By Dr. Leon Herszon
April 2, 2025
Today, generative AI can be a game-changer in problem-solving, especially in project management. Whether you’re a seasoned professional or just starting out, understanding the capabilities of generative AI can significantly enhance your effectiveness in tackling complex issues.
Understanding the VUCA World
We live in a VUCA world—one characterized by Volatility, Uncertainty, Complexity, and Ambiguity. This environment poses significant challenges for organizations and professionals alike. Recent statistics highlight a steep increase in volatility and uncertainty since 2019, making it crucial for us to adapt and innovate.
As we navigate this landscape, leveraging generative AI can provide us with the tools to analyze data, forecast trends, and automate decision-making processes, thereby enhancing our capabilities.
What is Generative AI?
Generative AI refers to systems that can produce content—be it text, images, or even music—based on the data they have been trained on. This technology goes beyond traditional AI, which merely analyzes existing data. Generative AI utilizes large language models (LLMs) to understand context and generate new insights.
At its core, generative AI is about creation. It allows us to generate original outputs that can mimic human creativity, making it a powerful ally in problem-solving.
How Generative AI Enhances Problem Solving
So, how can generative AI actually help us solve problems? Here are some key ways:
- Data Analysis: Generative AI can sift through massive datasets in seconds, providing insights and visualizations that would take humans much longer to generate.
- Trend Forecasting: By analyzing existing behavior, generative AI can help predict future trends, allowing organizations to make informed decisions.
- Automation: Routine tasks can be automated, freeing up time for more strategic thinking and problem-solving.
- Simulation: Generative AI can simulate various scenarios, helping organizations to test different approaches before implementing solutions.
Case Studies: Generative AI in Action
To illustrate the potential of generative AI, let’s look at some real-world applications:
Telecom Network Optimization
A telecom company faced inefficient network performance and high maintenance costs. By employing generative AI algorithms, they were able to predict traffic patterns and optimize resource allocation, ultimately saving significant costs.
Customer Retention Strategies
Another organization struggled with high customer turnover affecting revenue. Using generative AI, they identified factors leading to customer cancellations and developed targeted retention strategies that improved customer satisfaction.
Personalization in Retail
Walmart utilized generative AI to personalize product recommendations based on customer behavior. This not only enhanced customer experience but also drove sales by aligning product offerings with individual preferences.
Engaging with Generative AI: A Hands-On Approach
During our session, we engaged in a live exercise using generative AI tools. Participants were encouraged to create prompts that could help generate useful outputs tailored to their specific project management needs. This practice highlighted the importance of prompt engineering—crafting effective instructions for the AI to follow.
Effective Prompt Engineering
To get the best results from generative AI, it’s crucial to understand the components of a good prompt:
- Task Definition: Clearly state what you want the AI to do.
- Context: Provide background information to guide the AI’s response.
- Examples: Include examples of the type of output you expect.
- Style and Tone: Specify the desired tone or style of the output.
Creating Your Own AI Bots
One exciting application of generative AI is the creation of custom bots that can assist with specific tasks. For example, a project bot can ask questions about project details and provide tailored advice on project management methodologies based on user input.
This interactive approach fosters a collaborative environment where human expertise and AI capabilities come together to solve complex problems efficiently.
Future Trends and Considerations
As we look to the future, the integration of generative AI into various industries will only increase. However, it’s essential to be aware of potential biases in AI models and to implement guardrails to ensure ethical use. Collaboration with legal and cybersecurity departments is crucial to protect proprietary information when using generative AI tools.
Final Thoughts
Generative AI is not just a buzzword; it’s a powerful tool that can revolutionize the way we approach problem-solving in project management and beyond. By embracing this technology, professionals can enhance their capabilities, streamline processes, and ultimately drive better outcomes for their organizations.
Leon Herszon, Civil Engineer, Msc, PMP, is finishing his PhD studies and started his career at Shell managing HSE (Health, Safety, and Environment), Information Technology and strategic projects. He was invited by IIL to start operations in Brazil, and moved thereafter to the USA. Now he is based in NY acting as Senior Vice-President, managing global and strategic business. He also delivers presentations and training worldwide, including lectures at Rutgers Business School, and communicates in English, French, Portuguese, German, and Spanish.
