Systems | Development | Analytics | API | Testing

Leon Taiman, Global Practice Lead at Dell Technologies, discusses the strategic partnership between Dell, Cloudera, and NVIDIA to accelerate the adoption of Private AI on-premises for customers.

The AI Forecast welcomes back John Santaferraro, host of The Digital Analyst Podcast and CEO of Ferraro Consulting, for a candid look at what’s really coming next for AI in 2026.

The AI ecosystem is exploding with tools that promise to accelerate delivery, improve quality, and transform the way we work. Yet for many teams, evaluating these tools is overwhelming - flashy demos and marketing claims rarely answer the real questions: Will this work in our context? Can it scale? Is it sustainable?

Professional services speed up onboarding by helping teams get maximum value fast: through setting clear expectations, calibrating the tool, and working side-by-side so users learn by doing. This builds confidence and leaves teams fully empowered, which is valuable because they can achieve meaningful impact quickly without relying on long-term vendor dependence. — Mush Honda, Chief Quality Architect at Katalon.

Learn more: speedscale.com.

Learn more: speedscale.com.

Following your migration assessment, it is time to execute the transfer of your data and SQL queries into Google Cloud. This video dives into the specific tools and services that simplify migrating workloads from Snowflake, Teradata, Cloudera, and Databricks into BigQuery, Dataproc, and Google Cloud Storage.

AI is reshaping software testing, but real quality does not come from hype alone. This session explores where AI truly adds value in automation and where human testers remain essential. It focuses on building a practical partnership between AI and testers, where speed and scale are balanced with judgment, context, and responsibility.

AI is leveling the field across organizations. Everyone, from leaders to individual contributors, is approaching work with a beginner’s mindset, questioning what AI can do, what it cannot, and how it affects business, customers, and roles. This shared uncertainty challenges traditional leadership models and raises a fundamental question. How does leadership evolve when no one has all the answers?

Retrieval-Augmented Generation can sound convincing while still being wrong. This session focuses on moving beyond surface-level metrics and turning stochastic AI outputs into evidence-backed, verifiable results. It explores how to test the entire RAG pipeline, from ingestion and indexing to retrieval, grounding, and answerability, ensuring every response is traceable to the right source, policy, and user context.