How Machine Learning Automates Business Processes
From HR support to predictive analytics
The machines are here. They’re learning. And they’re coming for your business — with the power to build or destroy your ability to compete in the near future. As Margaret Laffan, Machine Learning Business Development Director for SAP says in Forbes,
“Those companies not considering investing and innovating will soon be outperformed by the new economy that runs on machine learning”
Machine learning is already changing the world. As a key subset of artificial intelligence (AI), it enables computers to act and learn on their own, without being specifically programmed, by utilizing data and experience rather than being explicitly programmed. Self-driving cars, Netflix recommendations, and virtual personal assistants like Siri and Alexa are some of AI’s best-known applications. Such features aren’t just for eCommerce and entertainment websites.
Automating Business Processes
One of the most immediate ways businesses use machine learning to improve their competitiveness is by automating back-office processes, the majority of which are high volume, rules-based functions that could seamlessly operate on a “lights out” basis, freeing up employees’ time for achieving more strategic company objectives.
Many companies are already delegating numerous mundane tasks to computers, from capturing data from optically-read or web-based forms to processing orders. Computer-to-computer interactions, such as IT, finance, and accounting, are the low-hanging fruit of AI.
Dan Wellers Global Lead, Digital Futures at SAP has said:
“By monitoring existing processes and learning to recognize different situations, AI significantly increases the number of invoices that can be matched automatically,”
“This lets organizations reduce the amount of work outsourced to service centers and frees up finance staff to focus on strategic tasks.”
According to a 2017 survey of 835 companies by Tata Consultancy Services, the majority of these back-office functions being automated through AI involve Information Technology. These AI-driven processes included:
- Detecting and deterring security intrusions.
- Resolving users’ technical problems.
- Reducing production management work by automation.
- Gauging compliance internally by using approved technology vendors.
HR Human resources (HR) and talent acquisition are other realms where AI can have a huge impact on reducing workload, preventing bias, and improving efficiency.
Chatbots and other applications of machine learning can handle a number of routine repetitive HR tasks, including:
- Screening and shortlisting job applicants from hundreds of résumés.
- Scheduling interviews, performance reviews, and other group meetings.
- Measuring and managing employee engagement Streamlining office workflows.
- Tracking and enhancing employee rewards and recognition programs.
- Identifying knowledge gaps or opportunities for employee development.
- Answering questions about company policies, benefits, office procedures, and even basic conflict resolution.
- Attracting and reaching out to top talent (through such sites as Glassdoor, LinkedIn or Indeed).
As noted by Sarah Williams of HPPY, an HR and employee engagement community,
“Properly applied machine learning technologies can save time through the use of predictive analytics to reduce time-wasting in recruiting and make the process more reliable and accurate.”
For example, she says international consumer goods giant Unilever directs candidates through three rounds of machine learning-based interviews and tests before meeting a human for the first time, saving the company’s recruiters 50,000 hours and shortening hiring time from the typical four months to just four weeks.
Machine learning can also be used to improve human productivity. For example, auto-generating weekly status reports and even monitoring hundreds of news items, social media mentions, and other sources for information about competitors and then issuing brief summaries to corporate stakeholders.
Such tools can be used not only for monitoring data but predicting outcomes, such as helping corporate teams prioritize product development and sales and marketing efforts. By 2022, one in five workers engaged in mostly nonroutine tasks will rely on AI to do a job.
Machine learning has the power to deliver value from brand new and enormous sources of data that were never before accessible through human means. For example, Shivon Zilis and James Cham suggest in the Harvard Business Review:
“Imagine if you could afford to have someone listen to every audio recording of your salespeople and predict their performance,”
“These data sources might already be owned by your company (e.g., transcripts of customer service conversations or sensor data predicting outages and required maintenance)”
Other advanced applications of machine learning can make companies more competitive by creating new products and improving the reliability of existing ones. Furniture manufacturer IKEA uses a machine-learning algorithm to analyze social media and search data to invent (and name) brand new products that solve problems commonly complained about online, for example, IKEA’s “Apple juice” furniture that wirelessly charges your smartphone or their “My Partner Snores Bed” mattress.
Machine learning is also excellent at examining vast amounts of historical sensor, logistics, and failure data from appliances, machinery, or vehicles. The models’ predictions could then recommend preventive maintenance, mitigate transportation or supply chain risks, or even detect anomalies in real-time that would indicate a failure is imminent. Eric Bussy, Worldwide Product Management at Esker has said:
“Many businesses have yet to understand the full potential of machine learning and cognitive computing for back-office processes,”
“The benefits range far beyond simply increased productivity and faster supply chains.”
Taking a “wait and see” approach with AI machine learning is no longer an option, especially for companies in competitive industries. As the 2017 closures of at least 8,053 stores (including major brand names like Payless, RadioShack, and Toys R Us) showed, retailers are being eaten by giants who are heavily investing in AI. 34 However, ignoring competitors who have robots isn’t just a problem for B2C. All companies face peril from lagging behind the technology curve.