Saxon AI

  The manufacturing industry has already been using   Artificial Intelligence (AI)   since the fourth industrial revolution. We can see robots doing heavy work on the production floor with great accuracy and efficiency. However, manufacturers didn’t get to use AI much in their business operations. Now, with the entry of generative AI in manufacturing, you have an opportunity to transform your back-office operations and drive business growth.  In a survey by  Capgemini , almost 50% of executives said that generative AI is disrupting their manufacturing business. So, how is generative AI going to transform the manufacturing industry?   Let’s explore.   Five Major Applications of Generative AI in Manufacturing   Improving product design and development   For manufacturers, time-to-market is an important metric. Given the competition, demand, and changing market dynamics, you have to innovate fast and produce faster. However, there are ...

  A survey by   Capgemini   found that 60% of business executives strongly advocated generative AI. Every industry – healthcare, manufacturing, banking, finance, etc., is tapping into the potential of generative AI technology. Retail, which is data-rich and touches customers’ lives directly, also finds some game-changing use cases for generative AI. The time is ripe for leveraging generative AI in Retail and embracing transformation. In this article, let’s explore five ways you can use   AI technology   for a competitive edge. Five use cases for generative AI in Retail Enhance CX with personalized product recommendations Imagine walking into a store and the products on display are tailored specifically to your preferences and needs. You would be more likely to buy, wouldn’t you? As a retailer, you can leverage generative AI to analyze your customers’ past shopping behavior, browsing history, and even social media activity to create hyper-personalized product rec...

  Generative AI   is widely accepted by users across the world. As gen AI evolves, we are exploring the business use cases for gen AI in various industries. Human resources management (HRM) is a key part of any business. Like any other operations, HR operations also involve mundane, repetitive tasks, keeping HR people occupied. Should these tasks be automated, HR teams can focus more on enhancing company culture and employee experience, which will translate into productivity gains and profits. Toward that objective, let’s explore how to leverage generative AI in talent management and how it will transform the people teams.  Five ways generative AI in HRM will transform business   Expedite talent acquisition and hire the best Talent acquisition is instrumental in building an organization’s workforce. The recruitment process starts with drafting the job description. And this is where challenges to recruiters start, too. Recruiters spend at least two hours writing a job...

  Intelligent Document Processing marks the ultimate convergence of OCR, Artificial Intelligence, and Machine Learning- a unified solution for   advanced data processing . It involves data extraction from diverse sources, all managed on a unified platform. Decades ago, financial organizations couldn’t fathom such a technology easing their operations the way IDP does. In this blog, we will take you through the top six use cases of   Intelligent Document Processing(IDP)   in banking and financial services and understand why financial firms should transition from manual data entry and template-based OCR to advanced IDP solutions for document processing. A deluge of documents and data   Globally, banks and finance revolve around astronomical volumes of data. Though hardcore financial documents with numerical data in debit, credit, profit, loss, and more are primarily structured, a monumental set of supporting documents is vital yet unstructured and perpetually chang...

  Augmented analytics are democratizing data and empowering businesses with faster time to actionable insights. But what’s augmented analytics? How does it help your business?   Let’s explore.  As we are integrating more digital channels into our enterprise ecosystems, the size and intricacy of business data are increasing. Extracting meaningful insights from this data has become complex. To execute processes in the data analytics lifecycle, such as integration, preparation, modeling, and visualization, you require expertise and sophisticated software tools. So, traditional data analytics is a manual, expensive affair requiring days to weeks of time to generate insights.  On the other hand, augmented analytics doesn’t require you to be a seasoned data professional to extract actionable insights from your enterprise data and make informed decisions. By enabling business users to analyze and extract value from data, it helps you drive data culture in your enterpri...