Many issues that are present with other uses of AI are present with this RPA version too , such as internal resistance to change and a skills gaps .
“ The primary challenge in integrating AI with RPA lies not in the technology itself – since both exist under a single platform – but in overcoming organisational limitations or frictions . Many organisations struggle with securing c-suite-level buy-in and scaling automation efforts successfully ,” explains Michael .
Some issues are more technical in nature , but both concern data . “ Data management and quality are absolutely critical in the integration of AI with RPA ,” explains Tim .
AI systems rely heavily on large datasets to make accurate predictions and decisions . If the data fed into an RPA system is inconsistent or of poor quality , it can lead to errors that undermine the effectiveness of the automation .
This , when used in a healthcare setting , for instance , could have the potential to lead to disastrous results .
Not as dangerous but equally as damaging are the security concerns . As RPA and AI technologies become more integrated and sophisticated , they gain access to vast amounts of sensitive data , potentially increasing what is exposed if a data breach occurs .
“ Organisations are often tentative in rolling out technologies like Gen AI due to compliance and security concerns , particularly when managing sensitive data ,” says Micheal .
Combine that with the challenge of integrating legacy systems with modern AI and RPA technologies and many organisations may struggle to retrofit
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