AI/ML in Media and Entertainment Image

AI/ML in Media and Entertainment

Purchase

Actionable Benefits

  • Identify key use cases for AI/ML in M&E, from compression efficiencies to personalization and workflow optimizations.
  • Evaluate the impact of AI/ML in M&E and assess opportunities to generate efficiencies, reduce costs, and increase yield.
  • Determine a timeline for future applications of AI/ML, enabled by upcoming technologies like XR.             

Critical Questions Answered

  • Where is AI/ML being used today in the M&E market? In what capacity and to what effect?
  • How is AI/ML helping companies adapt to the changing M&E landscape?
  • How big I the market opportunity for personalization (premium content/services and advertising) and content contextualization?          

Research Highlights

  • Detailed breakdown of key use cases for AI/ML in the M&E space.
  • Assessment of current and longer-term (beyond 5-year forecast window) market potential and impact for AI/ML in M&E.
  • Market sizing for ad tech market (total and video ad tech).
  • Market sizing for personalization and use of AI/ML in media workflows. 

Who Should Read This?

  • Service and content managers responsible for maintaining (i.e. reducing churn) or growing the customer base and spend.
  • Solutions vendors who need to assess current product offerings for any shortcomings and plan for future customer needs and demands.
  • Planners within telcos and network operators who need to identify future transformative trends and its impact on the network and the needs of its customers.

Table of Contents

1. EXECUTIVE SUMMARY

2. SETTING THE STAGE

3. AI/ML USE CASES IN M&E: PERSONALIZATION, PROCESSING CONTENT, AND METADATA

3.1. Automated Media Metadata Tagging
3.2. Automated Closed Captioning, Subtitling, and Localization via Natural Language Processing
3.3. Automated Content Monitoring and Compliance Marking
3.4. Content Recommendation and Personalization
3.5. Advertisement Personalization

4. AI/ML USE CASES IN M&E: WORKFLOWS AND ANALYSIS

4.1. Content Creation
4.2. ML for Encoding Optimization
4.3. Detection of Consumer Patterns, BI, and Analytics

5. NEW OPPORTUNITIES ON THE HORIZON

5.1. Contextual Commerce
5.2. Future Impact of XR and Other Enabling Technologies

6. KEY TAKEAWAYS AND RECOMMENDATIONS