Generative AI: Enterprise Use Cases

Generative AI, a type of AI that is trained to generate original content, is growing in both its consumer and business use cases. Particularly in the enterprise, generative AI is:

• Quickly automating and simplifying project workflows.
• Taking repetitive tasks off the plates of busy employees.
• Helping businesses maintain high quality and volume production standards.

Learn how generative AI is optimizing enterprise use cases across a variety of industries and tasks.

Also read: Generative AI Landscape: Current and Future Trends

And: 100+ Top Artificial Intelligence Companies 2023

Generative AI in the Enterprise: Table of Contents

• What Can Generative AI Be Used for in the Enterprise?
• How Enterprises Are Using Generative AI Today
• Using Generative AI Responsibly and Ethically
• Bottom Line: Generative Enterprise Use Cases

Generative AI Enterprise Use Cases

Some enterprises, like marketing and sales-driven companies, have quickly added generative AI into their content creation workflows, while others — like real estate, education — have been more hesitant.

Overall, more enterprise companies are looking to today’s top AI companies for assistance – most firms aren’t able to produce or support artificial intelligence without external support.

Still, generative AI’s integration into enterprises is expanding across industries and department types:

Code generation, documentation, and QA

For software developers and programmers, generative AI uses cases include writing, completing, and vetting sets of software code. Quality assurance is perhaps the most important emerging use case in this area, with generative AI models handling bug fixes, test generation, and various types of documentation.

Product and app development

Generative AI is now being used to code various kinds of apps and write product documentation for these apps. While apps are probably the most common type of product development for generative AI today, generative AI support is also going into projects like semiconductor chip development and design.

Blog and social media content writing

With the right prompts and inputs, large language models are capable of creating appropriate and creative content for blogs, social media accounts, product pages, and business websites.

Many of these models enable users to give instructions on article tone and voice, input past written content from the brand, and add other specifications so content is written in a way that sounds human and relevant to the brand’s audience.

Inbound and outbound marketing communication workflows

Inbound and outbound marketing frequently require contextualized email and chat threads to be sent to prospective and current customers on a daily basis. Generative AI solutions can create and send the content for these communications, and in some cases, they can also automate the process of moving these people to the next stage of the customer lifecycle in a CRM.

Graphic design and video marketing

Generative AI is capable of generating realistic images, animation, and audio that can be used for graphic design and video marketing projects. Some generative AI vendors offer voice synthesis and AI avatars so you can create marketing videos without actors, video equipment, or video editing expertise.

Entertainment media generation

As AI-generated imagery, animation, and audio become more and more realistic, this type of technology is being used to create the graphics for movies and video games, the audio for music and podcast generation, and the characters for virtual storytelling and virtual reality experiences.

Some tech experts predict that generative AI will constitute the majority of future film content and script writing, though creatives are understandably pushing back on that assumption.

Performance management

Generative AI uses cases include several business and employee coaching scenarios. As an example, contact center call documentation and summarization, when combined with sentiment analysis, gives managers the information they need to assess current customer service rep performance and coach employees on ways to improve.

Business performance reporting

Because generative AI can work through massive amounts of text and data to quickly summarize the main points, it is becoming an important piece of business performance reporting. It’s especially useful for unstructured and qualitative data that usually require more processing before insights can be drawn.

Customer support and customer experience

For many of the most straightforward customer service engagements, generative AI chatbots and virtual assistants can handle customer service questions at all hours of the day. Chatbots have been used for customer service for many years, but generative AI advancements are giving them additional resources to give comprehensive and more human answers without the help of a human customer support representative.

Optimized enterprise search and knowledge base

Both internal and external search benefit from generative AI technology. For internal employee users, generative AI models can be used to scour, identify, and or summarize enterprise resources when users are searching for certain information about their job.

Similarly, generative AI models can be embedded into company websites and other customer-facing properties, giving them a self-service solution to find answers to their brand questions.

Pharmaceutical drug discovery and design

Generative AI technology is being used to make drug discovery and design processes more efficient for new drugs. AI-driven drug discovery is one of the areas of generative AI that is receiving the most funding right now, so expect this particular enterprise use case to grow significantly in the coming months and years.

Medical diagnostics

Generative AI in medicine is still nascent, but that is changing quickly. Image generation and editing tools are increasingly being used to optimize and zoom into medical images, allowing medical professionals to get a better and more realistic look at certain areas of the human body. Some tools even perform medical image analysis and basic diagnostics on their own.

More on this topic: Generative AI in Healthcare

Inverse design

In medicine, manufacturing, and other materials-based industries, generative AI is being used in a process called inverse design. With inverse design, generative AI assesses missing materials in a process and generates new materials that fulfill the required properties for that environment.

Consumer-friendly data analysis

Although generative AI poses some crucial security concerns, it can also be used to heighten data and consumer privacy.

For example, generative AI can be used to create synthetic data copies of actual sensitive data, allowing analysts to analyze and derive insights from the copies without compromising data privacy or compliance.

Smart manufacturing and predictive maintenance

Generative AI is quickly becoming a staple in modern manufacturing, helping workers create more innovative designs and meet other production goals.

In the realm of predictive maintenance, generative models can generate to-do lists and timelines, make workflow and repair suggestions, and simplify the process of assessing complex data from sensors and other parts of the assembly line.

Inventory and supply chain management

Several components of supply chain management can be enhanced with generative AI. Route optimization, demand forecasting, supplier risk management, and inventory management can all be made smarter and more accurate with generative AI suggestions.

Fraud detection and risk management

This type of technology can analyze large amounts of transaction or claims data, quickly summarizing and identifying any patterns or anomalies in that data. With these capabilities, generative AI is great for fraud detection and risk management in finance and insurance scenarios.

Learn about other generative AI examples.

How Enterprises Are Using Generative AI Today

Generative AI enterprise use cases comprise a number of creative initiatives. Some are sticking with conventional subscription-based generative AI models, while others are building their own models and versions of these tools into their existing tool stack.

Here are a few examples of how major enterprises are adding generative AI to their processes today:

Professional services and business operations: Accenture uses case

Accenture, a major consulting firm, is using generative AI to help its clients create smarter business strategies, roadmaps, and operations.

Accenture’s clients span across banking, sales, customer service, legal, and other industries and are using Accenture’s generative AI services for enhanced search, document summarization, and automated communications.

Life sciences: Nvidia uses case

Nvidia recently released its BioNeMo Drug Discovery Cloud Service, which uses large language modeling to advance and speed up drug discovery, protein engineering, and research in genomics, chemistry, biology, and molecular dynamics.

Travel and hospitality: Expedia uses case

Expedia’s beta ChatGPT-powered travel planner lets users ask questions and get recommendations on travel, lodging, and activities. It also saves suggested hotels and venues through an intelligent shopping feature, so users can recall and easily book recommended lodging.

E-commerce and retail: Shopify uses case

Shopify now offers Shopify Magic to help retailers generate product descriptions and other product-related content with artificial intelligence.

Fintech and software development: Stripe uses case

Stripe, a financial services and SaaS company, is using OpenAI’s GPT-4 to power better documentation, summarization, and query management for developers that use Stripe Docs.

Also read: Generative AI Companies: Top 12 Leaders

Generative AI Use Cases: Ethics and Compliance

Generative AI is an emerging technology and there are still many unknowns. For example, most users aren’t familiar with how the models are trained or what data goes into their training.

Additionally, these models have wide-ranging capabilities that can both help and harm cybersecurity postures. And finally, generative AI models are quickly growing in their skill sets, posing a threatening alternative for many skilled workers’ careers.

So what can enterprises do to make sure they’re using generative AI responsibly and ethically, in compliance with security/privacy regulations? We expect responsible generative AI expectations to rapidly evolve as the technology matures, but here are a few tips to get started with responsible use:

• Only input depersonalized and nonsensitive data into large language models. Otherwise, your most sensitive data could become part of the tool’s training dataset and become exposed to third-party users and companies.
• Stay current with generative AI news and trends. The generative AI space is changing daily, and with that change comes news of companies that are getting the technology right. Yet some companies are taking dangerous and/or unethical steps in their AI development. Staying updated on all of these changes will ensure you only use the most credible tools and work with the most ethical AI providers.
• Create an AI usage and ethics policy for your business. There are policy templates that are publicly available that should cover how internal users in your organization are allowed to use AI tools and also how your business is allowed to invest in third-party tools.
• Offer career training to all employees. Employees are rightfully afraid that parts of their job will soon be outsourced to AI; to combat this fear and build up their career prospects, offer training and certifications that will help them to use AI in their jobs and build skills that cannot easily be demonstrated by AI models.

Learn more: Generative AI and Cybersecurity: Advantages and Challenges

Bottom Line: Generative AI Enterprise Use Cases

Because generative AI capabilities are changing on a near-daily basis, enterprise use cases for this new technology are evolving just as quickly. With this change comes new opportunities for enterprises to enhance their current operations.

Already, enterprises are leveraging generative AI for everything from writing marketing copy to discovering new pharmaceuticals. For enterprise leaders who want to incorporate generative AI into their business, the key is to consider what model works best for your business, what you’re trying to achieve, and how this new business factor will affect your employees and your customers.

Read next: Top 9 Generative AI Applications and Tools

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