In this article, we explore how agentic AI is revolutionizing fraud detection in the financial sector by combining real-time monitoring, multi-agent coordination, and dynamic threat analysis. We’ll also look at specific use cases and the challenges financial institutions need to address when implementing this advanced technology.

In this article, we explore the technical applications and real-world impact of agentic AI in banking, covering use cases such as fraud detection, personalized financial advising, and credit scoring. We will also outline the challenges associated with agentic AI, including trust calibration and regulatory compliance, and how Intelligent Core™ can help financial institutions harness the full potential of this technology.t all begins with an idea.

The retail industry is undergoing a profound transformation, driven by advancements in artificial intelligence (AI). At the forefront of this shift is agentic AI—a new type of AI with autonomy, decision-making capabilities, and goal-directed behavior. Unlike

Agentic AI systems operate autonomously, learning from real-time data and adapting to new circumstances without human intervention. This article explores how agentic AI is revolutionizing supply chains with new case studies, successful implementations, and technical insights.

Traditional wastewater treatment methods, which rely on chemical, physical, and biological processes, are increasingly struggling to keep up with new contaminants and growing environmental pressures​​. Enter artificial intelligence (AI)—an advanced technology that is revolutionizing wastewater treatment by optimizing processes, reducing resource consumption, and ensuring environmental sustainability.

The Fast Moving Consumer Goods (FMCG) industry thrives on agility, precision, and efficiency. With evolving consumer behavior, supply chain disruptions, and fierce competition, the sector must continuously adapt. Agentic AI is emerging as a powerful tool that can help FMCG companies stay ahead by autonomously making decisions, learning from real-time data, and optimizing operations across the entire value chain.

Hydrogen, especially green hydrogen produced using renewable energy, offers immense potential for decarbonizing industries such as manufacturing, transportation, and power generation. However, hydrogen production faces significant challenges related to process efficiency, cost optimization, and energy intermittency. Artificial intelligence (AI) is playing a pivotal role in addressing these challenges, making hydrogen a more viable and scalable clean energy source.

Small and Medium Enterprises (SMEs) operate in highly competitive environments where operational efficiency can make the difference between thriving or just surviving. While AI-powered solutions have traditionally been associated with large corporations, recent advancements in plug-and-play AI tools have made them accessible for SMEs.

This article explores five practical ways AI streamlines operations for SMEs, with real-world examples and case studies to demonstrate the impact of adopting AI-driven solutions.

Lean AI has emerged as a disruptive approach to deploying AI efficiently, focusing on smaller, more focused models that require fewer data resources and can be iterated quickly. For SMEs (Small and Medium Enterprises), Lean AI provides a path to plug-and-play AI solutions that reduce operational burdens, avoid large investments in infrastructure, and deliver measurable results fast.

Agentic AI systems can think, decide, and act on their own. These systems leverage advanced models, such as generative AI and reinforcement learning, to develop autonomy, make decisions toward specific objectives, and continuously learn from their environment​​.