Systems | Development | Analytics | API | Testing

Why Data Teams Are Best-Positioned For Agentic AI Success With Data Integration and MCPs

Building AI agents is the first step, and it’s positive to see enterprises exploring this avenue. But it’s only the first step. For true enterprise value, these agents must seamlessly connect to your data ecosystem through robust integration, standardized protocols, and be guided by knowledgeable data teams. The need to give AI agents access to data and connect them to the necessary tools and functions has led to the creation of the Model Context Protocol (MCP).

Hitachi: A Process Transformation Blueprint - Streamlining Operations with AI and Data Fabric

Learn how Hitachi America leveraged Appian’s process orchestration and Data Fabric to power AI and unify fragmented sales data across a 55+ company group conglomerate. By integrating siloed systems and delivering real-time insights to their sales teams, Hitachi empowered smarter cross-sell strategies and accelerated proposal delivery by 60%. The result: a 20% reduction in operating expenses, 4x faster time to market, and significant gains in development speed and data efficiency.

9 AI Agents Examples That Solve Real Enterprise Challenges

When ChatGPT hit headlines, many equated artificial intelligence with simple chatbots. Useful? Sure. But limited to isolated tasks and virtual assistants, they fell short of their full potential. That’s changing. Businesses are now entrusting AI agents with real decision-making power on complex tasks. These agents reason, adapt, and act autonomously—without waiting for human intervention. When they’re deployed directly into processes, they provide real value at enterprise scale.

Artificial Intelligence in Payment Processing: Efficient Investigations, Happier Customers

Artificial intelligence is one of the most impactful innovations the financial services industry has ever seen. From streamlining financial operations to enhancing customer experiences, artificial intelligence capabilities help financial sector organizations stay competitive in a marketplace that never stops shifting. The benefits of AI also extend to payment processes. Here’s a real-life example.

AI Agent vs. AI Assistant: Understanding The Differences

Thanks to artificial intelligence’s increasing influence in everyday life, many previously uncommon terms have become part of the zeitgeist, much like AI itself. Chances are, you’ve already come across the terms ‘AI agent’ and ‘AI assistant’. You might even have seen them being used interchangeably. While the two terms sound similar, what each of them represents is very different.

Demystifying the ATO Process: What Government Teams Need to Know About Cloud Security

Table of contents What is an ATO and why does it matter? DoD security levels Federal civilian security levels Key policies governing ATOs Common challenges in obtaining an ATO Streamline the ATO process with an approved cloud The federal government has made cloud computing a strategic priority. Government organizations that embrace the cloud gain security, flexibility, and cost savings.

AI Agent Framework: What it is and How to Choose The Right One

Just like every impressive building starts with a strong foundation, every remarkable capability in an AI agent can be traced back to its framework. AI agent frameworks or agentic AI frameworks make it possible to create smart, efficient AI agents that can serve as simple chatbots, facilitate agentic automation, or contribute to complex use cases in finance, supply chain, healthcare, manufacturing, and robotics as part of a multi-agent system. But what are AI agent frameworks?

The Evolution of Automation: Why Enterprises Are Turning to AI Agents

Process automation has been around for decades, but the tools under this technology umbrella have multiplied over the years. Robotic process automation (RPA) was an early tool for handling simple, routine tasks, and it’s still powerful to have in your intelligent automation arsenal. But when technologies like intelligent document processing, business rules, and workflow orchestration entered the scene, they brought new capabilities to the process automation suite.

What AI Approach is Right for You: LLM Apps, Agents, or Copilots?

The generative AI hype train doesn’t appear to be slowing down, with organizational adoption rising from 33% in 2023 to 78% by the end of 2024. In fact, bigger companies are leading the way in GenAI adoption, with the global AI market projected to grow annually by 36.6% between 2024 and 2030. However, GenAI growth isn’t following a linear path. Organizations are utilizing different AI approaches, depending on their specific use cases.

Prompt Engineering Best Practices You Should Know

Look around yourself. We are swarming in the world of data and AI. From students at school using ChatGPT to complete their assignments to professionals using AI for market research, content creation, or even debugging code, everyone is leveraging the power of large language models (LLMs). Mr. Smith isn’t Googling his tax questions anymore; he’s asking an AI assistant.