2020 is the year that cars would fly, time-travel would be possible, and life could be extended indefinitely, at least according to science fiction. Fiction through the effort of researchers and the stampede of scientific development has a tendency lately to become real. Artificial Intelligence is not one of those things.
AI means a lot of different things to a lot of different people, so let's break it down. According to Merriam-Webster, it is:
- a branch of computer science dealing with the simulation of intelligent behavior in computers;
- the capability of a machine to imitate intelligent human behavior.
Put into practical application, AI is the ability for a machine to react to a situation it has never experienced or learned about
Think about dropping a Los Angeles alley cat into the Kruger National park in front of an elephant. How would the cat react? It has never seen an elephant in LA, nor been on one of the plains of Africa. Now compare this to dropping a human into a similar situation: the human could generalize that because they once saw an elephant in a zoo, the human knows that the elephant is not interested in eating them.
Generalizing is not yet possible for a computer. Computers ‘learn’ by ingesting massive amounts of data about a specific situation. Algorithms that allow computers to generalize across unrelated topics are just starting to emerge and are the first step in a long process that may eventually end in computers being self-aware. What has gained prominence in the workplace is machine learning (ML).
The Brookings Institute does an excellent job of delineating ML from AI: “The core insight of machine learning is that much of what we recognize as intelligence hinges on probability rather than reason or logic.” In application, ML is the use of statistical, actuarial, and other mathematical models to identify trends at scale in large datasets.
It is impossible to know what the workforce will look like if computers become rational-thinking beings, but for now, ML is not coming for your job:
- ML relies heavily on manual data gathering and preparation in order to ‘learn’;
- Computers require humans to build the models that power ML;
- ML allows computers to react to well-defined problem sets (such as immediately determining if a credit card transaction fraudulent) but requires humans for quality-control and to resolve the outcome of the inference.
Machine Learning in the workplace
Imagine a world where you have the ability to understand an events’ likely outcome. This ability is already being applied equally across the financial, retail, manufacturing, logistics, and agricultural sectors.
Imagine a world where you have the ability to understand an events’ likely outcome. This ability is already being applied equally across the financial, retail, manufacturing, logistics, and agricultural sectors. Let’s look at one of the practical and proven uses of ML, driving sales. Here is the situation: an insurance agent answers a client question. Seems simple right?
During the conversation, the agent casually offers them ten potential coverages which the client does not already have. The individual has no interest in purchasing nine of the offered policies. The client comes away from the encounter with a feeling of being sold to and not counseled. In most sold-to situations it is instinctual to just say “NO”. What a lost opportunity!
With machine learning, the conversation will go differently in the insurance industry. In the near future, insurance agents will have access to a web platform that uses ML, providing context into their clients’ needs. They now know that their client has a 92% chance of needing a commercial property policy. Does the agent still waste time offering the client the other nine policies if there is zero chance they’ll purchase? Of course not. Within this context, the ML informed the agent that their client would need additional coverage. The agent only presents additional lines that would benefit the client who can then easily review these coverages. Both parties win.
The statistical analysis at scale made possible by ML will enable companies to serve their clients and provide valuable insight during interactions with customers.
The statistical analysis at scale made possible by ML will enable companies to serve their clients and provide valuable insight during interactions with customers. So while an individual’s job may see some change as the result of ML insights, machine learning will not be replacing them.
Future of Work à la ML
Technology is leading to changes in how the workforce spends eight hours a day for their career spanning 30+ years. Naturally, this change has led to uncertainty about the role of technology, specifically machine learning (or AI), in replacing workers. Understanding the capabilities of ML is a starting point to understanding how this technology is enabling workers, not causing job loss. A couple ways ML is changing the way businesses operate:
- Increasing retention by targeting clients with customized loyalty initiatives.
- Personalizing customer service to maximize every client encounter.
- Identify fraud early in the underwriting process, reducing costly claims.
- Smoother supply chains for just in time logistics.
Far more likely to contribute to systemic job-loss is new consumer preferences, technological advancements (like using nuclear fusion for power generation rather than coal), and the practice of offshoring jobs.
I'm the CEO at ReFocus, and I've interviewed many seasoned professionals resistant to our SaaS service because they feel threatened; they think this advancement will lead to job losses. This could not be further from reality. Ultimately, the promise of machine learning is efficiency. The average workday will contain far less mundane tasks, and create more enriching work experiences.
Because no one enjoys crunching numbers in an Excel sheet - wouldn’t you rather engage in meaningful conversations with customers you have a greater than 51% chance of making the sale to?