undefined

Abstract blue digital network with glowing data connections and depth.

Why quality engineering is now strategic for media companies

August 25, 2026

<h2>As software becomes the business, AI can help media companies prevent the failures that damage reputation and customer loyalty.</h2>

Visit our Media & Entertainment webpage

In the media and entertainment industry, every subscription, ad impression and livestream depends on software performing reliably. Quality is something customers rarely notice until it’s gone—and then social media ensures everyone else notices it, too. A failure during a global sports event or major cultural moment could easily blow up into a public, brand-damaging incident that impacts revenue. The prospect of high-stakes software failures has put the spotlight on quality engineering, elevating it from back-office safeguard to strategic business priority. AI plays a key role in this transition, by both automating quality engineering and helping to reduce the chance of failure. By applying AI where it matters most, media companies can shift quality engineering from a defensive function into a competitive advantage that protects revenue and customer loyalty. <h3>The new reality of digital quality</h3> Traditional automated testing excels at speed and repetition. Those characteristics served it well a decade ago when most failures were episodic, like a bad release or even an outage. But the interconnection of today’s digital media platforms means that a defect introduced in one release or data model can quickly propagate across millions of users, devices and regions. What’s more, there’s potential for high-profile failure all across the customer experience, from subscription sign-ups and logins to authentication and ad delivery. Static testing scripts aren't designed for these types of systemic quality failures. However, with the addition of AI, media businesses can expand what quality engineering can accomplish, making it adaptive, automatically adjusting as software evolves, and speeding test creation. For media and entertainment companies, AI-augmented quality engineering introduces key foundational capabilities: <ol> <li>Network self-healing through automation. Unlike their static counterparts, AI-powered tests adapt as applications evolve. For example, if a streaming app redesign moves playback controls, AI can recognize the new interface and update the affected tests automatically instead of forcing engineers to rewrite them. The result is less maintenance for quality engineering teams and higher reliability for release pipelines. </li> <li>AI-generated test coverage. AI shortens the time between software changes and quality validation. Instead of quality engineers manually translating every new requirement or code update into a test, AI does it automatically. For example, it can test playback or verify premium subscribers’ ability to download titles for offline viewing. </li> <li>Risk-based testing anchored in business signals. Instead of treating every defect equally, AI analyzes business indicators—startup times, buffering and ad performance—to focus testing where failures are most likely to affect revenue and customer experience. Risk-based testing frees quality engineers to focus on validating high-impact scenarios and improving overall quality.</li> </ol> <h3>Where AI-augmented quality engineering drives business value</h3> We recommend that media organizations focus their AI-powered quality engineering efforts at the business areas that directly influence revenue and brand perception: <ul> <li>Subscription, identity and commerce. This is the most valuable stretch of the funnel—and the most fragile. A quality breakdown here rarely shows up as an error on a dashboard. Instead, it shows up as a lost customer due to issues like a promo code being rejected at the final step, an incorrect renewal charge or a message that “you don’t have access to this title” on a plan that includes it. Many people would abandon the transaction after that type of experience and never return.Rather than test whether a page responded, AI-driven validation verifies the outcome—entitlement granted, correct amount charged—so it catches errors before customers do. </li> <li>Playback and experience quality. These types of viewing complaints are among the most common in streaming and the hardest for traditional quality engineering to spot. Take caption misalignment: As the stream plays and the player reports success, the system delivers a series of positive automated checks. Yet the on-screen subtitles run several seconds ahead of the dialog or spill past the safe area on a particular TV model. Neither problem trips a pass/fail assertion, but each one is obvious to the subscriber. AI-powered visual and semantic validation looks at the rendered frame the way a viewer would. It measures caption timing against the audio track and flags sync, rendering and degradation defects on specific devices. </li> <li>Live sports and tentpole events. Marquee events invert the normal risk equation: Traffic that would build over months arrives at a single moment. The failures here are the ones that trend on social media within seconds—a sign-in service that buckles as millions log in at once, or a stream that freezes at the decisive play. Because every viewer experiences the same moment together, a defect that would be minor on an ordinary day becomes a highly public incident. AI-supported load modeling, chaos experiments and failure simulations let teams rehearse these scenarios ahead of time.</li> </ul> <h3>Why re-architecting quality is a business decision</h3> Turning quality engineering into a strategic enabler is a business decision, not a technology project. That’s why a critical first step is assigning an executive sponsor to lead an audit of the cost of quality failures, such as failed subscription sign-ups, abandoned purchases and customer support costs. This step makes quality a board-visible metric, which is necessary for gaining C-suite support. As with any AI initiative, media companies will need to identify the business outcomes they’re trying to improve. For quality engineering, this means identifying the customer journeys that matter most to the business. These might include subscriber sign-up and login and authentication, or content discovery and playback. Once the business outcomes are established, the next priority is to build the foundation. Goals here can include stabilizing automation, reducing noise and focusing coverage where risk is highest. Each company needs to choose the actions that best address its testing challenges and meet its business needs. For example, we <a rel="noopener noreferrer" target="_blank" href="https://www.cognizant.com/us/en/case-studies/media-giant-jump-starts-qa-automation">partnered with a media giant</a> that took a two-pronged approach: It sought to quickly stand up a sustainable quality assurance testing capability in tandem with the rapid reduction of its regression testing backlog. Working together, we cleared 98% of the targeted backlog in just 60 days and established a scalable QA capability with reusable components and a clear path to advanced automation such as self healing. <h3>Next steps for quality engineering</h3> Building a strategic quality engineering effort doesn’t happen all at once. It also doesn’t require replacing every testing tool. (In our work with the media giant, we used their existing tools.) Most organizations will see the fastest returns by applying AI first to the highest-impact customer journeys and testing bottlenecks, and then expanding those capabilities over time as confidence grows.