The network feeds back the errors from the solution and uses them to update the algorithm a second time and so on , until ANN achives the desired output.
Binary integers and yes/no questions are used by an ANN to produce the result. For example, if a school wants to know if the students are citizens of India, it will enter questions like these into its input units:
- Was the student an Indian citizen?
- Does the pupil own a PRC?
- Is the student’s birth information available?
The answers to the three questions must be Yes Yes Yes (binary format: 1 1 1). This is how the college expects the “Indian student” to respond. If the actual output of the ANN is 1 0 1, it will now adjust its results until it generates 1 1 1 as the output. After this point, the ANN will automatically give the institution all of the student’s data.
As a project management tool, ANN forecasts cost overruns depending on a number of factors, including the scope of the project, the nature of the contract, and the skill of the project managers.
4. AI Chatbots
An artificial intelligence (AI) program known as a chatbot can mimic a discussion with a user in natural language over messaging services, websites, mobile apps, or the phone. The two primary categories of chatbots are as follows:
Rule -based:
Chatbots that respond to user questions in line with a set of predetermined rules are known as rule-based chatbots. For instance, when you ask the chatbot in the weather application for a forecast, the chatbot compiles the data from several sources and provides it.
Based on machine learning:
Chatbots created using machine learning are able to process inquiries and understand the root causes of problems. These bots learn from their previous interactions in order to respond to more challenging questions in the future.
Project management tasks can be automated by chatbots, freeing teams to collaborate and focus on high-priority billable work. For instance, Meekan, a chatbot, helps teams seamlessly align their schedules. The chatbot will look for a time and day for the meeting based on the team’s calendar when the team members ask it to set one up.
If a member is unable to attend the meeting, the chatbot will find a replacement time slot to ensure that nobody misses it.
In a manner similar to this, task management can be automated using bots like Howdy and PMbot. Additionally, these bots aid in task selection, monitoring task completion, and even in distributing prepared reports to all project stakeholders.
Conclusion:
Project management is a crucial area where artificial intelligence is positively changing how firms run. There are several uses for AI in project management. These systems provide better accuracy, strategy, and assistance for the project manager. The project managers’ emotional intelligence and creativity have both grown, and personal biases in decision-making have been eliminated.
AI has developed to the point where it no longer requires a lot of human input. For starters, it can complete more tasks in less time. The foundation of everything is data, therefore AI tools like a data warehouse can hold more data than ever.
For instance, BIM 360 Field is used in the building industry to photograph project locations. When your construction project teams require to perform terrain feasibility and environmental effect analyses, it is convenient.
References:
- https://www.toptal.com/project-managers/technical/ai-in-project-management
- https://www.forbes.com/sites/cognitiveworld/2019/07/30/ai-in-project-management/
- https://www.saviom.com/blog/applications-ai-project-management/
- https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-chatbot/
- https://www.javatpoint.com/ artificial-neural-network
- https://www.techtarget.com/searchenterpriseai/definition/fuzzy-logic
