Artificial Intelligence (AI) and Machine Learning (ML) are technologies that enterprises across industries have been keenly experimenting with to explore the utility they can bring. Is there an AI adoption within the M&E industry? Enterprises are seeking automation and can AI be the solution? Have we cracked the AI code or do we have miles to go? If automation is a goal, it should be a priority even in the current times.
Content recommendation (for OTT), Speech-to-text, and media recognition are some of the initial applications that have been attempted. Clients find vendor demos to be impressive but when they do a Proof of Concept (PoC) with their content, results are not. In video operations, frame accuracy is a necessity and AI models struggle to universally solve for this. And such specific nuances of getting it right, is what makes automation work. After trying multiple vendors, clients conclude that AI data is still not accurate enough to solve specific M&E use cases. However, they remain optimistic about the future possibilities.
So where is the issue? The use cases are not always effectively solved by any one vendor solution. Then we seek to assemble a multi-vendor solution either for accuracy and/or coverage. Such solutions also tend to have gaps. The complexity and effort required to solve for accuracy in a multi-vendor solution are not trivial. Which vendor solution works for what is not obvious for that enterprise’s specific use case. Be it a large firm or a small one, finding data scientists, leadership, and investment to fund the R&D effort to make AI work is not something enterprises can do.
To crack the impasse, what is needed is a media recognition AI platform that brings the best-of-breed AI models (Microsoft, AWS, IBM Watson, Google, etc.) and custom (for M&E-specific use cases) models to address the gaps and accuracy. Plus, we believe that consulting adjacent to the technology will be an absolute necessity to make AI work. Such consulting should be directed to impart suitable learning to AI models and make custom ML models to address customer-specific use cases. For delivering this consulting, talent with expertise in media and deep learning AI with computer vision knowledge is critical.
Observing the fundamental impact AI is making, CEOs are looking to do more with less, especially in the times of Covid-19. I believe, making AI work for client-specific use cases is critical to drive larger adoption. And an AI platform with the flexibility to bring the best-suited models together for creating high-quality data dovetailed with consulting offers the best chance to change the status quo.