Artificial Intelligence and Machine Learning’s Power. 2020 demonstrated that business models cannot rely on human intuition and, secondly, that data, artificial intelligence, and machine learning are essential business tools for operation. These have enabled organizations worldwide to react to uncertainty. This will be reflected in business strategies that will increasingly rely on technology to make predictions with data at low cost and with high accuracy, transforming industries and delivering the value that customers demand.
The raw material to drive a company’s business models is big data, i.e., massive data sets, so large and complex that they require computer models for processing and obtaining valuable information. This allows the leaders of organizations to have a deep understanding of what is happening inside and outside their operations and their customers or users. This results in informed decision-making and the ability to stay ahead of the competition, increase control of their operation, and respond quickly to unexpected situations.
The challenge for companies is to integrate, manage and use data correctly. According to Harvard Business Review, companies use less than 1% of unstructured data, and less than 50% of structured data is used to make decisions.
This information is critical for all industries. In the case of retail, data and artificial intelligence provide insight into every customer interaction to improve the customer experience, predict prices and demand, and make more accurate recommendations to support consumers in their purchasing decisions. In financial services, millions of transactions are made every day and by analyzing them it is possible to anticipate fraud and suspicious behavior.
It also makes it possible to streamline processes by analyzing customer risk and granting credit.
In the manufacturing sector, data can provide information that strengthens the production process to avoid risks and errors. For example, it is possible to know when it is necessary to perform maintenance on production lines, when and what inputs to buy, or to project the production needed to meet demand. On the other hand, telecommunications companies can benefit from data analytics and AI. This can be to make appropriate changes according to customer behavior, address frequently asked questions from users and provide support and even bring an offer closer to the consumer based on their needs to retain the customer.
At Google, we promote solutions that make it easy to take advantage of data and that adapt to the needs of companies, as well as to their budget, and at the same time democratize technologies such as artificial intelligence and machine learning. One of these is BigQuery, which drives the interactive analysis of massive data sets, enables fast queries for organizations to gain agility, and provides statistics through real-time predictive analytics. For example, Toyota Canada used it to build a learning model to analyze and understand the likelihood that users entering their site would return in the next 30 days to complete an action. With this, it was able to identify the customers it needed to prioritize in order to send them a personalized ad.
Cloud AutoML is another technology that allows leveraging data for better decision making. It allows developers with limited machine learning experience to create high-quality custom models for their business needs. One of the companies that leveraged this solution is Imagia, whose mission is to make medicine an accessible good.
With AutoML, it trained multiple AI models and executed learning methods to analyze conditions in chronic and age-related degenerative diseases to develop preventive diagnostics.
These cases and many more demonstrate the power of data and how the democratization of AI drives the business world. According to Gartner, by 2022, 70% of customer interactions will involve emerging technologies such as machine learning applications, up from 15% in 2018.
Analytics and artificial intelligence solutions are tools for companies to differentiate themselves from their competitors. That’s why, at Google Cloud, we support our customers in breaking down silos in their data. This allows companies to extract data in real-time and make it available to all collaborators.
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