OTTera Achieves 60% Scheduling Efficiency with AI Integration
By leveraging AI on Amazon Bedrock, OTTera has transformed its scheduling process, cutting creation time by 60% and paving the way for data-driven decision-making that enhances audience engagement and profitability.
Key Facts
- OTTera's AI scheduling cut creation time by 60%, enhancing responsiveness to viewer trends.
- Projected $6.3M Year 1 profit highlights AI's financial impact on operational efficiency.
- AI-driven insights can boost viewer engagement by 15-25%, improving content monetization potential.
- Transitioning from manual to AI scheduling reveals vulnerabilities in traditional media operations.
- Modernizing tech infrastructure is crucial for leveraging AI, ensuring competitive advantage in streaming.
Summary
Summary
OTTera, a white-label streaming service, faced challenges in scheduling that limited its operational efficiency. By implementing an AI-driven decision engine on Amazon Bedrock, OTTera reduced schedule creation time by 60% and projected a Year 1 net profit increase of over $6.3 million.
Background
OTTera operates in the streaming service industry, powering more than 2,000 FAST channels globally. Before deploying AI, OTTera relied on a manual, rules-based scheduling process that was heavily dependent on engineering expertise, which created operational bottlenecks and limited responsiveness to audience behavior.
Challenge
OTTera aimed to resolve two critical issues: the engineering dependency in its scheduling workflow and the fragmentation of valuable insights across multiple systems. These challenges hindered the ability to quickly adapt to changing viewer preferences and monetize opportunities effectively, ultimately slowing innovation and reducing revenue optimization.
Solution
OTTera partnered with AI/R Compass UOL and AWS to modernize its scheduling tool through a phased transformation. This included enhancing the tool for self-service control, integrating programmatic ad systems, and deploying AI agents powered by Amazon Bedrock, Amazon SageMaker AI, and other AWS services. The new system allowed operators to make natural-language requests for scheduling, which the AI processed to deliver ranked recommendations quickly.
Results
The implementation of AI-driven scheduling resulted in a 60% reduction in schedule creation time. Additionally, it is projected to increase viewer engagement by 15-25% and generate over $6.3 million in net profit in the first year. OTTera's new system transformed scheduling from a manual process into a data-driven revenue optimization tool.
Key Insights
- Modernizing infrastructure is crucial before operationalizing AI to achieve sustainable results.
- High-quality, unified data is essential for effective AI decision-making.
- Focus on business value rather than technology when prioritizing AI use cases.
- Successful AI initiatives should aim to enhance revenue and customer experience, not just improve productivity.
Customer Testimonial
“AI/R Compass UOL helped us level up our scheduler, taking it from an operational challenge to a strategic tool. Our team and clients can now make faster, more informed programming decisions, improving audience engagement, channel quality and performance, and revenue.” – Craig McEldowney, CTO & Co-Founder, OTTera
Entities Mentioned
Companies
Products
People
Organizations
Key Concepts
Definitions
- AI-driven scheduling
- A process that utilizes artificial intelligence to automate and optimize scheduling decisions, enhancing efficiency and responsiveness.
- FAST channels
- Free Ad-supported Streaming Television channels that provide content to viewers without subscription fees.
- Programmatic ad systems
- Automated systems that facilitate the buying and selling of advertising space in real-time.
- Data estate
- The collection of data assets within an organization that can be leveraged for decision-making and strategic advantage.
- AI Server
- A computing environment designed to run artificial intelligence applications and processes.
Use Cases
- →Automating scheduling decisions
- →Optimizing audience engagement
- →Enhancing content monetization
- →Improving operational efficiency
- →Real-time audience behavior analysis
- →Streamlining programming schedules
Frequently Asked Questions
How did OTTera improve its scheduling process?
OTTera improved its scheduling process by implementing AI on Amazon Bedrock, which replaced a manual, rules-based approach. This transformation resulted in a 60% reduction in schedule creation time.
What are the benefits of AI-driven scheduling?
AI-driven scheduling allows for faster decision-making, improved audience engagement, and better monetization outcomes. It enables organizations to respond quickly to changing viewer preferences and optimize content delivery.
What role does data play in AI scheduling?
Data is crucial for AI scheduling as it provides the insights needed for informed decision-making. A unified and real-time data layer enhances the reliability and effectiveness of AI-driven recommendations.
What challenges did OTTera face before implementing AI?
OTTera faced challenges such as engineering dependencies for scheduling tasks and fragmented data across multiple systems, which hindered their ability to scale and innovate effectively.
How can organizations benefit from OTTera's experience?
Organizations can learn from OTTera's experience by modernizing their systems, focusing on high-impact use cases, and leveraging AI to transform operational processes into strategic advantages.