Machine Learning and AI in the Workplace: The Future of Business Tools
Modern technology is leading us into a new industrial age, and big data and artificial intelligence (AI) stand to become the driving force behind innovation in the workplace. By automating previously manual processes, we have more capabilities to identify patterns in real-time and make predictions that can streamline how we run our businesses.
We are already seeing this at highly data-driven companies – the ones that promote a culture of using analytics for all business decisions. Eventually, it becomes so natural that it feels weird when decisions are made without the edge of analytics.
Machine learning technologies leverage enormous amounts of data to help make predictions that go way beyond the capabilities of manual processing. This results in a huge boost to workplace efficiency while decreasing the risk of human error. If it was possible to run a more error-proof business, wouldn’t you want technology that could help achieve it?
There are a few areas where machine learning can most improve workplace operations, and provide more efficiency, accuracy and productivity.
Customer Service and Retention
Unless you’ve been hiding under a rock, you’ve probably seen some press on the rise of bots, software programmed to seem human. Bots already help us in our personal lives – we ask them to add appointments to our phones or pull up the weather. Companies like Apple, Microsoft and Amazon are working on bots that can act as the proxy for things like ordering food, buying a ticket to a baseball game or scheduling an appointment.
By leveraging natural language processing, bots have the ability to completely revolutionize customer service and retention. Bots have been around for a while in customer service – when you call your bank they ask you for your account number and what you’re calling about before passing you onto a representative. But as they become more intelligent and advanced, bots are helping transform an industry typically known for time-consuming and headache-inducing interactions.
But it’s not just chatbots that are making an impact on customer service. Machine learning is also being implemented to better understand customer behavior and enable companies to provide better service.
When The North Face decided to emphasize better service for its customers, it implemented a new AI shopping tool on its website. With it, shoppers answer questions about what they need (for example, “I need a jacket for a skiing trip in February”) and the system figures out the best options for them. This not only helps the customer make a more informed decision, it saves them time and effort of going through numerous options that aren’t the best fit.
Operations is at the heart of any company. Whether it’s hiring people, creating company policies or negotiating contracts, operations teams will look to make their company run like a well-oiled machine. Many companies turn to business process management (BPM) to improve operations, corporate performance and efficiency – but also want the benefits of analytics and insights within those systems. Enter AI. With real-time analytics and continuous intelligence embedded within such tools, business processes can operate more intelligently and with more agility - having a significant impact on the bottom line.
Gartner gives as an example a food concession stand at an event. Using an intelligent BPM system, that vendor can look at characteristics such as oversupply of snacks and waiting times at different stands, and direct customers to where they will be served quicker. By incorporating AI, BPM isn’t limited to just traditional administrative workflow like expense report approval or new employee onboarding, but more operationally critical tasks – with insights now embedded within.
One major area in which machine learning can be helpful is cyber security. With the ability to track patterns and deviations from those patterns much quicker than the human eye, machine learning can help keep businesses’ and its customers’ data safer. Due to the vast amount of security data moving around every day, it’s next to impossible for people to manually track every single threat and potential risk each day.
Using machine learning, tracking and monitoring cyber risks can be automated and deployed in real-time. Whereas a security professional manually combing through vast amounts of data to identify risks or security breaches can be time-consuming, a machine learning model can continuously monitor threats and address them before attacks or breaches happen.
While machine learning and AI can radically improve the way we work, one thing is for sure: the human touch is still necessary. AI can make recommendations on how to improve operations and business processes, but it’s still up to people to implement them. Similarly, while a call center AI application can predict if a customer will be dissatisfied at the end of a call, it’s up to the company and the service representative to determine how to fix that and improve customer satisfaction.
We are merely beginning to tap into the power of AI in the workplace. New ways to leverage AI technologies in the workplace will continue to be developed and optimized through the coming years. As we create the right balance between humans and machines, we will find better ways to manage and grow our businesses.
(About the author: Mike Feldner is regional head at Mu Sigma)