Systems | Development | Analytics | API | Testing

Unlock the ROI of AI by Embedding It In Your Core Processes

A new MIT study reveals 95% of gen AI pilots fail. But that’s not an AI problem. It’s an implementation problem. The real issue is the messy, fragmented way AI is used. Too many organizations treat AI as a helper on the sidelines—chatbots, copilots, and assistants that wait to be called upon. While helpful, this approach barely scratches the surface of what’s possible. Real transformation happens when AI is embedded directly into the core operations of your enterprise.

What is AI Data Cleaning?

Before jumping into AI data cleaning directly, let’s first understand data cleaning itself. Data cleaning, also known as data scrubbing, is a critical data preparation step where organizations remove inconsistencies, errors, and anomalies to make datasets ready for analysis. The cleaning process may involve actions like removing null values, correcting formatting, fixing syntax errors, eliminating duplicate data, or merging related fields like City and Postal Code.

Perfecto AI for Retail Application Testing

Experience how Perfecto AI redefines retail application testing in this in-depth demonstration. Discover how our agentic AI navigates complex, real-world scenarios that challenge traditional test automation, such as discovering, installing, and validating retail apps across multiple countries and languages simultaneously. In this video, you will see: Transform your retail testing strategy with a platform that thinks, adapts, and executes beyond the limits of traditional scripting.

Demo: Build An AI Meeting Coach With Flink SQL, OpenAI, & Confluent

Learn how to build a real-time meeting coach system that uses Confluent's data streaming platform, Apache Flink SQL, and Azure OpenAI to provide sales teams with timely, contextual advice. In this demo, Brenner Heintz, Staff Technical Marketing Manager at Confluent, demonstrates the system's architecture, key components, and how it leverages company knowledge documents to enhance sales conversations.

IAM for Agentic AI : Episode 01 - Identity, Evolved for AI

In this inaugural video of our new series, Geethika Cooray, VP & GM of Identity and Access Management at WSO2, introduces the critical and evolving concept of IAM for Agentic AI. As generative AI continues to advance, so does the complexity of AI agents acting on our behalf or on their own. Discover why traditional IAM might not be enough to secure these powerful autonomous agents and learn about WSO2's vision for treating AI agents as first-class entities. Geethika also announces the launch of new agentic AI security capabilities in Asgardeo.

Ep 36 | Rebuilding AI from the Ground Up with Val Cook

When AI needs to think faster, the architecture beneath it matters more than ever. Val Cook, Chief Software Architect at Blaize, joins The AI Forecast to unpack how today’s AI systems demand more than raw compute, but more adaptable infrastructure. He and host Paul Muller explore the critical intersection of hardware and software, the growing importance of data flow machines, and the limits of traditional architectures in real-time environments.

The Role of the Human: How to Build HITL into Agentic QA

TL;DR: In agentic AI systems, unpredictable behavior, contextual nuance, and subjective judgment make full automation impossible — and that’s not a failure. Human-in-the-Loop (HITL) testing isn’t a step backward; it’s a safety net and learning engine. From reviewing ambiguous outputs to approving high-risk actions, strategic human involvement helps catch what automation misses.

The New Standard for AI-Driven Decisions

The strategy is strong, but the insight you need—clear, live, decisive—is missing. It’s buried in dashboards. Stuck in backlogs. Trapped inside tools that promised acceleration, but only slowed you down. We were told things would be different. That self-service would finally work. That AI would bring clarity. That decisions could move at the speed of business. But the promise fell short. You invested in business intelligence. What you got was a backlog.