Business impact is defined by increasing revenue and/or decreasing cost. So how can you achieve business impact with machine learning? There are three possibilities.
- Reduce Costs. Machine learning allows you to automate the mundane tasks that drain your time, budget and attention, allowing you to do what you do best. Any activity that requires less than one second of thinking is ripe for being replaced by machine learning. As CEO, I personally led a team that automated more than 21 percent of these roles over two years and the organization has now pushed that number to more than 30 percent in three years. My head of operations did a great job taking all the transaction processing jobs that followed a desktop procedure and replacing them with technology.
- Gain Efficiency. About 80 percent of the activities that we ask people to do are beneath their skill level. On average only 20 percent make use of their skills. A healthcare example is adjudicating trials. Every participant in a trial needs to be reviewed by a doctor. Let’s say 80 percent of the participants in the trial usually respond as predicted, the doctor still needs to look through all the data and sign off on it. The 20 percent that do not react as planned will require the doctor’s focus and consideration to uncover the cause of the anomaly. By applying machine learning to this space, the technology can quickly identify just the fraction of participants who did not exhibit the predicted response and fast track them for the doctors. These efficiency gains can help a pharmaceutical company save time and money (reduce cost) and bring a drug to market faster (increase revenue).
- Achieve Breakthroughs. Machine learning unlocks hidden insights and can find “signals” in the data that humans can’t find. Today’s enormous processing power allows machine learning technology to study millions and millions of records almost instantly. Examples of these breakthroughs range from being able to identify guns and knives in luggage, to predicting prostate cancer from a liquid biopsy. Both of these are real examples that can scale to produce major business impact by transforming the thinking of these companies.
But what about the jobs?
There is an unavoidable correlation between business impact and job impact, but the hysteria about artificial intelligence decimating the workforce is exaggerated. When Infinia ML CEO Robbie Allen (then CEO of Automated Insights) helped The Associated Press automatically generate written stories about quarterly corporate earnings, you might have thought the newsroom employees, fearing for their jobs, would revolt.
Instead, The Associated Press reported that “the reaction has been positive from staff, largely because automation has freed up valuable reporting time and reduced the amount of data-processing type work they had been doing.” A New York Magazine headline read that “Robots Are Invading the News Business, and It’s Great for Journalists.” The Verge reported that “computers are not taking journalists’ jobs” and were “freeing up writers to think more critically about the bigger picture.”
LinkedIn Co-Founder Reid Hoffman and co-author Chris Yeh share secrets for scaling your business at the CEO2CEO Summit.
Dec. 6, 2018 | New York City | Details and Registration today!
Jobs that humans should not perform
That said, the potential for change among workers whose jobs consist of repetitive mindless tasks does exist. Simply put, there are some jobs that humans should not have to do, but until now there has been no other way to get the work done.
Take the example of image analysis for security screening. With sufficient focus people can do this well, but due to the long hours and numerous distractions, performance can degrade. Often it is the very jobs that are critical to important missions like security and health that are most repetitive and vulnerable to human error. By contrast, machine learning doesn’t get distracted and does the same high-quality job at all times. This improved security is in turn a better situation for all people who depend on it.
Does this mean that machine learning will take away great jobs and displace much of the workforce? Absolutely not. Machine learning, artificial intelligence, and other technologies will make people more efficient by empowering computers to do the things that are more routine and more error-prone due to distraction, repetition and boredom. True, there are jobs that will eventually be replaced by machine learning, but these are such undesirable jobs that they typically average a turnover rate of around 30-35 percent.
As CEO you will likely hear arguments from HR about how they must radically change the way they train people to prepare them for the evolution to the machine learning-powered business world. Like training people in all jobs for the next career step, one needs to look at an employee’s current role and determine the natural evolution of their skill set. Ideally, if you have a division of transaction processors, you retrain them for more stimulating jobs that require they analyze what the transactions mean based on their experience processing hundreds of transactions per day. These people know the transactions better than anyone else in the company, they just need to be trained and given the “go ahead” to use their brain to think vs. do.
Having lived through this, I know this can be done without firing people, but I also know that the majority of the people in these roles are not going to go back to school to pursue PhDs and complete the requisite classwork and years of lab experience to become ML scientists. In fact, some won’t even be easily transitioned into more sophisticated versions of their former jobs. In those instances, there is a huge opportunity and demand for people to clean up the data! After all, ML solutions are only as good as the data that powers them.
When I worked at a Fortune 500 company, I was able to redirect 21,000 roles to analyze transactions, do data clean-up (admittedly this is grunt work, but transaction processing is also grinding work), or to deal with the new client work that was coming in the door. The reality is that it can be done, but it’s hard. Many companies will be tempted to take the easy way out and let people go if they don’t plan ahead and don’t fully understand the opportunity.
Machine learning can have a positive impact on your top line, your bottom line, and if you do it right, your people. Remember that although part of the cost reduction piece is reducing full time employees, it is also about making better use of your people. To do this, my advice is to start planning for the inevitable change in roles and define how skill requirements will shift. Commit to spending the same amount of time figuring out what you will do with your people as you do on all other components of your machine learning strategy.
Don’t ever forget that your people are the business. If you do this right, you will make business impact (increase revenues and decrease costs) by transitioning your people from uninspiring, high-turnover jobs to more impactful data-centered careers, which will be core to your future business success. As you have heard me say before, if you lose the people, you will lose the business. So plan now around these machine learning concepts as it will make you, your shareholders, and your people much happier.