- June 26, 2021
- Posted by: Aanchal Iyer
- Categories: Artificial Intelligence, Uncategorized
More than 1 in 2 (53 percent) of Indian IT professionals state that their company has accelerated the rollout of Artificial Intelligence (AI) based technologies due to the COVID-19 pandemic, a recent IBM survey revealed.
The research has revealed that while the adoption of AI was nearly flat over the last year, the momentum is gaining pace as the need for AI has been augmented by varying business needs, attributed to the global pandemic. AI is already influencing the way businesses function today – from automating key workflows, to how they communicate with their customers using virtual assistants, and even handling network security.
With present AI and Machine Learning (ML) capabilities, businesses can identify useful data more efficiently. Not only numbers or text, but diverse inputs that comprise written, spoken, or image inputs can also easily be analyzed today. This enables organizations to find and discover business-critical information from more diverse data sets.
This not only aids in the reduction of business expenses, but new trade opportunities are also created. We have witnessed AI algorithms quickly identify and select huge amounts of information during the COVID-19 pandemic, enabling the identification of similar diseases and their possible cures for speeding up the coronavirus research work.
ML advancements are expected to help businesses set up new revenue streams by assisting them to monetize their data in a better fashion. Having access to critical data points helps in numerous ways; for example, information concerning the efficacy of a specific drug on people in a specific age group is very useful to drug researchers and pharmaceutical companies.
Emerging AI and ML data processing algorithms and protocols will be more useful for businesses and will make way for new, profitable market opportunities in the post-covid-19 era.
Future-Proof and Robust Data Supply Chains
Automation and AI are effective tools for making the data supply chains anti-fragile and future-proof. Statistical models, data connectors, ML, visual storyboards, data-driven automation, and so on are some constructive methods to achieve this goal. Data supply chains are complex; however, it is still possible to make them robust and contemporary by integrating pensive processing at both the creation point as well as consumption of actionable insights.
Adoption of AI in Different Industries
Trusted AI, automation, and the rise of virtual agents using NLP and anywhere Hybrid are the top areas that have emerged as key business enablers in the last year owing to the acceleration of technology adoption.
The education industry, while assessing the student engagement of their digital content, is tracking the retina movements of the students. In radiology, videos and digital images are being analyzed to gain deeper insights. MRIs are now capable of tracking brain tumors more effectively. All this is possible only with AI.
AI and automation are going to serve an equally important role on the security front. Intelligent AI-powered systems and automation will make it more effective and easier to screen people compared to manual surveillance.
The Bottom Line
The ability to continuously learn and adapt to varying circumstances is what separates AI from other technologies. It has lead to innovative applications, such as face and voice recognition, intelligent data processing, and so on. One can say AI will have a better impact on businesses in the supply chain and retail industries, but its applications in other industries are promising as well.