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Using Artificial Intelligence to Track Project Performance

Using Artificial Intelligence to Track Project Performance

By Cynthia Snyder Dionisio
February 21, 2024

There are a multitude of ways we can use artificial intelligence (AI) to help us manage projects. This is the first in a series of articles that will explore different ways you can leverage various types of AI to help you save time and improve your outcomes. This article will focus on using AI for performance tracking.

There is no doubt that AI is great at analyzing data. In fact, a few weeks ago, I asked Chat GPT to come up with a quote that described how awesome AI is in analyzing data. Here is what it said:

“Artificial Intelligence is an intellectual powerhouse, sifting through mountains of data with unparalleled speed and precision, transforming the vast and chaotic landscape of information into a map of crystal-clear insights and possibilities.”

It sounds confident in its data analysis capabilities, but let’s look under the hood and see what is involved in “transforming the vast and chaotic landscape of information”. Before you can harness the power of AI, you need access to software and data. Let’s start with software.

For performance tracking, your project software should have the following features and functions:

Task tracking: entering tasks, establishing dependencies, setting milestones, determining the critical path, and tracking actual progress against the baseline.

Resource allocation: Assigning resources to tasks, identifying overallocated resources, material usage, and translating effort hours worked into duration and percent complete.

Cost and budget: Recording labor costs based on rate and effort hours, recording costs for physical resource utilization, developing a baseline cost, tracking against the baseline, identifying cost variances, and providing cost forecasts.

Reporting and dashboards: Visual presentation of various project metrics, such as resource consumption, cost and schedule variances, and forecasts.

Given that you are managing a project, you should have access to all this data and potentially the same information for previous projects as well.

You may be thinking–this is all part of my day-to-day information, so how does AI help me?  A valid question. AI can automate a lot of the analysis making the whole tracking and reporting process faster and more efficient. Here are some examples:

  • On a large project there can be significant physical and team resources. AI can analyze patterns of physical resource utilization to support just in time resource ordering, delivery, and usage. It can also look at peaks and valleys in team member utilization, thereby smoothing out over and under allocation. This promotes the most effective team composition and reduces team member burn out.
  • AI can analyze data in real time and provide up to the minute insights on project performance. You can set up notifications for when specified variance thresholds have been crossed to take prompt corrective action.
  • AI can use algorithms to analyze both past and current performance data to predict future outcomes (predictive analytics). This allows you to predict resource shortages, cost overruns and potential schedule delays before they occur. This reduces the probability and impact of risks to the project budget and schedule.
  • When you have hundreds of tasks, a large team, significant physical resources, and multiple vendors, it is impossible to keep your eye on everything all the time. However, this is an area where AI can really help. It excels in detecting anomalies in performance data. Identifying anomalies early allows you to address situations before they become issues, risks, or problems.
  • If you are using an AI program that has been trained in natural language processing (NLP), AI can take all the reporting data, generate reports that summarize the data, and articulate insights in easy-to-understand language. This ensures that stakeholders understand the data in the reports and frees up the time it would take to translate the data into a report. All you must do is review and edit the information to ensure it is accurate and relevant.

This sounds great, but how do you access these powerful capabilities? Currently most project management software does not come with artificial intelligence functions built in, but there are pre-built connectors that can link your project management tool with various AI services for analytics and data processing. You can also use Application Programming Interfaces (APIs) and middleware solutions. An API allows you to connect your software with external AI services. Middleware bridges different software applications, allowing them to share data. You can use middleware to connect your project management tools with AI services if you don’t have access to an API.

Another option you can use is interfacing with some of the more powerful business intelligence and data visualization tools, such as Tableau or Power BI. Both applications have machine learning and artificial intelligence capabilities. They can produce automated insights, use natural language processing, and can provide predictive analytics and insights.

Most project management tools have connectors to these tools. For example, Jira, Trello, Asana, and Smartsheet all have connectors. Microsoft Project has built in connectivity with Power BI since they are both Microsoft products. You can check your product sheets, software website or your vendor to find out if your software has AI capabilities or if it can interface with AI.

It may take some up-front work with setting up the connections needed to harness the power of AI to track project performance. However, for large projects, the investment in time will be well worth the payback in real-time analysis, resource optimization, predictive analytics, and insights.

For more information on how to use artificial intelligence, check out IIL’s course, Generative AI for Project Management.

Tools mentioned in this article are examples. IIL does not endorse any of the tools mentioned in this article.

Cynthia Snyder Dionisio

Cynthia Snyder Dionisio is the Practice Lead for IIL’s Project, Program, and Portfolio Management (PPPM) Practice. Cyndi has over 20 years of experience leading international project teams, consulting, developing courses, and facilitating training. She has received several awards, including the PMI Fellow Award in 2018 and PMI’s Distinguished Contribution Award in 2009. Cyndi is passionate about turning chaos in order, engaging with awesome teams, solving problems, and facilitating achievement.

Cyndi’s books include Hybrid Project Management, A Project Manager’s Book of Tools and Techniques, and A Project Manager’s Book of Templates.

Cynthia Snyder Dionisio

Cynthia’s books include Hybrid Project Management (1st Edition), A User’s Manual to the PMBOK Guide (1st Edition), A User’s Manual to the PMBOK Guide (5th Edition), A Project Manager’s Book of Tools and Techniques (1st Edition), A Project Manager’s Book of Templates (1st Edition). View all of Cyndi’s books here.

Disclaimer: The ideas, views, and opinions expressed in this article are those of the author and do not necessarily reflect the views of International Institute for Learning or any entities they represent.

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