George Santayana, in Soliloquies in England, wrote,
“Those who cannot remember the past are condemned to repeat it.”
That’s certainly true of the workplace. How many times have businesses championed a new initiative only to have it fail because the implementation was the same as the failed initiatives? Instead of repeating the past, today’s organizations can use it to improve the future of work.
Machine learning (ML) uses algorithms and statistical models to analyze and draw inferences from data patterns, so solutions can learn and adapt to situations without explicit instructions. In other words, machine learning takes data from the past and uses it to perform work in the future. It can combine with robotic process automation (RPA) to execute repetitive tasks. Artificial intelligence (AI) can also improve productivity and increase innovation.
Everyone has those tasks that they don’t look forward to doing. Maybe it’s preparing monthly reports or sales forecasts. Sometimes, it may be entering data from one application into another. Whatever the specifics, those tasks are often tedious and time-consuming. Depending on the nature of the task, RPA or RPA combined with ML can remove the routine tasks from an employee’s queue.
Take, for example, those monthly sales reports. Every month the sales manager prepares a forecast for upper management. Each salesperson provides a forecast to the manager using a standardized form. Of course, the manager has to hound at least one salesperson for the information, but eventually, all the forms are emailed.
With all the data collected, the manager has to import (or re-enter) the data into a spreadsheet that calculates the dollar value of sales based on where the client is in the sales cycle. Then adjustments are made based on “gut feel” before the forecast is sent to upper management. Wasn’t it easier to just pick a number?
Given that the process has been repeated every month for years, it’s well-defined and perfect for a rule-based RPA conversion. If each salesperson saves a completed form to share, RPA can download the files, import the data, and perform the calculations. Add a little machine learning into the process, and the sales forecast could be refined to take into account patterns across years, improving the accuracy of the forecasts.
Decades ago, Peter Drucker predicted that the vast majority of the workforce would be knowledge-based workers by 2020. Not only would they produce knowledge, but they would also need to access it. Without access to corporate information, employees would be unable to do their jobs efficiently.
How much productivity is lost when employees have to search for information? It’s estimated that employees waste between two and three hours per day looking for information to do their job. But those hours do not happen all at once; instead, they are sprinkled throughout the day, creating disruptions in an employee’s workday. Each interruption takes more time away from productive work as the employee needs time to refocus.
Suppose a salesperson starts the day with an urgent email from a client who is questioning his order. The salesperson has to locate the sales order and trace it through to shipping to ensure that the correct quantity was shipped. In between working on the client’s request, she needs to prepare for an upcoming meeting, but she’s having difficulty finding the attachment she needs to read. It’s getting close to noon, and she’s still looking for information.
What if the company had an AI-powered knowledge management system (KMS) that returned all the information associated with the client and sales order at one time? The salesperson could quickly see the quantity that was ordered and shipped. She’d have an answer for the client in seconds. What about the attachment? Suppose the salesperson was able to ask the system for the attachment and have it returned in seconds. Within the first hour at work, she answered a client’s email and is ready to read the attachment for the upcoming meeting.
Multiply that one experience across hundreds of employees and imagine the gains in productivity. Taking the time to let AI learn from the past can result in increased productivity that can transform the future of work.
Organizations must innovate if they want to survive; however, innovation requires creativity, and creativity takes free time. With AI-powered tools, employees can spend less time on repetitive tasks or looking for information. Instead, they can collaborate with other team members to identify new ways to meet customer expectations or to deliver a better customer experience.
ML-based technologies could even participate in the creative process. Imagine a team meeting with an AI solution. Employees are discussing ways to improve client claim processing but need to talk to someone who understands the process. Instead of contacting the department head, the team asks the AI solution to find the best person to talk to. Based on its evaluation of past information, the solution identifies the person with the most experience in claims processing—and it’s not the department head.
The team can quickly contact the individual for input to gain valuable insight into how the process is working and where it can be improved. They can accomplish that without interrupting the flow of the meeting, creating a better synergy for ideas. In the future, ML will work alongside employees to innovate across the enterprise.
Future of work.
Imagine a work environment where repetitive tasks are limited, and access to information was as easy as asking a chatbot. How would employees spend their time? They would have the flexibility to explore new approaches to old problems or design a new product offering. Employees could collaborate on initiatives that spawn innovation and interact with customers for better first-hand experiences.
People would achieve a better work-life balance while contributing to high-value tasks for a better return on investment. By using data from the past, organizations can imagine a workplace where employees have fun while at work. Not only can companies learn from the past, but they can also change the future of work.