Systems | Development | Analytics | API | Testing

Ep 39 | Enabling the Future of AI with Donna Beasley

AI can’t transform businesses if organizations aren’t prepared to embrace it. Donna Beasley, Chief AI Enablement Officer, joins The AI Forecast to explore thoughtful AI deployment in an organization. She and host Paul Muller dive into the challenges and opportunities of AI adoption, from ensuring data quality to driving organizational change management. They discuss the importance of collaboration across departments, strategies for overcoming resistance, and the critical role of training and meeting individuals where they are in their openness with AI.

Leading with Data: AI-Driven Strategies for Tomorrow

In this interview with Nick Johnson-Hill, VP Data Group for Crum and Foster, we dive into what it takes to lead a modern team of data professionals and leverage modern data strategies for creating maximum impact. Nick also discusses the role and effectiveness of leveraging Generative AI for augmenting data teams.

RAG for SQL Server, MySQL, Postgres - Best Practices for Secure AI + Database Integration

Retrieval-Augmented Generation (RAG) lets LLMs deliver current, context-rich answers by fetching live data—customer records, knowledge articles, metrics—from SQL Server, MySQL, and PostgreSQL. Reports suggest RAG can boost answer accuracy dramatically (in some cases up to 90%), making it compelling for BI, support, and operations. The challenge: enabling on-the-fly retrieval without opening security, compliance, or scalability risks. Executive takeaway: Don’t let LLMs write SQL.

From Data to Decisions: How AI-Powered Analytics Speeds Up Business Impact

Most organizations are swimming in data, but still struggle to turn it into clear decisions. AI-powered analytics bridges that gap by automating routine analysis, surfacing hidden insights, and making data accessible to everyone through natural language. Instead of just looking at what happened, teams can understand why it happened and what to do next. The result is faster, smarter decision-making and a stronger competitive edge. Provide your users with the latest AI-powered analytics features.

How to Debug Agentic AI: From Failed Output to Root Cause

In traditional QA, debugging means tracing a failed test step to a broken function, a missed config, or bad data. There's usually a clear defect, a fixable cause, and a predictable outcome. But in agentic AI systems where outputs are shaped by language, memory, tool use, and learned behavior failure is rarely that clean. Instead, it looks like: If Blog 4 taught us how to design tests that stress these systems, this blog is about what to do when those tests fail.

DreamFactory + Claude Code can build bespoke MCP Servers on your data

In this video, Terence demos how combining DreamFactory's MCP server and Claude code you can securely expose your data schema and allow Claude code to then generate bespoke MCP servers based on your data. This allows you to get the upside of using AI code editors like Claude Code while keeping your data secure.

Proving the Value of AI-Driven Automation for Banking Ops

Financial institutions face growing operational demands in an environment defined by regulatory complexity, legacy system inertia, and the rapid evolution of customer expectations. At the same time, IT leaders are under pressure to not only maintain infrastructure but also demonstrate value to their operations counterparts. The opportunity is clear: use technology to drive operational agility without disrupting existing systems. This is where Appian excels.