Articles

Like their cloud-native counterparts, many large or long-standing enterprises aspire to automate as much of their operations as possible. As a result, many of them get overly ambitious with their process automation goals, and attempt to roll out sweeping, company-wide digital transformation initiatives. While ambition is a good thing, many of these initiatives take years to complete, and often require ripping and replacing legacy systems. 

​Mark Wharton, IBML’s business development manager looks at how firms in the transport and logistics industry can apply data capture solutions to streamline information sharing in the supply chain.

In 2020, the Capture Software market grew 5% to reach $US5.1 billion in worldwide revenue, according to Infosource’s 2020-2021 State of the Global Information Capture Market report. This was despite a worldwide economic slowdown caused by the COVID-19 pandemic.

With enterprises set to triple the amount of unstructured data they have stored in the next four years, according to Gartner, enterprises are looking for efficient ways to manage and analyse that data. This trend has spiked a massive shift toward distributed file systems and object storage that enable enterprises to scale linearly (scale-out) in a cost-effective manner to address their performance and capacity needs. 

The latest Gartner Hype Cycle for Artificial Intelligence (AI) shows a large number of AI technologies are set to reach mainstream adoption within 2-5 years, including edge AI, computer vision, AI cloud services, composite AI and machine learning. However, Gartner research also found that only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so.

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