Modern firms face a flood of dense, unstructured reports. Turning these documents into usable insights takes heavy effort and is far from agile when quick answers are needed. VTS-AI tackles this gap. It integrates Visual Thinking Strategies, which emphasize evidence-based observation, linking, and thinking, into AI agents, so the agents can extract business insights from unstructured text, tables, and images at scale. The system works in three tiers (micro, meso, macro). It tags issues, links them to source pages, and rolls them into clear action levers stored in a searchable YAML file. In tests on an 18-page business report, VTS-AI matched the speed of a one-shot ChatGPT prompt yet produced richer findings: page locations, verbatim excerpts, severity scores, and causal links. Analysts can accept or adjust these outputs in the same IDE, keeping human judgment in the loop. Early results show VTS-AI spots the direction of key metrics and flags where deeper number-crunching is needed. Next steps include mapping narrative tags to financial ratios, adding finance-tuned language models through a Model-Context Protocol, and building a Risk & Safety Layer to stress-test models and secure data. These upgrades aim to make VTS-AI a production-ready, audit-friendly tool for rapid business analysis.

In the field of business data analysis, the ability to extract actionable insights from vast and varied datasets is essential for informed decision-making and maintaining a competitive edge. Traditional rule-based systems, while reliable, often fall short when faced with the complexity and dynamism of modern business data. Conversely, Artificial Intelligence (AI) models, particularly Large Language Models (LLMs), offer significant potential in pattern recognition and predictive analytics but can lack the precision necessary for specific business applications. This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of LLMs in generating actionable business insights.

Machine learning helps to group a humungous amount of data. An unsupervised algorithm looks for the patterns in the data and groups them accordingly. Researchers use this feature to work with sample data. A decision tree is an upside-down tree where you start with numerous options on top and move towards narrowing options. The learning algorithm takes a date set and uses rules to divide it into small groups and differentiate based on the features.

With computerized reasoning (AI) turning out to be progressively well known, you may believe that it's simply a popular expression or that it's restricted to fancy self-driving vehicles and showing you what to watch on Netflix. Indeed, we'll tell you, it's not. Indeed, AI is changing the manner in which we live. It straightforwardly impacts the manner in which we shop on the web, how we convey, and how we're engaged. Computer based intelligence innovation is wherever around us, from profound learning and normal language handling frameworks to independent vehicles and shrewd robots.

Tendü Yoğurtçu, CTO, Precisely, directs the company's technology strategy and innovation, leading research and development programs. There's no doubt that artificial intelligence (AI) and machine learning (ML) are increasingly important to organizations seeking competitive advantage through digital transformation. More than 75% of enterprises are prioritizing AI and ML over other IT initiatives, and they are hiring data scientists in droves to make those initiatives happen. However, most of those efforts are siloed within individual business functions rather than addressing digital transformation across the enterprise. Traditional analytics cannot handle the volume and complexity of data available to organizations today.

As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data. R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling. Data Science includes various fields such as mathematics, business insight, tools, processes and machine learning techniques.

Decision intelligence is a framework that supports data and analytics architects model, align, develop, implement and track decision-making models and processes. Decision intelligence is thought to have a huge impact on business results and performance, with Gartner forecasting that over 33% of organizations will have analysts that practice business intelligence by 2023. Decision intelligence connects business problems and applies data science to find appropriate solutions. For this to be achieved, stakeholder behaviors need to be analyzed and incorporated into the decision-making process. Data intelligence is best described as an amalgamation of data science, business intelligence, decision modeling, and overall management.

In response to what it says is customer demand for "relentless" automation, Oracle plans to release in November a series of updates to its Fusion Cloud ERP and EPM suite that add features designed to streamline the process of logging and tracking transactions, while offering enhanced, AI-based analytics meant to optimize business processes. "Organizations at large are really looking to us to help them to improve the speed, the accuracy of the business processes, and really weeding out those mundane, really non-value add tasks as much as possible," said Juergen Lindner, Oracle's senior vice president of SaaS marketing. See "The best ERP systems:10 enterprise resource planning systems compared," with evaluations and user reviews. Learn why companies are increasingly moving to cloud ERP and how to spot the 10 early warning signs of ERP disaster. Get weekly insights by signing up for our CIO Leader newsletter.

There won't be any business insights if the data quality is poor. When preparing data, I often go through many different approaches to reach a level of quality of data that can provide accurate results. In this article, I describe how unsupervised ML can help in data preparation for machine learning projects and how it helps to get more accurate business insights. For accurate predictions, the data must not only be properly labeled, de-deputed, broad, consistent, etc. The point is that the machine learning model should process the "right" data.

Machine learning and AI can transform unstructured dark data into valuable business insights. Learn how to process dark data and use the information to your advantage. To compete in modern digital environments, machine learning, deep learning and AI are increasingly accessible. By using machine learning and AI, companies can use dark data to acquire more competitive business insights. Dark data consists millions of unstructured data points that businesses accrue and store in multiformat data lakes.