Barcelona, June 16, 2020.- The prestigious PWC Consultant has published a study on Process Automation in companies. Below, we have made a summary with the most practical conclusions for management software professionals, applied to transportation and logistics companies.
A key automation lever is deciding how to start: with end-to-end processes or with discrete tasks. Within a function, or across the business, successful automation initiatives identify problems that technology can solve—as opposed to starting with a specific technology like RPA or AI and looking at where to apply it. And those problems often come down to data, or, more specifically, to helping the people who currently must wrangle that data. For example, they do this within applications working in ERP systems or between applications when they need to pull data, manipulate it and feed it into another application. Or they manage data across the company, with standardized data groomed for access by many applications.
Automation is about all of those things. And every company needs to take a serious look at how they’re approaching it, especially as new tools put the power to solve problems and streamline tasks into the hands of business users. “Low-code,” or self-service, data management and RPA tools enable finance, human resources or other teams to address data challenges in their function and eliminate repetitive tasks. More advanced tools, which require stronger collaboration with the IT department, are also designed for business users and take advantage of artificial intelligence (AI) to intelligently optimize business processes.
Natural language processing is an entry point on the journey to AI. Indeed, many companies are still selecting AI use cases and looking for practical ways to apply the technology. Automation may be the answer they’re looking for.
Intelligent automation quietly incorporates an ecosystem of technologies. Many companies, for example, use customer service chatbots on their websites. Such chatbots often start with rule-based automation and natural language processing but are later fortified with additional cognitive understandings of sentiment. Similarly, when insurance companies send drones to recognize the damage to rooftops after a hurricane, they can optimize machine learning image recognition models as an essential step to quick claims adjustment and payout.
More information on automation and software at AndSoft:
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