Artificial Intelligence in Project Management: The Present and The future

Research by AXELOS back in 2017 showed that 90% (of the respondents) believe that the level and complexity of risks will increase in Project Management. This was one of the direct indications to the fact that there was and is a need for additional support in project management.

It is a popular belief that Artificial Intelligence and Machine Learning can be the answer to increasing complexity. Sizable investment has already been made to make AI and ML available and become a key player in project management. According to Crunchbase, over 5000 start-ups were relying on ML in 2018 and in just a year, the number reached 9000. It is estimated by PwC that by 2030, AI will contribute $15.7 trillion to the global GDP.

The numbers are impressive and they make experts in the field inquisitive about the role of AI in project management. Will AI completely replace human involvement in the project? With AI an ML be sufficient to predict or identify all threats and take substantial measures to avoid them?

Well, the answer is ‘NO’. While AI has already started to supplement project management, the chance that it will completely remove human involvement is slim to zero. It will, however, supplement project managers and help in better project delivery.

At present application of AI in project management is in its nascent stage. However, there are a couple of tools that continue to provide utility to project managers.

Knowledge Base Expert System consists of an interference engine and a knowledge base and works on the traditional “if-then” principle. KBE take historical data and input from experienced project managers to provide an estimate of resource requirement.

Another popular tool is the Fuzzy Logic, which is based on the “true-false” logic. Fuzzy Logic can be used by project managers to determine project priorities. It can also improve cost-time trade-off which directly helps project managers in planning an optimal budget.

While the current employment of AI is limited, it holds tremendous scope and has the prospects to revolutionaries the project management. In the years to come, AI and ML will begin contributing to multiple levels of project planning and execution.

So, how will Artificial Intelligence and Machine Learning Help?

1. Make things more personalized

I would like to establish the essence of this point through an example: Which dine out would you like to go to? One where the waiter knows your name, the music you like and the dish you generally order or a dine out that has been customized for an average customer? AI has the potential to make communications more personalized. This will result in better response from the client. The customization can be implemented on various levels in a project as well, from communication with stakeholders to end-users.

2. Make work Faster

Artificial Intelligence has the capability to analyze historical data, error logs, etc. to identify suitable steps to take in a variety of situations. This makes the process of decision making faster by providing feasible and relevant steps.

3. Help in maximizing human Resources

AI can be used to keep a tab on the human aspect of a project as well. While in the simpler implementations, it can keep track of the deliverables and send appropriate reminders in cases a delay has occurred to manage the overall delivery of project, AI and ML also have the potential to allocate correct employees to correct positions. There have been numerous surveys where the characteristics of a person have been mapped to various roles. Such qualifiers along with the information of employees can make automated allocations possible.

4. Help in Predictive Analysis

Risk analysis is an important aspect of project management and plays a vital role in project success. Improper risk analysis can lead to late revaluations which can have serious implication on the overall budget and timeline performances. Risk analysis may require analysis of huge data. People, in general, are not very efficient in analyzing huge data and often miss out important but not so obvious patterns in data. This is where Machine learning can be very helpful. An intelligent, self-learning machine that can analyze historical data, issue logs and incoming requests to provide an optimized risk rating system will indeed be useful for project managers.

Limitations of AI in Project Management

We have established the utility of AI in today’s project management and the potential of AI for the future project management scope. However, while AI and machine learnings can provide numerous benefits, they have some serious limitations as well.

1.       AI and ML cannot communicate as humans do. There are several instances where a human touch is necessary. Several negotiations have to be done during a project and AI cannot make such negotiations.

2.       AI and ML cannot motivate people. A major role of the Project manager is to build a project team and keep them motivated throughout the project. Project managers often act as leaders and guide there team members in times of low morale or conflicts. This is an area where AI and ML cannot replace human beings.

3.       Current AI tools rely on people to input data. The efficiency of AI and ML is subjective to the quality of the data available. Current AI and ML tools are highly dependent on people to feed such data. If the person makes any mistake while feeding the data or feeds incorrect data, the final deliverables from AI tools will have defects as well.

Even with limitations, application of AI tools in Project management is growing at a considerable rate and it is becoming increasingly important for professionals to keep themselves updated with the current trends of Project Management. If you are a professional building a career in Project Management, we at Certification Planner can assist you in giving a post to your career. You can connect with us at support@certificationplanner.com or speak with our experts at +1 8553221201. Happy learning!