Low Down on Low-Code

In this episode, John Rymer and Rick Greenwald explore the real value of AI in business, distinguishing between operational efficiency and strategic advantage, and debunking common misconceptions about AI's capabilities.
Key Topics
The two ways IT systems deliver value: operational efficiency and strategic advantage
The impact of AI hype and market expectations
AI as a tool for operational efficiency and its limitations
AI's role in strategic advantage and innovation
The importance of asking the right questions when using AI for business reimagination
The distinction between AI research and AI application in business
The non-deterministic nature of AI and its implications for decision-making
The evolution of AI from a revolutionary to an evolutionary technology
The importance of critical thinking skills in leveraging AI effectively
The risks of over-relying on AI for competitive advantage

In this episode, Chris Condo shares his journey from software engineering at Microsoft to becoming a key voice in AI's impact on engineering and organizational change. We explore how AI is transforming software development, the evolving role of engineers, and the systemic changes needed for successful AI adoption.
Key Topics
Chris Condo's background at Microsoft and Forrester
Impact of AI on software engineering and organizational change
Adoption levels of AI in organizations and best practices
Skills engineers need for AI success
Systemic change required for AI integration
Reimagining Agile practices for AI-driven development

In this episode, Val Huber discusses the evolution of rules-based systems, the impact of AI on business logic, and how Gen AI Logic is transforming application development through context engineering and executable models.
Key Topics
The evolution of rules-based systems and their automation
The impact of AI on generating business logic and rules
The concept of context engineering and its role in AI training
Differences between rules and procedural code in AI applications
How APIs serve as logic containers in AI-driven architectures

This episode explores the evolving landscape of software pricing, the impact of AI and platform models, and how CIOs can navigate this complex environment. Experts Francis Carden and Andy Bartels share insights on future trends, pricing models, and strategic considerations.
Key Topics
The shift from perpetual licenses to SaaS and platform models
The challenges and opportunities of token-based and usage-based pricing
The impact of AI and large language models on software costs
The importance of economic analysis and risk management in software investments
The role of platform vendors and models in reducing technical debt

In this episode, John Rymer and Rob Koplowitz sit down with Dave Marcus to discuss his hands-on evaluation of the fast-moving world of vibe coding, low-code, and process automation platforms.
Dave explains why he chose a CRM-style application as his test case: it had a realistic data model, a third-party integration with Microsoft SharePoint, and a workflow component that would reveal how these tools handle real business complexity rather than simplistic demo apps.
A major theme of the conversation is the difference between prompt-driven development and visual tooling. Dave found that while prompting could generate surprisingly functional applications, the experience often became frustrating when he needed to adjust layout, test changes, or understand dependencies. Visual tooling, when available, often helped—but some platforms treated it as secondary or immature compared to prompting.
The discussion also digs into a critical enterprise concern: maintainability. John and Rob push Dave on what happens when applications need to evolve over time due to changing business, regulatory, security, and workflow requirements. Dave emphasizes that model-based systems still matter because they make dependencies, workflows, and governance visible in ways code-heavy systems often do not.
Another key takeaway is workflow orchestration. Dave argues that workflow is not just a diagram—it is part of the application’s operational logic and must be understandable to domain experts, not just developers. He notes that some pure vibe coding tools bury workflow in code, while some process-centric platforms add complexity that may be more than needed for simpler applications.
In the end, Dave’s conclusion is nuanced: there is no single winner. Different platforms are better suited to different use cases, and the market is evolving too quickly for simplistic “replace everything” narratives to hold up.
Key takeaways
A realistic evaluation needs a real application, not a toy demo.
Prompting can produce functional apps, but visual refinement and testing remain important.
Workflow and dependency visibility are essential for enterprise maintainability.
No single platform category is best for every scenario.
The market is evolving quickly, so these tools must be reassessed continuously.