Can Artificial Intelligence (AI) Enrich Content Collaboration? Or Is It Just a Lipstick?
Is Artificial Intelligence (AI) the new lipstick? Sure, it is being put on many pigs. Can artificial intelligence improve Enterprise File Sharing and Sync (EFSS), Enterprise Content Management (ECM) and Collaboration? We want to explore if we could find some obvious collaboration use cases that can be improved using machine learning. In this article, we will not venture into AI techniques or impact of AI or evolution of AI. We are interested in exploring how EFSS benefits from “machine learning” – a technique that allows systems to learn by digesting data. The key is ‘learning’ – a system that can learn and evolve vs. explicitly programmed. Machine learning is not a new technology; many applications, such as search engines (Google, Bing), already use machine learning.
In the past year, many large players, such as Google, Amazon, and Microsoft, have started offering AI tools and infrastructure as service. With many of the basic building blocks available, developers can focus on building right models and applications. Here are a few scenarios in Enterprise File Sharing and Sync (EFSS), and Enterprise Content Collaboration, where we can apply machine learning soon.
Search is a significant part of our everyday life. Google and Amazon have made the search box the center of navigation. For instance, a decade ago, the top half of the Amazon homepage was filled with links, which is now replaced by a search box at the top. However, search hasn’t taken a significant role in enterprise collaboration, yet. Every day, we search for files that don’t fit in a simple search criteria. Think of search that goes ‘looking for a design proposal from a vendor x I received six months back.’ Today, we manually sort through files to find an image that satisfies the above search criteria. We could use a simple query processing, a crawler, and a sophisticated ranker to surface file search results, based on estimated relevance. Such a search feature can continue to learn and improve to provide better results each time. Already, we have many such machine learning algorithms and techniques available to index files, identify relevance, and rank search results per relevance. Hence, applying to enterprise scenarios requires a focused effort from the solution providers.
Predict and organize relevant content
A technique in machine learning, called unsupervised learning, involves building a model by supplying it with many examples, but without telling it what to look for. The model learns to recognize logical groups (cluster), based on certain unspecified factors, revealing patterns within a data set. Imagine your files are automatically organized, based on the projects you are working on. Any file will have a set of related files just one click away. Won’t such a feature have a significant productivity boost?
Collaboration across different languages will be simplified with many advanced translation tools available today. Google Cloud Translation API provides a straightforward API to translate a string from and to many languages. Translation of user comments and meta data, such as tags, image information, can be very useful for any large organization that involves working with partners and vendors across the globe. With translation combined with machine learning, translation within an enterprise can improve by learning domain knowledge (medical, law, technology etc.) and internal jargon. Systems can extract right meta data, apply domain knowledge, and translate them for employees, partners, and customers, so they easily communicate and collaborate.
Interaction with EFSS applications need not be just clicks and texts. Users can have more engaging user experiences that include conversational interactions, e.g., users could just say “open the sales report that I shared with my manager last week.” Personal assistants, such Siri, Cortana, and Alexa, already provide such conversational interfaces for many personal and home scenarios. Though it sounds complex, some of the technology, such as automatic speech recognition for converting speech to text and natural language understanding, are available in Amazon APIs. Converting the conversation into an actual query might not be as complex as it sounds.
Security and Risk Assessment
Machine learning has an excellent application in monitoring network traffic patterns to spot abnormal activities that might be caused by cyber-attack or malicious activities. Solutions like FileCloud use some of these techniques to identify ransomware and highlight potential threats. Similar techniques can identify compliance risks to analyze if any documents being shared have any personal identifiable information (credit card) PII or personal health information PHI. Systems can predict and warn security risks before the breach happens.
These ideas are just a linear extrapolation of the near future. Even these simple linear extrapolations look promising and interesting. Many predict that, within a few years, almost every device and service will have intelligence embedded in them. In future, the concept of file and folders might be replaced by some other form of data abstraction. As AI and collaboration continue to evolve, resulting applications evolve exponentially better than our linear extrapolations, and our current thoughts could appear naïve. Hope it doesn’t evolve, as Musk puts it, “with artificial intelligence, we’re summoning the demon.”