Some reaches production anyway. Eighty percent of the organizations surveyed traced a production incident back to AI-generated code, and 53 percent said they knowingly ship code with critical issues. Those teams are not careless. They are under pressure to keep pace, and the review and testing process they built for humanspeed development strains and often fails under the volume. This is no longer a developer problem or a QA problem. A defect that escapes into production becomes an outage, a security exposure, a frustrated customer or a financial hit. Those are the issues your customers already look to you to resolve Telling developers to slow down is not realistic nor the right approach. The productivity gains are too attractive, and no competitive business should give them up. MSPs’ customers will keep adopting AI coding tools, with or without a plan for what surrounds them. In a recent survey, 92 percent of enterprises doubted their existing safeguards could catch AI failures before they reached users. Throwing people at the problem didn’t fix it either. Sixty-four percent of respondents increased QA headcount during the past year, and incidents still went up. Customers need guidance on how to make the end-to-end software release work at the speed of AI, not just the initial code writing. That, coupled with appropriate human governance and release controls, is what will allow your customers to gain both competitively and economically from the new AI era. From AI adoption to AI assurance The first wave of enterprise AI services focused on selection, deployment and integration. Partners helped customers pick tools and get them running. While that foundational work continues to matter, the next wave measures ROI. Customers now must show that AI-generated code is also reliable enough to go into production at close to the speed at which it is being created. That matures and moves the conversation toward release assurance, testing strategy, governance, observability and risk. This gives partners a straightforward place to start. Look at where code enters the client’s development lifecycle. Notice that the next step is always code validation, which is historically highly manpower intensive. It requires creating the right tests (unit tests, functional tests, system tests and many more) and making sure they cover everything the application is supposed to do, including how and where it should fail and on all desired endpoints. Think native apps on iOS or Android or various browsers on various operating systems to begin with, and you start getting a sense of how hard this is. Then one needs to run these tests on the actual devices of interest, diagnose any failures, fix the code or the test, and so on until one is certain of the code quality. Tough, isn’t it? And imagine what occurs when suddenly the code is being written at 10x the pace and lower quality than earlier. This is why customers are looking for help from trusted experts and partners. Providers can help bring the right tools to help verification keep pace with code writing, and when done well, actually free up to 90 percent of the time development teams spend on testing. Hence the earlier claim about being better for the customers both commercially and in terms of ROI. Software Shipped to Production with Known Issues in Past Year FREQUENCY PERCENTAGE Frequently 6% Sometimes 47% Rarely 37% Never 11% Source: Wakefield Research, surveys of business and engineering leaders Business Consequences Experienced from Software Defect, Past 12 Months 0% 10% 20% 30% 40% Negative media coverage 40% Loss of a major contract/customer 38% Regulatory inquiry or fine 34% Customer lawsuit / legal action 33% Stock price impact 30% Executive departure 27% Source: Wakefield Research, surveys of business and engineering leaders 14 CHANNELVISION | FALL 2026
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