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Unlocking Project Management Superpowers with Generative AI: Insights from Vanderbilt
I’m currently immersed in an outstanding course from Vanderbilt University: “Integrating Generative AI into Project Management.”
My tutor, Professor Bennett Landman, brings exactly the kind of approach I resonate with — pragmatic, clear, and grounded in real-world application. Like Professor Jules White, who guided me through earlier courses, he makes complex AI concepts both accessible and actionable.
In this blog, I want to share what I’m learning — not just for the sake of content, but to demystify AI, highlight its practical relevance, and maybe even ease the fear many professionals still feel when AI enters the conversation.
I’m proud to be learning from some of the best minds in the field — it aligns perfectly with my style, and I’ll admit, a touch of professional vanity. 😉
So let’s dive into a few key takeaways from a recent session that really stuck with me.
My tutor kicked things off by outlining four core areas we’d explore:
- Why GenAI? Understanding the fundamental motivation behind integrating this technology.
- People-Centric AI: How to ensure AI serves us, rather than the other way around.
- A Technical Example: Gaining a practical skill from the session.
- Exercises: Opportunities to apply what we learn offline.
The Human-First Approach to Project Management with AI
At its heart, project management is about empowering people to be their best. It’s about achieving a complete visual of the system and enabling individuals to be creative, expressive, and effective in their roles. The crucial point here is that we don’t want AI to be an overlord; we want AI to empower individuals.
As Bennett eloquently put it, we’re not trying to replace project managers or create robotic overlords. Instead, we’re seeking to find peace and gain perspective amidst the constant “fires” of daily project life. AI, when used correctly, can help us prioritize creative, responsible decision-making.
These computing tools are fantastic, but their purpose is to empower us. Think about custom software development – building dashboards, configuration scripts, or automated emails. While valuable, these bespoke solutions are expensive, require ongoing support, and often trigger additional development costs with upgrades and compatibility issues.
This is where GenAI steps in. Can we leverage these general-purpose models to replace many of these idiosyncratic tools, allowing us to simply “talk to our data” and get things done?
Moving Beyond the “Excel Therapist”
We all know the project manager’s reality: spreadsheets, deadlines, deliverables, progress reports. Excel (or Google Sheets) is wonderful, but getting data clean and ready for analysis, like a pivot table, is often incredibly time-consuming. Have we, as project managers, become “Excel therapists,” spending most of our time just preparing data?
GenAI offers a way to break this cycle. By automating the mundane, it frees us up to focus on the human factors that drew us to this field in the first place. The goal is to facilitate data-driven decisions in modern project management using tools like ChatGPT and other large language models for data manipulation.
Easy Things Quickly, Hard Things Slowly
Bennett’s core hypothesis, and one I wholeheartedly agree with, is this: we should try to do easy things quickly and do hard things slowly.
Things like sorting emails or creating templates are conceptually easy but consume a significant amount of our time. GenAI has the potential to handle these relatively simple, time-consuming tasks, allowing us to dedicate our humanity, creativity, and problem-solving skills to the nuanced, complex challenges that truly require our attention. We can use large language models to get those “easy but time-consuming” things done quickly.
People-Centric Integration: Communication, Reporting, and Ethics
So, how do we integrate GenAI workflows with a humanizing perspective? We need to accelerate communication and reporting – getting concepts out quickly and effectively. However, ethical considerations are paramount. When a computer is “in the loop,” we must ensure it’s not “the loop,” and that we’re not just shouting drivel into the void.
A Practical Example: The Power of Text Parsing
Bennett provided a fantastic technical example: text parsing. Project managers deal with mountains of text in various, often inconsistent, formats. People are creative, and that’s great, but it makes programmatic text extraction a nightmare. Imagine trying to extract email addresses when they’re formatted in 20 different ways, sometimes with parentheses, quotes, or even embedded markdown.
Historically, this was the domain of “Regular Expressions” (RegEx) – cryptic, complex patterns that could match text. While powerful, writing and maintaining a RegEx for even a slightly varied dataset could take hours, if not days, and would become impossibly complex with global characters, accents, or embedded formatting like HTML.
The GenAI Breakthrough:
Here’s where GenAI shines. Instead of writing complex RegEx, you can simply tell a large language model (LLM): “I’m trying to extract emails and names in varied text formats. Can you help?”
And the answer is a resounding “Yes!”
While an LLM might initially suggest a RegEx (which is ironic, given we’re trying to avoid writing them!), if you politely ask it to do the extraction for you, it will. It can manipulate large amounts of text very effectively and quickly, building a table of extracted names and emails. You still need to verify the output for accuracy, but it gets you “nearly all the way there” in seconds.
Beyond Email Extraction: The Generalization of GenAI
The implications of this capability are vast. We can use GenAI to:
- Clean up text data
- Parse dates, times, and zones from documents
- Standardize project documentation (e.g., “reformat this all in a consistent way”)
- Convert lists to tables and tables to lists
- Find and replace text
- Change first-person to third-person narrative
- Sort information
- Merge multiple files and remove duplicates
Essentially, much of the tedious data manipulation work that once required specialized programming knowledge (like Perl, as Bennett mentioned!) can now be done without bespoke software. This empowers us to focus on higher-value tasks.
The Bottom Line: Free Up Your Time!
My key takeaway, and one I encourage you to consider, is this: If a task is taking a lot of time, and it feels conceptually easy – like text manipulation, data extraction, or number formatting – give GenAI a try.
You can free up a significant amount of your day by transforming what feels like a “hard but easy” task into an “easy and effectively done” task that just needs a bit of checking. We’ll delve into the “checking” aspect in future sessions.
I’m incredibly excited about this journey of integrating GenAI into project management, and I look forward to sharing more insights with you all. Stay tuned!
