Key obstacles in building an efficient data foundation for artificial intelligence implementation

Intelligent Automation (IA) and Artificial Intelligence(AI) are slated to see a marked increase in utilization over the coming year as companies plan their post-COVID-19 revival. Investment in digitization to improve customer experience, enhance process efficiencies, increase revenues, and generate cost savings is high on the agenda.

Investment in digitization to improve customer experience and enhance process efficiencies is high on the agenda.

However, it is important to get the start right. Companies need to understand the critical importance of data structuring as a necessity for successful AI and IA adoption to gain overarching improvements in business processes efficiency and value.


Even as companies grow their data sets and collection capabilities, too many feel they do not have much in the way of strategically useful and actionable data, chiefly thanks to poor data management integration and the lack of a holistic view of all their operational processes.


Our research indicates that the three top challenges organizations currently face when implementing AI and IA are:

  • Managing and integrating large data sets interoperability

  • Protecting data security and privacy

  • Finding the balance between technology and the human touch

The first two challenges speak to issues being experienced by organizations in terms of getting data collection, analysis, and protection processes right from the ground up. This leads to greater AI and IA integration issues down the line because the organization’s data foundation is incomplete, inaccurate, or, at worst, compromised in some way.


To put it another way, the barrier to entry for true AI and IA adoption is dramatically higher today in terms of the volume, quality, and security and different types of data necessary for it to work effectively.

 
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