In today's data-driven environment, a common goal between businesses is to become more intelligent — to know where market opportunities are, where supply chain logjams are, and where process improvements can be made. Data science was the energy behind this movement, and data science is now becoming more intelligent itself. Thanks to incredible developments in artificial intelligence (AI) and its machine learning and deep learning sub-segments, businesses are achieving new levels of data analysis efficiency that affect their entire business. With AI technology sales expected to reach nearly $90 billion by 2025, the rising tide of AI adoption across industries will drive significant growth in the next decade. The existence of AI is appealing to both data scientists and business managers who are seeking to let machines crack the number to make the company smarter on a holistic basis.
Usually, following the money trail, a leading indicator of the growth path of a market segment can be found. Investors and venture capital (VC) firms are always looking for great opportunities for growth, and they are now finding one in the AI business. Forbes recently reported that the number of active AI startups has increased 14 times since 2000, and VC firms ' investment in these startups has increased 6 times in that period. Meanwhile, companies that both build and use AI applications are on a similar growth path, with jobs requiring a 4.5x increase in AI skills since 2013.IT Is a Big AI Beneficiary. It should come as no surprise that the department concerned with information full-time, namely the This agency, is perhaps the largest recipient of the capabilities of AI. A research by Harvard Business Review estimates that between 34 and 44 percent of global organizations surveyed use AI to help solve problems with employee technical support (imagine a smart response system to streamline common questions and troubleshoot others), automate internal system changes (machine codes can be used to determine where bottlenecks can be fixed), To ensure that employees only use approved vendor software (picture a smart licensing engine that keeps up with daily alerts to knows vendor subsidiaries and partners).
How else to find a home for AI?
Image recognition and tagging, patient data processing, localization and visualization, predictive maintenance, predicting and threatening security threats, and smart recruiting and HR management techniques are among the most common examples of AI in the business. But perhaps the most successful use is seen in the process of marketing and sales, where smart data use and the ability to learn from human interactions can bring great financial benefits. In a worldwide survey conducted by Statista, 87 percent of new AI adopters said they used or consider using AI for revenue forecasting and email marketing improvements. Although sales forecasting is often to some degree automated by software, with an AI agent monitoring and responding to customer interactions and changing market trends, it can be greatly improved. Likewise, email marketers can build the impression of one-to - one advertising for different audiences through better targeting and content creation. Often essential is the bottom line. McKinsey found that companies that benefit from AI initiatives and have invested in infrastructure to support their scale reach a profit margin of three to 15 percentage points. As a result of AI adoption, education, financial services, and professional services see the largest increase in their profit margins.
Here are a few examples of how specific companies in different industries are using AI in their businesses: according to the McKinsey report, tech giants like Baidu and Google invested between $20B and $30B on AI in 2016, investing 90% on R&D and implementation, and 10% on AI acquisitions. AI's current investment rate is 3x the growth of foreign investment since 2013.Netflix has also achieved impressive results from the AI algorithm it uses to customize recommendations to its 100 million subscribers worldwide, improving search results and avoiding canceled subscriptions from frustrated customers who could not find what they wanted (with a potential impact of $1B annually). Bloomberg, a financial data expert, uses technologies such as computer vision and natural language processing to increase the scope of information available through its omnipresent terminals used by financial workers to access market information. In queries, users can use natural language rather than specialized technical commands that are analyzed and executed by AI.Uber has a core group of mobile app designers, map experts, and autonomous driving teams to provide pre-packaged machine learning algorithms' as - a-service.' Using computer vision, these skills are used to better predict driving patterns and improve maps and build algorithms for their autonomous vehicles. And Royal Bank of Scotland has recently launched an AI bot that will answer its banking customer questions and perform simple banking tasks such as money transfers, with the goal of making online customer support as effective as face-to-face contact.AI and machine learning are revolutionizing how companies access and process data to become organizations that are smarter and more efficient. And in their enterprises, IT and data science teams are preparing for the immense benefits of AI.
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