Across media and entertainment, AI has rapidly shifted from a distant promise to an operational reality. Yet the transformation is uneven. Many leaders now recognize that early pilots deliver tangible gains, while true, scaled impact demands new workflows, new skills, and new forms of collaboration between humans and machines.
The panelists—from Warner Bros. Discovery, Google, Epidemic Sound, EY, and Kudelski Group—offer a clear picture of where AI is already working, where it is stuck, and what executives must do next. Their experiences show that AI is not simply an efficiency play; it is a catalyst for rethinking how content is created, monetized, and secured.
At its best, AI is shifting employees from repetitive execution to higher-value work, unlocking new revenue from existing intellectual property, and compressing the time from data to decision. But those gains will accrue disproportionately to organizations that take a deliberate approach to data, operating models, and culture.
One of AI’s most immediate impacts is in automating manual, error-prone workflows. At Warner Bros. Discovery, ad operations historically relied on spreadsheet-driven, repetitive tasks to execute media buys across brands such as HBO Max, CNN, HGTV, and Food Network. Simple steps—aligning client media plans with creative assets, keying data into multiple systems, and trafficking campaigns—consumed significant staff time.
Over the past year, the company has piloted AI tools that automate large portions of these processes. Instead of manually copying information into spreadsheets and ad servers, AI now ingests plans and executes key steps end-to-end, replicating the work of experienced coordinators with high accuracy. The outcome is not just faster execution; it is a reallocation of human effort.
Similar dynamics play out in creative work. At Epidemic Sound, a company that soundtracks billions of YouTube and TikTok views daily, AI is removing friction from an inherently complex task: matching music and sound effects to video. Historically, non-professional creators either avoided sound design entirely or reshaped video to fit whatever audio they could find.
Now, AI-powered tools allow creators to upload a video and receive an instant rough cut of sound effects timed to actions—door slams, footsteps, and more. Natural language search lets users describe the vibe they want (“a gloomy instrumental with cellos for a documentary”) rather than wrestling with technical music terminology.
Despite compelling use cases, most companies are far from fully realizing AI’s potential. As EY’s perspective underscores, organizations have spent two years learning what is possible and discovering they are not structurally ready to scale it.
Three obstacles recur:
Warner Bros. Discovery’s response is instructive. Instead of rebuilding its stack from scratch, the company is layering AI on top of existing systems and pushing vendors toward open, modular architectures. Longtime partners such as Google, once closed “full-stack” providers, now expose more open interfaces that make it feasible to integrate AI agents into legacy environments.
The next wave will be agentic workflows in media buying and selling: autonomous software agents that negotiate, adjust, and steward campaigns across platforms. For that to work, sell-side agents and buy-side agents must interact over shared “pipes.” It is not simply a technology challenge; it is a market coordination problem that touches SSPs, DSPs, and large advertisers.
As AI automates more tasks, employee anxiety is inevitable—especially among early-career managers and operations staff whose daily work is most exposed. Panelists emphasized that leaders must address both the fear and the opportunity head-on.
At Warner Bros. Discovery, skepticism surfaced in two stages: first, doubt about whether AI could actually match human quality; then concern that, once it did, roles would disappear. The company’s experience suggests a more nuanced reality. As workflows become more agentic, demand will grow for people who understand:
In other words, operational expertise becomes a design asset rather than a redundancy. Those willing to learn how AI works—and how to harness it—are positioned to move up the value chain into roles focused on strategy, optimization, and innovation.
Google’s experience highlights a complementary leadership challenge: transformation depends on leaders’ willingness to change themselves. Declaring an “AI-first” strategy is not enough. Executives must:
As one panelist noted, the industry’s recent emphasis on cost reduction has strained “courage levels.” To unlock truly transformative outcomes, leaders will need to reintroduce intelligent risk-taking into their organizations.
Perhaps the most underappreciated impact of AI lies in how it can reshape the economics of intellectual property. A media client working with EY posed a powerful question: could AI turn a single piece of content into multiple commercial products, each tailored to different markets, at a fraction of the traditional cost?
The answer is increasingly yes. In one case, an animated film developed for a handful of Western markets was fed into a multimodal AI model to generate seven graphic novels based on the same IP. A second model localized those graphic novels into new languages and geographies—far cheaper and faster than fully localizing an animated feature.
This approach unlocks a new flywheel:
As AI improves, these derivative products will move from “proof of concept” to market-ready quality. The implications are profound: the lifetime value of a core IP asset can multiply without commensurate increases in production cost. That expansion in revenue potential, in turn, creates demand for new roles in sales, product development, franchise management, and audience insight.
As organizations lean into AI, they must also reckon with a more dynamic threat landscape. On the upside, AI can simplify execution and democratize creativity. On the downside, it commoditizes capabilities and arms adversaries with powerful tools.
Security leaders emphasize that attackers—whether focused on content piracy, ransomware, or more subtle content manipulation—have a far higher risk appetite and far fewer constraints than legitimate businesses. They are already using AI to scale attacks that would have been impossible only a few years ago.
Defensive strategies therefore need to evolve as quickly as the threats:
Executives cannot assume that controls designed for slower, more predictable systems will suffice in an AI-driven environment. Security, compliance, and risk appetite must be integral to AI design, not retrofitted after deployment.
Cultural adoption often lags technological capability. One of the most effective interventions described by the panel was surprisingly simple: take the pressure off by starting outside of core business processes.
Google, for instance, convened hundreds of executives for an AI immersion focused on planning an 80th birthday party—hardly a mission-critical workflow. Participants were challenged to create nine assets in 90 minutes using AI: from mood boards and personalized video invitations to a simple website. The exercise introduced concepts like “super prompting” and showed how different tools can work together, all in a low-stakes context.
The result: a genuine “aha” moment for many leaders, who left with a concrete sense of AI’s power and a more playful, less fearful mindset. Inside other organizations, similar grassroots initiatives—such as loading years of user research into a notebook that employees can query via Slack—have had infectious effects, making AI feel like an everyday co-pilot rather than an abstract threat.
For leaders seeking to move from AI curiosity to impact, five practical actions emerge from the panel’s experiences:
AI’s trajectory in media and content is no longer a theoretical debate. It is a practical, operational challenge—and a strategic opportunity. The organizations that will thrive are not those that deploy the most tools, but those that are willing to rethink how work happens, how value is created, and how people and machines can collaborate to build something fundamentally better than either could achieve alone.