As AI makes its way into software development, engineers are coding quicker. Development accelerates, but many organizations are discovering that the biggest delays happen after code is written. To realize the full value of AI, engineering teams need more than...
Automation and AI
From Reactive to Predictive: How AI and Integration Transform Engineering Efficiency
The modern engineering landscape is defined by a relentless push for speed and a non-negotiable requirement for safety. For engineering and product leaders in regulated industries, the pressure to deliver complex mechatronics products has never been higher. ...
What is Atlassian Doing With Your Data?
Engineering leadership often faces a difficult choice between adopting cutting-edge innovation and maintaining strict data sovereignty. This balance is particularly delicate in regulated environments like medical device manufacturing, automotive engineering, and...
How Software Teams Can Measure and Maximize AI Coding ROI
AI coding tools are quickly becoming part of everyday software development. Tools like Cursor, GitHub Copilot, GitLab Duo, and other AI assistants are helping developers generate code, complete repetitive tasks, and review pull requests. Software organizations are no...
How to Blend AI Efficiency with Human-Centered Service in JSM
In IT Service Management, there is often tension between the drive for efficiency and the need for a high-quality customer experience. We are often told that AI and automation are the all-encompassing solutions, yet 95% of consumers still demand a human connection...
5 Key Takeaways from the PTC NEXT for Engineering Teams
PTC NEXT Spring 2026 showcased a clear vision for the future of engineering software: connected product data, AI-powered workflows, modern cloud platforms, and an increasingly integrated digital thread spanning CAD, PLM, ALM, manufacturing, and service operations....
Engineering AI Adoption Assessment
Get your AI maturity score Take the assessment to learn how to accelerate your product and software development lifecycle with AI-powered development from a personalized AI maturity roadmap.[dsm_icon_list _builder_version="4.27.6" _module_preset="default"...
Connecting AI Assistants to the Tools You Love with the Model Context Protocol
Artificial intelligence is quickly becoming part of the software delivery lifecycle. Development teams are utilizing AI assistants to summarize issues, generate code, review merge requests, explain pipelines, and automate repetitive tasks. However, many organizations...
Automating Routine Jira Tasks with the Help of Context-aware AI Agents
Any software developer knows the reality of engineering is not always smooth sailing. Between the deep architectural work and the innovative feature builds, teams are frequently bogged down by routine maintenance, bug fixes, and context-switching. The challenge for...
Proven Strategies for Resolving the ALM-PLM Gap in Automotive Engineering
As automotive manufacturers accelerate toward software-defined vehicles, the lines between mechanical, electrical, and software engineering are rapidly blurring. To compete in an era defined by electrification, autonomous systems, connectivity, and regulatory...
Automating Issue Creation in Jira with the help of Redmoon Software
Jira users across many teams rely on issues to plan, track, and complete work. Whether you are managing marketing campaigns, IT requests, or product updates, Jira helps keep work visible and organized. However, creating and managing issues manually can quickly become...
From Hype to Roadmap: Your AI Maturity in 90 days
AI shouldn’t be a moonshot—it should be an operating model. In this practical session, SPK experts reveal how to turn scattered pilots into a funded, responsible plan in just 90 days. We’ll walk through AI Launchpad, our assessment and roadmap that benchmarks your...












