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Top Use Cases for AI Analytics in 2023

Artificial Intelligence (AI) is the latest addition to the ‘list of unprecedented things’ which seems to have grown exponentially in recent years. Much of the conversation currently surrounding AI is about the future: will AI result in mass unemployment? A labor-free utopia? Terminator-style oblivion? There’s a place for that discussion, but we want to focus on how AI is being used right now, specifically how it’s transforming the field of data analytics and visualization.

The Biggest AI Trends for 2023


AI isn’t new: In 1952, Arthur Samuel, a computer scientist, developed an AI program to play checkers. Since then, things have evolved considerably, and today, the biggest news surrounds Large Language Models (LLMs). LLMs are a type of machine learning model that use deep learning techniques to process and generate human-like text. This means they can comprehend and generate text in ‘natural language,’ meaning they can understand written text and then generate their own. These are the ‘conversational AI’-type systems, like ChatGPT, that allow users to input complex prompts and generate new content.


AI is redefining user interactions, analyzing data to offer tailored responses and recommendations across sectors. ‘You might also like…’; ‘Customers also bought…’; ‘For you’ pages: all of these are the product of AI Recommender Systems. These systems aren’t new – Elaine Rich built one to suggest books in 1979 – but through greater data access, machine learning, and Reinforcement Learning from Human Feedback (RLHF), the systems have never been more powerful.

Content Creation

Generative AI can produce both written and visual content based on prompts from users. Generative AI utilizes machine learning techniques like Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs) generate realistic and creative outputs, such as images and text. The consensus seems to be that Generative AI is useful for producing a rough first draft from which an actual person can build on and edit. Teachers are no strangers to the power of Generative AI; the implications of this technology on student work has been a hot topic of late. In fact, the issue is so salient that there is now an AI specifically dedicated to identifying GAI content in academic writing.

Data Insights

LLMs mine valuable insights from vast datasets, fueling informed decision-making. Natural Language Processing (NLP) means business users can conduct their own data analysis and visualization. In the case of Astrato’s AI Insights, just one click produces text-based and visualized data insights. The feature works via an integration with OpenAI’s ChatGPT, and makes sophisticated data analysis accessible to business users.

Ethical AI 

As the power of AI has grown, and its use cases have expanded, alignment – ensuring AI is unbiased and responsible – has become a key focus. In March, a group of prominent AI scientists and other notable figures in the field of computer technology, signed a statement declaring: ‘Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war’. Signatories included two of the ‘Godfathers of AI’ (Geoffrey Hinton and Yoshua Bengio), the CEO of OpenAI (Sam Altman), and Bill Gates.

Analytics and Artificial Intelligence Trends in 2023

Data analytics and AI have an interesting relationship. AI requires data to be trained and functional, but it’s also used to analyze data more accurately and efficiently.

The prospects for AI in data analytics are exciting not only due to improved accuracy and efficiency, but due to increased ‘democratization’ – broadening access to such insights. Here is more detail on the top use cases for AI in Analytics in 2023:

Predictive Analytics

AI-driven predictive analytics is transforming how businesses forecast trends. By analyzing historical and real-time data, AI models can make probabilistic predictions with remarkable accuracy. From demand forecasting in retail to predictive maintenance in manufacturing, AI-driven predictions facilitate more accurate decision-making.

Healthcare Diagnostics

In the medical field, AI analytics is making significant strides. AI algorithms can analyze medical images, such as X-rays and MRIs, to detect anomalies that might be missed by the human eye. This early detection improves patient outcomes and saves lives.

Like any large organization, hospitals also have a substantial administrative team. You can find an example of a healthcare dashboard in Astrato here, which gives an idea of how Astrato’s AI offering could be incorporated in the future.

Fraud Detection

Financial institutions are leveraging AI-driven analytics to combat fraud. AI algorithms can identify unusual spending patterns and detect fraudulent transactions in real time, providing a robust defense against cyber threats.

Supply Chain Optimization

The complexities of modern supply chains are a prime candidate for AI optimization. AI technology can streamline inventory management, logistics, and demand forecasting, reducing costs and improving efficiency.

Energy Efficiency

AI-driven analytics are helping industries minimize their environmental footprint. By analyzing energy consumption patterns, AI identifies areas for optimization, reducing waste and energy expenses.

The intersection of AI and analytics has also given rise to innovative products like Astrato AI Insights. The Cloud-native Business Intelligences solution harnesses the power of AI to provide in-depth insights from complex data sets, enabling businesses to make data-driven decisions with confidence.


AI analytics is ushering in a new era of data-driven decision-making. The trends shaping AI’s trajectory in 2023 are indicative of its transformative potential. As industries continue to harness the power of AI analytics, we can anticipate even more groundbreaking use cases that will reshape business landscapes for years to come.

From an analytics perspective, the most exciting prospect is democratization: greater accessibility to data analytics and so more data-driven decision making.