1. Organizational change
One of the foremost challenges before cognitive automation adoption is organizations need to build a culture that encourages the human workforce to accept, adapt, and work alongside the digital workforce.
2. Technology maturity
AI is still at its infancy, it learns by example, most technologies like NLP, OCR or ML has not yet been perfected or matured, this leaves room for error and require close attention. Thus, it possesses a potential error rate.
3. Value realization
Cognitive automation is more expensive and may take longer to implement than traditional RPA tools in specific scenarios. AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task.
Today, RPA is being widely adopted by enterprises. As cognitive technologies slowly mature, more and more data gets added to the system and it will help make more and more connections. Now the time is right for businesses to look at combining RPA with cognitive technologies to stay ahead of the competition.
Talk to our Experts at (firstname.lastname@example.org) to kick-start your journey with Cognitive Automation.
Choudhury, A. (2019, February 13). Are Banks Ready to Embrace AI? Retrieved from GoMedici: https://gomedici.com/are-banks-ready-to-embrace-ai
IDC. (2019, March 11). Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, According to New IDC Spending Guide. Retrieved from IDC: https://www.idc.com/getdoc.jsp?containerId=prUS44911419