Todays IT departments have an amalgamation of DevOps, Waterfall, artificial intelligence, and OS/new release software, so quality assurance must be able to test and to verify the goodness of all these variegated systems. Yet, those of us who have led IT departments know that the QA function is habitually under-appreciated.Understanding that QA must broaden its reach to test such a broad spectrum of different systems, vendors have rolled out QA tools like the automated execution of test scripts that QA designs.This has generated a steady market in QA testing software, which Global Market Insights pinpointed at $51.8 billion in 2023, with a projected CAGR (compound annual growth rate) of 7% between 2024 and 2032.What IT departments should do now is strategize how a limited QA staff can best use these tools, while also developing the knowledge base and reach allowing them to cover the broad array of new applications and systems that QA is being asked to test.Performing QA With No Single Pane of GlassIf you are in system programming or network support, you know that there are over-arching software solutions that boast single pane of glass visibility. These systems provide an overall architecture that enables you to unify visibility of all of the different tools and functions that you have on a single screen. Not all IT departments invest in these expensive software architectures, but at least they do exist.Related:That isnt the case for quality assurance.In QA, the test bench is a hodgepodge of different tools and techniques spread out on a general tool bench. When a staffer performs QA, they pick whatever tools they choose to use from this tool bench based upon the type of application they are being called upon to test.If the application area to be tested is DevOps, QA is an iterative never done function that might use some test automation for workflow execution, but that also requires a high amount of collaboration between QA, development and end users until everyone arrives at a consensus that the application is production ready.In the AI environment, testing is also iterative and never finished. You work with development and user area subject matter experts to achieve the gold standard of 95% accuracy with what subject matter experts would conclude. Then you must periodically reaffirm accuracy because business conditions constantly change, and accuracy levels could fall.If the application is waterfall, it routes through the traditional path of development, unit test, integration test, regression test, deploy.Related:If the system is a new database or operating or infrastructure system release from a vendor, the new release is first simulated in a test environment, where it is tested and debugged. The new release gets installed into production when all testing issues in the simulated environment are resolved.Each of these test scenarios requires a different mental approach to QA and a different set of tools.Make QA a Strategic Function and Elevate its Standing?Test tool provider Hatica has stated,In the past, QA engineers were primarily focused on testing -- finding bugs and ensuring that the product worked as intended before it was released to users. However, this reactive approach to quality is no longer enough in todays environment. Before long, QA engineers will shift from being testers at the end of the process to quality strategists who are involved from the very beginning.In Agile and DevOps development, there already is an emerging trend for QA that confirms this. QA is immediately engaged in Agile and DevOps work teams, and the QA team provides as much input into the end-to-end DevOps/Agile process as development and end users. As IT departments move more work to Agile and DevOps, QAs role as a frontend strategist will expand.Related:However, in waterfall and new infrastructure release deployments, QAs role is more backend and traditional. It performs end of the line checkouts and is often not engaged in the initial stages of development. AI also presents a QA challenge, because a separate data science or subject matter expert group might do most of the system development and checkout, so QAs role is minimized.The Best Approach to QAThanks to the Agile/DevOps movement, QA now sees a more forward-thinking and strategic role. Yet at the same time, applications in the AI, waterfall, and infrastructure areas engage QA as more of a backend function.QA is also knee-capped by the lack of a single architecture for its tools, and by the brutal fact that most of the staff in QA departments are new hires or junior personnel. Quickly, these individuals apply for transfers into application development, database or systems, because they see these as the only viable options for advancing their IT careers.Understanding these realities, CIOs can do three things:1. Move QA into a more strategic position in all forms of application development. Like the IT help desk, QA has a long institutional memory of the common flaws in IT applications. If QA is engaged early in application development processes, it can raise awareness of these common flaws so they can be addressed up front in design.Accept as well that most QA staff members will want to move on to become a developer or an IT technical specialist and use QA as a grooming ground. To this end, the more QA gets engaged early in application planning and development, the more IT software knowledge QA staff will gain. This can prepare them for development or systems careers, if they choose to take these routes later.2. Ensure that QA staff is properly trained on QA tools. There is no uber architecture available for the broad assortment of tools that QA uses, so personalized training is key.3. Foster collaboration. In the Agile/DevOps environment, there is active collaboration between QA, development and end users. In AI development, CIOs can foster greater QA collaboration by teaming QA with IT business analysts, who often work side by side with user subject matter experts and data scientists. In new infrastructure release testing and in waterfall testing, more active collaboration should be fostered with system and application programmers.The more collaborative bridges you build, the more effectively your QA function will perform.