Here are five ways AI and ML will improve information management:
1. Machine translation gains near-human quality
Advances, such as AI’s improved understanding of information context, will allow machine translation to reach human-like capabilities this year. Human translators reviewing machine translations will likely find fewer errors, perhaps no more than they would in human-translated content. Automated translation of large content archives will increase the ability of organizations to share data by making it possible to search and discover previously “hidden” content. This will enable multinational and multilingual companies to give their employees access to all of their content, regardless of the language in which the original content was written.
2. Text-to-speech becomes almost perfect
Text-to-speech apps have always been useful, but they also have some quirks that transcribe some text inaccurately. With the training and continuous improvement of AI and ML models, the gap between speech and text transcriptions is narrowing. These advances will likely continue, allowing text-to-speech applications to approach 100% accuracy and allowing businesses to improve knowledge sharing by making content more searchable and accessible.
3. NLP Improves How Knowledge Workers Find Relevant Information
Advances in natural language processing (NLP) allow AI-based solutions to process language in a way similar to how the human brain works, allowing it to look for patterns in text, image and audio files. NLP can extract the meaning of speech contained in audio files, giving it the ability to understand a user’s intent. This allows systems to apply metadata tags to audio and video files, indexing these files in a way once reserved for text documents. This will improve the way knowledge workers search for information and enable information management systems to better deliver relevant information to end users, before they even start searching.
4. Cognitive search improves efficiency
Knowledge workers spend an enormous amount of time not find the information they are looking for. Several studies carried out in recent years have estimated that workers waste between 30% and 50% of their time searching for the information they need to accomplish their tasks.
However, advances in cognitive search capabilities will allow knowledge workers to spend less time searching for the information they need and more time using it, while focusing on what drives bottom-line results. Businesses will be able to go beyond simple keyword searches, accessing content in context with advanced indexing capabilities, including advances in metadata tagging.
By combining the insights with a domain- or organization-specific knowledge graph, businesses will make it easy for employees to find context-optimized content without wasting valuable time going through endless search cycles.
5. Synthesis reduces information consumption time, thereby increasing employee productivity
Today’s employees are drowning in information, and most traditional summary technologies don’t always offer much relief. They use an “extractive” approach, selecting a subset of relevant sentences in a document to form a summary. But these sentences don’t always align with each other, limiting the summary’s ability to fully capture the content.
Large language models (LLMs) have seen a huge surge in popularity recently, with OpenAI’s LLM ChatGPT making headlines. People are realizing the business value of this technology, and its continued advancements make it possible to automatically generate “abstract” summaries, producing a more human and effective result. LLMs can use abstract summarization to reorganize language in the text, adding words and phrases as necessary. As with other AI model enhancements, abstract summarization can provide employees with more accurate information more quickly, reducing the time they spend searching for it and improving their productivity.
Information management as a business enabler
AI and ML add significant value to the massive amounts of data that businesses collect every day. Recent advances make information easier to find and understand, providing organizations with insights that were previously very difficult to discover. New advancements in AI and ML are transforming information management into a key business enabler. This is a trend that will certainly continue into the future, but is also already happening by enabling innovative companies and organizations to derive real insights from their data.
What changes are you seeing in information management with the increasing use of AI and ML? Share with us on Facebook, TwitterAnd LinkedIn. We would love to hear from you!
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