Artificial intelligence promises to revolutionize project management; it can assist with data analysis, automate routine tasks, and predict project trends.
However, as a veteran project manager (read “slightly skeptical older person”), I am concerned AI will cause a tsunami of project failures before improving the state of the practice.
People tend to be lazy, and a fool with a tool is still a fool. Worse yet, a fool with a tool that produces well-written, convincing project deliverables is a very dangerous fool.

Attempting to Solve the Wrong Problem
Producing project charters faster (or communications plans, risk management plans, etc.) is not the right goal. Project management does not have a typing speed problem.
Unfortunately, AI vendors and people looking to implement AI tools with only a superficial view of what project managers do tend to pick these visible outputs as places to implement AI. In doing so, they miss the collaboration, analysis and coordination that must occur before these visible outputs can be accurately created. They only see the outwardly visible part of the PM iceberg.

It is very easy to use AI to generate these visible deliverables by providing some high-level project information in the prompt. Unfortunately, these will be a poor foundation to start from without undertaking all the under-the-surface work. To begin with, they have not engaged all the necessary stakeholder perspectives.
Pesky People Problems
Indulge this older person in a rambling story of questionable relevance. Back in school, I had a teacher who liked to tell us how much they would love teaching if it were not for all the pesky students. It was his not-so-subtle reminder that we caused most of his headaches and issues.
However, teaching without students is meaningless. His love of the subject matter may have led him to study it and eventually brought him to teach it, but the purpose of teaching is to spark a similar love for the field in students and help them navigate their learning journey. Someone who loves a topic but does not like interacting with students has a hobby but is not a teacher.
As a junior PM, I loved the science and theory of project management. I liked elaborating charters to plans, going from tasks to a WBS and schedule. Techniques like critical path, the theory of constraints, and resource levelling to convert estimates to timelines brought me joy. There was a purity and mathematical validity that was dependable and satisfying…and then always ruined by the inconsistencies of people.
People in the role of sponsors give us incomplete and conflicting requirements to deliver within unlikely deadlines. Our teams give us unrealistic estimates and then spend ages working on tasks they did not even list or estimate. Vendors lie to us to win work, and customers want our products to solve all their problems at no cost. Project management would be wonderful if it were not for the inconsistencies and unpredictability of people spoiling our plans.
Yet projects without the messiness and unreliability of people would be as worthless as teaching without students. Projects are people-driven. Projects are conceived by people, developed by people, and the outcomes are used by yet more people. However, these people are often busy, antisocial, located far away; they can be fickle, fallible, furtive and some other “f” words that come to mind.
There are No Short Cuts to Anyplace Worth Going
Attempting to plan our projects without sufficient engagement of all these problematic people is very tempting, but completely pointless. AI tools can draft project charters, write task descriptions, create work breakdown structures, and outline estimates and schedules in <1% of the time it takes us to do it manually—what progress! For agile projects, AI tools can create well-formed user stories, product backlogs, acceptance criteria, sprint plans, and complete release roadmaps.
All these outputs will be well written—likely better than 80% of those produced in their organizations previously. They will be detailed and appear plausible, which is especially dangerous. They will incorporate most industry norms (which is good), but they are almost guaranteed to fail.
They fail because they were generated without enough input from all the annoying people. The people who tell us they are too busy to start, our technology is old and needs updating, we cannot work with vendor X, and department Y has a competing project.
There is a saying that "No plan survives contact with the enemy," which has been repurposed for project management into "No plan survives contact with reality." We manage this risk by engaging with stakeholders to inject reality into our plans. In predictive/traditional environments, this involves extensive stakeholder interviews, detailed requirements gathering, and then lots of due diligence to review and get acceptance of statements of work, task estimates, etc.
In adaptive/agile environments, this involves learning about our customers and understanding their needs and frustrations, then working iteratively with our teams to identify tasks, draft short-term plans, build product increments, get feedback and refine our direction/plans accordingly. There may be less upfront stakeholder analysis and estimation, but it happens as details emerge. The people side of projects cannot be safely avoided, as this is where the realities of projects are surfaced.
However, it is shockingly easy with AI to generate convincing-looking project deliverables without proper stakeholder consultation.
A Recipe for Disaster
Using a short prompt, anyone can ask an AI tool to generate a project deliverable, and it will dutifully comply. Here is an example using ChatGPT-4 on April 17th, 2024.
Prompt: You are an expert project manager. Generate a project charter for a new software development initiative. The project aims to develop a mobile app for a large e-commerce company to improve customer experience and increase sales. The project charter should include the following sections:
1. Project Overview
- Brief description of the project
- Project objectives and goals
- Alignment with company strategy
2. Project Scope
- Inclusions: features and functionalities of the mobile app
- Exclusions: any items not covered in the project
- Assumptions and constraints
3. Stakeholders
- List of key stakeholders and their roles
- Stakeholder communication plan
4. High-Level Timeline
- Estimated start and end dates
- Major milestones and deliverables
Generate the project charter clearly and concisely, using bullet points where appropriate. The charter should be easy to understand, including non-technical stakeholders.
Response: Certainly! Here's a project charter for the new software development initiative to create a mobile app for a large e-commerce company:
Project Overview
- Brief Description: Development of a user-friendly mobile application to enhance customer experience and increase sales for a large e-commerce company.
- Objectives and Goals:
- Improve user engagement and navigation ease.
- Incorporate advanced features for a personalized shopping experience.
- Increase sales through enhanced mobile accessibility and functionalities.
- Alignment with Company Strategy:
- Supports the company's vision to expand digital presence and accessibility.
- Aligns with the goal of leveraging technology for market growth and customer satisfaction.
Project Scope
- Inclusions:
- User account creation and management.
- Product browsing, search, and purchase functionalities.
- Integration with existing payment gateways.
- Customer support chat features.
- Review and rating systems.
- Exclusions:
- Development of a new payment gateway.
- Back-end inventory management system overhaul.
- Physical store integration and functionalities.
- Assumptions and Constraints:
- Assumption: Customers will prefer mobile app usage over the website.
- Constraint: Must be compatible with both iOS and Android platforms.
- Constraint: Six-month timeline for the initial launch.
Stakeholders
- List of Key Stakeholders and Roles:
- Project Sponsor: Company CEO.
- Project Manager: [Your Name/Designated PM].
- Development Team: In-house software developers.
- Marketing Team: For promotion and feedback analysis.
- Customer Service Department: For input on customer needs.
- Stakeholder Communication Plan:
- Weekly project status meetings with the development team.
- Monthly update meetings with all stakeholders.
- Email updates after major milestones.
High-Level Timeline
- Estimated Start and End Dates:
- Start: June 1, 2024.
- End: December 1, 2024.
- Major Milestones and Deliverables:
- Prototype Completion: August 15, 2024.
- Beta Testing Phase: September 20, 2024.
- Marketing Campaign Launch: November 1, 2024.
- Official App Launch: December 1, 2024.
I deliberately asked for a concise response for brevity in this article, but the more detail you ask for, the more convincing these deliverables can seem. I find this disturbing, not because I fear losing my job as a project manager (I now think I have job security for the rest of my career rescuing these doomed endeavors), but because of the stupidity of creating estimates and plans on such poor input data.
Some immediate questions that come to mind about this charter include:
- Where did the six-month timeline come from?
- Do we have a team with the required skills?
- What technology stack are we using?
- What products are we selling, how many, and in what markets?
- Apparently, it aligns with our corporate strategy, which is good, and it seems we do not even need to know what that strategy is.
If six months is too long, that is no problem. Ask it to adjust the schedule to four months, and presto, it will—without any pesky engineer interactions.
"Use at your own risk," I hear you say, "If you are stupid enough to generate a charter this way, you deserve the consequences."
These are valid points, but people are time-pressured and lazy. Most people do not even read articles like this anymore. I suspect that for every person who has read this far, 50 people have used AI to unwittingly generate an impossible project plan.
PREPARE to use AI Smarter for Project Management
Used correctly, AI is a boon. We can use AI to improve our plans, look for omissions, and suggest improvements. It can identify risks and suggest supporting tools and techniques we may not have considered.
However, we should use it after we have engaged with all the pesky people who list problems—and not as a replacement for talking with them.
PREPARE is a mnemonic for more responsibly using AI within project management. It focuses on improving artifacts we create with the consultation of relevant stakeholders and collaboration with our teams. The PREPAIR mnemonic stands for the following attributes:
- Protect – Protect your data (anonymize it) to prevent sharing sensitive information.
- Revisions – Ask AI to make improvements or updates to enhance the deliverable.
- Exclusions – Identify what is not included, but perhaps should be.
- Problems – Check for errors in the process or output.
- Assessments – Evaluate potential risks and suggest risk responses.
- Resources – Consider additional tools, technologies or methodologies that could support and enhance project outcomes.
- Explore – Investigate industry trends that could impact the project's direction or implementation.

So, instead of using AI to generate a project charter in one minute (then engaging a team of 10 people and spending six months failing at it,) I suggest we do this work the old way by talking to all the relevant stakeholders (sponsors, customers, architects, teams, vendors, etc.) to learn their goals, desires, issues, and constraints, etc.
Then, go through the process of engaging the people who will do the work in forming the estimates, along with consulting historical and industry data. Apply contingency, convert estimates to budgets, and do all the usual project management work.
Then, engage AI in what it is good at—making stuff up and creating lists. Ask it to review our plans and suggest improvements.
However, first, we need to protect our data. AI services might seem like helpful assistants we can have conversational chats with. Some even have friendly names like "Claude," but they are not your friends. They are front ends to large language models (LLMs) that learn based on the data they are trained on—including any data you give them, along with your prompts and responses.
Let’s look at each stage of PREPARE more in depth:
1. Protect Your Data
So, consult your organization's policies on the use of AI tools. Even if your organization has not yet developed AI usage policies, ensure you anonymize your data and do not share, upload or mention any proprietary, customer or non-public product information.
Learn the basics of data anonymization, synthetic data generation, and data masking. Tools such as µ-ARGUS, sdcMicro, and Anonimatron are free and/or open-source solutions that can help protect your data and IP.
When we know our data is anonymous and protected, we can engage AI with the remaining PREPARE-inspired prompts.
In the following examples, I am using [project charter] as the manually created deliverable I want to improve. However, this could be any project output, such as [project plan], [WBS], [Product backlog], [Persona List], etc. Likewise, I am using [groundwater analysis] as my domain, but it could be [software development], [residential solar generation] or whatever your project domain happens to be.
2. Revisions – Ask your AI agent of choice to critically review your [project charter] deliverable you created yourself, looking for ways to improve it.
Prompt: You are an expert project manager. Analyze the attached [project charter] and suggest improvements for its structure, flow, layout, readability, and clarity. Explain the reasoning behind your recommendations.
3. Exclusions – Ask what might be missing.
Prompt: Is anything missing from the document? Check the content for poorly explained content, omitted details, and missing sections commonly found in such documents in my industry of [groundwater analysis]. Explain why this element would be a helpful addition.
4. Problems – Look for errors, problems and conflicts.
Prompt: Are there any potential conflicting statements or errors in the document? Check for mutually exclusive statements, contradictions, and statements that may be incorrect or questionable for a [project charter] used in the [groundwater analysis] industry.
5. Assessments – Evaluate risks including threats and opportunities. Ask for a candidate risk list. It is always good to be forewarned of potential threats and opportunities.
Prompt: As a project management expert, experienced at executing projects in the [groundwater analysis] industry, create a list of potential risks (threats) and opportunities that might be associated with the project. For each risk, outline the potential impact, and a risk response plan.
6. Resources – Suggest supporting tools, techniques and documents. Ask for suggestions on additional tools or techniques we might use to make our case better. I might be old, but I am always on the lookout for more effective approaches, new tricks and better ways to succeed.
Prompt: As an expert business analyst, project manager, and product manager; consider the goal and purpose of my [project charter] in the [groundwater analysis] domain. Recommend supporting tools, techniques and deliverables that might help support the case I am making. Explain your suggestions with rationales of why they might be helpful and provide links and references to examples and explanations.
7. Explore – Search for alignment (or disruptions) from your industry norms. We should look for shifts in the market that may indicate our product/project is headed in the right direction for our industry (or deliberately forging a new path.)
Prompt: You are an expert in [groundwater analysis,] well versed in the latest developments, research, and industry trends. Is there anything in this [project charter] that indicates a deviation from the most probable market direction? Explain how the document aligns with industry trends and if any parts deviate. Support your points with links to relevant sources.
These prompts and acting on the results to improve our deliverables will help us be more effective and manage risk. They leverage the strength of AI while recognizing the complexities of people-based projects that require further analysis and collaboration.
Projects are Complex Adaptive Systems
Projects behave as complex adaptive systems (CAS). They are a network in constant movement pulled by stakeholder groups with competing demands, such as sponsors that want things fast and low cost, but of a high quality; customers who want things to be easy, attractive, comprehensive and cheap; and development teams who want meaningful work, career development, work flexibility, and high pay.
Projects do not get completed without managing these opposing forces, and there is no universal best way to do it. From a system's thinking perspective, today's projects operate in domains 2) Complicated and 3) Complex of the Cynefin framework:

The Cynefin framework is a way for us to classify how simple or chaotic a system is. It describes varying levels of complexity and suggests approaches for dealing with them. It starts in the lower right quadrant of 1) Simple environments. Here there is order with a clear cause and effect. This was the idealized view of projects I held as a junior project manager doing my planning without the unpredictability of people. In simple environments, universal best practices can be used over and over with predictable results.
Then we progress through 2) Complicated domains and into 3) Complex domains. In these environments, the cause and effect between project variables may only be possible to understand after events have occurred. Projects using new technology or working in emerging fields add extra complexity to projects. This is why adaptive/agile approaches build and assess small increments of product to determine viability. Their short iterations and feedback cycles mirror the Probe – Sense – Respond guidance for complex environments.
Understanding the complexities of projects and the variables at play can help us manage them more effectively. AI has a huge future in assisting us in the below-the-surface activities.
PREPARE for the Future
AI is a great tool when used appropriately. It can help us plan and improve our project management processes, stakeholder engagement, team interactions, project outputs, and their eventual outcomes.
However, it also has the potential to hamstring us if used to generate project charters, plans and estimates without the required stakeholder engagement and due diligence.
I am a fan of AI, just not the premature document creation capabilities of it. I used AI to improve this article after I had written it. I also used AI to create the PREPARE mnemonic from my initial list of Errors, Omissions, Risks, etc. improvement ideas that did not spell a recognizable word.
We can use AI to help interview all the necessary stakeholder groups and undertake the appropriate analysis. For example:
Prompt 1: I am meeting with a representative from the Finance group to discuss the goals for the ABC e-commerce project. Create a list of questions I should ask to understand the project scope and requirements from their perspective. Also, create a list of questions to capture their definition-of-done and project success criteria.
Prompt 2: We have an initial orientation meeting with the lead IT architect for the XYZ CRM project. Create a list of questions we should ask to clarify the infrastructure needs for the solution. Also, create a list of questions to explore the security, compliance and regulatory requirements we need to be aware of.
I would probably not use the exact questions these prompts generate in my interviews. However, the value comes from the topics it suggests. Most likely, it will suggest something I had not thought to inquire about, but could be helpful.
Projects are people-driven, complex adaptive systems. If we resist the temptation to use AI to generate convincing (but flawed) documents that lack underlying consultation and analysis, we can really benefit from AI.
Using the PREPARE mnemonic, we can use AI to assist us in vetting, improving and supporting our plans and processes. This smart use of AI plays to our strengths as a PM—and AI as a supporting and enabling technology.
[Note: For more articles from Mike Griffiths, visit his blog at www.LeadingAnswers.com. Mike first wrote this article for ProjectManagement.com in April 2024 here.]