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With the advent of process automation and machine learning (ML) technologies, companies are increasingly faced with new data and information, and may not know how to take full advantage of the pressure to adopt new tools.
In fact, in Deloitte’s State of AI in the Enterprise survey, 39% of respondents identified data issues as one of the top three challenges facing AI initiatives. It’s like finding a needle in a haystack with a metal detector that’s too sophisticated to use—a waste of time, resources, and a false sense of competitiveness.
But how are industry innovators like Field Service Organizations (FSOs), which typically send technicians to remote locations to install, repair or maintain equipment, rise to 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. However, traditional methods are no longer sufficient to demonstrate business value to their customers.
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As companies begin to focus on offering results-based service models, they need to prepare to launch services like predictive maintenance so they are unlikely to revert to a break/fix model of continually upgrading legacy systems. However, the evolution to an outcome-based model involves a level of digital transformation that presents many challenges. This can create a highly complex IT environment and include many applications and systems with different update and release cadences or security features, often resulting in high IT maintenance costs and potential business disruptions.
Additionally, replacing a legacy system that cannot make the most of data while simultaneously promising compatibility with AI can lead to project delays and additional costs.
Address data and AI-enabled technology flaws
Optimizing the productivity of a company’s workforce and providing a great customer experience is challenging in today’s on-demand world. To deliver greater business value to customers, FSOs need to use data and intelligence to anticipate and meet customer needs. However, this type of innovation requires breaking down data silos and coordinating processes across the organization to deliver customer insights to employees.
Also, with AI-embedded software, organizations have the ability to automate repetitive tasks, process complex data sets, and more. However, with 80% of companies already using some form of automation technology or planning to do so in the next year, it will be difficult for them to begin the process of delivering the value AI promises without a third party doing it better. AI and data solutions.
Maximize data and AI investments
Using a combination of data and AI has many benefits, especially for organizations like FSOs working to better serve customers, by ensuring that optimized scheduling of employees can respond to predicted service tasks.
In cases like these, data and AI work hand in hand; For example, data collected from IoT sensors can help AI predict asset performance and schedule optimization using data such as maintenance history. Typically, empirical data helps FSOs proactively respond to potential service issues by predicting when a customer’s product will need repair, and ensuring that parts and technicians are available at a given time.
AI also helps internal employees by automating customer interactions by improving chatbot and customer relationship management (CRM) tools.
As we move into a more modern and automated future, organizations need to understand their data silos to experience the full potential of AI. When data is used effectively with AI, organizations can solve various problems, which paves the way for organizations to leverage predictive scheduling while meeting customer needs.
Kevin Miller is the CTO IFS.
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