AI agents are becoming practical tools that autonomously perform tasks, support decision-making, and adapt to business needs. By starting with focused, high-value use cases and ensuring strong data governance and human oversight, organizations can unlock real value while building long-term capability.
A data-centric approach to AI prioritizes improving data quality over tweaking models or code. As AI shifts toward unstructured data like text and images, traditional tools fall short. Data and analytics architects can address these challenges using four key pillars: data preparation and exploratory analysis, feature engineering, data labeling and annotation, and data augmentation. These pillars enable the creation of high-quality, AI-ready datasets, enhanced by modern tools like automation, low-code platforms, and synthetic data generation for scalable, intelligent systems.
Explore the critical differences between AI agents and RPA. Learn their strengths, limitations, and how businesses can combine both to drive intelligent, scalable, and future-ready automation strategies.
AI agents rely on a layered architecture: Data (storage & retrieval), Model (learning & decision-making), and Deployment (scalability & reliability). Developers must choose between open-source tools (flexibility) and commercial solutions (support & integration).
Key considerations include context management, prompt engineering, error handling, security, and scalability. AI agents are transforming customer service, sales, and software development, with future trends pointing toward specialized AI, proactive automation, and AI-assisted coding.
This blog provides a practical roadmap for business leaders looking to adopt AI agents to streamline operations, enhance efficiency, and drive innovation. It covers key steps, including identifying opportunities for AI agents, selecting the right technology, deploying AI solutions, and ensuring long-term success through maintenance and ethical considerations. Whether you're just starting your AI journey or refining existing implementations, this guide helps businesses harness AI agents effectively while mitigating risks.
Data silos are the natural result of decentralized systems and tooling decisions that optimize for individual departments rather than the organization as a whole. Common entities like "client," "customer," or "user ID" often differ across departments, complicating data integration -- custom ETL (extract, transform, load) processes (read: spaghetti code) that are challenging to scale and maintain. It doesn't have to be that way.
ChatGPT and GenAI have upended content creation and interaction with customers. As "newness" wears off, we settle into a (reasonably) reliable and predictable trajectory. Organizations have gone from "let's see how this works" to "we need to make this work for us ASAP." And now, GenAI opens the door to a bigger technology change: agentic systems.
Data Integration has become a key focus for organizations aiming to unlock value from their rapidly growing data. Cloud-scale data stores – databases, file stores, and the range of big data types – have led many to adopt a data lake house platform, Snowflake and Databricks most prominent among the many options.
Uncover the risks of openwashing in AI tools like ChatGPT. Learn about the Linux Foundation's new tools for ensuring model transparency and protecting your interests.
OpenAI, Google, and Meta streaking towards multimodal AI, enabling more human-like interactions and revolutionizing various applications.
Agile methodologies are now recognized as a structured way to work through ambiguous and complex software problems with evolving solution requirements. Practices such as sprints, backlogs, timeboxing, scrum, can be applied in a variety of…
