Learning to Use AI

 

Although AI is a powerful new technology, there will likely be missteps as we learn to use it. Technology users have been lulled into expectations that software will deliver results with minimal effort. Checking local weather on a smartphone, shopping online, or ordering a ride from Uber are easy tasks. For project management, software can schedule tasks and manage complex requirements. This can be accomplished without insight into how the software logic works. However, for AI, we need to step back and understand the process, sometimes questioning the results. AI is not a turnkey solution, yet we treat it like it is. Think about using a large language model (LLM) like ChatGPT. The user asks a question and receives a response. Most users do not think about how the response is generated or what data was used to produce the result.  To clarify a vague answer, a new prompt can be entered requesting more details.

As project managers, we need to know more. AI methods can include supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, self-supervised learning, and genetic algorithms. Each algorithm has a different process for calculating results and has different data requirements. AI prediction is a probability. When you receive a response from an LLM, do you understand the probability that the answer is correct? You can ask for the source to help validate the answer. 

The way out of this forest of possibilities is education and training. We don’t need to be data scientists or software engineers, but we have a responsibility to investigate and understand how the algorithm provides answers. There is a learning curve with AI technology, and business users can be trained to enhance their knowledge so they know how to acquire optimal results from AI technology.