Council Post: Five Ways AI Can Transform Project Management—And Four Roadblocks (2024)

Yaniv Shor is the founder and CEO of Proggio and the author of the book Time to Deliver, a must-read for project managers.

With all the buzz around ChatGPT, artificial intelligence (AI) is a hot topic across nearly every business sector and application.

Project portfolio management (PPM) is no different. Even Gartner has predicted that by 2030, AI will take over and “eliminate” as much as 80% of project management tasks, including data collection, tracking and reporting.

While the promise of generative AI (the likes of ChatGPT) may be a bit further off in this field, there are many possibilities for AI within PPM, but it’s also not without some significant challenges that must be overcome.

Five AI Use Cases For Project Management

Let’s talk about what it can do before discussing some pitfalls and concerns you need to be aware of:

1. Natural Language Processing (NLP)

If you’ve ever asked Alexa anything or said, “Hey Siri…” you’ve used NLP, a form of AI that can recognize and respond appropriately to natural spoken language.


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In the context of PPM, being able to query your system of record about specific needs can drastically improve data insights and save time. For example, instead of poring over Gantt charts and spreadsheets and comparing those to manpower management, you can simply ask the platform, “Do I have resources for [insert specific task or project] next week?” Or, “Who can fill in for James when he’s on holiday next week?”

With NLP, the system can help you find resources with similar skills and availability to plan appropriately.

2. Data Collection And Analysis

Gartner is right: The potential here is huge. After all, chasing down data to report to management is a large part of the PPM function. AI will give us the ability to do that much quicker and arrive at performance insights and risk assessments much faster.

Not only will this help guide decision-making, but it will enable project managers to do what they should be doing: coaching and supporting teams.

3. Scenario Planning

AI’s super-human ability to analyze data and deliver insights at incredible speed provides unprecedented clarity that can guide realistic scenario planning. Let’s say you’re planning next year’s project roadmap. You already have 50 projects in process, and you want to add 40 more.

With AI-powered scenario planning, you can see, in real time, whether you realistically have the resources to support that plan, whether you should put some projects on hold, which you should prioritize, and perhaps discover you’ll need to hire additional talent.

4. Accelerated Decision-Making

Because AI enables these real-time insights, an AI-driven PPM platform allows you to see various parameters, dependencies and roadblocks more clearly to make better-informed decisions and adapt on the fly.

Prior to AI, in that same meeting from above where you’re planning next year’s roadmap, you’d have to discuss potential scenarios, adjourn the meeting, go back to the drawing board to work on revised plans and then reconvene in three or four days to review the options.

AI can accelerate that process, so you can stay on track with timelines and business objectives, not to mention gain or maintain a competitive advantage by being able to move faster.

5. Risk Analysis

The ability to analyze more data from more sources—and do it faster—can provide better insight into risk potential.

Especially as projects become more complex and involve cross-team coordination, there are a lot of factors that contribute to risk: the more tasks, people, and dependencies, the higher the risk of delays, resource shortfalls, missed OKRs and, ultimately, project failure.

Using AI to analyze real-time and historical data can help PPMs identify those roadblocks and risks sooner and help you devise and navigate mitigation strategies.

Four Potential AI Pitfalls For PPM

While all of this sounds amazing—and some of this capability already exists in the market—there are a few areas of caution that organizations should consider before diving headfirst into the AI-driven solution pool:

1. Don’t adopt AI for the sake of AI. Decide what functions or capabilities you need first, and then find an AI tool that delivers. With so much hype around AI, it’s tempting to think you have to have it, but it’s a waste of time and resources if it doesn’t serve your needs.

2. Take an incremental approach. Going all-in on AI right out of the gate can be a mistake, completely upheaving your entire workflow and exposing potential shortcomings in the solution only after it’s been fully implemented. As with any new solution, a managed approach to adoption and implementation is essential. You’ll want to ease into the process to minimize risk, assure user confidence and understand what’s working and what’s not throughout the process.

3. Prepare to standardize. One of the biggest problems in many organizations is scattered and siloed data, which prevents them from gaining insights that depict an accurate and complete picture. An AI solution will have the same problem if you don’t standardize it. If marketing is using one tool and product development is using another, an AI solution can’t magically integrate that data. It’s essential that everyone is on the same system in order to be on the same page.

4. Beware of “black box” solutions. One of the biggest risks that also diminishes trust in AI solutions is that they don’t explain how and why they arrived at that solution. ChatGPT doesn’t cite sources, for example. In a business context, users need to know where the data is coming from and how the AI solution arrived at its conclusions because there may be nuances that the platform doesn’t understand. Just like in middle school algebra, an AI platform should show its work, not just the final answer.


There’s no doubt AI has tremendous potential to improve the efficiency, operational workflow, clarity and accuracy of the project management function. And there are tools that are already implementing these capabilities and showing promise.

For organizations looking to jump on the AI opportunity, a careful, results-oriented approach with phased adoption is the most prudent strategy.

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I am YouChat, a large language model from I have access to a wide range of information and can provide assistance on various topics. I can help answer questions, provide insights, and engage in discussions.

Regarding the concepts mentioned in the article you shared, let's discuss each one in detail:

Artificial Intelligence (AI)

Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence. AI technologies aim to simulate human cognitive processes, such as learning, reasoning, problem-solving, and decision-making. AI has gained significant attention across various industries and sectors due to its potential to automate tasks, improve efficiency, and provide valuable insights.

Project Portfolio Management (PPM)

Project portfolio management involves the centralized management and control of an organization's projects. It focuses on aligning project activities with strategic objectives, optimizing resource allocation, and ensuring the successful delivery of projects. PPM helps organizations prioritize projects, monitor progress, manage risks, and make informed decisions.

Natural Language Processing (NLP)

Natural Language Processing is a subfield of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP technologies allow computers to process and analyze text or speech data, enabling applications such as speech recognition, language translation, sentiment analysis, and chatbots. In the context of PPM, NLP can be used to query project management systems, extract insights from project-related data, and facilitate communication and collaboration.

Data Collection and Analysis

Data collection and analysis are crucial components of project management. AI can enhance these processes by automating data collection, integrating data from various sources, and providing real-time analysis. By leveraging AI technologies, project managers can gain faster access to performance insights, identify risks, and make data-driven decisions.

Scenario Planning

Scenario planning involves analyzing different possible future scenarios and their potential impact on projects. AI can assist in scenario planning by analyzing large amounts of data, identifying patterns and trends, and providing insights to support decision-making. With AI-powered scenario planning, project managers can assess resource availability, prioritize projects, and make informed decisions about project roadmaps.

Accelerated Decision-Making

AI can enable accelerated decision-making in project management by providing real-time insights, identifying dependencies and roadblocks, and facilitating adaptive planning. With AI-driven PPM platforms, project managers can access relevant information, evaluate different parameters, and make better-informed decisions quickly. This can help organizations stay on track with timelines, adapt to changing circ*mstances, and gain a competitive advantage.

Risk Analysis

Risk analysis is a critical aspect of project management. AI can enhance risk analysis by analyzing large volumes of data, identifying potential risks, and providing insights to develop mitigation strategies. By leveraging AI technologies, project portfolio managers can identify and address risks earlier, improve project outcomes, and ensure successful project delivery.

These concepts highlight the potential benefits and challenges of integrating AI into project portfolio management. While AI offers exciting possibilities for improving efficiency and decision-making, organizations should carefully consider their specific needs, adopt AI incrementally, standardize data, and ensure transparency and explainability in AI solutions.

Please let me know if you have any further questions or if there's anything else I can assist you with!

Council Post: Five Ways AI Can Transform Project Management—And Four Roadblocks (2024)


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