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 to make machines ‘smart’ and ‘intelligent’ 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.
Barriers to AI adoption
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.
1. Disparate data
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.
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.
2. Right use cases
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, then the whole idea of deploying the technology will go in vain. Hence, business analysts should:
- find tasks that are tedious, repetitive, and time-consuming,
- check where there are recurrent human errors, and
- identify jobs that generate high revenue but low employee job satisfaction.
If you are new to AI adoption, then it is preferable to opt for AI offerings like robotic process automation to automate tedious tasks. Once you implement AI for low-level tasks, you can learn and experiment with more mature business use cases with time.
3. Skill Gaps
Organizations require experts and professionals to analyze AI use cases, innovate AI offerings, and experiment with various business use cases for successful AI deployment. But, relying on a small group of IT analysts and experts for AI deployment is unsustainable, of course. Leveraging AI brings in bigger questions around both, technical and business facets. And only AI experts can clear these questions well. Hence, companies should have enough IT professionals and analysts who can address all the issues that emerge while implementing AI. Due to this, companies should hire experienced and skillful candidates who are creative enough in developing robust AI models for the specified use cases. Besides, companies should carry out rigorous training for their employees, instructing them about AI opportunities.
Before capitalizing on this innovative and disruptive technology, AI, companies should overcome the challenges that are mentioned above. Doing so will enable them to enjoy AI’s potential of automating and innovating services with high operational accuracy and efficiency.
