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The adoption of AI in businesses across diverse industries is in its infancy. Enterprises must consider how they could put the technology to work. They should ask themselves technical questions and understand technology capabilities, along with crafting effective strategies, determining the governance structures and practices needed for responsible AI, and expediting the move from experiments to full-scale AI adoption.
The rate in development of and growing interest in AI spurring is colossal. This will continue to have amplifying organizational and societal implications, influencing accountabilities of decision-makers and policy analysts. AI has come a long way since it was considered just as a machine that can think like humans, reason, and make decisions. These capabilities now have advanced to general human-level AI, performing and interpreting like humans and over them in some way.
AI is taking center stage in businesses' strategies as it is continuing to show its potential across almost every industry. Understanding the capabilities of the technology deployment might be challenging for some businesses, but effective measures and steps toward innovations can help harness the power of AI. In our research findings, the adoption and exploitation of AI technologies, such as machine learning, deep learning, natural language processing (NLP), and computer vision have fairly taken the top positive trends and initiatives in 2020. It is also expected that the adoption of such technologies will ascend in the coming years.
While these technologies are seeing huge uptake across enterprises, we found that the number of articles on AI and business strategy is also growing. Today, increased network and data processing and advances in hardware have brought AI to the commercial level. The technology is expected to be pervasive in almost every new software product and related service in the next coming years.
With effective implementation of AI technologies, enterprises can handle operational costs, minimize risks, elevate productivity levels and create exceptional customer experiences. The application of AI can manifest in all sorts of ways relying on an organizational need and business intelligence (BI) insight derived from the data the company collects. Though AI has aroused both fear and opportunities, it provides a wide array of resources to individuals and organizations.
Following the positive as well as negative trends, we can say that there will be big winners and losers as collaborative technologies, robots and AI transform the nature of work. The proportion of organizations using AI in some form has significantly grown in the last couple of years, and that figure is extremely likely to rise further in the coming years. AI tools and technologies are often used to detect problematic clauses and could even be deployed in the future to analyze which businesses are likely to fail based on historic trends. AI is also becoming critical in rethinking risk assessment in a bid to exterminate money laundering and fraud.
In the past, organizations have sought to classify their customers using only a very limited number of data attributes, including name, address, and date of birth. But, with the advent of machine learning-based predictive technologies, they can have large access to their customers' identification processes and could assist in the fight against any sort of fraud.
Therefore, it is significant to see both negative and positive trends in the context of one another. Because negative trends tend to drive positive trends. Talent shortages and protectionism, for instance, are part of the business drivers behind artificial intelligence. Solutions based on this technology can assist organizations in easing talent shortages by performing and automating tedious and menial tasks and lessening the need for human resources, including offshore labor.
