June 04, 2021
As companies navigate unwelcomed uncertainties provoked by the COVID-19, they must also realize the potential of using cognitive and digital tools to curb the crisis. AI and machine learning hold great possibilities in empowering firms to develop and start adapting digitally-enabled AI services. Not only does AI help serve untapped markets but also gives them the opportunity to redefine their current business model.
By 2030, the global AI market is estimated to grow to USD 15.7 trillion, says a survey by PwC. This also means that AI adoption could boost certain economies by 26 percent. But for businesses to get the best of AI, they need to start understanding the technology as a necessity rather than a luxury. The survey further showed how AI adoption accelerated from 62 to 70 percent.
As a result of the survey, around 94 percent of respondents said they have adopted AI or are planning to adopt the technology in their services. As companies rethink their strategies, it is likely possible for the technology to develop a competitive advantage and boost decision-making capabilities while paving a pathway toward the future.
Amid the global pandemic, we’ve noticed certain countries with the highest AI adoption.
Countries across the globe have started engaging with the government and technology firms seeking different ways to fight the challenges caused by the crisis – contactless thermal screening and contact tracing.
Similarly, tech organizations, healthcare sectors, and even startups have developed AI-powered diagnostic guidance systems that can help models predict the spread of the virus.
In fact, technology like AI and machine learning has become the key differentiator to create a competitive advantage throughout this transition.
As a result, AI is on the verge to help businesses transform while ensuring they achieve business goals.
As organizations move from the AI implementation phase to the upscaling phase at the enterprise level, the type of challenges faced were business-related.
In 2019, the nature of the challenges was purely technical related i.e. lack of in-house skills or inability or explaining the model. Such challenges were due to a lack of understanding of how AI models work, insufficient data, a dearth of AI talent, and budget constraints.
However, in 2020, the nature of the challenges changed and they’re focused on business i.e. measuring the RoI from initiatives involving AI and the selection of relevant AI use cases. A drastic shift is also an indicator that it is natural wherein businesses are now more focused on demonstrating how to scale AI at the enterprise level.
The change projects a clear message showing that organizations are becoming more matured and they’re willing to adopt AI.
Although organizations are transitioning and accepting AI, they must not overlook the challenges associated while strengthening AI adoption. To stay resilient amid the transition, organizations should start developing a structured approach toward AI implementation.