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Companies across the world are increasingly turning to AI implementation for their smooth business operations. The technology has become very constructive in performing a wide range of tasks that are complex and cumbersome for humans, bolstering employee productivity. The use of AI can also aid enterprises to combat cybersecurity risks and thwart them from potential data breaches. With the growing capabilities of AI across diverse business functions, here is the key driver of the technology.
Enterprises nowadays are investing heavily in AI technologies to unleash the power of their business. They are adopting the technologies to perform tasks ranging from planning, envisaging, and predictive maintenance to customer service chatbots and more. As more and more enterprise tasks will be performed by AI, companies can see a huge transformation in their businesses and drive efficiency. Though businesses are in the early stage of AI adoption, they are yet to learn how to implement this technology to get the most out of it.
AI has been around for several decades transforming all aspects of businesses and lives. The current surge in AI research and investment has given an incredible rise in AI applications. These applications do not just promise to yield better business outcomes but enhance the human experience as a whole. The technology is currently being applied to a wide array of industries ranging from healthcare, retail, and food tech to banking, logistics, and transportation. Applications of AI have also expanded to real estate, entertainment, and gaming in recent years, and are expected to develop further in the coming years.
AI is capable of improving customer experience as it is rapidly transforming the way companies interact with their customers. To enhance efficiency and ability to deliver services, companies are invested in customer service AI solutions. Key players in the customer experience field are also looking for AI to augment intimacy to understand customers profoundly, drive customization and create personalized journeys.
Artificial Intelligence has a positive impact on the development of a business. The greatest potential value AI offers can influence both top-line-oriented functions, such as marketing and sales, and bottom-line-oriented operational functions like supply chain management and manufacturing. The technology is not only proving beneficial for large businesses that seek to obtain high competitive gains but small businesses are also being benefited from better strategic development using AI.
The increased proliferation of technology in the last few years has given the rise to a new threat landscape, forcing businesses to explore advanced defensive strategies. By integrating the power of AI into cybersecurity, security professionals will have a powerful resource to safeguard vulnerable networks and prevent potential data breaches. AI can significantly draw instant insights, resulting in lessened response times. This disruptive technology can assess user behaviors, infer a pattern, and detect all sorts of irregularities in the network, making it much easier to identify cyber vulnerabilities quickly.
Undoubtedly, AI has a huge potential to transform industries and provide decision-makers with opportunities to drive improved business excellence. This is why technology is being admired by every type and size of business and acknowledged as the most innovative and advanced technology of the 21st century. It has affected our lifestyle either directly or indirectly and is likely to take over some major everyday tasks soon. As AI is continuously revolutionizing the real world, it has some its limitations.
The prediction power of an intelligent algorithm is highly dependent on the quality of the data fed as input. Even in quality sources, biases can be hidden in the data. The time and effort required to clean and prepare an appropriate set of data should not be underestimated. In the self-driving automotive industry, for instance, most of the effort is spent on labeling hours of videos. This has led to the creation of an entire offshore industry for video labeling. Conversely, in the financial industry, the reconciliation of the data from front to back is already problematic, and data referential are often plagued with quality issues. Having an effective data quality program in place is a prerequisite to any large-scale artificial intelligence initiative.
The results of intelligent algorithms are opaque and not verifiable. They deliver statistical truths, meaning that they can be wrong in individual cases. The results could have a hidden bias difficult to identify. The diagnosing and correcting of those algorithms are very complex. This is majorly because there is no explanation as to why the algorithm provided a positive or negative answer to a specific question that can be disturbing for a banker's rational mind, for instance. This is often a blocking point for the use of AI in trading.
As AI is becoming more and more pervasive in today's information age, there is a concern over how people can trust that it reflects human values. Somewhere AI has not been able to build trust among people. People who are completely unaware of this technology and don't understand how it is being used by companies to make a decision find it difficult to comprehend its functioning.
AI can be the best defensive technology in a company's cybersecurity arsenal and, thus, it is becoming increasingly integral to information security. AI impacts nearly every aspect of people's lives and uses in multiple applications. However, AI is no longer placed solely in the hands of the good, as malicious actors are adopting technologies such as AI and machine learning faster than security leaders. The use of these technologies by cybercriminals can have a pessimistic impact on all businesses seeking to protect their most precious asset, data.
AI systems are dependent on what data we put into them. By putting bad data, they can create implicit racial, gender, or ideological biases. But this is the fact that most AI systems will continue to be trained using bad data, making this an ongoing problem. Since more and more companies are looking to deploy AI systems across their operations, being acutely aware of its bias and working to minimize them is an urgent priority.
