Generative AI is spreading like wildfire and it’s taking the global industry to the next level. A recent study from McKinsey found that the implementation of AI could lead to $4.4 trillion per year in global productivity. The wildfire is blazing through most industries, with companies looking to leverage artificial intelligence to optimize various processes and remain competitive amongst their AI-using counterparts. But how exactly are companies using AI to optimize their productivity? And where is the future of AI headed?
Use case: image generation and product recommendations
Last summer major furniture company Wayfair launched a new AI product, Decorify. The application is intended to help customers looking to redecorate their living spaces by providing visual design suggestions. Users can upload an image of their living space, choose the design styles that best resonate with them, and then receive a photorealistic image of the recommended interior design plan with links to the furniture featured in the visual. The product is intended to serve customers who struggle to make design choices that optimize the dimensions of their given space, and serve as a connector to the company’s furniture offerings.
While the company is still working on upgrading and finetuning the product to better replicate Wayfair’s furniture offerings, it is pushing the needle on how generative AI can be used to fulfill customer needs in an engaging way, while also promoting the brand itself.
Use case: patient communication
Mass General Brigham (MGB) is a non-profit integrated health care system used by Massachusetts General Hospital and Brigham and Women’s Hospital — the two largest hospitals in Massachusetts. MGB has been piloting a new feature involving Large Language Models (LLMs) — a type of generative AI that understands and generates human language text — to help physicians respond to patient messages. While MGB has used generative AI in the past to create educational videos on pediatric conditions and care strategies for providers around the world to learn from, the organization is now piloting a product that can help physicians respond to timely patient questions. Research from LLM product testing showed that 82% of AI-generated responses were safe to send to the patient (i.e. lacking misinformation or insensitive language), and 58.3% of those cleared responses did not need further editing from the physician.
Researchers at MGB are still working to drive those numbers up before the LLM product is widely available, but the results offer a promising solution to the issue of little face time with providers. LLMs could be the key to reducing providers’ time spent on administrative tasks, therefore increasing their in-person time with patients.
Use case: multi-divisional predictive analytics & content creation
Salesforce, a cloud-based software company specializing in CRM products and services, launched Einstein GPT in March 2023. The general AI software is integrated with OpenAI to generate emails, facilitate basic customer service, devise personalized marketing content, and summarize and update knowledge articles. Einstein GPT is designed to service employees in different industries with different needs, and thus was created separately for employees in each department (e.g. Einstein GPT for Marketing, Einstein GPT for Sales, etc.).
In addition to integrating with Salesforce’s CRM platforms, Einstein GPT also provides data analytics on items such as marketing trends, customer engagement, and industry trends to help employees assess their current strategies. Although the product is new in the market and continues to be improved, Einstein GPT serves as a good example of how AI can be integrated into CRM software and serve the needs of employees across a variety of divisions.
Use case: product marketing
Coca Cola, the largest manufacturer of carbonated soft drinks, is renowned for using AI in unique ways to promote new product launches. In September 2023, the company released a limited-edition drink with a futuristic concept called ‘Y3000,’ created through a mixture of customer input and AI. After collecting a bunch of customer feedback on the emotions, colors, and flavors that they pictured for the year 3000, Coca Cola used AI to synthesize the feedback and create the visual concept and the taste profile of the beverage. The drink was produced with a QR code on the packaging that would take customers to a website where they could filter photos with a custom AI Cam that visualized how their bedroom, residential street, etc., could appear in the year 3000.
The company used a similar AI-generated filter to promote their beverages during the holiday season. Users were able to create their own holiday cards using AI-generated images that cycled through iconic holiday branding assets to create personalized imagery. Using the newly upgraded AI imagery software, Dall-e-3, Coca Cola has been able to leverage AI to develop a buzz around their new product launches, while also creating vibrantly themed creative.
Use case: managing knowledge center infrastructure
Adidas, a leading retailer of athletic merchandise, is using generative AI for its extensive information storage capabilities. Using an AI data management tool, engineers can access the company’s knowledge base through a conversational interface and receive answers to questions both general and hyper-specific. As the Vice President of Enterprise Architecture, Daniel Eichten, remarked, the implementation of AI into their database has taken a major administrative load off their shoulders, allowing them prioritize core aspects of their LLM projects.
The AI product also offers secure data storage, reducing the amount of time spent on data coding, in addition to database keeping. Adidas’s use of generative AI for knowledge management is a clear demonstration of how AI can be widely used to reduce time spent on database management – a task that is universally shared by companies across various industries.
The examples above illustrate the incredible versatility and capability of generative AI. Whether it is for data storage, image generation, client communication, predictive analytics, or product marketing, companies across all industries have found ways to leverage AI to meet their objectives. All the projects listed here are still being fine-tuned, yet current results show there is much to be gained from hopping aboard the AI train. You never know where it might take you. Subscribe to our newsletter for more case studies, insights and best practices.
1 https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/
Punnya is the Summer 2024 Public Relations and Media Intern at Data Axle. Originally from Boston, Punnya is pursuing a double major in Communications and Psychology at Boston College. As a storyteller, she is excited to use her writing skills to uncover the newest trends in data and adtech. In her free time, Punnya can be found reading the latest epic fantasy novel or getting her nose on new fragrances at the nearest perfume shop.