Gartner’s projections indicate that by 2027, 40% of generative artificial intelligence solutions will be multimodal, integrating text, image, audio, and video, a significant evolution from the 1% recorded in 2023. This advancement comes in a context where a recent McKinsey report highlights data management as one of the main obstacles to the adoption and scaling of artificial intelligence in the business environment. With a growing volume of unstructured data, ranging from legal contracts to customer interactions, extracting relevant information from this data has become a persistent challenge.
Traditionally, converting raw data into useful intelligence requires considerable engineering effort, often involving managing multiple machine learning models and designing complex workflows. This reality generates fragile and costly workflows that require constant maintenance, underscoring the need for more efficient solutions, especially when over 80% of business data is considered unstructured.
In response to this need, Amazon has launched Amazon Bedrock Data Automation, a platform that enables the automation of generating useful information from unstructured multimodal content. This new tool offers organizations the ability to extract valuable data without needing deep knowledge in artificial intelligence or managing complex machine learning infrastructures. The ease of use provided by Bedrock Data Automation aims to facilitate the adoption of artificial intelligence and enable the development of efficient and responsible solutions.
The functionality features a unified API that reduces the complexity of processing multimodal content, ensuring accuracy and cost efficiency. Furthermore, it is designed under principles of responsible artificial intelligence, enhancing transparency and avoiding complicated integrations. Its cross-region inference capability ensures efficient handling of unforeseen traffic, utilizing distributed resources in different AWS regions and optimizing performance without incurring additional costs.
The adopted pricing model is transparent and predictable, based on the processed content type and the output generated, making cost estimation easier compared to traditional token-based models. Among the most prominent use cases are intelligent document processing, media asset analysis and monetization, voice analysis, and agent-guided operations, showcasing its potential to catalyze innovation and data-driven decision-making in various industries.
The intelligent document processing field anticipates significant growth, moving from $10.57 billion in 2025 to $66.68 billion in 2032. Organizations in sectors like finance and healthcare are leveraging automation to enhance efficiency in tasks like digitizing medical records and fraud detection. With Bedrock Data Automation, workflow simplification in this field is significantly sought after, eliminating manual steps and reducing processing times.
In the media and entertainment industry, Amazon Bedrock Data Automation emerges as a key tool for monetizing digital assets such as images and videos. Advanced content analysis capabilities optimize asset management, enabling applications ranging from contextual ad placement to content moderation.
Moreover, in the voice analytics arena, Amazon’s new tool is transforming how companies analyze customer service interactions, providing call centers with tools to enhance both customer experience and service quality. This integration of processes within a single system allows unstructured voice data to be converted into valuable insights, thus boosting performance and operational agility.
With its focus on automation and data processing, Amazon Bedrock Data Automation stands as an essential resource for companies to effectively implement artificial intelligence, accelerating implementation time and improving operational efficiency.
via: MiMub in Spanish
