Business processes influence many different aspects of our daily lives as consumers and workers, whether we realize it or not.
Whatever our requests or tasks—ordering your morning latte, applying for a job, finding the right song on a streaming music app, picking out a new car with just the right shade of leather seats—there’s a process involved.
#AI will transform #BusinessProcesses in 5 ways: 1. Flexibility, 2. Speed, 3. Scale, 4. Decision making, 5. Personalization. @hjameswilson & @pauldaugh discuss their book: #HplusM
But many processes are slow, inefficient or otherwise ill-suited to serve the fast-moving demands of today’s customers. As we detail in our recently published book, Human + Machine—Reimagining Work in the Age of AI, many companies are already using artificial intelligence to automate processes to some extent. But if AI is deployed primarily to displace human workers, short-term productivity gains are about as good as you’re going to do.
Our research of over 1,500 companies makes one thing clear: the most lasting, impactful performance boosts happen when people and AI-enabled smart machines work together. Taken steps further, we see AI propelling a reimagination of business processes, driving improvements in five key areas: flexibility, speed, scale, decision making, and personalization.
In a recent Harvard Business Review article, we discussed the opportunities in human-AI “collaborative intelligence” and the five specific areas of business processes poised for transformation. The following briefly summarizes each of those five areas:
Traditionally, car manufacturing was a rigid process with automated steps executed by “dumb” robots. For Mercedes-Benz, this inflexibility presented growing challenges. Increasingly, its most profitable customers were demanding individualized S-class sedans, but the automaker’s assembly systems couldn’t deliver the customization people wanted.
Mercedes replaced some of those robots with AI-enabled “co-bots” and redesigned its processes around human-machine collaboration.
Mercedes can now individualize vehicle production according to the real-time choices consumers make at dealerships, changing everything from dashboard components to the seat leather to tire valve caps. As a result, no two cars rolling off their Stuttgart assembly line are the same.
Some processes must be executed on the spot—detection of credit card fraud, for example. A bank has just a few seconds to determine whether a given transaction should be approved.
HSBC Holdings developed an AI-based solution that improves the speed and accuracy of fraud detection. By monitoring millions of transactions daily, AI seeks subtle patterns that signal possible fraud.
Again, humans have a critical role to play. Algorithms and scoring models for combating fraud have short shelf lives and require continual updating. That requires data analysts and financial fraud experts at the interface between humans and machines to keep the software a step ahead of the criminals.
Many business processes are hamstrung by poor scalability, particularly if the process requires intensive human labor with minimal machine assistance. This includes recruitment and other HR functions.
Unilever, seeking to accelerate recruit evaluation as part of efforts to improve diversity, adopted an AI-based hiring system that assesses candidates’ body language and personality traits in part by how they answer job-specific questions over video. Within a year, the new system helped Unilever broaden its recruiting scale, as job applicants doubled to 30,000, and the average time from application to hiring decision shrank from four months to four weeks.
The more tailored the information people receive, the better the decisions they can make. For workers on the factory floor or service technicians out in the field, making the right call can have a huge impact on the bottom line.
Consider “digital twins,” virtual models of physical equipment that companies such as General Electric use to monitor turbines and other industrial equipment. By collecting data from many machines, GE amassed a wealth of information on normal versus aberrant performance. Its Predix application uses machine-learning algorithms to predict when a specific part in a specific machine might fail.
This technology fundamentally changed a decision-intensive process. With Predix, workers are alerted to potential problems before they become serious and they have information at their fingertips to make decisions that, in some cases, could save GE millions of dollars.
Through AI, what some consider the “holy grail” of marketing is attainable: providing customers with individually tailored, on-demand brand experiences at vast scale.
Pandora, the music streaming service, applies AI algorithms to generate personalized playlists for each of its millions of users based on preferences in songs, artists and genres.
This is just one example of AI technology doing what it does best—sifting through piles of data to recommend certain offerings or actions—to help humans do what they do best—exercise intuition and judgment to make a recommendation or select the best fit from a set of choices.
The need for new roles and new talent
To be sure, reimagining business processes involves more than implementing AI technology; it also requires a commitment to helping employees develop “fusion skills” enabling them to work effectively at the human-machine interface.
Most activities at the human-machine interface require people to do new and different things (such as train a chatbot) and to do things differently (use that chatbot to provide better customer service). So far, however, only a small number of the companies have begun reimagining their processes to optimize collaborative intelligence.
But the lesson is clear: Tomorrow’s business leaders embrace collaborative intelligence today to transform their operations, their markets, their industries and—no less important—their workforces.
Read more about Process Reimagined, our research detailing how people and smart machines, together, are reinventing how work is done.