AI in USE ✨: Enhancing Communication and Automating Workflows
Discover how AI is elevating writing ✍️, automating documentation 🗃️, and tracing the roots 📜 of conversational technology.
Welcome to the latest edition of AI in USE, where we bring you compelling examples of artificial intelligence transforming industries and driving innovation. In this newsletter, we explore three AI use cases that highlight the power of AI to augment human capabilities and automate complex tasks.
This week, we introduce DeepL Write, an AI-driven tool that enhances writing by offering context-aware suggestions to improve grammar, tone, and style, making it a valuable resource for multilingual users. Next, we spotlight Scribe, a platform that automates the creation of process documentation, revolutionizing how teams capture and share their workflows. Finally, in our Rediscover section, we take a look back at ELIZA, the pioneering natural language processing program developed at MIT in the 1960s, which laid the groundwork for modern conversational agents and chatbots.
📝 From writing assistance to automated documentation and early AI conversational breakthroughs, these use cases showcase AI's ability to enhance and simplify the way we work.
🈳 DeepL
Targeted Industry: Technology
Organization Type: Scale-up
AI Purpose: Augment
Type of AI Model: Generative
AI Application Type: User experience
Target Group (AI User): User (employees)
Use Case Description:
DeepL, a German AI company recognized for its translation accuracy, launched DeepL Write in January 2023. This AI-powered tool is designed to enhance human writing by providing context-aware suggestions that improve grammar, tone, style, and clarity. It supports multiple languages, making it especially useful for non-native speakers aiming to produce polished, natural-sounding text. DeepL Write offers an intuitive user interface where users can input their text and instantly receive refined versions with alternative suggestions, helping to elevate the quality of their writing.
Key Features:
Context-aware grammar and style suggestions.
Supports multiple languages (including English, German, French, and Spanish).
Integration with DeepL’s translation service for seamless language transitions.
Advanced options for customizing tone and style.High security standards for data privacy.
Results:
Since its launch, DeepL Write has received positive feedback for its ability to provide nuanced and contextually appropriate writing enhancements. It has been particularly effective for users in multilingual contexts, enabling smoother and more professional communication. The tool's ability to integrate seamlessly with DeepL’s core translation services further augments its utility, making it a comprehensive solution for both writing and translating needs.
Launch Date: January 2023
Retrieve the case (including its sources) on the AI in USE website
✍️ Scribe
Targeted Industry: Technology
Organization Type: Start-up
AI Purpose: Automate
Type of AI Model: Generative
AI Application Type: User Experience
Target Group (AI User): Operation teams, Customer support teams, Product managers, HR / L&D professionals
Use Case Description:
Scribe is an AI-powered platform designed to automate the creation of process documentation, including standard operating procedures (SOPs), user guides, and training manuals. The platform captures user workflows directly from web or desktop actions and automatically generates step-by-step visual guides. With Scribe AI, users can create detailed documentation instantly, without the need for manual input, and the tool can even provide contextual information such as who, what, when, where, and why for each process.
Key Features:
Automatic capture of web and desktop workflows.
AI-generated documentation, including text, screenshots, and contextual information.
Custom branding and sensitive data redaction capabilities.
Instant sharing via links, PDF exports, or embedding in other platforms.
Editing and enhancement of existing documentation.
Results:
Scribe has significantly reduced the time required to create detailed documentation, with reports of up to a 15x faster process compared to traditional methods. It has been adopted by over 1 million users across various industries, particularly enhancing efficiency in onboarding, training, and customer support.
Launch Date: May 2023
Retrieve the case (including its sources) on the AI in USE website
⏮️ {REDISCOVER} MIT
Targeted Industry: Education and Research
Organization Type: Non for profit
AI Purpose: Augment
Type of AI Model: Supervised
AI Application Type: R&D / Product development
Target Group (AI User): Scientists / Researchers
Use Case Description:
ELIZA was an early natural language processing program developed by Joseph Weizenbaum at MIT between 1964 and 1966. It was one of the first programs to demonstrate that computers could simulate human-like conversation. ELIZA used pattern matching and substitution techniques to respond to user inputs, creating the illusion of understanding and empathy. The most famous script within ELIZA was "DOCTOR," which mimicked a Rogerian psychotherapist, engaging users in dialogue by asking open-ended questions. This led many users to perceive the program as having human-like understanding, even after they were informed it was just a simple algorithm. ELIZA generated significant interest and debate about the potential and limitations of artificial intelligence, particularly in its ability to engage in human-like conversations.
Key Features:
Pattern Matching: Recognized keywords in user input to generate responses.
Script-Based Responses: Used predefined scripts like "DOCTOR" to simulate conversations.
No True Understanding: Responded based on rules, not actual comprehension.
User Engagement: Created an illusion of understanding, leading users to engage deeply.
Script Flexibility: Could be programmed with various scripts for different conversational roles.
Results:
ELIZA's creation marked a pivotal moment in the history of AI, laying the groundwork for the development of chatbots and conversational agents. The program's impact was profound, as it showed that even a simple system could generate a powerful psychological response from users. Weizenbaum himself was surprised by the reactions to ELIZA and later became a vocal critic of AI, cautioning against the overreliance on machines for tasks requiring human judgment and empathy.
Launch Date: 1966
Retrieve the case (including its sources) on the AI in USE website
📢 Stay tuned for more exciting AI use cases in our next weekly edition. Thank you for being part of the AI in USE community! Remember to visit our website at AI in USE ✨ to consult the whole library of use cases.
Disclaimer: This content was (obviously 😉) built with the assistance of AI.
We hope you found these AI use cases inspiring! Please leave a comment and let us know which use case intrigued you the most. Your feedback helps us improve and grow!