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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.

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.

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 integration has long been one of the most time-intensive parts of enterprise IT. Connecting multiple systems, reconciling formats, and ensuring data reaches its destination reliably often requires weeks of preparation before the first record moves. But with AI-powered integration, that timeline compresses dramatically. What once took weeks can now be designed, validated, and delivered in minutes.

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 Outdated information cripples your decision-making, fuels operational inefficiencies, and leads to missed opportunities—from slow fraud detection to irrelevant chatbot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.

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.