Business Decisions at the Edge │ AI, Artificial Intelligence, Location Intelligence
Futurists may delight in predicting a time when business decisions will be made entirely by intelligent algorithms, but today’s innovative business executives are making nearer-term arrangements. They are finding ways to use big data analytics and artificial intelligence (AI) to help their employees address two persistent business challenges: providing faster service and making better sales decisions. In both cases, the new approach combines the best of machine learning with the best of human insight to produce better business outcomes.
The power of that approach will be felt across a wide swath of the economy, including manufacturers, retailers, banks, insurance companies, real estate firms, utilities, transportation, and the oil and gas industry. Indeed, nearly any business where customer interaction or equipment service is key to success can benefit from this fast-developing concept.
Supported by big data analysis, location insight, and artificial intelligence, employees are beginning to make on-the-spot choices regarding service and sales in a practice called decisions at the edge. That term—the edge—may be new to some business leaders. It is the place or places, often far from the central office, where a company’s products and services reach the consumer.
When companies create AI-based, location-aware tools that help employees respond more precisely to customers’ needs and relieve middle managers from repeated intervention in the sales or service process, those companies can boost revenue and reduce the cost of sales and service. Meanwhile, executives who take a more traditional approach to customer interaction will lose opportunities to boost revenue if they cannot equip their employees with AI-based decision support tailor-made for specific clients and locations.
To bring more efficiency and repeatability to decisions at the edge, companies are capturing and digitizing best practices in sales and service. They are then incorporating those best practices into user-friendly platforms that offer employees real-time options for dealing with customers, service decisions, and potential sales. And they’re adding location intelligence to deepen the insights into customer behavior and service decisions and to further customize the suggested best options.
It’s a powerful tool, but it’s not full automation. The algorithms developed through machine learning cannot discern all the subtle shades and tones of a particular situation, so the employee (often in a face-to-face conversation or over the phone) remains in the best position to choose from among options suggested by the system. This approach keeps business interactions at the edge moving swiftly and minimizes the need for constant consultation with middle management, which can frustrate customers.
Key to this improved interaction with customers are big data analytics that examines past customer encounters; location analytics that factors in where and when the transaction takes place and how that affects outcomes; and AI that sorts through that complex data to suggest a slate of simple, localized options to guide frontline employees.
Though new, this approach is quickly gaining prominence among industry thought leaders. Indeed, Gartner recently framed the issue as a trend that industries will ignore at their peril. In a report on the top strategic technology trends of 2018, the analyst firm notes that “over the next few years every app, application and service will incorporate AI at some level.” It will become so widespread, the report says, that customers and clients should challenge their service providers to demonstrate how they plan to use AI functions to keep pace.
At a recent symposium on business trends, David Cearley, Gartner vice president and fellow, said that AI holds the potential to transform the workplace through machine learning and the sharing of those insights in ways that help employees and improve results.
