Systems | Development | Analytics | API | Testing

Organizations waste an average of $12.9 million annually due to poor data quality. Traditional ETL processes built on hard-coded rules break with schema changes, batch processing misses real-time insights, and manual mapping creates bottlenecks that delay critical business decisions by days or weeks.

Data teams spend 45% of their time on data preparation, which stifles business growth and delays critical insights. With the ETL market projected to grow from $533 million to $1.28 billion by 2034, businesses face an overwhelming array of choices. Yet traditional ETL tools require specialized coding expertise that non-technical teams simply don't have, creating dangerous dependencies on overburdened IT departments.

AI agents have exploded. And we use this term intentionally. Tech vendors rushed to put out agent products that don’t stand up to enterprise use with results that were mostly underwhelming and sometimes even catastrophic. Appian took a different approach. While companies are now scrambling to validate the efficacy of their AI, Appian’s AI is field-tested, so the products you get are ready to use and safe to deploy from day one.

Just a few years ago, AI’s effect on software development was debatable — would it be as transformative as everyone predicted? But its impact is undeniable. In a 2024 survey, a majority of developers reported using AI in development, a sharp increase from the year before. In a short period of time, developers adapted, integrating agentic AI into their daily operations to boost productivity. Quality assurance teams are not far behind.

In a previous blog post, we introduced one of the most significant advancements in Qlik Predict to date — multivariate time series (MVTS) forecasting, bringing enterprise-scale accuracy and context to complex prediction problems. But MVTS is only part of the story. Over the past few months, we’ve continued to enhance Qlik Predict with several powerful updates designed to make predictive AI more trustworthy, fair, and connected for our customers.

Is your artificial intelligence (AI) underperforming? You're not alone. Stale, fragmented data is a leading obstacle to optimal AI performance. Outdated information cripples your decision-making, fuels operational inefficiencies, and leads to missed opportunities—from slow fraud detection to irrelevant chatbot responses. AI’s rapid evolution makes real-time data even more critical: That’s why real-time data streaming isn’t merely an option.

Combine your data with generative AI to analyze and improve customer experience.

Quality assurance has always been about balance, ensuring every scenario is covered without drowning teams in complexity. Yet, as systems grow and requirements multiply, keeping up with coverage demands has become one of the hardest parts of modern QA. That’s where AI-powered test model generation steps in, not to take over, but to amplify how testers design, visualize, and expand coverage.

In just a few steps, you can link your SQL Server database to ChatGPT using DreamFactory, eliminating the need for custom API development. DreamFactory automatically generates secure REST APIs for your database, enabling ChatGPT to interact with your data effortlessly. Here's a quick breakdown: What You Need: SQL Server instance, DreamFactory account, API key, and OpenAI's ChatGPT API access.

A burgeoning cutting-edge technology has been fundamentally transforming how we build automation inside disruptive businesses: agentic AI. The impact of agentic AI is already shaping up to be massive. And agentic adoption is soaring: Kong's Agentic AI in the Enterprise report found that, of those with visibility into their organization’s plans, 90% say their companies are actively adopting AI agents.