Leadership Assisted With Artificial Intelligence
How can Managers leverage AI for their Leadership related tasks
Hey, fellow Leader 🚀,
I am Artur and welcome to my weekly newsletter. I am focusing on topics like Project Management, Innovation, Leadership, and a bit of Entrepreneurship. I am always open to suggestions for new topics. Feel free to reach me on Substack and share my newsletter if it helps you in any way.
By the time I am writing this article, Artificial Intelligence is a hot topic, and we are still to see how it will carve the landscape for all IT professionals in particular. The change is too recent and tools are rapidly evolving making any prediction or analysis challenging and making correct assessments. In this article, I will cover some concerns about using Artificial Intelligence solutions, starting with the technology’s limitations.
Currently, the literature across the internet is gazing at the wonders of Artificial Intelligence. Is like getting the latest PlayStation console into your home, setting it up, and marveling at every bip the machine does. After some hours in, the feeling of wonder and novelty starts to fade away and we realize that not all games are the pinnacle of human digital craftsmanship. I suspect we will have the same feeling with Artificial Intelligence when the dust of novelty starts to settle.
When natives see technology that they do not understand they call it “magic”. Regarding AI-related tools and practices, we are still behaving like natives, until we learn and understand how to use this technology.
What will hold AI back to deliver its great potential?
Artificial Intelligence on its own is not capable of providing value. It needs a very important resource to capitalize on its promises: Data. Not only any kind of data but quality data. A manager needs good quality information for decision-making, in the same way Artificial Intelligence tools require the same effort on the data. Since the technology is very new and if the human part fails to provide good data for AI engines, the results given by the engine will be misleading. As leaders, is important that we understand how these tools work to allow them to provide quality results.
For this kind of technology to be used regularly, the software used to manage the team, the planning, or any other tool used by management, needs to have a built-in AI engine. This means the tools will be as good as the implementation of these AI engines. Nowadays will be easier for a product to lead by implementing some AI features. However, not all popular tools today have these engines built in, and may take them some time to adjust. This means the wonders of AI will take some adjustment time to take shape unless you change drastically today the team’s toolset to get an advantage early on. This drastic measure can put a team using the latest AI techniques but will have an overhead just based on the learning curve and cost of change.
What will be the potential uses? And dangers?
The main common for Project Managers will be resource allocation and task management recommendations. Planning has the potential to become mostly automatic if the Project Manager provides the needed information and setup early on. This means the Project Manager should research or have training for implementing the AI part correctly.
One of the dangers of using Artificial Intelligence without having a critical eye is that AI engines can provide poor-quality results without the manager realizing it. The output is as good as it was the input and the data used in its generation. This means there is always a human factor somewhere.
One game-changing feature will be performance reviews. If an AI can tap into Development tools and measure the code quality, bug correction, and other metrics, it can provide insightful feedback about the developer’s performance. Hard data can be easily extracted and used to evaluate the developer’s performance. The only problem here comes if there is no method of evaluating metrics on a developer’s performance in the first place. There is a significant amount of IT Development teams that don’t have any kind of metrics to start with. Adding an AI-based tool can help speed up the process of evolving the team’s performance evaluation practices, but it lacks the mindset and the experience to properly analyze the numbers for the human part of the equation.
So, what to do?
The Leadership roles today need to start having a more comprehensive knowledge of Artificial Intelligence to lead products and teams for better outcomes. Any leader should have some sort of AI literacy, not only at a technical level but also at practical usage as well.
Not all AI engines are made equal, and there is even some bias that differs results between engines. So is important to have some knowledge about how the AI works. Without this knowledge, a leader risks making decisions based on poor tool usage of low data quality. Be mindful that the AI engines behave like a black box and some of the outputs need some human oversight.
For that reason is important to think about practical uses and play and experiment with AI-based tools for checking the results. Especially if you have some edge cases in your project or team, it will help you determine which would be the best tool to use and to create awareness of what a misleading output would be like.
To recapitulate, I would suggest the following:
Core AI fundamentals: Learn about what are the different types of AIs and how they work.
Specific project management tools (e.g. Microsoft Project): It’s recommended to check or search for training material based on the latest versions with AI.
Hands-On Learning: Experiment with the different tools with edge cases and see how they behave.
Critical Thinking: It’s not just a cliche, but keep in mind the tool’s limitations and evaluate the quality of the AI output.
For the AI fundamentals, I will add some resourceful links for future Atomic Monday editions.
Cheers,
Artur