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Trend Watch: AI Helps Businesses Make Decisions at the Edge

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.

The Human-Machine Interface

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.

AI and location intelligence for smarter business decisions

Systems for supporting decisions at the edge do not replace human interaction—they enhance them. They use a combination of machine learning and GIS tools for location intelligence to help employees make quick decisions that increase customer approval and corporate sales.

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. He advised, “Explore intelligent apps as a way of augmenting human activity and not simply as a way of replacing people.”

Digitizing and Sharing Best Practices

An immense amount of data factors into every decision at the edge, and increasingly that data derives from the Internet of Things (IoT). For a sales situation, this includes customer history; buying trends for the time of year; the demographics of the area; and details about extra options that have appeal in that location, whether it’s a particular region, state, city, or neighborhood. For an equipment service decision, factors include history of repairs, details on the useful life of a part, equipment sensor readings, effects of local weather, current stock, the customer’s expected rate of use, and the cost tradeoffs between replacing several related parts over some months or replacing a full assembly all at once. That trove of data is increasing rapidly as IoT-connected sensors proliferate across every sector of the business world.

No manager, of course, can compute that data and analysis in her or his head, but the computerized guide has perfect memory and is not affected by mood changes, stress, or other circumstances that can interfere with good customer relationships. The electronic assistant can draw on the complete record of the company’s business interactions and can be continually updated.

Some business leaders may not realize how close they already are to developing a digitized guidance system for their employees, or how simple the first steps can be (see sidebar). Most businesses keep large electronic databases of sales and service transactions. But if a database just sits there as an archive, then its rich veins of information cannot help employees make decisions.

Artificial intelligence can sift through those files, organizing them according to time of day; locale of the customer or service site; season or weather at the location; and preferences of the client based on ZIP code, business sector, calendar, or some personal background.

AI helps redefine the business edge

Key to 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.

To find and digitize best practices, the company—with the help of specialists in data analysis, business strategies, and programming—can interview top sales managers and service providers to gain an understanding of the important details of the decisions and approaches that lead to great results. That, in turn, can reveal hidden patterns related to geography, season, and history with similar customers in similar places. No single person could see those patterns across the company, but with artificial intelligence, that corporate wisdom can be shared with employees at the edge.

To illustrate the power of the approach, let’s examine two scenarios where AI and location analysis deliver business benefits—one for sales, another for service.

Sales Decisions on the Edge

Imagine that a customer has just bought a new product such as a high-end TV; a sales rep, with a tablet computer in hand, is presented with several scenarios: selling the customer related products (say, home theater speakers), an extended warranty, or additional services—perhaps the installation of a home theater system that includes the speakers.

The intelligent system knows to present the sales rep with a small range of incentives that have led to sales in past transactions: HDMI cables at no cost, an extra year of warranty coverage, same-day installation, or other niceties.

With the help of a location analytics tool known as a geographic information system (GIS), the slate of options shown to a sales rep in the Los Angeles area will differ from the one shown to a Philadelphia sales rep, because customers gravitate to different products/services and respond to different sales tactics in those locations. With hyperlocal intelligence, employees at two Philadelphia stores a few miles apart will be guided toward different options. In both cases, instead of the middle manager trying to remember the best slate of offerings and incentives for every circumstance, there’s a data-driven, repeatable process of delivering the best outcome for the customer and the business.

The intelligent system provides a set of best choices, not a final judgment. It democratizes the company’s data to permit decisions at the edge. Such a system also allows the person who is dealing directly with the customer to consider the nuances and emotional atmosphere of the interaction when planning a response. In the process, it saves time and money by minimizing the number of people needed in the decision chain.

Though eminently helpful, this computer-assisted guidance is not robotic. AI-based systems analyze the situation and suggest several avenues, but ultimately, the employee decides which option to pick or action to take.

Location analytics helps redefine the business edge

What we mean when we say “decision-making at the edge” is that machines are used to speed up the old-fashioned way of going up the ladder to a decision-maker, then coming back down to the customer to communicate the decision.

Service Decisions at the Edge

In the service realm, we can envision a technician called to repair an HVAC unit at a manufacturing plant in, for instance, Dallas.

Before the technician leaves the service depot to answer the call, the system runs analytics on the unit in question, searching the knowledge base for recent repair jobs and possible causes of malfunction. To improve the technician’s decisions at the edge, artificial intelligence built into the system takes into account the machinery’s location, analyzing a hyperlocal weather report from the period since the unit’s last servicing to factor in the effects of rain, heat, humidity, hail, and more. Of course, a technician heading out to a service call in Minneapolis may receive different guidance based on that city’s climate or local style of doing business.

Once the AI system sorts through that cache of big data, it presents possible causes to the technician, who can ensure that the truck is stocked with appropriate replacement parts. On site, the technician quickly resolves the issue in the most efficient manner. Benefits include improved time to resolution, less input from supervisors, greater productivity of technicians, higher customer satisfaction, and a competitive advantage over service companies using non-digital approaches. Additionally, the development of this virtuous cycle creates an ongoing dynamic that leads to better sales and more efficient service.

Billions Going to AI  

The strategy of empowering workers to make decisions at the edge isn’t limited to the private sector. Cities, public utilities, and charitable organizations also can gain from the efficiency that results when workers have the power to make quick decisions and can relay a digitized guide that offers a menu of the best options available to deal with an established client, a resident in need of city services, or a potential donor to a charitable cause.

Artificial intelligence helps redefine the business edge

While futurists debate what the workplace will look like and whether AI could someday replace all customer interactions, today’s business realities already include this powerful approach that mixes machine learning and human insight to increase efficiency and profits.

In the business world, the impact is not necessarily felt every day by the CEO or senior executives. The daily impact is experienced by the people at the edge who interact with customers or other businesses and need to make decisions in a face-to-face manner. In simple terms, employees gain the benefits of having a digitized mentor at their fingertips while middle and upper management are freed to spend more time developing new products, services, and markets.

The big-data analysis and machine intelligence needed to empower such decisions have existed for some time and already are being employed in various business sectors. Their potential as a time-saver and business generator is real and could be transformative. Indeed, a recent article in the Harvard Business Review points out that corporations invested $5 billion in artificial intelligence or machine learning in 2016—an amount that is expected to increase to $100 billion over the next eight years.

Machine Intelligence: A Supplement, Not a Replacement

Any mention of machine intelligence often prompts warnings from futurists and trend watchers about people losing jobs to machines.

Two recent reports by Forrester address this issue. In one, the analyst firm concludes that automation will create a net loss of nearly 10 million U.S. jobs in the next 10 years. The other Forrester report notes that artificial intelligence, automation, or machine learning will open up new jobs for data scientists, automation specialists, and content curators and continue to transform the workplace. But the report concludes that automation will cause a net loss of 7 percent of US jobs by 2025.

And yet, full automation is not likely at the edge—at least not anytime soon. Decision-making at the edge means that machine intelligence and GIS tools for location intelligence combine to speed up or replace the old-fashioned way of going up the ladder to a decision-maker, then coming back down, where the edge worker communicates the decision to the customer.

Despite reasonable worries about automation, customer relations remains a nuanced art. The goal is to give employees a rich base of understanding to guide their actions. That, in turn, can make the empowered employees more valuable and less replaceable, as they learn to work more quickly and with less supervision.

Benefits to Middle and Upper Management

Why is this crucial to middle managers and C-suite executives, who typically are closer to the center of an organization than to its edge?

One of the benefits of sharing data and intelligence for decisions at the edge is that it frees up middle and senior managers to develop business opportunities, explore partnerships, find new efficiencies, build employee skills, and invest in community betterment projects.

Executives with strong problem-solving skills can spend time helping data analysts uncover the factors that make for successful customer interactions or service decisions. Employees benefit when those insights are digitized and accessible during customer interactions. If executives offer machine intelligence and analysis in the right way to employees at the edge, they can improve employee productivity and customer satisfaction. By its very nature, that process helps create a culture of continual improvement.

Big data analytics, location intelligence, and AI can create large strategic advantages, but the combination is a simple and practical concept at its core.

The growing use of machine intelligence simply enhances a company’s ability to digitize best practices, relate them to nearly any location or context, and then transmit that knowledge to employees at the edge. Businesses that ignore this new approach may be in danger of losing their competitive edge.

 

Dive deeper into digital transformation and decisions at the edge in this podcast interview with Jay Theodore.

(Photo by Joshua K. Jackson, Unsplash)

About

As Esri’s chief technology officer, Jay Theodore guides the long-term vision for the ArcGIS platform. Jay is passionate about harnessing innovative ideas to increase the value companies gain from location intelligence, geoscience, computer science, and technology. He takes great pride in working with outstanding software developers, architects, and product engineers. Jay earned a master’s degree in computer science from Florida Institute of Technology, where his research focused on finite element analysis and modelling (FEA/FEM), computer graphics, and composite structure design for Space Station Freedom. He also holds a bachelor’s degree in computer engineering.

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