Artificial intelligence (AI) has permeated enterprise operations to the point that it now determines an organization’s success, including in the area of project management. In a report, Project Management Institute (PMI) examines how six AI technologies are affecting today’s project managers and will affect project management operations in the future.

PMI’s AI Innovators: Cracking the Code on Project Performance (2019) found that in the next three years, project professionals expect overall AI usage to jump from 23% to 37% and the majority of respondents (81%) said their organizations are currently being affected by AI technologies.

SEE: The ethical challenges of AI: A leader’s guide (free PDF) (TechRepublic)

“Project leaders are in the earliest stages of adopting AI to streamline–and improve–project work. AI technologies are already contributing to higher productivity and better quality,” said Mark Broome, chief data officer at PMI. “For example, technology is decreasing the amount of time project managers need to spend on activities like monitoring progress and managing documentation–they can rely on AI for these more administrative tasks. The time saved can then be repurposed to more strategic and creative tasks and planning.”

Along with monitoring progress and managing documentation, project managers also spend a lot of time finding patterns, efficiences, and redundancies in projects–tasks that can be expedited by AI and machine learning tools, said Ronald Schmelzer, principal analyst at Cognilytica.

Top AI technologies affecting project management

  1. Knowledge-Based Systems:

Knowledge-based systems use machine learning and natural language generation to create the documentation for the individual, Schemlzer said. “Documentation is the bane of everyone’s existence,” he added.

In the next few years, the impact of knowledge-based systems on organizations will jump from 37% to 71%, according to the report.

“Natural language processing and machine learning algorithms will assist the project manager in developing accurate project plans,” Broome said. “Learning from a plethora of previously executed projects and associated project management artifacts will be utilized to train AI to effectively assist the project manager in all aspects of project management including charter development, time and resource estimation, communications, risk identification and management.”

2. Machine Learning:

Machine learning development is in its earliest stages of use for project management, Broome said, nonetheless, 31% of organizations have been impacted by machine learning and 69% expect a high or moderate future impact, according to the report.

Machine learning is at the core of many other AI technologies on this list, Schmelzer said. Overall, machine learning’s expertise is in finding patterns. For example, machine learning could look at a schedule, study patterns in a schedule, and spot areas where projects can be sped up, Schmelzer said.

3. Decision Management:

AI can play a key role in helping project managers make crucial decisions. Currently, 29% of organizations have already been affected by decision management, but 68% expect a high or moderate future impact, the report found.

“As decisions need to be made throughout the project, project managers will rely on predictive models to assess options and select those that provide the highest likelihood of a positive outcome,” Broome said.

This is also where machine learning algorithms come in, Schmelzer added, as they can show what features of a product consumer are or aren’t using, for example, and help project managers make decisions accordingly.

4. Expert Systems:

Going hand in hand with decision management, expert systems also provide project managers with expert thinking. Some 21% of organizations have already been impacted by expert systems, and 64% expect a high or moderate future impact, the report found.

“You can actually have machines automatically create these things called decision trees to help you,” Schmelzer said. “It’s like taking the ideas of the expert and putting them into machine learning.”

The tool can look at patterns of expert decisions and draw insights based on those, providing the project manager with expertise, Schmelzer said.

5. Deep Learning:

A machine learning technique, deep learning has impacted 21% of organizations and 63% expect a high or moderate future impact, the report found.

“Advanced forecasting and deep learning models will assist in predicting work effort activities, tracking project progress and updating forecasts as the project progresses,” Broome said.

Deep learning is very good at recognition, speech, and conversational systems. Deep learning models are very data hungry, said Schmelzer, so the more data a project manager has, the more accurate the deep learning system will be.

6. Robotic Process Automation:

“Robotic Process Automation (RPA) appears to be one of the first technologies to support project managers’ administrative tasks,” Broome said. “Many more advancements should be anticipated in the future.”

This anticipated advancement is accurate, as 21% of organizations have already been impacted by RPA, but 62% expect a high or moderate future impact, the report found. RPA is most beneficial for repetitive and cumbersome tasks, Broome said.

“RPA is the gateway to AI,” Schmelzer said. “It has gotten a huge push because of how relatively easy it is to automate systems that have traditionally not been automated because of human bound and paper bound stuff.”

An example of RPA in action is with invoices, which normally involves someone approving payment invoices and placing them in a payment system, but many of those steps can be automated with this tech, Schmelzer said.

Characteristics of successful AI systems for project management

In the coming years, AI will continue growing in the enterprise. As these six technologies develop, project managers will see greater use of their capabilities. To reap the greatest benefits from AI, project managers should adopt AI systems that pull information from multiple sources, provide deeper opportunities for better decision making, offer efficient and detailed project planning, and forecast results accurately, said Daniel Stang, research vice president at Gartner.

For more, check out How project managers are essential to AI deployment on TechRepublic.

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