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How AI Will Change the Future of Project Management

An eminent entrepreneur Gartner predicts that by 2020, Artificial Intelligence will have wiped out 1.8 million jobs. However, it would also be responsible for the creation of 2.3 million new jobs. Meaning that in effect, the rise of AI would create more jobs than it would eliminate. Therefore, the prevailing myth about Artificial Intelligence is dissipated, and it also has a positive impact on businesses from a Project Management view. Artificial Intelligence has the competence to bring out an outstanding revolution in the domain of Project Management. Some of them may look like routine improvements, but they integrate over time to enhance the overall long-term efficacy of project management for businesses looking to adapt AI especially through Project Management Software.

1. Minimizes Unsolicited Delays.

In a Survey, it is found that managers spent 54% of their time on project management-related administrative tasks. However, organizations now expect AI to cut that time to half, freeing, employees to work on higher-value activities. Intelligent bots are helping to automate project management duties, such as project documentation and quality check activities. They can also interact with workers to provide instant access to the status of various projects, dependencies and critical project-related information. AI chatbots save huge amounts of time in fielding queries and requesting updates. These intelligent assistants also help reduce the workload associated with keeping track of various project management activities. The use of these automated systems reduces human error and keeps aspects of bias in check by systematically and reliably keeping project management records.

The primary job of a project manager is to make sure that the strategic vision of the senior management is realized through the various activities that the business performs. However, project managers spend most of their time on the administrivia of handling projects, rather than shepherding activities in more strategic ways. It’s clear that intelligent systems are making a significant impact by eliminating much of the necessary but low-value activity of

project management. This type of intelligent assistance can help focus the organization on the projects at hand and not on the minutiae of project management activities.

One might think that incorporating a sophisticated AI-powered project management software in the business might not be a great idea-cost-wise. But the potential savings that can be attributed to the proper utilization of AI far outweighs its cost.

AI can streamline and automate many a repetitive task, allowing both project managers and team members to focus on the more complex activities involved in the project. It increases the quality of work while reducing the cost of labour. In general, cost reduction seems to be a big reason for AI adoption.

2. Predictive Analytics

The advent of dashboards and scorecards built the monitoring generation, which later gave rise to a new question: “What will happen?” To answer this, data analysts first used advanced statistics, data mining, and advanced data analytics. Technologies including machine learning, neural networks, and deep learning now help data scientists face the

challenges raised by the need for prediction.

On a Holistic view, when leveraging data mining methods, complex statistical models, and machine learning technologies, advanced analytics allows for making effective data-based decisions and building sentiment analysis or recommendation systems that lead to predictive analytics.

Predictive analytics considers the following four axes: prediction, speed, business, and accessibility.

  1. Prediction: Predictive analytics goes beyond the standards that allow for producing simple descriptions. In brief, descriptions let decision-makers understand the current state of their business, while predictions empower them to implement action plans knowing what may or may not happen next.
  2. Speed: The strength of prediction capabilities also lies in the ability to come up with actionable outcomes quickly, compared to its contemporary methodologies
  3. Business: These analytics are business-oriented by their very nature, far from statistical research where you just search for trends within a dataset about past activities.
  4. Accessibility: The combination of the three previous axes tends to strengthen the need for business accessibility. The link between the prediction solution and its outcomes
  5. and the decision-makers who will put it into action requires it to be as simple and as natural as possible.

3.Enhancing visibility for Early Risk Detection

AI is much better at performing repetitive and routine tasks than a human being. This allows for better administrative control and also enhanced visibility in projects that require it. Consider- a construction project is using a project management software with AI capabilities-  this can alert the risk of fatal accidents by simply executing the routine safety checks, which otherwise might be overseen by the safety inspector.
On the other hand, the software will be able to ascertain risks that a person might not be able to see. And such risks can be nullified, once detected. AI enhances the visibility of projects across the spectrum, which enables early detection of risks, which, can be tackled before they pose a threat to the completion or quality of the project.

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