A Practical Guide: How Project Managers Can Leverage Large Language Models Across the Project Life-cycle

Artificial intelligence (AI) is becoming an increasingly valuable tool in project management, offering data-driven insights, automation, and enhanced decision-making. However, much of the discussion remains abstract, leaving many project managers uncertain about how to integrate AI effectively into real-world projects.

This guide provides a practical approach to using Large Language Models (LLMs) such as ChatGPT, Copilot, and Gemini at each stage of the project life-cycle—from bidding and planning to execution, lessons learned, and closure. It explores how LLMs can serve as a teacher, mentor, or guide, helping project managers refine strategies, challenge ideas, and streamline workflows while saving valuable time.

While LLMs offer significant advantages, they must be used with caution—Caveat Emptor (let the buyer beware - so to speak) always applies. Your professional training as a project manager provides the guardrails for planning and execution, and LLMs can complement this by offering insights into unfamiliar subjects or areas where you may lack formal training. Understanding prompt engineering and critically evaluating AI-generated responses are essential to maximising their value.

Additionally, data privacy and security remain paramount, ensuring that sensitive company and client information is protected. Future guides will delve deeper into specific use cases and best practices.

1. Bidding and Proposal Development

Challenges:

One of the earliest challenges for project managers arises in bidding and preparing proposals. Accurately estimating timelines, resources, and costs can be daunting, especially with incomplete information.

How LLMs Can Help:

LLMs can assist in creating compelling proposals and analysing project requirements:

• Drafting Proposal Sections: LLMs can generate initial drafts of project plans, executive summaries, and cost breakdowns based on your inputs.
• Analysing Requirements: By pasting sections of an RFP or client requirements, you can ask LLMs to highlight key details or suggest clarifying questions.
• Competitor Analysis: While the free version doesn’t have real-time access to external data, LLMs can help generate lists of questions and considerations to guide manual research.

Security Tip:

??????? Avoid sharing sensitive details, such as proprietary financial data or client-identifying information, when using LLMs. Instead, abstract sensitive content into generalised examples.

Practical Tips:

??????? Start by asking LLMs for a structure or outline for your proposal, and refine it based on your project specifics; Try - “Please give me a formal proposal outline for delivering an engineering development project.” Then as “Is that all?”.? You will be surprised at the value of the extra information.

??????? Using the ?+ Create button in ChatGPT (for example in Version 4o), you can load your project training materials and guides, such as the PMBOK, into your own LLM so that you are assured the framework follows along professional lines.

??????? In the same way as you can get guidance for proposals, you can also get support for a Project Charter or a Project Management Plan.? Drafts can be produced very quickly giving you the framework to add detail and accuracy.

2. Project Planning and Scheduling

Challenges:

Planning involves multiple moving parts, from setting milestones to allocating resources. Traditional project planning methods often rely on static Gantt charts and timelines, which can fall apart when unexpected changes arise.

How LLMs Can Help:

LLMs can assist in brainstorming and creating initial drafts of project schedules:

• Milestone Identification: Ask LLMs to suggest key milestones based on project objectives.
• Task Breakdown: Provide an overview of the project, and LLMs can help break it down into manageable tasks and dependencies.
• Risk Considerations: LLMs can generate potential risks and mitigation strategies for consideration during planning.

Security Tip:

??????? When discussing project details with LLMs, ensure that confidential timelines or project-specific data is excluded. Use anonymized task descriptions where possible.

Practical Tips:

??????? Use LLMs to generate task lists and milestone descriptions, and then integrate them into project management tools like MS Project, Trello or Asana.

??????? When defining milestones, provide a brief description of your project, including key deliverables and deadlines. Then, ask: "What major milestones should I include for a project that involves [brief project details]?".? This will generate an initial set of milestones, which you can refine based on your project's specifics.

??????? Instead of manually breaking down a project, feed the LLM your high-level objective and ask: "Can you break down this project into key tasks with dependencies?". You can refine the output by adding or adjusting dependencies based on resource availability or project constraints.

??????? "What are the most common risks for a project involving [describe your project]?" Then, follow up with: "How can these risks be mitigated?". This will give you a structured risk register that you can adapt for your risk management plan. Ask for the risks to be in the format ‘There is a risk that…, caused by…., resulting in….’.

3. Resource Allocation and Optimization

Challenges:

Resource allocation is a constant balancing act—ensuring team members are neither overworked nor underutilized. Manual allocation often leads to inefficiencies, missed deadlines, or budget overruns.

How LLMs Can Help:

While LLMs does not have direct integration with resource management tools, it can assist in brainstorming strategies:

• Workload Balancing Tips: Ask LLMs for best practices on balancing team workloads.
• Skills Matching Ideas: Describe your team members’ skills, and LLMs can suggest task assignments that align with their expertise.
• Predictive Considerations: Although LLMs cannot analyse real-time data, it can help outline scenarios where resource shortages may arise based on project complexity.

Security Tip:

??????? Refrain from sharing personnel-specific details, such as employee names or sensitive role descriptions. Use generalized profiles instead.

Practical Tip:

Use LLMs to review or refine your initial resource allocation plan before implementing it in your preferred project management software.

Workload Balancing Tips:

??????? If you're struggling with balancing workloads, ask the LLM: "What are some best practices for balancing workloads in a project where I have [number] of engineers working on [describe key tasks]?". Example: "I have a team of five engineers working on hardware design, software integration, and testing. How should I allocate work to avoid bottlenecks?".? The LLM can suggest techniques such as rotating tasks, load-sharing strategies, or prioritisation frameworks like the MoSCoW method to prevent burnout.

Skills Matching Ideas

??????? List your team members’ skills and experience, then type in the LLM: "Based on these skills, how should I allocate tasks to optimise efficiency?".? Example: "I have Alex (embedded systems specialist), Priya (RF design expert), and James (testing lead). How should I assign them tasks in an integration project?".? The response might suggest that Alex focuses on firmware development, Priya leads hardware validation, and James manages integration testing, ensuring no skill gaps in critical areas.

Predictive Considerations

??????? Ask the LLM to identify potential bottlenecks before they arise: "What resource constraints should I anticipate in a project that involves [describe complexity, e.g., multiple suppliers, tight deadlines]?". Example: "I'm managing a project with multiple suppliers, hardware dependencies, and a strict deadline. What resource shortages might I face?". Risks might be highlighted such as supplier delays affecting integration, testing bottlenecks due to insufficient lab space, or key personnel being overloaded. It can then suggest proactive mitigation strategies such as buffer time, cross-training, or parallel work-streams. Ask for a workload tracking template in Excel.

4. Execution and Monitoring

Challenges

Effective project monitoring ensures that tasks progress on schedule, risks are identified early, and corrective actions are taken proactively. However, tracking multiple work-streams manually can be inefficient, leading to overlooked dependencies, delayed responses, and incomplete visibility into team workloads. While MS Project is ideal for complex scheduling, simpler tracking methods such as Excel-based task monitoring can provide more flexibility for day-to-day execution.

How LLMs Can Help with Monitoring Tasks

LLMs can assist project managers in maintaining oversight and responding quickly to emerging challenges:

• Real-Time Status Updates – Provide key task details, and LLMs can generate structured status reports, highlighting completed, delayed, and at-risk activities.
• Early Risk Detection – LLMs can analyse ongoing project issues, predict bottlenecks, and suggest mitigation strategies based on project complexity.
• Task Progress Summarisation – Instead of manually consolidating updates from different teams, LLMs can synthesise information into clear summaries for dashboards or stakeholder reports.
• Team Load Monitoring – By describing workload distribution, LLMs can help identify resource constraints and suggest adjustments to balance workloads effectively.

Choosing the Right Monitoring Approach

• Excel provides a lightweight, flexible solution for tracking progress and highlighting overdue tasks with custom formulas and conditional formatting.
• MS Project is ideal for managing task dependencies, resource allocation, and generating automated Gantt charts for detailed project scheduling.
• Hybrid Approach – MS Project can be used for structured planning, while Excel can facilitate ad-hoc tracking and reporting.

Security Tip

??????? When using LLMs to assist with project monitoring, avoid sharing sensitive project metrics, proprietary data, or personal performance details. Focus on summarising trends, progress indicators, and generalised risk insights.

Practical Tip

Use LLMs to draft a preliminary task monitoring report based on team updates. Refine it with real-time inputs, prioritise issues, and adjust schedules dynamically to enhance responsiveness in project execution.

Real-Time Status Updates

??????? Provide brief task updates and let LLMs generate a clear, structured report for stakeholders. Example Prompt: "Here’s my team’s progress: Hardware design is 90% done but awaiting a supplier part. Software integration is ongoing with minor delays. Testing starts next week. Can you summarise this for an executive update?".? Benefit: Saves time on report writing while ensuring clarity.

Early Risk Detection

??????? Describe a delayed task or potential bottleneck, and LLMs can suggest root causes and mitigation strategies. Example Prompt: "A software feature is two weeks behind due to unclear requirements. What are the likely risks, and how can I mitigate them?"

Benefit: Identifies risks before they escalate, allowing faster intervention.

Task Progress Summarisation

Instead of consolidating updates manually, ask LLMs to generate structured task status summaries for quick decision-making.

Team Load Monitoring & Workload Adjustments

??????? Describe team assignments and let LLMs suggest ways to balance workloads and avoid burnout. Example Prompt: "I have 5 engineers: two focused on hardware, two on software, and one on testing. The hardware engineers are overloaded, while the testing engineer is underutilised. How should I re-balance the workload?".

Benefit: Prevents bottlenecks and ensures efficient resource utilisation.

5. Risk Management

Challenges:

Projects are inherently risky—whether from scope creep, resource constraints, or external factors. Many PMs rely on static risk registers that don’t evolve dynamically as the project progresses.

How LLMs Can Help:

Managing risks effectively is crucial to ensuring project success, yet traditional methods of identifying and mitigating risks can be time-consuming and inconsistent. Large Language Models (LLMs) offer a powerful way to streamline this process, providing structured risk identification, proactive mitigation strategies, and clear documentation.

LLMs can help project managers define risks using a structured approach, ensuring clarity in descriptions such as: "There is a risk that [event], caused by [reason], resulting in [impact]."

This format ensures risks are well-defined and actionable. By prompting LLMs with project details, managers can quickly generate a comprehensive risk list tailored to their specific challenges.

Beyond identification, LLMs assist in developing practical mitigation strategies. For instance, if a supplier delay is a concern, LLMs can suggest actions such as pre-ordering critical components, securing backup suppliers, or redesigning workflows to minimise dependencies. This allows teams to take proactive steps before risks escalate.

Additionally, LLMs can draw insights from lessons learned in past projects, highlighting recurring risks in specific industries and offering best-practice solutions. This helps organisations avoid repeating common mistakes and strengthens overall risk management.

To formalise risk tracking, LLMs can generate Excel-based risk registers, pre-populated with structured risk statements, likelihood and severity ratings, and mitigation plans. These ready-to-use templates reduce administrative effort and ensure risks are continuously monitored throughout the project lifecycle.

By integrating LLMs into risk management, project teams can improve efficiency, enhance decision-making, and maintain a structured approach to identifying and mitigating uncertainties. Whether used for brainstorming risk scenarios, refining mitigation plans, or automating documentation, LLMs offer a practical tool for strengthening project resilience.

6. Project Communication and Collaboration

Challenges:

Effective communication is the backbone of successful projects. However, keeping everyone on the same page, especially in large or distributed teams, can be difficult.

How LLMs Can Help:

LLMs can help streamline project communication:

• Drafting Emails: Provide key points, and LLMs can draft clear and concise emails for stakeholders.
• Meeting Summaries: Input meeting notes, and LLMs can generate summaries and action items.
• Conflict Resolution: Describe team conflicts, and LLMs can suggest resolution strategies based on common management practices.

Security Tip:

??????? Avoid including sensitive information in LLMs-generated communication drafts. Review all drafts carefully before sharing them externally.

Practical Tip:

??????? Incorporate LLMs-generated communication drafts into collaboration tools like Slack or Microsoft Teams.

7. Lessons Learned and Closure

Challenges:

Many project teams rush through the closure phase without properly documenting lessons learned. As a result, valuable insights are often lost, leading to repeated mistakes in future projects.

How LLMs Can Help:

LLMs can assist in organizing and summarizing post-project insights:

• Generating Lessons Learned: Ask LLMs to generate a list of questions to guide team reflections.
• Drafting Postmortem Reports: Provide data and team feedback, and LLMs can help draft comprehensive closure reports.
• Knowledge Organization: LLMs can suggest structures for storing lessons learned in a way that is easily accessible.

Security Tip:

??????? Sanitize any sensitive project details from the data shared with LLMs to ensure confidentiality.

Practical Tip:

??????? Use LLMs to create a lessons-learned report template that can be reused across future projects.

Final Thoughts: The Role of LLMs in Project Management

While Large Language Models (LLMs) may not yet offer real-time integrations or access to live project data, they are proving to be valuable tools for project managers. Whether used as a brainstorming assistant, content generator, or risk analyser, LLMs can enhance decision-making, improve estimates, and help optimise resource planning.

However, as with any tool, they should complement—not replace—critical thinking, experience, and collaboration. Project managers should always review and refine LLM-generated outputs, incorporating feedback from their teams to ensure accuracy and relevance. Additionally, data privacy remains a priority, and any information shared should be carefully sanitised and anonymised to maintain security.

When used with a practical mindset, LLMs can be a game-changer—offering efficiencies and insights without the need for premium services or complex integrations. The real value lies not in blindly relying on AI but in strategically leveraging it to enhance human decision-making.

Opening the Conversation: The Future of LLMs in Project Management

With AI tools becoming more accessible, project managers have a growing set of options to streamline workflows, improve reporting, and anticipate risks. But are these tools always beneficial?

??????? ?How do you see LLMs fitting into project management?

??????? ?Where do you find them most useful—and where do they fall short?

??????? Do you think AI-generated insights can improve decision-making, or do they introduce new risks?

We’re at an exciting crossroads where AI is beginning to shape the way projects are planned and delivered.

Let’s discuss: What are the pros and cons of using LLMs in project management? Are the risks of misinterpretation too great?? Could you become complacent in just accepting the answers without challenge?? What training is available and how can PMs get further support?

Please share your thoughts!