Artificial intelligence and machine learning are surely on the priority list for many enterprises. Faced with a sudden implication of digitisation and change in consumer behaviour, enterprises are looking for a rapid solution to bend the curve on efficiency and agility. Artificial intelligence and machine learning come for the rescue of companies that laments the death of traditional working models. AI and ML in enterprises can improve their revenue while reducing the cost spent over feeble things.
Smart enterprises are already leveraging enough use cases from AI and ML. The emerging technologies have the potential to make the most sense for employees, stakeholders and customers. Artificial intelligence is defined as the application that normally requires human intelligence, but are powered by machines. Applications of AI increases scope in diverse industries like e-commerce, manufacturing, human resources, accounting, customer relations, marketing, etc. Despite various technological disruptions, the core aim of AI service remains at the consumers’ interest to leverage flexible, adaptive and interactive solutions. An advanced AI algorithm offers far better speed and reliability at a much lower cost as compared to its human counterparts. Machine learning is no different. It triggers business processes and helps enterprises serve consumers in a better way. From involving customer experience to developing products, there is no area of the modern enterprise where machine learning has not touched. Algorithmia conducts an annual survey on AI and ML applications in enterprises. The report titled ‘2021 Enterprise Trends in Machine Learning’ unravels the changes AI and ML have brought in today’s working system. The survey is based on the reply from 403 business leaders and practitioners who have insights into their company’s digital efforts. Some of the key highlights of the report are listed below,
- Around half of the business leaders and practitioners who took part in the survey indicated that they have plans to spend more on AI and ML for their enterprise in 2021.
- Around 76% of respondents said that they have not reduced the size of their AI/ML teams, with 27% reporting they had increased it.
- More than 76% of organizations say they prioritize AI and ML over IT initiatives, and 64% say the priority of the applications has increased relative to other IT initiatives in the last twelve months.
- In 2021, 20% reported AI/ML budget increase of more than 50% from FY2019 to FY 2020.
- The percentage of respondents who indicated they have more than five AI/ML use cases has increased by 74% year-on-year.
- Organizations proved that they still stumble at the beginning of the AI/ML lifecycle with 49% respondents saying they experience challenges with the integration or compatibility of their ML technologies.
- As the AI/ML market matures, 71% of all organizations have hybrid environments, and 42% have a combination of cloud and on-premises infrastructure.
- Buying a third-party solution costs 19-21% less than building their own infrastructure. Meanwhile, the time required to deploy the AI/ML model is 31% lower for organizations that buy a third-party solution.
General applications of AI and ML in enterprises
Cognitive insight: Cognitive insight refers to the AI/ML algorithm that is used to detect patterns in vast volumes of data and interpret their meaning. These applications are being used to predict customer’s shopping choice, identify credit frauds, analyze warranty data to detect product quality, automate personalised targeting of digital ads and provide insurers with detailed models.
Voice recognition and voice search: Voice recognition and voice search is mainly used to focus on fulfilling the customers’ experience. Enterprises can make the brand content more appealing by using a more conversational tone, incorporating longer tail keywords and implying question and answer, and short answer content formats.
Fraud detection: The chances of being a victim of online scam have multiplied over years. This accelerates the need for fraud detection models. Many enterprises use machine learning applications to weed out likely cases of fraud. The fraud detection applications work by learning the characteristics of legitimate transactions and then scanning incoming transactions for characteristics that deviate.
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