Are you an AI evangelist or fatalist? Today AI is often profiled as either the end of the world as we know it, with robots taking all of our jobs, or the answer to all our problems, where AI is the ultimate solution to save the planet. It’s ironic that such a complex technology often provokes such a simple, binary response. And that’s a problem, because as anyone who actually works with AI will tell you, the truth is less dramatic, at least in the short term, but far more relevant here and now. So in which enterprise areas are we seeing AI first, and which use cases will further advance its development and use? These are applications that stand to gain traction rapidly:
1) AI augmentation and decision automation
One reason why some people believe that AI will deprive people of their jobs is that they confuse AI with automation. Research firm and consultancy Gartner commented in a news release late last year, “2020 will be a pivotal year in AI-related employment dynamics, as artificial intelligence (AI) will become a positive job motivator." Also, according to Gartner. AI will create 2.3 million jobs in 2020, while eliminating 1.8 million. Says Gartner analyst Svetlana Sicular, “’Unfortunately, most calamitous warnings of job losses confuse AI with automation — that overshadows the greatest AI benefit — AI augmentation — a combination of human and artificial intelligence, where both complement each other."
One example for how this can be used is within decision optimization. In an expanding global market, industries wrestle with increasing complexity. Globalization, innovation, and competition are all powerful forces, with businesses frequently tasked with producing or delivering more from fewer resources, using leaner, faster operations. One consequence of globalization is that demand for product and services may shift quickly in markets across the world. Imagine a manufacturing company selling products in 50 markets. A sudden increase in raw material prices in one region or new trade tariffs will make it important to be able to adjust demand and possibly pricing on short notice. Here, AI can help companies create an overview of a large number of factors simultaneously to produce a plan for how to adjust demand planning and pricing. Historical data can be used to learn to make or propose decisions to make them both quicker and more intelligent. With very large sets of data from multiple markets it may be hard to pinpoint what is actually important. AI can help companies detect anomalies and patterns as well as raise alerts when data points go outside certain intervals. This way, some decisions can be automated by the AI. Based on past actions and specified priorities, your AI-enhanced business software could, for instance, present a daily top-5 list of decisions to make.
Using AI for anomaly detection, the human would focus on making decisions on how to manage anomalies, which may require more human qualities, like creativity or empathy when judging human reactions and consequences. Finding this balance to optimize how humans and AI can work together will be crucial to succeed with an AI strategy in the long term.
2) AI-enhanced predictive maintenance and service
High-profile AI stories such as those about driverless trucks always grab headlines. In reality, for most companies, AI's impact is more likely to be seen first in how the truck is maintained and serviced—which algorithms will use what sensor data to predict the truck’s specific needs in context, ahead of time, whatever the climate, whether the truck is on the open road or in the service bay. In fact, AI will play a major role in maintenance in many, many industries. McKinsey found that for manufacturing operations, predictive maintenance enhanced by AI allows for better prediction and avoidance of machine failure by combining data from advanced internet of things (IoT) sensors and maintenance logs as well as external sources. Asset productivity increases of up to 20% are possible, and overall maintenance costs may be reduced by up to 10%.
Asset-intensive industries, such as manufacturing and energy, are ideal applications for AI given that, these days, most of the key pieces of equipment are fitted with loads of sensors that generate mountains of IoT data that can form the foundation for building machine learning algorithms. Based on the data, AI can turn maintenance from preventive to truly predictive.
Manufacturing lines or energy plants connected to software with built-in AI capabilities may not only use IoT data to detect, for example, where temperature levels are too high using sensors. Using machine-learning algorithms, the system can learn from experiences and connect this data to production scenarios. For example: The temperature level on the production line is too high, which has in the past created a need for maintenance. This experience can now be used to automatically create a work order in the enterprise software and dispatch service staff to fix the problem, without any manual work. In adding AI capabilities to this, by creating an AI-powered route scheduling solution (decision optimization), the software could even learn how to optimize the workforce schedule to service equipment at geographically spread-out locations at top efficiency. This is just one example of how IoT, automation and AI can work together to optimize predictive maintenance and service.
3) AI-supported system interaction
The area in which AI perhaps is most advanced already is within interaction with people or systems. AI-powered voice assistants represent a major opportunity for many organizations, both internally and externally. The key is to use it for the uncomplicated queries or transactions that occur in great volumes. These tasks can be uncomplicated in nature but still require you to log in to an application and perform a short series of actions every time you do it, which in the long run takes a significant amount of time.
In a company-internal setting, AI chatbots have a great potential to make this process more effective. One example could be when employees are to call in sick, ask for leave or simply want to find and access certain items within their enterprise software. Making it possible to access this information, and take action on it, using your voice, or by chat, enables significant time- and cost savings. The added AI capability can in time refine the process, so the path to executing the task will be even smoother and quicker.
Externally, taking calls at a service help desk is a natural application for AI chatbots, as the calls often pertain to simple questions such as a hours of operation or when an engineer is due to arrive. The AI-powered approach is going to become increasingly important not just in terms of the quality of service you can deliver, but in the context of growing skills shortages of service providers.
As many contact centers are now developing omnichannel solutions to include voice, e-mail, social media and chat as contact options, the AI capability could help to identify your preferred contact option and guide you quicker through the process in the future.
Bas de Vos is director of IFS's creative think tank, IFS Labs.