Artificial Intelligence (AI) is a term that’s been creating ripples since its inception in 1956. From IT to finance, AI has impacted every industry and businesses associated with them. While some are skeptical of the impacts of AI, others think of it as a game-changer. It is also expected to influence project management principles in a big way in the coming days.
Project management addresses planning and applying specific processes and principles to ensure project success. As per PMBOK guidelines, there are five different steps in project management:
- Initiation.
- Planning.
- Execution.
- Monitoring and Control.
- Closing.
The data-interpretation capability of AI can provide real-time insights into project metrics. It can enable project managers to make data-driven decisions based on past experience. For instance, Cap Gemini uses the cognitive computing system IBM Watson to improve resource deployment in projects through efficient resource planning.
In this blog, we discuss different ways AI can help in project management.
Importance of AI in Project Planning
A project comprises a series of tasks designed to meet a specific objective. It can be developing a new product/service, constructing a bridge or building, house renovation, upgrading the data system, implementing new business, etc.
Irrespective of the nature and size of the assignment, every project manager strives to meet the project goals and objectives, i.e., deliver within time and budget. Effective project planning paves the path for a project’s success.
Project planning is an essential component of project management. It establishes the project scope and defines the objectives to achieve them. A project plan enlists how the project will be executed, monitored, controlled, and closed. The plan must include every project constraint, such as the costs, risks, resources, and deadlines. The project plan consists of six essential steps, namely:
- Creation of a task list.
- Formation of a budget plan.
- Preparation of a risk management plan
- Production of a communications plan
- Making of a project schedule.
- Allocating appropriate resources to suitable tasks.
AI-based tools assist project managers in handling different tasks during each phase of the project planning process. It also enables project managers to process complex project data and uncover patterns that may affect project delivery. AI also automates most redundant tasks, thereby enhancing employee engagement and productivity.
As per Gartner, AI will eliminate 80% of today’s manual project management tasks by 2030. AI machines will take over everything from planning to data collection, tracking to reporting, and so on.
Solutions of AI in Project Management
From scheduling to analyzing the working team’s patterns, AI has become an obvious benefit to project managers.
Here’re some applications of AI that aid in the project management cycle :
Knowledge-Based Expert Systems (KBE system)
A knowledge-based expert system is a computer program that exhibits the knowledge and analytical skills of one or more human experts regarding a specific problem. The system captures the human expert’s experience and codes this in a computer so that any user can understand it.
Here is a diagrammatic representation of the architecture of a KBES.
The knowledge engineer/human expert feeds information into the KBES. Often this information is declarative, i.e., the expert would state some facts, rules, or relationships into the knowledge base. The inference engine would then use the knowledge base as a data file to determine the knowledge and provide the output.
In most cases, experts use “IF-THEN rules” to input knowledge. The if-then rule takes the form as such:
If <condition> then <solution>
The solution will be more narrowed and distinguished depending on the rules (if) that are fed into the system. For example,
For example,
“If the mammal stands on two feet and wears clothes, then it is a human being.”
Here, “two feet” and “wears clothes” are the conditions, and based on that knowledge, the KBES can distinguish the solution as a “human being.”
Knowledge-based systems work across several applications. Here is a list of KBES applications:
- Classification – The network identifies an object based on the mentioned characteristics.
- Diagnosis – The KBES deduces any malfunction from data.
- Monitoring – The system compares data from a past system to predict patterns.
- Scheduling & Planning – The KBES develops or modifies a plan based on the project.
For instance, in the medical field, a KBES helps doctors diagnose diseases better. A KBES is also used in industrial equipment fault diagnosis, avalanche path analysis, and cash management.
Another example of a KBES is the Attendance Capturing & Recording System (ACRS). This automated system saves time for both managers and employees. The system reduces errors and avoids disputes by eliminating manual record keeping. The ACRS has several advantages such as:
- Individual information of employees
- Faster information searching
- Easy generation and review of reports
- Full database backup
- Specific authorization and security levels for employees
Artificial Neural Networks (ANN)
An artificial neural network (ANN) is a computing system that simulates the human brain and processes information. Like the human brain, an ANN has neurons called processing units, which are interconnected by nodes. These neurons consist of input and output units. The input units receive the information, following which, the neural network learns about the data to produce one output report.
ANNs use a process called backpropagation, which is an abbreviation for backward propagation of error, to process the perfect output. The ANN records the information and compares the fed information’s initial solution with the known actual solution. The solution’s errors are provided back into the network and used to modify the network’s algorithm the second time around, and it keeps on going until the ANN can reach the desired output.
An ANN uses yes/no type questions with binary numbers to produce the result. For example, an educational institution wants to know if the students are Indian citizens, and it has input units fed with questions such as:
- Was the student born in India?
- Does the student have a PRC?
- Does the student have a birth record?
The institution wants the “Indian student” responses to be Yes Yes Yes, (binary format- 1 1 1) for the three questions. Now, if the ANN’s actual output is 1 0 1, it will adjust its results until it delivers the output as 1 1 1. After this phase, the ANN will automatically give the details of every student to the institution.
As a project management tool, ANN predicts cost overruns based on several parameters such as project size, contract type, and project managers’ competence. It also helps in automating project activity sequencing based on the functional requirements. ANN is also used in civil engineering for prediction, optimization, system modeling, and classification.
The replication of past cost trends in highway construction and estimation of future costs trends in this field in Louisiana, USA, is an example of ANN application in project management.
Fuzzy Logic
Fuzzy logic is defined as a many-valued logic form that may have truth values between 0 and 1. It was designed to allow the computer to determine the distinctions among data which is neither true nor false. Loosely based on the Boolean true or faulty logic, this logic concept determines partial truth. For example, we may come across situations where we can’t decide whether a statement is true or false. At that time, fuzzy logic helps in developing reasoning that will solve the crisis. The fuzzy logic algorithm solves a problem after considering all the available data. After that, it makes the best possible decision based on the case. It imitates the way a human will make a decision.
Here is a dramatic representation to show the difference between fuzzy logic and Boolean logic.
Fuzzy logic uses membership degrees within the interval 0 (no membership) to 1 (full membership) to represent the range of possible values. With this approach, it evaluates the “degree of truth” of propositions.
Fuzzy logic is used in various fields. Some of its standard applications are:
- The aerospace industry where it provides altitude control of spacecraft and satellites
- Larger organizations where fuzzy logic enables decision-making support systems and personal evaluation
- Natural language processing and various other intensive applications in AI
- Knowledge-based expert systems
This technology works out favorably for construction project managers who want the least cost routing to work out the logistics of having material supplied on-site. Plus, they can model probability distributions to assess risks in construction projects.
This fuzzy model is based on two primary indices, schedule performance index (SPI) and cost performance index (CPI). It transforms the SPI and the CPI and evaluates the overall project status (output). This approach helps fuzzy logic to replicate the risk and uncertainty in the project.
AI Chatbots
A chatbot is an AI software that can simulate a conversation with a user in natural language through messaging applications, websites, mobile apps, or telephone. There are mainly two types of chatbots, namely:
- Rule-based chatbots: These chatbots follow a set of established rules to respond to questions of the user. For instance, when you ask for a weather forecast, the weather application chatbot fetches the data from different sources and responds with the information.
- Machine Learning-based chatbots: These chatbots are based on machine learning and can process the question and understand the meaning behind the problem. These bots use data from the previous conversation and learn to handle more complex questions in the future.
In project management, chatbots can automate repetitive and mundane tasks, thereby helping teams collaborate and focus on high-priority billable work. For example, the chatbot Meekan helps teams automatically match their schedules. The team members can ask the chatbot to schedule a meeting, and the bot will check the team’s schedule to book a date and time.
If a member can’t make it to the meeting, the chatbot will find an alternative slot and ensure that no one misses the meeting.
Similarly, bots such as Howdy and PMbot can automate different aspects of task management. These bots also assist in task selection, keeping track of the tasks completed, and even distribute created reports to all stakeholders involved in the project.
Moreover, managers can integrate chatbots with third-party software such as project management tools. The team members will perform all commands directly in their chat rooms, while the project manager will get an entire history of all tasks executed by all teammates. Chatbots can also monitor every change made to the source code and report bugs in any code line. It can then link it to the person who made a mistake. This solution helps in evaluating and improving team performance.
How AI helps Project Managers
Project managers can turn wayward projects around by adding applied intelligence to performance monitoring tools. This way, you can track progress and stay forewarned of potential risks that threaten project delivery. What’s more? You get to rule out projects devoid of any profitable outcome.
With your main goal being to avoid any surprises as you near the end of project delivery, the areas where AI provides conflict-resolution are:
Automated Risk Estimation
Every project is prone to risks. AI can accurately predict the number of defects or quality in general in managing projects. Using AI models at different stages of projects can help identify and alert teams if the process is risky. For example, AI can observe the actual progress and compare it to the planned schedule. Based on that data, AI can:
- Alert potential delays
- Point out the underperformance based on KPIs
- Recommend ways to bring the project back on track
You can factor in budgeting and scheduling constraints to make informed decisions on risk management.
With machine learning, you can retrieve parametric information as and when required. For instance, you can use past data such as planned start and end dates to predict future projects’ realistic timelines. The system can add an upper and lower bound to these dates to account for delays within reason. If the system indicates high confidence in a particular project, successful delivery is guaranteed.
Adaptive Resource Management
To ensure your projects remain on track, the right people must work on them. AI delves into the history of past projects, which give you real-time information on resource engagement. For instance, the manager can compose the project team and assign roles and responsibilities to individual team members. AI tools also ensure that the project managers manage the project effectively and stick to deadlines.
AI helps your staff remotely access real-life training material, allowing them to enhance their skills and knowledge quickly. Based on this, you’d know if your resources are ready to be deployed. You can add extra hands or take people off the project if a disparity arises in the hours required versus projected availability. This reduces the time taken to onboard them on to new projects. As a result, project delivery is quickened, with your clients gaining clarity on project deployments.
Predictive Analytics
Predictive analytics is used to make predictions about future events. It can generate future insights based on historical data and analytics techniques such as statistical modeling and machine learning. Companies can use past and current data to reliably forecast trends and behaviors.
Project managers can estimate the effort and resources needed to complete a project more accurately. Predictive analytical tools contain the exception handling feature, which points you to an excess or shortage of the right resources. The idea behind predictive forecasting is to strategically identify and block risks before they take over the project. It helps you prepare the optimum project schedule.
Here is a rundown of all the benefits that project managers get from predictive analytics:
- Gain an overview of the project risks and close identified gaps.
- Improve project outcomes by prioritizing necessary actions.
- Realize early value from the projects.
- Negate financial losses by improving project oversight.
- Drive greater efficiency by eliminating unnecessary project characteristics.
Intuitive forecasting is a statistical approach that validates your project. Not only does it point you to the correct number and type of resources (both human and technical) needed, but it also reduces labor costs.
Conclusion
Artificial intelligence is changing how companies work, and project management is a crucial area positively affected. The applications of AI in project management are comprehensive. These solutions provide the project manager with enhanced accuracy, strategy, and support. It has also improved the project managers’ emotional intelligence and creativity and negated individual decision-making biases.
We’ve reached a stage where AI has reduced the need for excess human intervention. For one, it can complete more tasks in a shorter time frame. With data at the heart of everything, AI tools like a data warehouse have the storage capacity to hold more information than ever.
For example, BIM 360 Field is used in construction to capture photographic evidence of project sites. It is convenient when your construction project teams have to conduct terrain feasibility and environmental impact studies.
SAVIOM Solution
SAVIOM has over 20 years of experience helping multinational clients manage their resources efficiently and effectively. With over 20 years of experience, this Australian-based MNC has a global presence across 50 countries and has helped 100+ clients meet their specific business goals. Saviom also provides tools for project portfolio management, professional service automation, and workforce planning software. So, SAVIOM can help your business to establish an efficient system geared towards your specific business challenges.
