September 2025

By: Bryan Reynolds | 04 September, 2025

This article outlines the urgent need for enterprises to rethink software asset management in the era of artificial intelligence (AI), introducing the AI Governance and Asset Management (AI-GAM) framework. It argues that traditional approaches to software asset management are inadequate for overseeing AI assets—which include models, data, and infrastructure—due to their dynamic, high-risk nature. Integrating global governance standards such as the EU AI Act, NIST RMF, and OECD AI Principles, the article presents a phased roadmap and maturity model to build ethical, scalable, and regulatory-compliant AI programs. By embedding governance within the lifecycle of AI asset management, organizations can minimize risk, maximize ROI, and position themselves for long-term strategic advantage.

By: Bryan Reynolds | 03 September, 2025

This executive guide demystifies cloud-native architecture and its business impact, going well beyond technical jargon to explain how modern organizations can drive agility, resilience, and value creation. It explains core concepts like microservices, containers, and managed Kubernetes, highlighting the cultural and operational changes required for a successful transition. The article provides a business-focused analysis of cloud-native ROI, total cost of ownership (TCO), cost comparisons between managed versus DIY Kubernetes, and delivers a phased, actionable framework for getting started. Designed for business leaders, the guide underscores that cloud-native transformation is a pivotal business strategy essential to closing the agility gap and remaining competitive in today’s digital economy.

By: Bryan Reynolds | 02 September, 2025

This article provides a comprehensive analysis of Anthropic’s new web-based Claude Code AI assistant, discussing its features, benefits, and strategic importance for enterprise software development. It highlights how Claude Code democratizes advanced, agentic AI-driven development for a wider audience, contrasts it with other leading tools like GitHub Copilot, Amazon Q Developer, and Tabnine, and offers actionable insights for IT leaders evaluating AI coding assistants. The piece covers practical adoption strategies, integration considerations (especially with Azure DevOps), and outlines best practices for successful enterprise rollout while also summarizing key market trends in AI-driven coding.

By: Bryan Reynolds | 01 September, 2025

This comprehensive guide explores Devin.ai, the world's first autonomous AI software engineer developed by Cognition Labs. The article cuts through the hype to provide an in-depth look at what Devin is, its architecture and workflow, pricing structure, costs (both direct and hidden), and its real-world effectiveness based on case studies and independent tests. It compares Devin to other AI coding agents and assistants, analyzes its practical ROI, and offers strategic advice for business and technology leaders considering AI integration into their software development pipelines. The piece ultimately concludes that Devin excels in highly structured, large-scale, repetitive development tasks, but requires significant oversight and is not yet a complete replacement for skilled engineering teams.