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With the advent of process automation and machine learning (ML) technologies, companies are increasingly faced with new data and information, as well as increasing pressure to adopt new tools. that they may not know how to make the most of.
In fact, at Deloitte’s The State of AI in the Enterprise In the survey, 39% of respondents identified data issues as one of the three biggest challenges they face with AI initiatives. It’s like finding a needle in a haystack with a metal detector that’s too complicated to use — wasting time and resources and creating a false sense of competition.
But how come industry innovators, such as field service organizations (FSOs) that often send technicians to remote locations to install, repair or maintain equipment, reach up to meet the challenges of an increasingly automated world? The answer lies in organizational changes to replace legacy technologies, disrupt data silos, and take full advantage of artificial intelligence (AI).artificial intelligence) to its full potential.
Replace old technologies
FSO has traditionally focused on optimizing efficiency and service quality through process improvement and management software updates. However, traditional methods are no longer sufficient to demonstrate business value to their clients.
As companies begin to focus on delivering results-driven service models, they need to be prepared to roll out services like predictive maintenance, so they don’t risk falling back to the break/fix model as they continually upgrade older systems. However, the process of evolving into a results-based model with regard to digital transformation poses several challenges. It can create an IT environment that is overly complex and includes many applications and systems with different update and release cadences or security features, which often leads to high IT maintenance costs and potentially high costs. may cause business disruption.
Additionally, replacing an old system with one that cannot make optimal use of data while promising AI compatibility can lead to project delays and additional costs.
Addressing data and AI-enabled technology gaps
Optimizing the productivity of a company’s workforce and delivering a great customer experience is a challenge in today’s on-demand world. To deliver greater business value to customers, FSOs need to use data and intelligence to meet and anticipate customer needs. However, this type of innovation requires breaking down data warehouses and coordinating processes across the organization to provide employees with customer insights.
In addition, with embedded AI software, organizations have the ability to automate repetitive tasks, handle complex data sets, etc. However, while 80% of companies already use some form of automation technology or plan to do so within the next year, it may be difficult for them to begin the process of delivering the value that AI promises without a third party guiding them on solutions. best data and AI solutions.
Maximize data and AI investment
Using a combination of data and AI offers a lot of benefits, especially for organizations like FSOs that work to provide the best service to customers, by ensuring optimized employee schedules are available. can fulfill predictive service tasks.
In cases like these, data and AI work together; For example, data collected from IoT sensors can help AI predict asset performance and schedule optimizations using data like maintenance history. Often, empirical data also helps FSOs proactively respond to potential service issues by predicting when a customer’s product needs maintenance and thus ensuring parts and technicians are available at all times. available at a given time.
AI also assists internal staff by automating customer interactions through chatbot enhancement and customer relationship management (CRM) tools.
As we move towards a more modern, automated future, organizations will need to embrace their data stores to experience the full potential of AI. 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 the CTO of IF.
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