AI can expedite manual processes that exist throughout an enterprise, but the nature of the human insight is necessary to rationally manage these processes and make contextual decisions.
Are you an AI evangelist or an AI fatalist? People seem to have polarized perceptions of this technology, either that AI will answer in all the places humans fall short or that I will render humans useless in their careers. However, the true key to success lies in merging AI’s strengths and the human brain together in ways they can support each other. The key areas where AI can make a real difference today are within human augmentation and decision automation, AI-enhanced predictive maintenance and service, and AI-supported system interaction.
AI augmentation and decision automation
One reason why some believe AI will replace humans in the workplace is because they mistakenly associate it with complete automation. Rather, people need to view AI’s “employment” through a different lens that differentiates mechanical tasks from insight-driven or emotive-oriented tasks. AI can significantly expedite manual processes that exist throughout the enterprise, but the nature of the human insight is necessary to rationally manage these processes and make contextual decisions, with much of the manual burden alleviated by AI.
Contrary to common concern, AI in the workplace will actually stimulate the need for more human workers to manage this technology and the resulting momentum augmentation will bring. Although it may be for different kinds of jobs, Gartner specifically predicts AI will lead to a net of half a million new jobs in 2020.
Human augmentation can additionally be illustrated through AI’s propensity for decision optimization. Globalization, innovation and competition are taking over the marketplace, meaning businesses are constantly tasked with producing more goods and services in an atmosphere where various factors are constantly affecting demands and resource supplies. Here, AI can take these factors into account to help human workers map out this activity to better tailor strategy around fluctuating demands and changing rates.
AI can learn patterns from historical data and then propose decisions that help workers make both quicker and more intelligent plans of attack. Additionally, the technology can raise alerts when patterns or unexpected data points go outside historically-typical intervals. This way, AI can facilitate decisions through informed recommendations for humans to then carry out the next steps that require more advanced human judgement.
AI-enhanced predictive maintenance and service
There is a lot of buzz around autonomous vehicles, as an example, however maintenance operations are where AI more quickly can serve as an asset. Implemented algorithms can use sensor data to predict the vehicle’s specific needs for whatever the situation might be.
Another use-case of AI and maintenance/service is manufacturing. Here, AI-enabled predictive maintenance can help operations steer clear of machine failure before it even happens, increasing asset productivity by up to 20% and reducing maintenance costs by up to 10%, according to McKinsey.
In manufacturing, and other asset-intensive sectors, data generated from IoT can form the foundation upon which predictive algorithms can be developed. Manufacturing lines or energy plants connected to software with built-in AI capabilities may use IoT data to detect 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 could be too high, which previously might have led to an expected failure. This experience can now be used to automatically initiate preventive work orders in the enterprise software and dispatch service personnel to troubleshoot without any work interruptions.
By creating an AI-powered route scheduling solution (decision optimization), the software could even learn how to optimize the workforce schedule to more efficiently service dispersed equipment. This is just one example of how IoT, automation and AI can work together to optimize predictive maintenance and service.
AI-supported system interaction
The area where AI is already quite advanced is in its interactions with people or with other systems. Organizations should leverage AI speech or chat technology for uncomplicated queries or transactions that occur frequently. These tasks can be uncomplicated in nature, but still require someone to log into an application and perform a short series of actions every time they do it, which in the long run takes more time.
AI chatbots have great potential to make business internal processes more effective. This could be helpful when employees need to call in sick, ask for leave or want to find and access certain items within their enterprise software. Voice technology enables workers to speak, or chat if preferred, to access needed information and action it efficiently, refining the process to be more seamless and expedited in the future.
Externally, taking calls at a service helpdesk is a natural way to use AI chatbots as the requests – like establishing opening hours or determining when a service worker will arrive for a job – are often simple in nature. This AI-powered business approach will become increasingly important not just in terms of quality of serviced offered, but also to make up for skill shortages across service providers.
Now you tell me; are you an AI evangelist or fatalist? Though many employees in the enterprise fear AI will replace them at work, there’s enough to mitigate these anxieties and can help them understand how the technology can really complement many of their everyday responsibilities. The areas of augmentation and decision automation, predictive maintenance and service, and system interaction are three of the strongest places we’re already experiencing AI’s valuable impact and where we’ll continue to see the technology benefitting in the near future.
Bas de Vos is Director of the Innovative Think Tank for IFS Labs at IFS.
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