{"id":9304,"date":"2018-09-27T05:05:47","date_gmt":"2018-09-26T23:35:47","guid":{"rendered":"http:\/\/www.allerin.com\/blog\/?p=9304"},"modified":"2018-09-24T05:08:26","modified_gmt":"2018-09-23T23:38:26","slug":"enterprises-face-these-3-challenges-while-adopting-ai","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/enterprises-face-these-3-challenges-while-adopting-ai\/","title":{"rendered":"Enterprises face these 3 challenges while adopting AI"},"content":{"rendered":"

\"barriers<\/a><\/p>\n

For quite some time now, there has been a lot of buzz around AI and its promise to disrupt industries altogether. From digital assistants to robotic process automation to self-driving cars, AI has offered cool and innovative applications, which have only been the subject of science fiction. Today, AI has reached a level of precision where it can understand human emotions too. The power of AI<\/a><\/u> to make machines \u2018smart\u2019 and \u2018intelligent\u2019 has triggered a lot of industries to invest in AI projects. The decision of leveraging AI to aid digital transformation is pretty understandable. But, companies should first analyze the potential barriers to AI adoption so that they can enjoy successful AI implementation.<\/p>\n

Barriers to AI adoption <\/strong><\/h1>\n

While companies think of leveraging AI for transforming their existing workflows, they should keep in mind these potential hurdles, and plan their journey with AI accordingly.<\/p>\n

\"barriers<\/a><\/p>\n

1.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>Disparate data<\/strong><\/h1>\n

We already know that AI systems work well when trained appropriately. But for training AI systems, companies should first collect big data from diverse sources. Data covering every facet of a specific issue will help AI experts to build robust AI models efficiently.<\/p>\n

With this aim, companies collect data from multiple sources, including videos, audios, and texts, in real time. But analyzing and integrating the data becomes difficult sometimes. For addressing this gap, companies should leverage the right analytics tool to have a seamless analysis of the growing data.<\/p>\n

2.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>Right use cases<\/strong><\/h1>\n

One of the critical responsibilities on the shoulders of analysts is to pick right business use cases, where AI can automate and improve services. If companies fail to identify right AI use cases<\/a><\/u>, then the whole idea of deploying the technology will go in vain. Hence, business analysts should:<\/p>\n