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With the advent of process automation and machine learning (ML) technologies, companies are increasingly confronted with new data and information, as well as the mounting pressure to adopt new tools they may not know how to take full advantage of.
In fact, in Deloitte’s State of AI in the Enterprise survey, 39% of respondents identified data issues as one of the top three greatest challenges they face with AI initiatives. It’s like finding a needle in a haystack with a metal detector that is too complicated to use — a waste of time and resources and a false sense of competitiveness.
But just how are industry innovators, such as field service organizations (FSOs) that typically dispatch technicians to remote locations to install, repair, or maintain equipment, rising to meet the challenges of an increasingly automated world? The answer lies in organizational changes to replace legacy technologies, break down data silos and fully leverage artificial intelligence (AI) to its full potential.
Replace legacy technologies
FSOs have traditionally focused on optimizing service efficiency and quality through process improvements and management software updates. Yet, traditional methods are no longer enough to show business value to their customers.
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As companies start focusing on offering outcome-based service models, they need to prepare to launch services like predictive maintenance, so they don’t risk reverting back to the break/fix model where they are constantly upgrading legacy systems. However, the evolution to an outcome-based model involves a level of digital transformation that poses several challenges. It can create an IT environment that is overly complex and includes numerous applications and systems with different update and release cadences or security features, which often leads to high IT maintenance costs and possible business disruptions.
Additionally, replacing a legacy system with one that cannot utilize data optimally while simultaneously promising compatibility with AI can lead to project delays and additional costs.
Address data and AI-enabled technology deficiencies
Optimizing the productivity of a company’s workforce and providing excellent customer experience is challenging in today’s on-demand world. To offer greater business value to customers, FSOs need to utilize data and intelligence to both meet and anticipate customer needs. However, this type of innovation requires breaking down data silos and coordinating processes across the organization to provide employees with customer insights.
Additionally, with AI-embedded software, organizations have the ability to automate repetitive tasks, process complex data sets, and more. However, while 80% of companies are already using some form of automation technology or plan to do so over the next year, it can be difficult for them to start the process of delivering the value AI promises without a third party walking them through the best AI and data solutions.
Maximize data and AI investments
Using a combination of data and AI has a lot of benefits, especially for organizations like FSOs that work to provide the best service to customers, by ensuring optimized scheduling of employees are able to respond to predicted service tasks.
In cases like these, data and AI work hand in hand; for example, data gathered from IoT sensors can help AI predict asset performance and schedule optimization by using data such as maintenance history. Typically, experiential data also helps FSOs actively respond to potential service issues by predicting when a customer’s product needs maintenance and thus makes sure parts and technicians are available at a given time.
AI also helps internal staff by automating customer interactions through the enhancement of chatbot and customer relationship management (CRM) tools.
As we move toward a more modern, automated future, organizations will need to get a grasp of their data silos to experience AI’s full potential. When data is used effectively with AI, organizations can solve a variety of problems end to end, paving the way for organizations to leverage predictive scheduling while meeting customer needs.
Kevin Miller is CTO of IFS.
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