The algorithms are coming for your jobs. We hear that refrain over and over, but does anyone really buy into that narrative? When people are completely replaced by machines, something is lost in almost every case. Be it personal service, creativity, leadership, or advanced decision making, people have valuable contributions to make.
The near-term future of machine learning systems will be driven by enabling, not replacing people. However, is not enough to simply state, “Enable your employees with AI.” There needs to be some substance behind that; an answer to the question, “How exactly do we achieve that goal?”
The futurists are well meaning but predicting the implications of machine learning in the workplace requires a deep understanding of neural networks; specifically, their limitations. It also requires an understanding of how people interact with ML based systems, an emerging area of research in the AI field. Given those two areas of focus, I have written up a few tips for building systems that make people more efficient and effective at their current jobs.
Done right, employees will embrace practical, highly functional AI. Done wrong, AI does not live up to its promise because is not used. It is the software equivalent of a paper weight and a complete waste of money. The fastest way to get employees on AI’s side is…
Automate Their Most Hated Work
I ask groups a simple question, “What task do you regularly do that you never want to do again?” The responses I get will haunt your work-related nightmares. Mind numbing tasks like looking through a 300-page bid for errors. Trying to guess the right keywords to search for then reading through several hundred resumes to get a handful of qualified candidates. Spending months searching the internet, conferences, industry groups, and calling around to find new suppliers.
Replace the manual error search with an anomaly detection algorithm. It reads through the bid and presents the user with the highest anomaly scored items for review.
Replace the manual resume search with a sematic search algorithm. It reads through a job description and finds the resumes that match the requirement. Those get presented to the user for review.
Rather than removing a person from the process altogether, take away the drudgery. This frees the employee to exercise their judgement which is where their real value comes into the task. It also allows for continuous training for the machine learning algorithm. ML improves with consistent user feedback so interfaces like these turn a good algorithm into an amazing algorithm.
Find Hidden Time Sinks
This is another employee satisfaction issue that machine learning can resolve which also returns a lot of productivity to the business. According to IBM, employees spend 2 days a week on knowledge and people search activities. These are the perfect chores for machine learning to automate. I listen for people to say things like, “I look on the company intranet for…” or “I’ve got a few really good resources to find…” or “I search online…”. These are my verbal cues for a knowledge or people search task.
Mass, generic communications and outreach are also high effort but low skill. Let machine learning handle that too. People are needed to build the outline and proof the final draft. ML can handle everything else from transforming the outline into content to sending it out and managing responses. Here again, we keep people involved in the process at critical points and allow them to provide the algorithms with feedback to learn from.
Any sort of bulk screening (like screening out inappropriate posts) or error checking (like code reviews) tasks are huge time sinks that ML can automate. There are many more. The key factors to look for are low skill, time consuming, repetitive tasks. The structure of the data does not really matter anymore. Machine learning algorithms can handle text, video, pictures, and structured data points with high degrees of accuracy.
Automate Process, Prioritization, & Administrative Tasks
Processes and administrative tasks are perfect for automation. Anything that has a well-defined, stable, rule-based workflow can be automated. There is already a ton of software out there to do this. However, on top of every process and/or workflow is a layer of administration. Someone is forced to spend their time keeping the process going. Machine learning can handle the basic decision making around administering a process. Using the logs from the administration tasks, an algorithm can learn to classify what action to take based on the state of each item in the workflow.
Prioritization is another area machine learning can automate to make employees’ lives more productive. Communications are a great example. Machine learning can prioritize emails based on content or sender putting the highest priority items at the top of the employee’s attention. The same goes for phone messages or competing action items. Let machine learning classify importance so people do not have to scan through 999 unread, high priority emails to find the 3 that are actually relevant.
A great example of this concept can be applied to Agile. Everything from estimation to prioritization can be automated using machine learning. These are simple classification tasks with infrequent review needed to continuously train the algorithm. That allows less time to be spent managing agile and more time to be spent building the product. It also allows machine learning to optimize the development process. With limited resources, how does the business build the highest earning products possible? When I hear fastest, shortest, highest, quickest, etc. I am probably hearing an optimization problem. Machine learning is exceptional at solving those making it a good tool for processes and continuous improvement.
Free Employees for Higher ROI Tasks
I own a car that essentially drives itself. I live in both San Francisco and Reno, so I make the drive between the two cities often, putting the car into self-driving mode for most of the trip. I noticed something interesting happened when I do that. You would expect my mind to wander or for me to have very little to do. I have begun to work on other driving tasks that I do not typically have the free focus to do. I do not have to worry about staying in the lane or keeping my distance safe. Instead I am looking for a driver who is about to meander into my lane or any number of other one-off scenarios. The car is handling the simple, repetitive tasks while I am looking ahead for more complex scenarios that I have been freed up to manage.
That is how AI enables employees rather than replacing them. What people realize when they are not doing all the monotonous tasks is how much of the big picture they have been missing. Monotonous tasks put blinders on employees. AI allows employees time to look around and look ahead. People with that latitude become more strategic, forward thinking employees. Both return higher value to the business. AI’s impact on the enterprise is amplified by this effect. Not only does automation increase productivity, it increases the value of productivity. Employees spend their work hours doing higher value tasks; actually, maximizing their potential rather than being limited by the nature of the tasks that must get done.