R&D doesn’t lack data—it lacks signal. AI-driven knowledge discovery only works when answers are grounded in trusted, contextual enterprise data, not probabilistic guesswork. Most AI tools break trust before they create value. Treating research data like generic internet content strips away context, provenance and scientific rigor. Boring, reliable AI wins in 2026. Knowledge discovery that is governed, explainable and embedded into real R&D workflows is what turns AI from pilots into lasting outcomes.

In today’s data-driven world, cloud storage is no longer optional—it’s essential. Organizations rely on platforms like Amazon S3 for durability, scalability and cost efficiency. With support for objects up to 5 TB, S3 has become the backbone for data exchange, analytics and ETL workflows. But moving large files between systems and S3 can be challenging.

ICYMI: Hazem El Zayat Catches Up with 10 Minute Martech on Why Strategy Should Always Come Before Tech

The Progress DataDirect Autonomous REST Connector makes it easy to connect to REST APIs using ODBC or JDBC without writing custom code. At the heart of this capability is the Autonomous REST Composer, a powerful UI tool that helps users quickly create REST configuration files, or .REST files.

This post explores practical approaches to adopting AI responsibly in SaaS products, with a focus on ethical decision-making, sustainability and long-term value. It outlines key considerations teams can use to evaluate where AI adds real impact without unnecessary complexity.