Tag: Data and Artificial Intelligence

Key Takeaways:

1. GenAI is relevant for both service providers and end users
2. GenAI drives scalability and efficiency
3. GenAI has massive self-improvement and integration capabilities

Introduction

The digital payments lifecycle has evolved significantly since its inception, with total transaction value expected to reach USD 17.72 trillion in 2024 according to Statista. This has partly been made possible by a constant evolution in various technologies, such as data encryption, biometric authentication, magnetic secure transmission, near-field communication, blockchain, artificial intelligence and machine learning. 

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Key Takeaways:

1. AI agents massively expand the potential of automation
2. Complex business processes can be dealt with without manual intervention
3. New levels of efficiency, productivity, accuracy and informed decision-making
4. Striking the balance between AI abilities and human creativity is vital

AI Agents Explained: The Future of Task Automation and Productivity

Artificial intelligence (AI) agents are programs that can perform tasks and achieve pre-defined goals through interactions with the environment around them and collecting data. They use algorithms and data input to process information using machine learning models, react to variables and circumstances, and work towards human-set objectives.

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Banking Experience 2025: Trends Shaping Customer Expectations

All over the world, traditional banking models are being disrupted by changing customer experience expectations. For example, by 2028, nearly 80 million Americans are expected to be using digital banking services – that’s nearly a quarter of the entire population.

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As Big Tech dives deeper into artificial intelligence, Microsoft, Meta, Amazon, and Alphabet are collectively expected to surpass $200 billion in capital investment this year. These expenditures are primarily focused on data centers and AI-driven tools. While these tech giants tout AI’s potential to enhance services and cut costs, Wall Street is skeptical about whether the returns will justify such massive spending.

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Key Takeaways:

• LLM agents deliver contextual content in a range of applications
• They can support interaction, collaboration and analytical insights
• Retail and education are two sectors already making full use of the technology
• Ethical use is vital, especially as the technology advances

Understanding LLM Agents: Types and Applications in AI

Large Language Models have formed one of the cornerstones of artificial intelligence growth in recent years, thanks to their ability to generate written content that’s detailed, contextual and relevant to the user’s needs. With 67% of businesses prioritizing LLM adoption by year-end, LLM agents are proving to be particularly useful, from creating project plans and writing code to summarizing meetings and supporting research.

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