What can we really use Artificial Intelligence for in Project Management? Are there any practical AI tools that can make our work easier? The answer is yes. And, in this article, Yaniv Shor, founder and CEO of Proggio, looks at five real-world applications for AI tools in Project Management.
Proggio is the adaptive enterprise project management platform that powers operational excellence through visualization, clarity, team alignment, and seamless execution.
In this article, Yaniv looks at:
And then he looks at our five Project Management applications using AI tools for:
- Performance Analytics
- Risk Assessment
- Process or Task Automation
- Natural Language Processing (NLP)
Yaniv ends with:
- The Caveats: Consider These Things when Evaluating AI Tools
- Summing up AI Tools in Project Management
So, enough from me. Over to the expert…
Where are We with AI Tools Today?
There’s been a tremendous amount of hype around AI, particularly Generative AI like that employed by ChatGPT, Microsoft Bing, and others. Every day it seems there’s a new ‘transformational’ potential application in business, medicine, and even sports or entertainment.
In many cases, the hype is a little ahead of the delivery curve. The groundbreaking functionality or output is entirely possible, but not quite practical or ready for prime time. Sure, ChatGPT can analyze billions of healthcare records, but it’s not going to be diagnosing patients anytime soon.
The Potential of AI Tools
What AI can do, however, is make the background processes, rote tasks, and data analysis that often bog down our work far more efficient. AI can leverage computer power beyond any human capacity for data ingestion, processing, and summarization or analysis. AI is already proving to be an incredibly valuable tool to assist and augment – but never replace – human expertise.
For project managers, this is extremely promising. We are constantly bogged down with routine tasks and have far more data available to us than we can possibly analyze or leverage. We desperately need tools to assist with those mundane, yet vital tasks, so that we can become more strategic, more insightful, and better equipped to navigate the inevitable, unexpected twists and turns inherent in every initiative.
The Need for Trust in our AI Tools
But we also need solutions we can trust. And, for many, therein lies the rub. AI still feels very futuristic, not ready for real-world use. We’re skeptical. And we certainly don’t want to trust the success of a major project—and therefore our careers—to unproven, black-box technology.
AI Tools in Project Management
The reality is, however, that AI is already making a real-world impact on the field of project management. A number of solutions are bringing these capabilities to the forefront, helping PMs and PMOs accelerate processes and streamline workflows to achieve operational excellence through automation.
Let’s look at five real-world applications of AI and how it’s transforming multiple PM functions while giving PMs more time, energy, and resources for strategic operations. We’ll consider:
- Performance Analytics
- Risk Assessment
- Process or Task Automation
- Natural Language Processing
Using AI Tools for Performance Analytics
One of the biggest reasons projects fall behind schedule is because we lack the tools to realistically anticipate how long tasks and processes will take. Throw in a few roadblocks, like key personnel being out of the office or attending to an emergency task, and things quickly go off the rails.
Using a platform that incorporates AI-based data analytics can help you better understand:
- How long tasks historically take,
- Where workflows commonly break down, and
- What resources are typically most scarce?
By collecting and aggregating data across the organization around task assignments, time to completion, and resource allocation (both for the project in question and others that are competing for the same resources), AI tools can provide a window into performance analytics that would be virtually impossible for a human to compile.
This not only helps organizations plan better and set more realistic timelines, but also helps identify bottlenecks that often impede progress, so you can address them and improve future performance.
Using AI Tools for Forecasting
Insights into past performance give Project Managers a powerful window into the future. Once we know empirically, based on historical data, how projects have typically played out in terms of timeline, resource allocation, and budgeting, we can forecast more accurately what we will need, to make future projects successful.
Setting Realistic Expectations
For example, you may be running 50 projects a year and you now have 40 more to add. However, you currently only have the resources to accommodate an additional 15-20 projects. You need to come up with a game plan to make it work.
Maybe you’ll start by prioritizing the projects that are the most mission-critical. You may also need to make some decisions about which to delay and which to cancel altogether. Or maybe you’ll need to hire additional talent or bring on contract support. Have you allowed enough runway in the timeline to accommodate procurement needs?
Modern project management requires the ability to forecast with accuracy and develop various scenarios to adapt to ever-changing conditions. Tools like Proggio are one way to power resource planning and automate labor cost calculations. This will allow you to better forecast time, resources, and budget. And, in doing so, to account for every team member, including external resources.
‘What if’ Scenario Planning in Real-time
This predictive capability makes it much faster and easier to conduct more accurate scenario planning. Running a typical ‘what if’ analysis can take days or weeks with a prolonged process that involves:
- Discussing potential scenarios during a planning meeting,
- Returning to your desk to run the scenarios, and
- Then reconvening in yet another meeting to discuss the results and decide which way to go.
With AI, you can do it all right on the spot by making a few adjustments to the variables you want to test, and then see the predicted results instantly. This not only speeds up the process from ‘what if’ to a decision but also gives stakeholders a glimpse into the factors and conditions that go into this type of analysis and how even minor changes can have ripple effects across other tasks and projects. It also helps to reduce the level of uncertainty involved in making project choices because you can play out the scenario across the timeline to get a full sense of the impact of the decision before you commit.
Using AI Tools for Risk Assessment
Risk assessment is a key part of project management. But often our risk metrics are limited in scope.
Spreadsheets and most static Project Management tools rely on Project Managers to assign risk scores or severity, which means they often don’t take into consideration the full breadth of factors that put tasks at higher risk.
For example, a task might be deemed medium risk because it carries a large number of downstream dependencies. But if we don’t also know about the capacity or availability of resources to complete the task in the first place, that can amplify the risk. Or, if it’s a process that has repeatedly failed in the past, that should drive the risk assessment higher. But that relies on the Project Manager to remember and account for that history in their assessment.
How AI Lowers Risk
AI-based risk assessment tools, like Proggio’s, look at the individual task plus many other factors that contribute to risk from across the organization.
For example, they can consider the amount of cross-team coordination required. The more people involved in the process with multiple dependencies, the higher the risk (see our video on Metcalfe’s Law). AI tools that can look across the organizational structure and leverage the full breadth of data available can provide a much more accurate risk assessment to aid in risk management.
Using AI Tools for Process or Task Automation
One of the simplest, but most valuable uses of Artificial Intelligence in Project Management is process automation—intelligent workflows that make everyday Project Management tasks easier, faster, and more accessible.
For example, AI can streamline project planning with automated template creation and customization. Do you need to duplicate a repeating project or task? In Proggio, you can simply choose a frequently repeated task or project, create a template in just a few clicks, and modify it with any specific details you need. The AI tool will automatically build out the timeline, resource allocations, budget, and task assignments in seconds!
AI-based automation can also accelerate project reporting with AI-driven logic for automatically generating periodic updates and on-demand reports. Simply set it and forget it: tools like Proggio can automatically generate reports in any format (Excel, PDF, etc) and even email them to designated stakeholders for effortless updates. This dynamic view capability already exists in tools like Proggio, providing real-time data and the ability to instantly adjust cross-functional plans with drag-and-drop ease to see exactly how changes impact everything throughout the portfolio.
We have a video on Robotic Process Automation and Intelligent Automation, for more detail.
Natural Language Processing (NLP) AI Tools
Natural Language Processing (NLP) is a form of AI that allows users to speak or write in their own language into a User Interface to:
- Run queries
- Test scenarios
- Understand the impact of changes
Think of this like Alexa or Siri for your Project Management AI tool. Rather than having to write out a carefully scripted query or formula to obtain data from a spreadsheet, you can simply type or speak a query or direction into a chatbot-style interface. For example, in the future you might ask a tool like Proggio to ‘create a template from Manufacturing Plan B’ or ask it ‘Do I have the resources to kick off a new product launch in Q2?’
This is especially useful for people who aren’t Project Managers but still need to be able to use PM tools effectively. These are people like your colleagues from marketing, personnel, or engineering teams.
The Caveats: Consider These Things When Evaluating AI Tools
While the potential is certainly there for AI to transform the way we work in Project Management, it’s no panacea. Launching an AI-based platform in your organization requires some prerequisites and a comprehensive approach. When evaluating a move to an AI-based solution, consider these four factors:
Do you have the data to support it?
For AI solutions to work, they must be able to pull from historical data to deliver the kind of functions discussed here. If you don’t have the data required to begin with, you can still use the tool—just know that it will take time to build those datasets to leverage its full functionality. The AI tool has to learn how your organization operates before it can help to optimize it.
Equally, it’s fair to say that many organizations have historic data that is riddled with errors, gaps, and other inaccuracies that bias it – often towards a rosier portrayal of events. This is, needless to say, only going to result in poor forecasting from your AI tool. Cleaning up that data can be daunting and overwhelming to the point that it might be better to start from scratch.
Are you willing to standardize?
For an AI tool to be effective, your organizational data must be consolidated: not siloed with different departments and multiple tools. That means everyone must standardize on the same platform and shared formats to achieve the kind of:
- Cross-departmental integration
- Shared business KPIs and
- Comprehensive resource allocation
that allows for realistic planning and execution without leaving blind spots. It’s impossible to accurately account for resources, timelines, and dependencies if everyone’s data is on different systems.
The tool must show its work.
Remember your high school algebra? Getting to the right answer was only half the battle—showing how you arrived at it was equally important.
The same is true for the solutions that AI tools offer. The tools must be set up to provide evidence of where the analysis, predictions, and forecasts come from, if they are to build confidence in the system and help users understand how the various factors and variables come into play to provide predictions and recommendations. A black box solution not only
‘Crawl, Walk, Run’
This mantra applies to virtually any technology implementation. And it starts with identifying what problem you’re trying to solve.
Don’t just add tech for tech’s sake – or because everyone else is doing it. Focus on the desired outcome. Then start gradually, building momentum and proof of concept, before expanding. For Program and Project Management, that might mean deploying an AI tool first, only in the Project or Program Management Office (PMO).
Once that is successful, you can then onboard engineering, manufacturing, and finally marketing, for example. This iterative approach builds trust, confidence, and commitment as the organization sees the proven advantages the new platform delivers.
Summing up AI Tools in Project Management
It’s easy to see how Artificial Intelligence can save time, accelerate insights, and help us reach faster, better-informed decisions. And, because of this capability, many also fear AI will displace humans by creating a fully automated solution for day-to-day project management.
But, the reality is that every project still needs humans to drive strategy, champion the cause, engage stakeholders, build investment, and motivate the team. Rather than replace project managers, AI tools actually allow us to leverage our talent and coaching skills to be more strategic, and therefore more vital than ever, to project excellence.
What Do You Think?
Now, it’s your turn: let us know in the comments where you see the most potential for AI tools in your work… or where you’re already seeing results!