Artificially Intelligence, AI, is the current next big thing. I enjoy reading what it can do for an organization and watch it create the initial version of this essay. However, AI presents challenges to organization management and training people to analyze and use its value.

I remember the first time I learned that the pivot table function in Excel would give you bad numbers, when all the data was not there, causing it create incorrect data and leading to incorrect conclusions. It forced me to challenge every result for reasonableness and accuracy. So too with AI.

Numbers have a high status, not incorrectly. But they can leave out a lot and not deal with all realities.  

When I am using AI generated wording I will put in italics.

The Changing Landscape of Management

One of the biggest changes of AI is the elimination or limiting of current repetitive tasks. You many need less bodies to do number crunching, but an organization will now people who can question the results and connecting them to realities where there will be missing numbers or non-existent information and data to begin with. This shift requires a deep understanding of AI technologies and their implications for business strategy.

Data Literacy and Analytical Skills

I remember way back in my college days how difficult to learn the skills needed to pass a statistics class. Now, understanding data is just a basic to survive in the business world. As AI systems rely heavily on data to function, managers must be proficient in interpreting data outputs and deriving actionable insights. This involves not only understanding statistical methods and data visualization techniques but also being able to communicate data-driven findings to stakeholders in a clear and compelling manner..

Change Management

Below, in the first paragraph what AI wrote. The next paragraph I will point out what missing from this statement.

Implementing AI solutions often entails significant changes to existing workflows and processes. Managers must be adept at change management, guiding their teams through transitions and addressing any resistance that may arise. Effective change management strategies include clear communication, training programs, and providing support to employees as they adapt to new ways of working.

As I read and watch AI stores, I noted how positive in tone the output, specifically in its writings, In condescending tone of to any past different way of working. One of the keys of change management is understanding the successes and habits of the current and past periods. There is value there. Yes, there are desirable improvements to be made, but to get buy in and best results you need to take this information and merge into past practices. People know when they are respected and know when they are not. If this respect is not there, change at its best will be suboptimal and may be sabotaged.

The Unknown unknowns

Experience is a teacher. When you have new workers, they are not going to know what questions to ask of the data. One of the more important aspects of managing is the role of the teacher. The manager needs to guide the staff on what questions to ask and what experiences the newer employees might not have. Data is not complete story. More so in the past, managers cannot assume their new employees can fully judge the data.

Conclusion

AI management requires top managers to acquire a diverse set of new skills, ranging from data literacy and technological acumen to change management and ethical leadership. As AI continues to reshape the business landscape, leaders must adapt and evolve, embracing a culture of continuous learning and collaboration. By developing these competencies, top managers can effectively navigate the challenges and opportunities presented by AI, driving their organizations toward a future of sustained success and innovation.

Here is another example of positive language AI uses to make it seem it is all knowing. It is not. It can be extraordinary useful (and worth it’s cost) but to manage it optimally its limitations must be understood. I think one useful example in understanding may be the example of usings robots in the warehouse. The most successful projects have the robots and people working together, in a process to increase the efficiency of the warehouse.  Robots free warehouse personnel of repetitive and boring tasks, but the people there can deal with frequently changing realities of work, better and more fully than a programmed machine can. Another example is calling customer service on a non-typical problem and the frustration to get beyond the recorded voices to have the real issue resolved. So, really useful AI, must be managed and used wisely.