Key Recommendations Prioritize employee augmentation for immediate value. Outside of the predictable genAI priority of “do more with it,” “see business value” is the next objective for those with use cases already in production. The fastest way to accomplish this is practitioner augmentation — automating repetitive tasks. Today, only 38% of mature genAI adopters in streaming have even identified employee productivity as a key value opportunity. Adopting genAI capabilities that are already part of the tools you use — like creative suites — is critical to reduce the learning curve and improve output quality. While most know this in theory, an embrace of employee augmentation will be critical to generate sought business results. Fund legacy tech modernization initiatives. The biggest challenge streaming organizations of all maturities see today — outside of poor output quality — is legacy systems hindering their progress. While the details of what ‘legacy systems’ entail vary by organization, establishing a working group — with a budget and staff — to facilitate modernization efforts will ensure organizations can see returns based on their genAI investments. Prioritize trusted partners. For those just starting, use premade solutions. While the furthest along adopters are still working to fine-tune their own models, poor performance and skills shortages limit business impact. For many proprietary model adopters, it’s due to their partner approach they’ve gotten this far. As organizations are orienting to genAI business value — or just starting out — partner selection is critical. Focus partner selection on two fronts: 1) those who can help with legacy systems modernization and data efforts and 2) those who can bring premade vertical specific solutions to bear across generative modalities. Prioritize MSPs and strategic tech providers who can supplement your skills, abstract away the complexity of managing a genAI stack, and assure your ownership over your data. Adopt a federated strategy. Today, most M&E firms either follow a centralized model of genAI management or are running fully decentralized. Both of these approaches utilize a single team to be in charge of everything from adoption, to policy advisement, to technical development. While centrality allows for effective governance and decentralization allows teams to move fast, both introduce risk. Centrality limits adoption and decentralization reduces risk awareness. As modalities and tooling proliferate, this model must adapt. Federation with a central team reviewing for privacy, data security, policy adherence, and training efforts at the organization level and deferring development and adoption to the team level allows for faster integration into daily workflows. 15 © FORRESTER RESEARCH, INC. ALL RIGHTS RESERVED. © FORRESTER RESEARCH, INC. ALL RIGHTS RESERVED. 14
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