Generative AI is one of the most exciting and rapidly advancing fields in technology today. In 2022 and 2023, we saw massive leaps forward with models like GPT-3 for natural language processing, DALL-E for image generation, and AlphaFold for protein folding. The capabilities of these large language models seem to expand almost daily.
Generative AI refers to AI systems that can create brand new content, whether text, images, audio, video or more. This stands in contrast to most modern AI solutions which relies on categorizing, recommending or predicting based on existing data. Generative models can synthesize completely novel outputs based on their training data.
Nearly every industry from healthcare to finance stands to be disrupted by these advances. Within enterprises, generative AI can lead to immense productivity gains, new products and services, and savings on time and costs. As models continue rapidly improving, adoption will only grow.
In this article, we'll explore some of the most promising enterprise applications of generative AI through real-world examples and case studies.
Grammarly leverages generative AI to rephrase and rewrite content. This allows them to suggest more natural phrasing while preserving the original meaning.
For Grammarly, conciseness, formality and other dimensions of writing improving suggestions are now powered by AI models rather than hard-coded rules. This makes the writing suggestions more flexible and human-like.
Semrush uses generative AI to automatically rewrite marketing copy for clients. This saves hours of human time while providing fresh, unique versions of the text. Semrush just has to provide the core message and keywords, and the AI generates new variations.
Adobe showcased an AI system that generates realistic images and art from text captions. This allows even non-artists to transform ideas into images. Adobe trained the model on billions of image-text pairs to learn these artistic associations.
Possible applications include automatically generating product images from descriptions for e-commerce or manufacturing. Marketers could also ideate social posts, infographics and other graphical content in seconds. As generative models grow more advanced, the outputs will only get more impressively photorealistic.
What makes this case unique is that the images are trained using legal-only images from Adobe-owned database. By that, Adobe delivers a copyright-compliant AI image generating service - an unique quality among current solutions.
Note-taking and productivity platform Notion relies on generative AI to automatically summarize documents for users. When importing or creating a long-form document in Notion, it uses a summary model to pull out key points into a tl;dr digest.
This enables users to quickly get the core ideas from sales proposals, research reports, long emails and more without having to read through everything. Generative AI saves users time while allowing Notion to provide value-added features.
Generative AI used in Todoist helps the user to split a larger task into smaller chunks to be executed separately. It also supports the user in achieving goals by breaking tasks down or making them more actionable by rewriting a particular task. Also, the AI assistant can give tips on completing a particular task.
Management consulting firm McKinsey built an internal generative AI assistant called Lilli. Lilli uses LLM to answer consultants' questions on deadlines, HR issues, project details and more. It also summarizes long documents into digestible bullets.
Consultants have found Lilli saves them huge amounts of time compared to digging through company resources to find answers themselves. It also gets smarter over time as the model trains on more internal data. McKinsey is exploring uses like automating data analysis and report generation as well.
Humane has created an AI assistant device called "Humane AI Pin" that serves as an alternative for a smartphone. It uses conversational AI interact with the user.
This device aims to challenge the concept of smartphone and reshape the way people use mobile devices - instead of carrying the touchscreen in their pockets, users can just tap the pin and start talking.
Slack, the popular workplace chat platform, has prototyped an AI feature to summarize Slack conversations. This uses natural language processing to analyze threads between colleagues and pull out key concepts, decisions and action items.
The summary gives users a quick recap of long, complex conversations without having to reread everything. It also highlights the most important points and next steps. This showcases how generative AI could make enterprises more productive and efficient.
Also, according to the company statement, up to 47% of users struggle to find the information they need, and 32% employees made wrong decisions due to lack of information they need. Introducing Slack AI is the way to tackle this challenge.
Generative AI is already transforming enterprise software and unlocking new productivity gains. Use cases like intelligent assistants, content summarization, task automation and image generation demonstrate the immense possibilities. Virtually every industry from retail to healthcare can benefit from generative AI's unique strengths.
As models continue rapidly evolving, so too will practical business applications. Companies that strategically leverage generative AI will have a competitive advantage. However, thoughtfully managing risks around data, biases and misuse will be critical as well. Overall, generative AI opens up a new world of opportunities to enhance products, services and workflows. The next few years will reveal even more disruptive and transformative use cases as these technologies mature.
More real-life use cases and applications of generative AI, as well as hands-on expert experience, will be available during the Data Science Salon Austin Conference! Don't hesitate to get your ticket now!