Even though AI-powered chatbots have been around for decades, Generative AI technology has never been as omnipresent — or impressively versatile and intelligent — as it is today.
Less than a year after Open AI released its groundbreaking LLM ChatGPT to the public, it’s clear that Generative AI will change the way work is done across industries. As of mid-2023, 37% of advertising and marketing professionals in the United States have already used AI to assist with work-related tasks.
Adoption isn’t quite as high in other industries — yet. But as the technology evolves in a snowball fashion, we will see these numbers grow exponentially across the board.
We’ve gathered examples of Generative AI use cases from 10 key industries to help you plan for the AI-powered future. Keep reading to find out how to navigate the changing technological landscape and innovate your business with Generative AI.
(Or, if you’re eager to take action now, use our Generative AI Wizard tool to get a free list of ideas for how your business could be using artificial intelligence.)
What is Generative AI?
Generative AI is a category of artificial intelligence tools that generate outputs (e.g. text, images, video, code, etc.) based on user prompts. The quality of these outputs is closely related to the training data each specific tool “learns” from — and the structure and characteristics of which it works to replicate.
In mid-2023, Generative AI is definitely enjoying a moment in the mainstream spotlight. Over the last couple of years, AI tools have become widely available to the general public. The fact that many of them rely on advanced natural language processing technologies (i.e. they “understand” plain language prompts and contextual cues) and package the complex workings of their AI into user-friendly interfaces makes it possible for pretty much anyone with access to a computer and an internet connection to see Generative AI in action. The most popular examples of such tools currently include:
While the majority of tech savvy-ish internet users start off using simple Generative AI tools out of curiosity or for entertainment, businesses across all industries are racing to adopt — and monetize — this technology. The possibilities are endless.
Key Generative AI use cases
Below, we dive into the most popular uses of Generative AI. Note that these are industry-agnostic — we’ll dive into more specific business applications in the next section.
Perhaps the most obvious way to utilize Generative AI is creating new media — including text, images, video, and audio.
As mentioned above, Generative AI “learns” from data sets. The technology that makes this process possible is called machine learning, and it relies on artificial neural networks that mimic the workings of the human brain.
Modern Generative AI models are trained on vast data sets that include publicly available media as well as prompts and feedback from active users. This allows them to create:
- Text on par with the writing abilities of a native speaker of any given language
- High-quality images ranging from photo-like hyperrealism to interpretations of existing art styles
- Realistic and animated video clips
- New music, realistic voice recordings, and modifications of existing audio samples
And the quality of these outcomes is good. Here’s an example of how Midjourney interpreted a simple art history-related prompt:
Augmented and virtual reality
Virtual spaces (like Meta’s Metaverse) and experiences (e.g. clothing try-ons) are typically powered by Generative AI. The technology generates and enhances the quality of virtual spaces, characters, landscapes, textures, etc. Generative AI can also realistically superimpose virtual elements onto real images or footage, in real time. This process is best illustrated by popular social media filters that change facial features.
Finally, Generative AI works to… improve the way Generative AI works.
We’ve already mentioned the training data sets that Generative AI uses to “learn” and produce quality outcomes. This data typically comes from many sources and may include:
- Publicly available information
- Curated sets built by researchers
- Proprietary sets with an organization’s internal information
- User-generated content
- Synthetic data
Synthetic data is created by Generative AI systems based on available information in a process known as data augmentation. It’s used to expand and diversify available data sets, helping artificial intelligence models learn (to recognize objects, understand text, etc.) through exposure to more examples.
Data augmentation can be as simple as creating variations of an image, e.g. a mirror reflection, a version with noise in the background, a version with a different contrast level, a rotated version, etc. Training AI to correctly interpret all variations improves its ability to consistently recognize and “understand” similar images.
Applications of Generative AI across industries
1. Marketing and sales
As mentioned before, marketing is currently leading the pack in terms of Generative AI adoption.
Here’s how Generative AI is used across sales and marketing to save time and effort, and efficiently manage resources:
- Content generation and curation. Generative AI can assist in creating various types of marketing content, including blog articles, website and advertising copy, social media posts, product descriptions and more — at scale. It can also curate content by identifying trends and suggesting materials worthy of sharing.
- Personalized marketing content. Generative AI can be used to personalize marketing messaging for individual customers or specific target audiences. By analyzing customer data and preferences, AI can create personalized test variants for ads, emails, landing pages, etc.
- Segmentation and targeting. Generative AI models can analyze customer data to identify meaningful segments based on demographic, behavioral, or purchasing patterns — and help businesses target these specific groups with tailored marketing communications.
- CRM. Generative AI can enrich customer and prospect records by generating data-based insights and strategic recommendations.
- Analytics and lead scoring. Generative AI can analyze customer data and historical sales patterns to predict customer behavior, sales trends, customer lifetime value, the likelihood of churn, and more. This, in turn, allows businesses to optimize marketing strategies, prioritize leads, and increase sales.
How does this work in practice? Generative AI marketing platform Jasper helped their client, digital marketing agency Mongoose Media scale their content production and get a 166% increase in organic traffic in just 2 months. In the same time, the agency’s team saved 240 hours, increasing the efficiency of their workflows by 400%.
2. Customer service
Customer service professionals can expect to witness a true revolution in their niche in the coming years. Here how businesses will be using Generative AI for customer service:
- Chatbots and virtual assistants. Generative AI is the magic behind customer service chatbots that already handle simple inquiries and provide instant support on many websites and social media accounts. NLP-powered agents can answer frequently asked questions, guide customers through self-service options, provide order tracking information, and assist with basic troubleshooting. They make 24/7 support possible and significantly reduce average response times.
- Personalized customer interactions and upselling. AI support tools can generate tailored responses based on a customer’s individual preferences and past interactions with a business. By understanding individual customer needs, AI models can also suggest upgrades, similar products, or tailored offerings, effectively increasing upselling opportunities — and revenue.
- Voice and speech assistance. Generative AI can be used to provide accessibility-forward voice-based customer service. AI-powered voice assistants can understand spoken language, process simple requests, and provide assistance using conversational speech.
- Call transcription. Generative AI can transcribe support calls, extracting key information, creating comprehensive summaries, and streamlining call center operations.
- Multilingual support. Generative AI tools can generate accurate translations in real-time, allowing businesses to provide support in multiple languages without investing in large multinational teams.
We’re currently seeing well-established players in the industry update their product offerings to include AI features. Business messaging platform Intercom, for example, recently added GPT-powered article generation and conversation summary tools to their software.
Generative AI is able to do some of the time-consuming heavy lifting associated with large engineering projects, including:
- Generative design. Artificial intelligence systems can create designs based on human-devised plans and parameters.
- Design optimization. Generative AI can speed up the design iteration process and improve the quality of outcomes by generating and evaluating multiple design variations.
- Predictive maintenance. Generative AI models can analyze sensor data from construction machinery to detect anomalies in real time and predict future maintenance needs. This, in turn, can increase the lifespan of expensive equipment and reduce project downtime.
Looking for real-world examples? Tribe AI partnered with Togal to build an AI-powered construction estimation software that automatically and accurately detects, labels, and measures project spaces, cutting project takeoff times down from weeks to minutes. Watch this video to learn more about the game-changing technology:
Although AI won’t be able to emulate the expertise and judgment of legal professionals anytime soon, it is able to streamline their work.
- Document drafting and review. Generative AI models can draft legal documents based on templates and available client information. They can also streamline the review of lengthy legal documents by identifying key information and highlighting potential errors.
- Case analysis. Generative AI can analyze vast amounts of text, identify relevant information, extract insights, and generate comprehensive summaries.
- Due diligence. Specialized AI tools can analyze contracts, extract key information, and flag potential risks, streamlining due diligence processes.
- Regulatory compliance and risk assessment. AI can analyze inputs against legal regulations and highlight compliance risks.
Harvey is a company pioneering the legal AI space. Backed by OpenAI (the creators of ChatGPT), this startup is engineering a “copilot” for lawyers.
“We want Harvey to serve as an intermediary between tech and lawyer, as a natural language interface to the law,” said Harvey co-founder and Tribe AI alum Gabriel Pereyra in a recent interview. “Harvey will make lawyers more efficient, allowing them to produce higher quality work and spend more time on the high value parts of their job. Harvey provides a unified and intuitive interface for all legal workflows, allowing lawyers to describe tasks in plain English instead of using a suite of complex and specialized tools for niche tasks.”
5. Private equity
Generative AI creates many exciting, high-stakes opportunities for the private equity sector:
- Bespoke trading and investment strategies. Artificial intelligence models can develop algorithmic trading strategies by analyzing vast amounts of market data and historical trends.
- Credit scoring. Generative AI can analyze credit data, financial statements, and market trends to assess a client’s eligibility for specific banking products and services, streamlining the loan underwriting process.
- Personalized wealth management. By analyzing customer goals and risk preferences, AI can suggest tailored investment portfolios and wealth management strategies.
- Fraud prevention. AI can scan data in real time and highlight anomalies (e.g. unusual transactions) that suggest fraudulent activities.
- Regulatory compliance. Private equity is a heavily regulated industry. Generative AI can streamline compliance monitoring and reporting processes by analyzing regulatory requirements, monitoring business operations, flagging compliance risks, and generating reports.
The team at Tribe AI is empowering major players in the investment industry to make the most out of AI solutions. Find out how we helped a leading PE firm build a proprietary AI-powered investment engine.
6. Consumer goods and retail
On top of the marketing and sales use cases listed above that apply to this industry, Generative AI is used for:
- Product and packaging design. Generative AI technology can be used to generate new designs or variations of existing products. It can also help brands expand product offerings based on market trends, user data, etc.
- AR-powered try-ons. Generative AI is the science behind augmented reality apps that allow customers to virtually try on or experience products before purchasing. This technology is most commonly used to support clothing and furniture sales.
- Feedback analysis. Artificial intelligence can analyze reviews and online mentions at scale and extract useful data: brand sentiment, recurring product issues, common requests, etc.
You don’t have to work in this industry to be familiar with the tools listed above, e.g. AR try-ons. Popular retailers have been using augmented reality to make the experience of online shopping more “real” for years. Popular examples include Ikea’s Kreativ app and Warby Parker’s handy glasses try-on feature.
7. Enterprise business management
Large companies can use Generative AI for sales support, customer service, and marketing content generation — but some enterprise-specific use cases include:
- Workflow optimization. Generative AI can assist with time-consuming tasks across departments and roles, improving a business’s overall efficiency. Automating repetitive tasks reduces the risk of human error, buys teams more time for strategic thinking, and helps fewer people get more done — optimizing the organization’s payroll needs.
- Risk management. Generative AI can analyze internal data and generate assessments for proactive risk management support.
- Analytics. AI can aggregate and analyze all kinds of business data (KPIs, OKRs, marketing and sales results, revenue stats, and much more) to extract insights, produce visualizations, and generate reports.
On top of general workflow optimization, here’s how Generative AI can be used in manufacturing:
- Demand forecasting. Artificial intelligence systems can analyze historical sales data and market trends to accurately forecast demand, helping manufacturers efficiently manage inventory levels.
- Supply chain optimization. Based on supply chain information and demand forecasts, AI models can optimize production processes and logistics to minimize costs and improve overall supply chain efficiency.
- Quality control. AI can detect and flag defects or anomalies during the manufacturing process in real time.
Generative AI models don’t only learn human languages — they are also trained to learn the syntax and structure of programming languages. Developers can use artificial intelligence for:
- Code generation. This includes writing new code, generating autocomplete suggestions for projects in progress, improving existing code, and detecting errors in human-written code.
- Cybersecurity. Generative AI can help mitigate cybersecurity threats by monitoring network traffic and user behavior for anomalies and suspicious patterns.
- Operations support. Generative AI models can predict network outages, optimize resource allocation, and automate routine tasks to improve the overall efficiency of IT teams.
In the coming years, patients and medical professionals alike can expect to benefit from Generative AI technology. Healthcare-specific use cases include:
- Drug discovery and development. Generative AI models can analyze vast amounts of scientific data to accelerate and automate the discovery of novel drug formulations.
- Electronic health record (EHR) analysis. AI tools can analyze patient records to identify patterns and extract insights, helping medical professionals form diagnoses and plan treatments.
- Personalized treatment planning. By analyzing data and extracting insights at scale, Generative AI also improves the bandwidth of medical organizations to provide personalized — and more effective — treatment.
- Medical image analysis. AI models can analyze medical images (X-rays, CT scans, MRIs, etc.) to detect abnormalities, support accurate diagnostics, and streamline treatment planning.
A great way to get started with AI in healthcare is investing in an NLP-powered assistant. Startups like Nabla are working to perfect software that saves clinicians hours every day by taking notes and updating patient records. You can also work with a company like Tribe to tailor an off-the-shelf solution to your business needs or build something bespoke.
The (manageable) risks of using Generative AI for business
While Generative AI offers a magnitude of exciting possibilities, early adoption does come with some challenges — as is the case with any technology.
Currently recognized risks of using Generative AI include:
Unreliable, biased, or misleading outputs
Modern Generative AI tools are excellent at producing grammatically correct text, realistic images, etc. But the meaning behind these outputs should always be verified by a human expert.
Remember: AI models are only as good as the data they are trained on. There is a lot of false and biased information out there that can make its way into training data sets. Even without malicious intent, Generative AI end users risk producing false, misleading, or potentially offensive content.
The datasets that Generative AI is trained on may also include copyrighted material — and occasionally, AI models generate outputs that infringe on intellectual property rights.
A good way to mitigate the risk of violating copyright laws is to never publish AI generated content without careful review. Treat AI-generated content as a first draft, and always have a human editor improve the output. When in doubt, run the text through an online plagiarism checker or seek your legal team’s advice.
Regardless of your niche, data privacy, security, informed consent, and ethical considerations should always guide your implementation of Generative AI.
But if your business operates in a regulated industry — e.g. government, finance, or healthcare — the way you can and can’t use AI may be defined by relevant regulatory bodies and guidelines like GDPR, HIPAA, FINRA, etc. Compliance is not optional — violations can result in fines or suspensions. Make sure to consult your organization’s legal team before implementing any AI tools that will interact with your customers and your data.
As the adoption of Generative AI spreads across industries, many people without prior exposure to artificial intelligence will become end users. Without the experience, they may have trouble interpreting and validating outputs or understanding the legal and ethical considerations of using Generative AI. As with any new technology, adequate training is going to be crucial.
All of these risks can be considered the AI industry’s growing pains. Working with an experienced team of consultants when implementing AI solutions will help you gain a competitive edge while staying compliant, sustaining ethical standards, and upholding legal obligations.
Get started with Generative AI to fuel business growth
Generative AI is helping businesses across industries save time, cut costs, and embrace new opportunities for innovation and growth.
Don’t wait — embrace digital transformation and start thinking about how you can use Generative AI to improve your operations and bottom line today.
Tribe.ai is an expert Generative AI consulting company that connects businesses with experienced consultants and data scientists. Contact us to learn more about our services and get started with your Generative AI project — or use our Generative AI Wizard to see how AI can help your business.
Curious how we work with clients?
In only four weeks, our team can help you unlock the power of Generative AI for your business.
Here’s what you can expect from the process:
- Week 1: Discovery. We identify use cases specific to your business, set performance benchmarks, and conduct stakeholder interviews to map out your success.
- Week 2: Rapid prototyping. We bring your Generative use case to life by building an MVP tailored to your use case. We test various LLMs to identify the one that delivers the best results for your specific needs.
- Week 3: Fine-tuning. We measure and evaluate the performance of your product against established benchmarks and success criteria. Based on the results, we fine-tune your model and the prompts it uses to optimize the outputs.
Week 4: Implementation plan. We develop a comprehensive implementation plan, outlining the necessary steps to seamlessly transition your MVP into a full-scale production solution. We consider factors such as scalability, maintainability, and integration with existing systems, ensuring a smooth and efficient deployment. Get Started Now.