Turn AI experiments into measurable gains
RetailNOW panelists offer VARs, ISVs and MSPs a practical playbook for provable AI ROI – and it starts with saying no to complexity.
Build an adaptive AI maturity model
TechHouse CEO Kathy Durfee shares a framework to help businesses align AI adoption with proper oversight and human review.
Five AI services every solution provider should be building today
How IT solutions providers can transform AI buzz into high-margin, repeatable managed services for retail clients.
The evolution of test management: How AI is shaping the next chapter
As software development accelerates with AI, test management must evolve to maintain code quality and prevent bottlenecks across release cycles.
The top 10 security risks enterprises must solve before Agentic AI scales
As agentic AI shifts focus from information accuracy to autonomous action, enterprises must secure every connected workflow and execution path.
Soft skills are the secret to success with AI coding tools
As intelligence tools change how engineering teams write software, clear communication and code review are becoming vital skills for your developers.
The business risk of opaque AI in software development
Opaque AI agents create massive liability. Software developers must prioritize explainability to protect customer trust and drive retention.
Editor’s notebook for ISVs: AI reality checks and steady leadership
ISV leaders must navigate failing artificial intelligence rollouts, rising application vulnerabilities, and economic stress.
Scale software automation with AI assisted code tools
Using AI to automate repetitive boilerplate setup helps QA engineering teams eliminate pipeline friction, scale workflows, and improve test quality.
In regulated industries, faster testing still has to be defensible
Devs must balance the pressure for faster AI driven releases with the strict requirements for documented validation and clear audit trails.
Stop your AI agents from spending too much money
AI agents solve complex problems through judgment but can run up huge bills without economic guardrails. Learn how to govern agentic spending.
The ABAP dev crisis is real – AI code generation in the only viable fix
The SAP ecosystem faces a massive talent drain as developers retire. Organizations must leverage AI tools to bridge the skills gap before 2027.
The high cost of undocumented engineering decisions
Technical turnover costs more than just hiring time. It destroys the institutional memory and rationale behind every major architectural decision.
Why AI Testing Demands Multi-Dimensional Evaluation
Traditional pass or fail testing models fail to capture the probabilistic nature of AI. Teams must shift to multi-dimensional scoring for quality.
Why the design phase remains essential in the age of AI
AI can generate functional code but it cannot replace the empathy and strategic thinking found in the design process to solve human problems.
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Ownership assigned after launch is not ownership
When engineering teams wait for a governance review to define accountability, the system already decided.
The checkbox that isn’t a control: Rethinking human-in-the-loop for ISVs
Relying on a final review screen fails to catch systemic AI errors. Software vendors must architect genuine oversight directly into the product.
AI application generation needs design, discipline and determinism
Scaling AI application generation requires moving beyond prompt-driven workflows to embed architectural and design constraints into development.
Modern monetization: The route to software business success
Stop relying on intuition. Use granular customer usage data to improve software products, optimize AI investments, and boost adoption.
