Are you an AI optimist or pessimist? BAS DE VOS argues for a more pragmatic view of artificial intelligence.
Artificial intelligence (AI) can make a real difference today, particularly within the areas of human augmentation and decision automation, AI-enhanced predictive maintenance and service, and AI-supported system interaction. The key to success lies in using the strengths of the AI and the human brain together where they support each other.
Are you an AI evangelist or fatalist? Polarised perceptions stop us from seeing how much is already possible. Today AI is often profiled as either the end of the world as we know it, where robots will take 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 it’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 areas will we see AI first and which will be pioneering the development? Here are the ones I predict will gain traction rapidly.
AI augmentation and decision automation
One reason some people believe that AI will deprive people of their jobs is that they confuse AI with automation. According to research and advisory company Gartner, “2020 will be a pivotal year in AI-related employment dynamics, as artificial intelligence will become a positive job motivator.
“AI will create 2.3 million jobs in 2020, while eliminating 1.8 million.”
Gartner analyst Svetlana Sicular says, “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 optimisation. In an expanding global market, industries constantly wrestle with increasing complexity. Globalisation, innovation and competition are all growing fast, with businesses constantly tasked with delivering more, from fewer resources, using leaner, faster operations. One consequence of this is that demand for products and services may shift instantaneously. Imagine a company that operates in 50 markets. A sudden increase in prices in one region, or new regulations, will make it important to be able to adjust demand and possibly pricing on short notice. Here, AI can help you 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 it may be hard to pinpoint what is actually important. AI can help you to detect anomalies and patterns as well as raise alerts when data points go outside certain intervals. This way, some decisions can be automated by AI. Based on past actions and specified priorities, your AI-enhanced business software could, for instance, present your daily top five list of decisions you should action.
Using AI for anomaly detection, people would focus on making decisions on how to manage the anomalies, which may require more human qualities like creativity or empathy when judging human reactions and consequences. Finding this balance to optimise how humans and AI can work together will be crucial to succeed with an AI strategy in the long-term.
AI-enhanced predictive maintenance and service
High profile AI stories like driverless vehicles always grab the headlines. In reality, for most companies it’s more likely to be how assets are maintained and serviced that AI will impact first – which algorithms will use what sensor data to predict the asset’s specific needs in context, ahead of time, whatever the climate and whether in the open air or under cover. In fact, AI will play a major role in maintenance in many industries.
Management consulting firm McKinsey & Company found that 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 percent are possible and overall maintenance costs may be reduced by up to 10 percent.
Asset-intensive industries like facility management are ideal applications for AI because key equipment is increasingly fitted with sensors that generate mountains of IoT data, which can form the foundation for building machine learning algorithms. Based on that data, AI can turn maintenance from preventive to truly predictive.
Equipment 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 machine-learning algorithms, the system can learn from experiences and connect this data to operational scenarios. For example, the temperature level is too high, which has in the past led to 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. Adding AI capabilities to this by creating an AI-powered route scheduling solution (decision optimisation), the software could even learn how to optimise 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 optimise predictive maintenance and service.
AI-supported system interaction
The area where AI is perhaps the most advanced is interaction with people or systems. AI-powered voice assistants represent a major opportunity for many organisations, 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 time.
In a company-internal setting, AI chatbots have a great potential to make this process more effective. One example could be when employees 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 actioning 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 in the future.
Externally, taking calls at a service helpdesk is a natural way to use AI chatbots, as the calls are often simple requests like establishing when a service technician is due to arrive. This AI-powered approach is going to grow increasingly important, not just in terms of the quality of service you can deliver, but because of increasing skills shortages. As many contact centres are now developing omni-channel solutions to include voice, email, social media and chat as contact options, the AI capability could help to identify your preferred contact option and, in the future, guide you through the process much faster.
Bas de Vos is the director of the innovative think tank IFS Labs at global enterprise software provider IFS.
This article originally appeared in the Dec/Jan 2019 issue of FM.
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