The subject of AI has become unavoidable in relation to multimedia content over the last 18 months, with 1 in 5 of the new billion-dollar startups joining Crunchbase’s ‘Unicorn Board’ in 2023 being artificial intelligence companies, the rate of investment and development is phenomenal, creating an exponential variety of ways on which AI technologies can be used. From images to videos, AI is enhancing our capabilities to process, analyse, and create content in ways previously unimaginable.

While the potential benefits of AI are monumental, distinguishing between reality and marketing hype has proven challenging. Content owners find themselves in a dilemma, struggling to make informed decisions about the actual benefits of AI for their content and the intricacies involved in achieving the result they ideally want.

AI tagging and AI visual search tools have emerged as significant aids for content owners and users alike. These tools facilitate quicker and more effective content tagging and retrieval. That statement comes with a caveat however as the performance of these AI tools varies across different content types. AI requires tailored training to identify specific objects that users commonly seek, leading to a landscape filled with hype, noise, and a certain reticence surrounding the achieved performance and Return on Investment (ROI).

Customers can effortlessly choose assets within their existing Capture platform, select their preferred engine, and instantly receive automated descriptions and sets of keywords for each asset. The AI engine’s output can be assessed alongside existing keywords, allowing users to accept, reject, or merge the generated data seamlessly.

Once customers identify the right engine or blend for their content, the process can be automated for content tagging upon ingestion. Given the constant learning capabilities of AI tools, tags can be updated at any frequency, enhancing both accuracy and relevance of search terms.