The imperative is clear: reduce overhead, improve reliability, and deliver services that evolve continuously without compromising performance.
Compressed timelines are now standard in media operations. Five-year roadmaps have collapsed into five-month cycles, driven by rapid shifts in technology, evolving content distribution models, and increasingly software-centric workflows. In this environment, operational efficiency has moved beyond technical optimisation, now playing a central role in shaping business strategy and long-term viability.
This urgency isn’t limited to consumer-facing innovation. Internally, media organisations are rethinking how they build and maintain platforms. The imperative is clear: reduce overhead, improve reliability, and deliver services that evolve continuously without compromising performance.
Efficiency Over Features
While innovation and feature development remain essential for differentiation, platforms are increasingly recognising that long-term competitiveness also depends on how systems are architected, deployed, and sustained. Operational agility and resilience are becoming just as critical as the features themselves.
A practical framework for navigating this shift centers on three principles: agility, automation, and AI. Agility enables faster iteration through smaller, modular releases. Automation standardises deployment and reduces human error. AI enhances both front-end personalisation and back-end operations, accelerating development and improving system resilience.
Meeting service-level agreements and managing Total Cost of Ownership (TCO) requires architectural flexibility, not just rapid development. Monolithic systems, though often associated with legacy deployments, have historically provided stability due to their early implementation and centralised control. However, modern platforms increasingly favour modular architectures that offer greater flexibility and scalability. Many organisations are finding value in combining both, modernising incrementally rather than replacing entire systems. This hybrid approach preserves reliability while enabling innovation.
These changes also bring technical debt into sharper focus. Quick fixes and legacy code may offer short-term relief, but they accumulate long-term costs. Unaddressed, they slow development and increase risk. Treating technical debt as a continuous responsibility is now essential to maintaining platform adaptability.
Automation and AI: Tools for Speed and Resilience
Operational efficiency increasingly depends on how well automation and AI are integrated into deployment and maintenance workflows. Automation reduces human error and accelerates rollouts. Strategies like canary rollouts — which release updates to a small subset of users before full deployment — and blue-green rollouts, which switch traffic between two identical environments to minimise downtime, are now standard practice.
To support these methods at scale, teams rely on CI/CD pipelines — automated workflows for integrating, testing, and releasing code. These pipelines reduce configuration bottlenecks and allow engineering teams to focus on higher-value work. Frequent, low-risk upgrades are a necessity in a market where responsiveness drives relevance.
AI builds on this foundation by enhancing the software development lifecycle. It supports faster development, improves code quality, and enables systems to detect and resolve issues without manual intervention. Its role in observability — the ability to understand system behaviour in real time by analysing logs, metrics, and traces — is especially critical. AI doesn’t just monitor; it interprets, predicts, and recommends.
This capability is proving valuable in regulatory contexts. For example, the EU’s NIS2 directive requires modernisation of tech stacks and the replacement of deprecated components. AI helps identify vulnerabilities and manage dependencies, turning compliance from a reactive burden into a proactive process.
Beyond automation, AI is helping teams offload repetitive and low-complexity tasks such as subtitle generation, metadata enrichment, and predictive monitoring. This allows developers and operators to focus on higher-value activities, improving both productivity and innovation capacity.
Infrastructure and Resource Planning
Software architecture alone doesn’t guarantee scalability. Infrastructure must be equally adaptable. Cloud-native technologies such as containers and orchestration tools enable platforms to scale dynamically across public, private, and hybrid environments.
Meanwhile, managed services models offer operational flexibility by outsourcing infrastructure oversight and maintenance, allowing teams to focus on core development. These tools help manage demand spikes without overprovisioning and simplify future migrations.
Strategic planning extends beyond infrastructure to the Bill of Materials (BOM), a comprehensive view of ownership costs that includes hardware, cloud services, and third-party software. Understanding the BOM helps teams identify underutilised assets, reduce redundancy, and align investments with long-term software roadmaps.
As platforms grow more complex, this kind of planning becomes indispensable. With multiple services and vendors in play, clarity around cost structure enables better decision-making and more efficient resource allocation.
Increasingly, autonomous AI systems, sometimes referred to as agentic systems, are taking on such complex maintenance tasks as replacing deprecated libraries and optimising deployment configurations. They are also beginning to support predictive planning with their ability to anticipate traffic spikes, forecast subscriber behaviour, and adjust resource allocation accordingly. For example, in regions where seasonal events like Ramadan drive peak traffic, AI can ingest historical data and automate infrastructure scaling to meet demand. By operating continuously and independently, the systems reduce manual oversight and improve system resilience.
This evolution supports a move toward continuous deployment. Rather than relying on scheduled upgrades, platforms now evolve through ongoing, automated releases. The result is faster launches, quicker adaptation, and more consistent innovation.
Rethinking Platform Readiness
Agility, automation, and AI are no longer optional. Their absence signals risk, not differentiation.
But readiness is just as much cultural as it is technical. Teams must be willing to rethink legacy processes, embrace iterative development, and invest in systems that support continuous improvement. This means moving from reactive problem-solving to proactive optimisation, an approach that requires alignment across engineering, operations, and leadership.
Scaling smarter means connecting deployment speeds with measurable outcomes such as uptime, latency, and cost control. It also involves refining workflows incrementally, using automation and feedback loops to improve reliability and efficiency over time. To move in this direction, organisations might start by asking what indicators will help them assess the impact of frequent deployments on service quality and team performance, and how they can introduce automation in ways that support gradual process improvements and cross-functional collaboration. These questions don’t require immediate answers, but they do invite a shift in perspective – from scaling fast to scaling with intent.
By Ali Amazouz, Business Development Director MENA, Viaccess-Orca
