Media & communications

Many organizations have not yet realized the full extent of AI’s promise. According to Slalom's August 2025 Global AI Insights survey, only 15% of respondents from the M&C industry described their AI use cases as aligned to end-to-end transformation across the enterprise. For everyone else, AI is being implemented in silos. This patchwork approach to rolling out AI toolsets and determining use cases to prioritize, along with technical challenges such as legacy applications and data infrastructure, create obstacles to achieving cross-functional value.

Taking an enterprise-wide approach enables organizations to drive both innovative advancements that add significant value and cost savings that can be reinvested for growth. For example, AI-driven network operations can shift from manual alarm responses to predictive management, with intelligent agents prioritizing and automating actions. In another example, many US movie studios struggle to earn back their production costs due to extra expenses from manual tasks. By automating processes like footage tagging and dialogue transcription, AI helps cut costs and accelerate production, allowing creative teams to focus more on the storytelling that customers love.

But these kinds of AI use cases are only possible when data is aligned with the needs of modern AI-driven applications. With many M&C companies undergoing network transformations to better compete against cloud and tech giants, there is now a clear opportunity to future-proof operations by combining network modernization with a transformation of the underlying data that powers AI.

Laying the foundation for AI also means having clear ROI targets tied to a concrete business strategy, which includes AI strategy, to better prioritize focus areas. ROI measurement requires creating baseline and target metrics tied to the overall strategy so that when savings are achieved, they can be reinvested in the business and aligned with the most important business focus areas. 

Advanced AI is enabling deeper, real-time insights for more contextualized and targeted delivery of advertising, content, and services. Delivering this kind of personalized experience, however, must be balanced with an effort to avoid overwhelming customers with too much noise. Personalization is as much about the how as it is about the what. Respecting boundaries and avoiding excessive notifications while delivering customer value will yield positive results.

A critical internal roadblock to successful hyper-personalization is legacy technical debt. Core back-office systems, including billing and provisioning platforms, often struggle to support the fluid demands of real-time pricing and customized bundles. Media organizations face similar urgency, needing to bring data together across streaming, theme parks, and ad sales to successfully build a unified customer view that tech rivals natively possess.

A solution for bridging this gap is to accelerate data sharing and governance across siloed business units. Modern data platforms and API layers can act as AI overlays to legacy infrastructure, enabling dynamic functions like real-time pricing and service adaptation without the disruptive cost of a full system replacement. This strategy delivers a robust, governed data layer for a frictionless, human-centered experience that customers demand in an AI-powered world.

While broader data modernization initiatives will open wider opportunities (and be required over time), a more tailored approach can also yield significant impacts. Organizations that clearly define gains from AI can take a faster approach with modular data modernization, focusing on making the necessary data-specific use cases or functional teams available to rapidly achieve personalization goals.

The increasingly competitive M&C market requires a new kind of radical cooperation, which means looking beyond internal capabilities to embrace ecosystem-driven innovation. Companies must collaborate across complementary territories to build national ecosystems of services, providing the unified platform and scale necessary to effectively compete with powerful tech entrants. This cooperation is the engine that drives strategic debundling/rebundling, offering customers a personalized and private a la carte selection of streaming, connectivity, and smart solutions.

The benefits of the ecosystem extend beyond improving customer experience. MovieLabs, for example, provides a framework designed to update media creation and asset protection by implementing secure, cloud-based methods. It simplifies workflows for production, post-production, visual effects, and distribution teams by promoting a collaborative and interoperable media environment. This approach can minimize workflow challenges, support scalable cloud operations, and improve security, allowing studios, vendors, technology providers, and creators to collaborate effectively. The value of such an ecosystem is both operational and strategic; it provides benefits related to time, cost, and creativity while also establishing an industry-wide platform for innovation, collaboration, and secure content transfer.

Such cooperations are also essential to turn the network pipe itself into a monetizable asset with vertical-specific B2B solutions, for example, co-creating private 5G for manufacturing or dynamic network slicing for telesurgery. The prospect of the best surgeon in the world being able to help sick patients anywhere shows how compelling the impact can be. Effectively productizing and marketing these complex B2B services, however, requires a unique skill set, which makes building these capabilities essential for success.

Establishing an AI-first culture where the organizational structure itself is adaptive, fluid, and designed for efficiency has proven difficult for many companies. Slalom’s August 2025 Global AI Insights survey shows that employee resistance remains a significant blocker, with 38% of M&C organizations citing employee distrust and job-security concerns as a primary barrier keeping the organization from realizing value from AI. Such resistance can scuttle an otherwise promising initiative. If employees are not utilizing AI technology, how can the organization point to any measurable value being derived from it?

To increase adoption, leaders must craft a transparent and compelling vision about AI’s role and how it will benefit employees, customers, and the broader organization. This can be a hard case to make, because, unlike other initiatives, change and adaptation will be constants with AI.

Critical thinking, creativity, and digital literacy skills will therefore be essential for all teams. Organizations that foster experimentation, not just with AI use cases but also team structures, will be better able to find the right equation for achieving value. As teams are successful in this journey, they will showcase these wins in a way that allows the organization to learn from the insights and build momentum across teams.

Source: Slalom’s August 2025 Global AI Insights survey

In 2026, M&C companies need to do more to capitalize on their biggest assets. For communications orgs, growth in Network-as-a-Service (NaaS) offers new life to core infrastructure, turning the network pipe into a fluid, customizable platform. As network convergence continues, this platform offers the opportunity to deliver the services customers demand.

For media and entertainment, monetization centers on audiences. Companies are using data to drive tailored advertising, focusing on secure audience matching and converting their audience data into revenue. This creates an opportunity where interactive commerce and content blend, shifting focus from generic display ads to contextual, interactive, and shoppable commerce integrated deeply into content.

Productizing data, however, will require investment in data unification. Data clean rooms and advanced ad-tech strategies that maximize first-party customer signals can help turn data into a monetizable asset. With a unified data architecture, innovative network services (like NaaS) and modern data platforms can be leveraged as profitable offerings.

GenAI is now augmenting nearly every creative stage: from initial ideation and script analysis to complex visual effects, localization, and editing. The primary goal is economic: accelerate timelines, expand creative capacity, and reduce cost to maintain a competitive edge in the race for proprietary content.