Profitability now runs through data and AI

The media and communications industry isn't short on challenges: consolidation has picked up pace, new entrants keep chipping away at traditional revenue, and the profitability everyone expected from streaming investments still hasn't fully materialized. Video bundles and broadband, once the industry's steadiest revenue lines, are now under real pressure. Meanwhile, subscribers have gotten less forgiving , the moment an experience feels mediocre, they're ready to cancel.

That pressure is pushing 2026 into a different playbook. Cost-cutting alone isn't enough anymore; leaders are now building strategies that go after revenue growth, customer value, and efficiency together. AI sits at the center of that shift, no longer confined to internal tools and back-office use cases, but increasingly a board-level priority shaping the customer experience directly.

Here are three shifts we see defining the industry's path to profitability this year.

1. Get the data foundation right, then build the culture to keep adapting

A lot of organizations are stuck running disconnected proof-of-concepts because their underlying data infrastructure was never built to support AI at scale. Before any AI initiative can produce measurable value, the data foundation - clean, connected, governed has to come first. Without it, every AI effort stays a pilot. But infrastructure alone isn't the finish line. The bigger shift is cultural: organizations that build the internal habits, workflows, and decision-making structures to keep adapting as AI capability evolves will consistently outperform those treating AI as a one-time rollout. A strong data foundation only pays off when the organization around it is built to keep evolving with it.

2. Win on experience - through personalization, partnerships, and less friction

Customer expectations have gotten harder to predict, which means generic personalization no longer holds attention. The organizations retaining customers are the ones going deeper , understanding intent, not just profile data and pairing that with simpler, less cluttered experiences. Hyper-personalization and simplification are turning into the two strongest defenses against churn.

That experience increasingly depends on who you build it with. Audiences are tired of fragmented experiences that require five apps to get what used to take one. Companies that build strong alliances and ecosystem partnerships are better positioned to deliver the unified experience customers are demanding, rather than trying to build every piece internally.

3. Turn what you already own into new revenue and competitive speed

With top-line growth harder to find, companies are looking inward , finding new ways to monetize content libraries, data, and platforms they already own rather than only chasing new revenue lines. This is turning existing assets into fresh profit centers instead of static cost centers. That same logic applies to content production. Streaming platforms increasingly differentiate on speed and volume of content as much as quality, and generative AI is being pulled deep into production workflows , not to replace creative work, but to accelerate the parts of the pipeline that used to slow it down, giving platforms a real edge in how fast they can compete.

Where this leaves the industry

None of these three shifts work in isolation. The organizations that come out ahead in 2026 will be the ones that stop treating AI as scattered pilot projects and instead weave it through the entire value chain, from the data foundation up through the content that reaches the screen. That means strengthening ecosystem partnerships, prioritizing simplification and hyper-personalization to fight churn, and building the AI-first adaptability that lets a company sense market shifts and react before competitors do. The real test in the year ahead isn't who experiments with the most new technology. It's who turns that experimentation into a genuinely more efficient, customer-centric, and profitable business. Increasingly, that means media and communications companies have to start operating less like traditional broadcasters and telecoms, and more like the technology companies they're now competing against.

At Saguna Consulting, this is the kind of transformation we help media and communications clients navigate, from building the AI and data foundations that make personalization and automation possible, to the broader technology transformation work that turns disconnected AI pilots into an adaptive, enterprise-wide capability.

‍