Ask any CTO at a major OTT platform what keeps them up at night, and you’ll rarely hear ‘content generation with GenAI’. Instead, the answer is usually something far more mundane. It’s the legacy code that’s been piling up for years—code that nobody on the current team really understands anymore. It’s the thousands of customer support tickets flooding in every day, burying human agents. Or it’s those bloated tech systems that take forever to run and are draining a million dollars a year from the budget.
After working closely with these platforms over the past couple of years, I’ve witnessed a fundamental shift in how streaming companies approach Generative AI. Rather than chasing the headline-grabbing applications, they’re using it to automate the herculean operational tasks that have historically consumed enormous resources and slowed innovation to a crawl.
Here’s what that transformation actually looks like on the ground.
Engineering Modernization: Decoding Digital Archaeology
Most major OTT platforms are built on years, and sometimes even decades, of accumulated legacy code. Upgrading a backend stack or migrating logic to modern languages traditionally meant 12-18 months of painstaking work: risky, expensive, and frankly soul-crushing for developers who’d rather build new features.
The transformation happening now is striking. Using tools like Copilot and Cursor, engineering teams are:
Decoding legacy systems in seconds – GenAI analyzes codebases that predate current staff, explaining undocumented logic written by developers who left years ago. What once required weeks of archaeology now takes minutes.
Compressing timelines dramatically – Full-stack modernization projects that consumed 12+ months are now being completed in weeks. Version upgrades, security patching, and architectural refactoring that earlier required dedicated teams can now happen continuously.
Accelerating new development – GenAI generates initial code from requirements, which developers then refine. This allows complete application re-development and re-architecture with fewer resources and compressed timelines.
The benchmark that’s emerged? Most platforms have set a challenging target of improving developer productivity by at least 40% through GenAI-assisted code migration, debugging, testing, and security remediation. It’s ambitious, but the early data suggests it’s achievable.
From War Rooms to Self-Healing Systems
In traditional OTT operations, you only find out something’s broken when it’s already too late; ideally, it starts with a dashboard that flashes red and worsens when frustrated users start complaining on social media. That’s when teams finally rush into a war room and start the painful process of manually digging through logs across all your different systems, trying to track down what went wrong. By the time you figure it out, your viewers have already had a lousy experience.
Leading platforms are now implementing AI-driven observability that transforms this equation. Intelligent agents continuously analyze logs across legacy applications, and when anomalies surface, they:
- Detect the anomaly before users notice
- Identifythe root cause through historical pattern analysis
- Push actionable alerts with remediation suggestions directly into collaboration tools
The ultimate goal? Creating feedback loops where systems eventually self-heal by automatically applying fixes based on past successful resolutions. We’re not there yet, but the foundation is being laid.
Quality Engineering: Trimming the Monster
When you build software incrementally over the years, your test suite inevitably becomes unwieldy. I’ve seen platforms with tens of thousands of test cases where 20-30% are redundant, outdated, or conflicting—the accumulated detritus of rapid development cycles and team turnover.
Rather than forcing quality engineers to audit this mess manually, GenAI is being deployed to:
Deduplicate intelligently – Identify and eliminate redundant tests that provide no incremental coverage.
Auto-generate based on requirements – Create comprehensive test scripts directly from updated specifications.
Maintain continuous optimization – Keep test suites lean and fast, enabling deployment velocity that was previously impossible.
Customer Support: Creating Super-Agents
There’s a persistent misconception that GenAI in customer support means replacing human agents. What I’m seeing tells a different story: it’s about dramatically amplifying what humans can accomplish.
Currently, resolving even simple issues often requires agents to log into five or more separate backend systems (payment processors, user databases, content delivery networks, subscription management platforms, etc.) to understand why a customer’s billing failed or their stream is buffered.
GenAI is replacing that friction with natural language interfaces. Now, agents are empowered; they simply ask, ‘Why was this user’s last payment declined?’ and receive instant, contextualized answers pulled from across the entire system architecture.
The impact is measurable: considerably faster ticket resolution and significantly reduced onboarding time for new agents who no longer need months of training on complex internal tools and systems.
Operations Beyond Streaming: The Theme Park Challenge
For OTT platforms that also operate physical entertainment properties, such as theme parks & resorts, the operational challenge extends beyond digital infrastructure. Consider the feedback loop: 1,000+ guests comment daily, each requiring human review, profanity filtering, categorization, and routing.
GenAI automation is transforming this process:
Intelligent categorization – Automatically tagging comments by department and issue types (e.g., food quality, ride safety, ticketing problems, etc.)
Sentiment and urgency analysis – Distinguishing between general dissatisfaction and situations requiring immediate management intervention.
Smart aggregation & routing – Grouping related issues by department and sending consolidated reports instead of overwhelming managers with hundreds of individual notifications.
Internal Tools: Eliminating the ‘Tech Tax’
One of the most insidious productivity killers in large organizations is the complexity of internal systems. Enterprise resource planning platforms, HR portals, project management tools: each with its own arcane interface, each requiring specialized knowledge to navigate effectively.
Problems that should take minutes to resolve (pulling a budget report, checking project status, verifying approvals) often consume days as requests bounce between departments and specialists who know which obscure menu to access.
Leading platforms are now deploying secure internal GPTs that act as unified interfaces. Employees ask questions in natural language; the system queries the relevant backend platforms and returns answers. It’s about eliminating what I call the ‘tech tax’: the enormous time cost of simply doing business in a complex organization.
What’s Coming: The 2026 Horizon
While current deployments focus on operational excellence, the roadmap for 2026 shows platforms preparing to tackle more customer-facing applications:
AI-powered media planning – Multi-agent systems handle end-to-end advertising workflows, including campaign planning, setup, optimization, and reconciliation, with minimal human oversight.
Natural language content discovery – Enabling users to find content through conversational queries rather than precise keywords. ‘Show me something funny but not too long’ or ‘find that cooking show we watched last month’ become valid search inputs. Mood-based and time-based search that understands context.
Licensed character content generation – Disney’s recent three-year agreement with OpenAI exemplifies this shift. Using Sora, consumers will create short AI-generated videos featuring over 200 licensed characters from Marvel, Pixar, and Star Wars, with selected content potentially showcased on Disney+. This moves GenAI from being an operational tool to a creative consumer platform.
The Real Revolution Is the One Most People Don’t See
The public conversation around GenAI in media still centers on creativity: new content formats, personalized experiences, and AI-generated media. But inside OTT platforms and media organizations, the most meaningful transformation is happening elsewhere.
GenAI is being applied to the most complex, least visible problems the industry has carried for years: legacy code no one fully understands, fragile operations that demand war rooms, bloated test suites that slow releases, fragmented internal tools, and support systems that don’t scale. Solving these problems doesn’t just reduce cost; it restores speed, resilience, and confidence across the organization.
What matters most isn’t a specific model or tool, but a shift in focus. Leading platforms are directing GenAI toward engineering modernization, enabling AI-driven observability, optimizing continuous testing, augmenting support, and simplifying internal interfaces, systematically eliminating technical debt and reducing operational friction before layering on new consumer or advertising experiences.
The impact compounds. Product teams ship faster. Advertising teams gain flexibility without added operational overhead. Support organizations scale without burnout. And customer-facing innovation becomes easier because the foundation beneath it is no longer brittle.
This is the quiet GenAI revolution in the media & entertainment industry, not about replacing creativity, but about removing the technical debt and operational friction that have long constrained it. The platforms that win won’t be the ones that generate the most AI content; they’ll be the ones that operate better, adapt faster, and lead as media and advertising continue to evolve.
