How is Infosys identifying and mitigating the hurdles thwarting AI’s business implications?
While digital natives are comfortable adopting AI, traditional large enterprises are yet to embrace it extensively. While the potential of AI is lucrative, yet enterprises aren’t exactly rolling it out because of factors such as the absence of a clear strategy, lack of organized data, skills shortage, and functional silos within the organization. As per a Mckinsey study, a mere 17 percent of respondents said their companies have mapped out the potential areas in an organisation where AI can succeed.
Organizations who are early adopters of AI, are struggling to accrue the benefits because they have not been successful in scaling and democratising AI. Also, only 18 percent have a clear strategy in place for sourcing the data that enables AI work.
The dearth of trained talent with AI skills also plays a role in slowing adoption. At the same time, to adopt AI seamlessly, organisations need to take additional measures to ensure better security, governance, and change management. Having said that, we’re likely to see most of the stated hurdles being overcome as technology evolves at breakneck speed. For instance, data synthesis methodologies are now available to combat data challenges in AI. With the emergence of techniques such as transfer learning and meta learning, reduces the need for high volume data. Aspects like the explainability of AI, elimination of bias and ensuring AI is used ethically are becoming mainstream helping in adoption of AI in the enterprise context.
This story is from the April 2020 edition of DataQuest.
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This story is from the April 2020 edition of DataQuest.
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