Today’s technology landscape is largely unrecognizable from that of a decade ago, and as technology and multimedia content continue their rapid evolution, keeping the resulting data generated in check is more challenging than ever.
According to Statista, video content accounts for more than 80 percent of all worldwide internet traffic. Today video is a mainstream form of communication, education, and obviously entertainment, adopted for convenience under the pandemic and now an ingrained part of corporate interaction. As video archives and data swell, generative AI is driving down by orders of magnitude the cost barriers to making video content for the purposes of communication and education. Applying artificial intelligence (AI) to data management, analysis, and communication is set to become a focal point for enterprises.
Creating value through enterprise data flywheel with AI
The prime goal is to extract maximum value from abundant but scattered multi-modal data to put intelligence across all workflows that companies do, which AI-driven multimedia (which we will term AI x multimedia) technology can help achieve. To do this requires robust computing capabilities to support aggregation, tagging and classification, analysis, and recommendations. Let’s examine these attributes in more detail.
Firstly, aggregation: to do this effectively, high-efficiency management and uploading capabilities are essential. High-speed encoding technology enables fast conversion to streaming formats. AI tools such as per-title (thematic) encoding (PTE) and perceptual streaming engine (PSE) image enhancement deliver high-quality, high-efficiency streaming services, optimizing bandwidth use to control costs. Within the process of effective aggregation, it is also important for enterprises to consider multimedia data protection to safeguard business information and assets. This is where digital rights management (DRM) protection comes in, to ensure comprehensive content encryption and combat piracy and unauthorized distribution and access.
In terms of tagging and categorization, AI models can perform video auto-tagging and audio-visual analysis. This helps bring the otherwise gargantuan task of labeling and classifying extensive content under control, enabling the transfer of considerable time and effort to more strategic activities.
Lastly, when it comes to analysis and recommendations, enterprises can use AI to deliver hyper-personalized recommendations and execute semantic searches to parse video data and extract what is required. This can be particularly advantageous in the context of internal training, which is a corporate priority. According to a 2023 Deloitte study, nearly two-thirds of companies struggle to hire people with the skills they need, and 43 percent see training or reskilling as the best way to bridge the gap.
Tracking information, progress, and effectiveness
Multiple systems exist to make life easier for trainers and learners. Smart tagging helps to automatically break down training videos into atomic content, which refers to structured content broken down into smaller parts, classify them according to content, and accelerate content retrieval. Video and document management systems can make it easier to organize a massive number of training videos. Meanwhile, a backend analytics system is on hand to monitor each individual employee’s learning progress and recommend the most appropriate content for self-development, transforming what can be a stressful process into one that is enjoyable and enriching.
Generative AI assistants x atomized content for precise learning
The rapidly growing sophistication of generative AI assistants together with content atomization makes a mass of learning material instantly accessible, enabling users to quickly locate essential learning points from dispersed content. When watching an instructional video, a viewer can interact with an AI assistant to ask questions and receive answers immediately. During the conversation, generative AI with annotated data sources can provide additional understanding of those sources or access further relevant information or video content.
With the assistance of AI x multimedia streaming, training initiatives can take on a new level of focus and efficiency. Program supervisors can use the technology to organize fragmented enterprise information as well as monitor employee learning progress and application. More than 40 percent of Fortune 500 companies conduct online training, and research has shown that companies that use technology in their training generate over 25% more revenue per employee than those that don’t. The integration of technology enhances efficiency, enabling easier access to and more frequent opportunities for learning and career development.
The volume of enterprise video data is set to continue to expand, and employing technology to make it accessible and useful is a must. Given the pressing need for effective training and its impact on effectiveness, choosing the right infrastructure to enable it while managing costs will be a distinct competitive advantage.
Kevin C.H. Lee is General Manager of Multimedia Technologies, KKCompany Technologies.
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