Effective project management plays a vital role in any corporate establishment. It helps the company to achieve its short and long-term goals and make the best use of the available resources. AI or Artificial Intelligence helps in achieving this through its sophisticated characteristics and capabilities like analysis, prediction, adaptability, human-like learning mechanism, etc. When the right type of AI tools are used appropriately, It becomes easier for the project managers to manage the team, avoid risks and multiply productivity. In this post, we are going to discuss different ways in which AI can be used to streamline and improve project management
Comprehensive project planning to avoid delays
The key objective for any project manager is to achieve the project goals within a stipulated duration and budget. For that, they need competent project planning from start to the end.
Ideal project planning defines solid processes like execution strategy, monitoring, optimization, impoverishing, and hierarchically structured steps. All these processes are governed by quantifiable metrics like resource availability, risk calculation, schedule, specific costs, allotted team, scope, targets, and cut-off dates.
However, it is not uncommon for the project managers to jump the cut-off date- many times by a significant gap. It can be avoided with effective project planning and AI can help in achieving this. The high-end predictive capabilities of AI help in streamlining the process of project planning by analyzing complex patterns and accurately identifying the factors that directly or indirectly influence project delivery. It empowers project managers to set standby provisions/guidelines to maintain a smooth and uninterrupted process.
Besides, AI can be used for automating various repetitive or formal processes so that human capital can be used for more productive and mission-critical tasks. It will have dual benefits. On the one side, the repetitive tasks would be executed faster and more accurately and on the other, the managers can extract maximum value out of human resources. With its sophisticated analytical and logical capabilities the AI can also be an important asset for tracking project development and providing actionable insights.
Improved outcomes with time
Inspired by the human neurons, the ANN (Artificial Neuron Network) computing system uses interconnected nodes of processing units with a logically designed input/output mechanism. In simple terms, the information is received by input units which are used by neural networks for learning and generating the output report.
The key process in the above method is backward propagation of error for generating a correct result. After recording information the ANN compares its initial solution with the standard solution. Then it feeds the errors into the network again. This information is reused to optimize the network’s algorithm to get the right output. This process is repeated until the desired result is obtained.
For getting results the ANN uses objective yes/no questions.
Easy transferring of expertise to other team members
Using a sophisticated learning mechanism, the KBE system (or Knowledge Based Engineering) efficiently absorbs the specific expertise of a human specialist and converts it into codes that are then fed to the system. It allows any new user to easily understand and utilize it.
The expert feeds relevant information into the system which is used as a data file by the inference engine to decide the knowledge and accordingly deliver the output.
The user generally inputs the information using a standard If-then declaration which can be as simple or as complex as required. One can enter multiple conditions, different combinations, etc.
KB system functions across different applications like Classification, diagnosis, monitoring, monitoring, scheduling, and planning. Along with other characteristics it also possesses some sophisticated capabilities like an advanced prediction of patterns through data history analysis and plan modification based on the current project status.
Advanced prediction capabilities
Unpredicted cost overruns inflate the project budget and may disrupt the speed. ANN or Artificial Neuron Network helps in avoiding through its advanced cost overruns prediction capabilities. Working on concrete parameters like managers’ competency, type/size of contracts and project magnitude can forecast the cost overruns.
ANN is also extremely useful in fields where classification, system modelling, and prediction play a decisive role- like civil engineering. Hierarchical task sequencing is another use of ANN. This automated sequencing helps in logically connecting different steps to achieve the desired output more quickly and efficiently.
Automatic risk estimation
By deeply analyzing the different phases of a project, AI can predict the general quality and number of risks involved. It enables the teams to understand the degree of risk involved and accordingly plan the project. Likewise, the system prevents the team from any mid-project surprises by comparing actual progress with the scheduled outcome. In that capacity it allows the team to identify and work on the gaps to achieve the desired outcome in the stipulated time.
It can identify Potential delay alerts and can generate KPIs-based performers list to identify underperformers. It can also deliver actionable insights to realign the project to complete it in time
Conclusion
For a strategic business growth it is very important to handle the projects responsibly and achieve the objectives. Project management is one of the major areas where the advanced capabilities of AI can help in achieving the desired output and improving the quality of the d. There are many ways in which the AI's capabilities can be utilized to help project managers effectively accomplish the project with the minimum possible period without compromise on the overall outcome. Various characteristics of AU like deep analyses reduction accuracy, comparing between past and present data, and adapting to the current changes help managers to competently manage the project and achieve outcomes without jumping the timeframes
Jitendra Bhojwani
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