AI Driven PM: AI Agents Are The Next Big Thing in Project, Program, and Portfolio Management

"Organizations will not be able to compete globally without putting in place project management processes and continuing to develop their project managers to become leaders within the organization," I wrote in Stop Playing Games. That leadership role is evolving faster than ever with the upcoming release of OpenAI’s AI agents, expected in January. These tools are set to revolutionize how project managers approach their work, freeing us from the mundane and allowing us to focus on strategy, innovation, and team growth. Let us not spend our time looking backward, reporting on what has occurred. Instead, let us apply our skills in predictive analytics to elevate project delivery and drive transformative results.

What Are AI Agents?

AI agents are intelligent digital assistants designed to automate repetitive tasks, process large amounts of data, and provide real-time insights. They combine machine learning, natural language processing, and data analytics to support decision-making and free up time for more strategic and creative activities. These agents can integrate with tools like Jira, Google Sheets, and communication platforms, seamlessly fitting into existing workflows. By handling routine operations, AI agents allow project managers to focus on innovation and leadership, transforming the way we approach project management.

How AI Agents Will Transform Project Management

1. Simplifying Multi-Project Portfolio Management

Managing dozens of projects at once often feels overwhelming, with timelines and resources constantly competing for attention. AI agents will track project timelines across portfolios, flagging potential resource conflicts and suggesting real-time adjustments. By analyzing historical data, they can even predict delays before they occur, helping us optimize resource allocation and align all projects with broader organizational goals.

2. Strengthening Stakeholder Relationships

Stakeholder management is often the make-or-break factor in a project’s success. AI agents can analyze email threads, meeting notes, and communications for sentiment, identifying dissatisfaction or concerns early. They can propose tailored responses based on stakeholder preferences and even generate follow-up reminders, ensuring no relationship falls through the cracks. This proactive approach builds trust and strengthens collaboration.

3. Enhancing Change and Risk Management

In volatile project environments, change and risk are inevitable. AI agents will monitor metrics like sprint velocity, budget usage, and resource utilization in real time. They can identify risks such as potential delays or scope creep and suggest contingency plans. By simulating various outcomes, these agents provide managers with actionable options, enabling quicker and more informed decision-making.

4. Optimizing Team Productivity in Agile Projects

Balancing team workloads in Agile environments is no small task. AI agents will continuously monitor task distribution, identify under- or over-utilized resources, and recommend sprint adjustments. For instance, they can reassign tasks to prevent bottlenecks or suggest pacing changes to keep teams on track. This ensures teams remain productive without risking burnout.

5. Improving Customer Experience

Customer satisfaction is often the true measure of project success. AI agents will analyze feedback from surveys, customer service tickets, and user interactions, highlighting pain points and opportunities for improvement. They can even generate customer satisfaction scorecards to track progress over time, helping us exceed expectations and deliver consistent value.

6. Ensuring Compliance and Data Integrity

In industries like healthcare, finance, and technology, compliance is critical. AI agents will automatically audit project data against regulatory requirements, flag inconsistencies, and recommend corrective actions. They can simulate compliance scenarios, ensuring our projects stay audit-ready and meet industry standards without manual oversight.

7. Delivering Budget Insights and Control

Budget management is one of the most stressful aspects of any project. AI agents will track expenses, calculate metrics like Net Operating Value (NOV), and provide predictive insights into budget overruns. They can suggest adjustments to resource allocation or project timelines to keep costs in check while ensuring goals are met.

8. Empowering Team Development

Strong, engaged teams are the backbone of every successful project. AI agents will track performance metrics for individual team members, identify skill gaps, and recommend training opportunities. By offering personalized feedback, these agents help create a culture of continuous improvement, ensuring teams remain motivated and high-performing.

A New Frontier for Project Management

The introduction of OpenAI’s AI agents is more than just an upgrade—it’s a turning point for our profession. These tools will allow us to delegate routine tasks and focus our energy on strategy, leadership, and delivering meaningful results. They empower us to work smarter, not harder, by providing actionable insights, improving communication, and optimizing resources.

Leadership in project management has always been about more than completing tasks; it’s about driving change, inspiring teams, and creating measurable impact. With AI agents, we’ll have the tools to do this better than ever before. January marks the beginning of something extraordinary. Are you ready to embrace the future of project management? I know I am.

AI Driven PM: Leveraging AI to Transform Portfolio Management and Project Ranking

In today’s competitive landscape, prioritizing the right projects is critical to success. With so many factors to consider—ROI, strategic alignment, risk, and resource availability—keeping a balanced and optimized portfolio can seem daunting. Enter AI, a powerful tool that can support project managers in making data-driven decisions, ranking projects based on potential impact, and continuously adapting as conditions change. Here’s a look at how AI can redefine portfolio management and project ranking, transforming the way we strategize and execute.

Define and Align: Setting the Foundation for AI in Project Ranking

The first step in bringing AI into portfolio management is defining the project scoring criteria. Without clear metrics, even the best AI algorithms won’t deliver meaningful insights. Start by identifying essential criteria that reflect your organization’s core values and goals:

• Strategic Alignment: How well does the project align with core business objectives?
• Financial Impact: What’s the projected ROI based on historical data and current trends?
• Risk Level and Resource Needs: Is the project feasible within current resource constraints?
• Customer Impact: Does this project address key customer needs or enhance satisfaction?
• Innovation Value: Does this project push the boundaries of what your organization is known for?

With these metrics in place, AI algorithms can help predict project success by analyzing historical data, market trends, and customer insights, providing a preliminary score for each project. This clear, data-driven start enables project managers to make informed decisions that reflect both current needs and future goals.

Data is the Fuel: Integration and Quality Matter

A powerful AI model needs quality data to thrive. Collect data from various sources—historical project outcomes, resource availability, CRM systems, and external market data. This provides the AI with a well-rounded dataset that improves its predictions. Examples include:

• Historical Performance Data: Collect success rates, budget adherence, and customer satisfaction scores from past projects.
• Resource Data: Understand resource capacity and skills available across teams to ensure efficient project assignments.
• Market Trends: Analyze market shifts and competitive dynamics, giving the AI insight into project feasibility and relevance.

Regular data updates ensure AI models stay relevant, and high-quality data integration paves the way for accurate AI-driven recommendations.

The Power of Prediction: Using AI to Score and Rank Projects

Once data is in place, AI can begin scoring and ranking projects. Machine learning models, such as regression analyses or decision trees, help predict the potential impact of each project. Techniques like clustering and natural language processing (NLP) can group similar projects and evaluate descriptions to assess strategic fit. Here’s how it works:

1. Predictive Models for ROI and Risk: Train models to predict each project’s ROI and potential risks based on past data.
2. Clustering for Similar Projects: Group similar projects to find high-priority candidates based on historical success.
3. NLP for Strategic Alignment: Analyze project descriptions to quantify alignment with core organizational priorities.

With weighted scores calculated for each project, AI provides a ranked list. Project managers can then balance this AI-based ranking with their own experience and insights.

Optimizing the Portfolio: Balancing Projects for Maximum Impact

Project ranking is only half the battle; managing the portfolio requires balancing resources, risks, and rewards. AI can simulate different portfolio compositions, using optimization algorithms to recommend the best mix based on current constraints. Some techniques include:

• Optimization Algorithms: Use algorithms to balance resource allocation, budget, and deadlines.
• Scenario Analysis: AI simulations offer insights into project combinations, showing likely outcomes based on variables like resource availability or changes in budget.
• Dynamic Re-Ranking: Continuously monitor live project data, adjusting rankings as new information becomes available.

AI doesn’t just provide a static project ranking; it ensures that the portfolio remains balanced and adaptable, continuously optimizing for maximum impact.

Human Insight: Adding the Final Touch

No matter how powerful AI becomes, it can’t fully replace human insight. Regular portfolio reviews allow project managers to validate AI’s recommendations, taking into account strategic shifts, new information, and nuanced context. The human touch is essential for:

• Validating AI Recommendations: Ensure AI’s rankings align with on-the-ground realities and strategic changes.
• Managing Ambiguities: Address uncertainties that data alone may not capture.
• Adjusting Criteria as Needed: Based on ongoing results, adjust criteria weights and scoring algorithms to refine AI’s approach.

By blending AI’s analytical power with human expertise, project managers can elevate their decision-making process, ensuring that each project delivers value aligned with broader goals.

Continuous Learning: The AI Feedback Loop

As projects are completed, updating the AI with actual outcomes creates a feedback loop, helping the AI refine future predictions. This cycle of continuous improvement enhances accuracy and keeps the AI model responsive to changing conditions. Steps include:

• Post-Project Analysis: Feed final data back into the model to improve accuracy.
• Trends and Recalibration: Identify shifts in priorities, such as increased focus on customer impact, and recalibrate AI scoring to reflect these.

With each completed project, AI learns and adapts, making future rankings and recommendations even stronger.

The Future of Project Portfolio Management

AI has the potential to revolutionize portfolio management by delivering data-driven insights, streamlining project ranking, and optimizing portfolio balance. By harnessing AI's predictive power and marrying it with human oversight, organizations can achieve unprecedented clarity and focus in project selection and execution. As Ralph Waldo Emerson wisely noted, “Do not go where the path may lead, go instead where there is no path and leave a trail.” Embracing AI in project portfolio management isn’t just about following trends; it’s about paving a new path to strategic success.

AI Driven PM: Uncovering Project Overruns with ChatGPT

As project managers, we’ve all faced the challenge of figuring out what went wrong on a project after it's finished—why it ran over time, blew past the budget, or failed to meet expectations. Recently, we tackled this issue head-on by comparing two versions of a project plan—an initial one and a second from about six months later. Using ChatGPT, we dove into the details of the project to uncover the real sources of cost overruns and time delays, providing crucial lessons that any project manager can apply.

The analysis started with comparing baseline and actual data for each task. We fed both project plans into ChatGPT and guided it through specific prompts to pinpoint where the project diverged from the original plan. For example, tasks like “Requirements Definition” took longer than expected, and development costs were significantly higher than estimated. By leveraging ChatGPT’s ability to process large amounts of data quickly, we identified the exact points where things went off track. This kind of insight is only possible when you have baselined project schedules that are regularly updated, something every project manager should maintain.

One of the key findings came from identifying new tasks in the later project plan—ones that hadn’t been accounted for initially. This led us to uncover scope changes, such as additional development work or change requests, which drove up costs and extended timelines. Using ChatGPT, we could filter out irrelevant tasks and focus on the most impactful areas. The right prompts, like “What tasks are new in the updated plan?” or “Which tasks show the greatest cost overruns?” helped zero in on the problem areas, making the analysis both efficient and thorough.

In addition to identifying overruns, we used ChatGPT to formulate questions that project managers can ask before a project begins. Prompts like “Have task durations been validated by the team?” or “What’s your process for managing scope changes?” can help uncover potential risks before they escalate. ChatGPT can also be a great tool for facilitating lessons learned sessions, where you can use specific questions based on real data to guide meaningful discussions about what worked, what didn’t, and how to improve next time.

Key Steps to Analyze Your Project and Uncover Lessons Learned:

If you’re interested in using ChatGPT to analyze your project and discover lessons learned, here are some key steps you can follow:

1. Gather Your Project Documents:

   • Start by compiling your project schedules, baseline plans, and any updates that show actual progress. Be sure to include key metrics such as task durations, start and finish dates, baseline costs, and actual costs.

2. Cleanse Your Data:

   • Make sure your project files are free of unnecessary or incomplete data. Remove tasks that are irrelevant to the analysis (e.g., placeholders or completed without impact) and ensure that baseline and actual metrics are aligned. Ensure tasks are clearly labeled to make comparison easier.

3. Identify Key Areas for Analysis:

   • Use ChatGPT to assist in comparing baseline versus actual data. Start with prompts such as:
     • “What are the differences in task durations between the two project plans?”
     • “Which tasks exceeded their baseline costs the most?”
     • “What tasks appear in the later version but not in the earlier one?” These questions can quickly highlight the tasks where things went wrong.

4. Run Comparative Analysis:

   • Analyze specific metrics such as cost overrun, delays in task completion, and scope changes. Use detailed prompts like:
     • “Show me the tasks with the highest variance in planned and actual completion times.”
     • “Which tasks were added after the initial plan, and how did they impact costs?”
   • This will allow you to isolate the tasks driving overruns.

5. Turn Findings into Actionable Lessons:

   • Once the analysis is complete, use ChatGPT to help craft questions for future lessons learned sessions. For example:
     • “What would you change in task estimation to avoid overruns like those in Development?”
     • “How could earlier identification of resource bottlenecks prevent delays?”
     • “What processes need to be in place to control scope creep effectively?”

6. Document and Share Lessons Learned:

   • Summarize the key findings from your analysis into a structured document that identifies specific overruns and their causes. Include clear lessons and actions that can be applied to future projects, ensuring that the knowledge is shared across teams.

Prompts to Try in Your Own Analysis:

Here are some additional prompts you can use when diving into your own project data with ChatGPT:

• “Compare the baseline cost and actual cost for each task in my project plan.”
• “List the tasks that caused the most time delays and explain how they impacted the overall timeline.”
• “Identify the tasks where rework occurred, and what impact it had on project costs.”
• “What scope changes were introduced, and how did they affect both time and budget?”
• “How did resource allocation contribute to delays or overruns?”

By leveraging ChatGPT for these types of detailed project reviews, you can uncover insights that might otherwise be missed, turn project data into meaningful lessons learned, and prepare more effectively for your next project. Whether you’re identifying scope creep, resource bottlenecks, or task delays, this approach ensures a clearer understanding of where things went wrong—and how to avoid similar pitfalls in the future.

AI Driven PM: Claude Projects is a Game Changer!

Let me share my experience with a tool that's quickly becoming a game-changer for my projects—Claude Projects. I've been using this tool extensively, and I have to say, it's been delivering incredible results. While its capabilities are particularly outstanding in the realm of software development, I'm convinced that its benefits would extend just as effectively to other types of projects.

Claude Projects is designed to streamline processes, enhance collaboration, and spark innovation in ways that I've found transformative. One of the features I really appreciate is its ability to upload and integrate various project documents into its knowledge store. I can upload everything from technical specifications and design documents to coding standards, architecture diagrams, and even historical project data. What this does is allow Claude to develop a deep understanding of the project's context, goals, and constraints—something that’s critical for any project, but especially in software development. For instance, when I upload our project's software architecture documentation, Claude provides suggestions and insights that align with the existing system design, helping to maintain consistency and reduce potential integration issues.

But that’s just scratching the surface. The custom instructions feature is another game-changer. It allows me to tailor Claude's behavior to meet the specific needs of my project. Whether it's preferred coding styles, naming conventions, documentation standards, or project-specific terminology, I can ensure that when Claude assists with code generation or review, it adheres to the practices we've already established. This feature also allows me to define the structure and format for development tickets or user stories, which has significantly reduced the time spent on ticket creation and refinement.

One of the most innovative features of Claude Projects is its ability to analyze front-end designs, such as those created in Figma. By uploading your Figma output, Claude can dissect the design and suggest a list of features based on the visual and functional elements of the UI. This integration is particularly valuable during the initial stages of development, where aligning the front-end design with backend functionality can make or break the project. Claude's analysis ensures that nothing is overlooked, and it often provides feature suggestions that enhance the user experience while maintaining design integrity.

What makes Claude Projects even more valuable is its role as a brainstorming partner. With its knowledge of our repositories and architecture, it helps generate lists of potential features based on our project goals and existing functionality. This has been particularly useful in our agile environment, where continuous improvement and feature ideation are essential. The tool can even break down complex features into smaller, manageable tasks, taking into account our microservices architecture or module dependencies.

I’ve been maximizing Claude Projects' impact by uploading a diverse set of documents—everything from technical documentation like API specs and database schemas to project management artifacts, business documents, and even historical data like postmortems from previous projects. This comprehensive input allows Claude to offer more nuanced and context-aware assistance, helping me make informed decisions, anticipate challenges, and identify opportunities for innovation.

While my primary use of Claude Projects has been in software development, I have no doubt that its powerful features would be just as beneficial in other types of projects. Whether you're managing construction, finance, or marketing initiatives, the ability to upload comprehensive project documentation and tailor AI-driven assistance to your specific needs is a significant advantage. Claude Projects is not just a tool; it's a catalyst for achieving excellence in project management across any industry.  Give it a try!