Generative AI in business: top 5 use cases every company should consider

The term “generative AI” (Gen AI) refers to a type of artificial intelligence capable of producing content on a par with humans.

To accomplish this, Gen AI solutions learn to identify patterns, structures, and features in the vast amount of data they have been trained on. The algorithms then use this knowledge to reproduce the same parameters in the newly generated content.

Large language models (LLMs) like OpenAI’s ChatGPT are one of the primary generative AI examples. But the potential applications of generative AI in business go far beyond text generation.

Platforms like Synthesia.io, Runway, and Wondershare Filmora help create and enhance video content. Advanced graphic design tools like DALL·E 2 and Canva’s AI Image Generator are already competing with human designers. Additionally, it is now possible to create royalty-free music using tools such as Ecrett Music, Soundraw, and MusicLM, which require nothing more than text prompts or the selection of specific moods and themes.

Aside from content creation, effective generative AI use cases in business include automating customer service and support tasks, personalizing the client experience, improving companies’ analytics capabilities, modeling complex scenarios, and more.

This is where the technology’s true value lies.

What you need to know before using generative AI for business

When investigating generative AI use cases for your business, there are two main paths you could take:

The first option is to use commercially available software such as ChatGPT, Synthesia.io, and others. These platforms offer user-friendly interfaces and integration tools, making adaptation relatively simple even for those with limited AI experience. Aside from integrating commercially available Gen AI solutions with enterprise applications as-is, you can also fine-tune them with your own datasets to significantly improve model accuracy.
The second option is to choose an appropriate AI foundational model, such as GPT-3, BERT, or their successors, and either use it without customization or train it with your data. This approach offers a higher degree of customization and control over the AI’s behavior and outputs but requires a more substantial investment in terms of technical expertise, resources, and time.

There is also a third way to use generative AI in business—i.e., building generative AI models from the ground up. We would not recommend this route unless you are a unicorn startup backed by Microsoft, Google, and Tesla with the computing resources and technical expertise to feed 300 billion words to your system.

What are the top 5 generative AI use cases in business?

Disclaimer: This article will not delve into industry-specific use cases for generative artificial intelligence. Instead, we will tell you which processes and tasks this cutting-edge technology can supplement or completely automate.

Our top-five generative AI use cases look as follows:

Automating customer support
Streamlining content marketing activities
Achieving full-on business process automation
Improving and democratizing data analytics
Enhancing employee education

Let us go through them one by one.

1. Automated customer support that maintains a human touch

One of the immediate generative AI use cases in business revolves around providing instant responses to customer inquiries received via live chat, phone calls, and emails.

In addition to fully automating customer service, businesses can tap into generative AI to augment the work of human specialists. Intelligent assistants confidently take over tasks like information search, call summarization, and call transcript analysis. This empowers customer support managers to identify common issues faced by their clients, highlight problematic areas where customer service is lacking, and use the feedback to fine-tune their products and services.

Hyper-personalization of customer service is another way to use generative AI in business. By analyzing subtle patterns in call recordings, such as word choices, speech rate, and tone of voice, Gen AI can help organizations adjust communications and come up with tailored offerings to improve customer engagement and loyalty.

2. Content marketing that yields tangible results

Marketing departments have so far been the key beneficiaries of generative artificial intelligence. From boosting the predictive power of recommendation engines to tapping into intelligent ad placement, there’s no digital marketing task that Gen AI cannot enhance.

The majority of marketing-related generative AI use cases in business, however, focus on content creation.

Gen AI crafts contextually relevant and coherent content on any given topic in mere seconds. In comparison, experienced writers spend 2–6 hours polishing a 1,000-word blog post.

It shouldn’t come as a surprise that Gen AI is already producing 25% of all digital content.

Forward-thinking brands use generative AI tools to write and edit social media announcements, blog posts, product descriptions, articles for link-building, sales emails, and copy for presentations. In some cases, they even fire in-house writers to reduce content marketing costs.

However, there’s a hitch (or, rather, several hitches).

Large language models tend to hallucinate, presenting false or fabricated information in response to user questions. This drawback stems from the fact that LLMs are trained on large amounts of data that might be incomplete or erroneous.

Furthermore, while generative AI business applications such as ChatGPT can now access search engines in real time to obtain specific information, the search results may be incomplete or completely unrelated to user queries.

Search engine optimization (SEO) is another area where generative AI use cases are limited. Despite the availability of specialized ChatGPT SEO plugins, such as SEO Core AI and Bramework, most Gen AI tools merely suggest keyword ideas and content topics instead of conducting comprehensive keyword and competitor research like Ahrefs and Semrush do.

3. Business process automation that brings value

Compared to rule-based or even AI-infused BPA tools, generative AI business applications are broader and more complex. Their transformational power comes from Gen AI’s capacity to comprehend natural language.

Given that language-based tasks comprise 25% of all work activities, generative AI use cases in business encompass various processes and workflows, including:

Performing managerial activities, such as prioritizing tasks in project management applications, scheduling meetings, and organizing emails
Searching for accurate information across your IT infrastructure and summarizing content through a conversational interface
Creating standard or custom documents and reports automatically
Entering information into technology systems

Gen AI’s key advantage is its ability to continuously learn from new data and refine its capabilities. While deep learning-based IPA solutions do that, too, they are exposed to less training data from the onset and therefore have lesser decision-making potential.

4. Data analytics that is accessible to anyone

By tapping into generative AI use cases in business, our clients can take the concept even further, enhancing self-service BI and AI-augmented analytics systems in several ways:

Strategic decision making. While BI tools help comprehend complex business data, generative AI applications in data analytics include the development of potential strategies, trend forecasting, and automatic report generation.
Higher level of automation. Whereas self-service BI simplifies and automates data analysis for end users, generative AI can automate the creation of insights, predictions, and content from operational data. These insights can then be accessed via conversational interfaces or converted into graphs using the appropriate prompts.
Proactive analytics. Self-service BI is often reactive, meaning your employees need to query data to gain insights. Generative AI business applications can be proactive, providing real-world solutions without requiring explicit queries.
Scenario modeling. Generative AI can assist users in making complex decisions by simulating possible outcomes or generating data-driven proposals.

Gen AI can potentially reduce the cost of data analytics, too, since your company won’t have to train an AI model from the ground up. To reap the full benefits of generative AI-assisted analytics, however, you’ll still need to source and format your data for model training. Check out our data preparation guide to elevate your knowledge in this field.

5. Employee onboarding and education that fosters innovation

There are numerous AI implementation challenges that undermine organizations’ ability to innovate. These include technology roadblocks manifesting themselves late in the development process, failures to scale AI proof of concepts (PoCs), and ethical issues surrounding AI adoption.

According to 49% of business executives, the ethical and moral implications of artificial intelligence remain the most significant barrier to digital transformation.

With so many promising use cases for generative AI in business, it is natural for your employees to be concerned about being replaced by intelligent and highly productive algorithms. Additionally, employees might be hesitant to abandon the technology tools they’ve been relying on for years, regardless of how useful and intuitive they are.

From creating personalized learning paths for your workers to automatically developing training materials, quizzes, and other educational content, Gen AI can speed up the work of your learning and development (L&D) team while improving learning outcomes.

The technology can also streamline the hiring process for new candidates by assisting your HR teams with CV screening and preparing job interview questions based on the applicant’s profiles.

These generative AI business applications are only the tip of the iceberg.

Not every company is sold on Gen AI just yet, and there’s still a lot to be figured out, both on the technical and business sides.

That’s why only 33% of IT executives are currently considering generative AI as the top priority for their organization, even though 86% of the respondents expect the technology to play a significant role in their organizations in the future.