Generative AI is dissolving the economic logic that made standardized enterprise software the only practical choice for most companies. What replaces it will be shaped not just by the rapidly evolving capabilities of this new technology, but by leaders willing to ask a harder question: Which workflows do we actually need to own?
"When you talk to people about breaking them down, they feel like they're going to get flattened. This negative perception of breaking down siloes can impact the organization's ability to solve the siloes in the first place."
"When we first shipped Copilot, foundation models were not powerful enough to use Copilot to command the applications. This meant Copilot was a passive partner in documents: it could answer questions but missed the mark when it was asked to take action on the canvas directly."
The new feature via the Actual Result field enables you to record precise outcomes for each test step, improving traceability, audit readiness, and collaboration across your teams.
Industry professionals are realizing what's coming next, and it's well captured in a recent LinkedIn thread that says AI is moving on from being just a helper to a full-fledged co-developer - generating code, automating testing, managing whole workflows and even taking charge of every part of the CI/CD pipeline. Put simply, AI is transforming DevOps into a living ecosystem, one driven by close collaboration between human judgment and machine intelligence.
Manual database deployment means longer release times. Database specialists have to spend several working days prior to release writing and testing scripts which in itself leads to prolonged deployment cycles and less time for testing. As a result, applications are not released on time and customers are not receiving the latest updates and bug fixes. Manual work inevitably results in errors, which cause problems and bottlenecks.
Your coding apprentice can build, at your direction, pretty much anything now. The task becomes more like conducting an orchestra than playing in it. Not all members of the orchestra want to conduct, but given that is where things are headed, I think we all need to consider it at least.
Integrating databases into the CI/CD process or the DevOps pipeline is overlooked in the current DevOps landscape. Most organizations have adapted automated DevOps pipelines to handle application code, deployments, testing, and infrastructure configurations. However, database development and administration are left out of the DevOps process and handled separately. This can lead to unforeseen bugs, production issues, and delays in the software development life cycle.
