AI for Executives: How Machine Learning Is Impacting the Next Generation Workforce
The term “artificial” doesn’t really do the next generation, with the attitude of “how we will get things done,” justice.
Artificial refers to a machine doing the work rather than a human, and the “Augmented Intelligence” might be more appropriate. Many agree that repetitive tasks and to-dos should be done by someone other than humans.
Take a robotic vacuum, for example. As I write this, I am vacuuming or should I say Ivan is vacuuming. It has an intelligence of where it has covered and what areas of my home need the most attention. It doesn’t slack, cut corners or decide it is too tired to get the job done. Simply put, it is more efficient than me. I also have way more visibility into what is going on with the vacuum, so in that sense it is much more efficient as well.
Now for a definition of Artificial intelligence (AI) – this one from wahtis.com gives the best understanding within the context of executive management:
Artificial Intelligence is the simulation of human intelligence processes by machines. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.
Because hardware, software and staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings, as well as access to Artificial Intelligence as a Service (AIaaS) platforms. AI as a Service allows individuals and companies to experiment with AI for various business purposes and sample multiple platforms before making a commitment. Popular AI cloud offerings include Amazon AI services, IBM Watson Assistant, Microsoft Cognitive Services and Google AI services.
Now let’s look at examples of how AI is being applied. Let’s start with Human Resources (HR) and workforce management. It is interesting it is referred to as Human, wonder if that will evolve as new AI functionality is brought to the table.
AI tools much like databases are only as smart and good as the data that is input into them. When it comes to HR practices the potential for bias is inherent, thus the Human part. You have to remember that people determine what data points should be used in the training of an AI program or process, and people hold biases some even unconscious.