In the first two parts of this series ahead of CES 2026, we assessed the emerging AI capabilities we expect to gain prominence at the Las Vegas tradeshow: from generative video and synthetic talent emerging in mainstream programming, to recommendation systems progressing from algorithmic filtering to agentic capabilities that autonomously curate content.
These developments hold significant potential for how media companies produce, distribute, and monetise their content. Yet realising this value requires navigating the new architectural complexities that emerge as AI embeds itself deeper into core operations.
This is a challenge that has not yet been recognised by the vast majority of organisations to date. But as companies move from early adoption to real-life deployments moving in 2026, we expect them to start waking up to the realities of production-scale AI.
This means establishing governance as foundational infrastructure; architecting systems that protect intellectual property; and designing for continuous adaptability rather than static optimisation. Below we outline more detail as to what this will look like.
Governance as a foundation
As AI systems assume greater autonomy within media operations, from generating broadcast-ready content to independently curating viewer experiences, governance can no longer function as a retrospective consideration. It must instead be architected as foundational infrastructure from the outset.
Media companies require comprehensive observability and auditability across the full stack – ensuring that as synthetic content, personalised experiences, and agentic systems proliferate, organisations maintain visibility into decision-making processes, intelligence accumulation points, and ethical and regulatory boundaries.
The organisations scaling these capabilities successfully in 2026 will be those that recognise robust governance frameworks as an enabler rather than a constraint to innovation. Without foundational observability and auditability embedded throughout the entire modern stack, even the most sophisticated AI advancements risk becoming feature successes rather than lasting strategic assets as systems grow more complex and audience expectations around transparency evolve.
Architecting to secure IP
Equally, as AI systems process increasingly vast volumes of proprietary content, audience data, and creative assets, protecting intellectual property becomes not merely a technical requirement but a strategic imperative that defines competitive positioning.
This ability to architect systems that protect owned data represents a new form of competitive advantage. It involves moving beyond reactive security measures to proactive architectural thinking where IP protection is foundational to how these systems are designed, deployed, and governed.
Such protection must take into consideration where processing occurs, who retains intelligence generated from proprietary assets, and whether organisations preserve strategic advantage as capabilities advance. When generative video systems process years of broadcast footage to create new content, or agentic recommendation engines interpret decades of viewing behaviour, the question quickly becomes whether media companies can maintain control over the intelligence that defines their market position.
As we enter the 2026 AI landscape, media enterprises need to start prioritising architectures where IP protection is embedded at the design phase, rather than retrofitted after deployment. The systems being built today must ensure organisations extract maximum value from their own data while maintaining secure control over the intelligence that sets them apart from competitors.
Remaining agile for long-term adaptability
We are witnessing media AI capabilities advancing faster than most organisations can absorb. Yet, the developments explored in this series represent only the beginning of the potential that AI capabilities represent for the entertainment industry. The pace at which this landscape is evolving exceeds what traditional deployment cycles can accommodate, and organisations that don’t make architectural flexibility a priority will only fall further behind as these developments accelerate.
This requires modular architectures that can integrate new models and tools without extensive rebuilds, so enterprises can be prepared to adopt new capabilities rapidly without re-engineering entire production pipelines. As recommendation systems progress from algorithmic filtering toward conversational and agentic curation, platforms need infrastructure capable of incorporating these advances without extensive system overhauls.
Equally critical is preserving optionality as market economics shift. Much of the current AI stack is built on top of highly subsidised infrastructure, with many API-based offerings running at a loss to capture market share. When economics inevitably adjust to reflect true costs, organisations locked into single providers will struggle. Those that prioritised architectural flexibility will capitalise on new opportunities as competitors remain constrained by legacy decisions.
Designing for lasting impact
As 2026 approaches, we expect organisations to start realising that to capitalise on all of the opportunities at play, they must build resilient, adaptable and future-proof architecture.
The trends we anticipate, from generative video advancement to agentic personalisation, require not simply adopting new capabilities but architecting the underlying systems that make those developments governable, secure, and adaptable over time.
At Stelia, we partner with organisations to build the resilient foundations required for production-scale AI, ensuring investments made today deliver lasting competitive advantage. We look forward to connecting with customers and partners at CES as together we advance innovation at a pivotal moment for AI.
