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:
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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.
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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.
To further assist potential customers in navigating generative AI business applications, ITRex has prepared several blog posts and guides:
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A comparison of traditional and generative artificial intelligence
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A summary of generative AI pros and cons
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A rundown of generative AI applications in healthcare, pharma, retail, supply chain management, and banking
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An overview of factors affecting the cost of generative AI in business
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An introduction to AI cloning and Gen AI’s role in it
Additionally, we encourage you to download this Generative AI Guide for Business Leaders. Our free eBook assesses the technology’s impact on businesses across various industries, identifies the most promising generative AI use cases, such as customer service and product design, and provides actionable tips for implementing generative AI in business.
Without further ado, let’s discover how to use generative AI in enterprise settings.
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:
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Automating customer support
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Streamlining content marketing activities
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Achieving full-on business process automation
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Improving and democratizing data analytics
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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.
But what is an example of generative AI in customer service?
Expedia Group, a travel technology company behind some of the world’s leading holiday and flight booking platforms like Hotels.com and Vrbo.com, integrated ChatGPT into the Expedia app.
Instead of searching for flights and accommodations on Expedia’s website, users can now ask the AI-powered personal assistant for travel advice the way they’d consult a travel agent. ChatGPT can come up with recommendations on travel destinations, hotels, and transportation. Users can then bookmark the suggested locations in the app and check their availability on selected dates.
To leverage generative AI in business, Expedia has trained OpenAI’s technology to identify and understand a staggering 1.26 quadrillion variables, including date ranges, hotel location, room type, and price requirements. The intelligent assistant also uses Expedia flight data to compare current prices to historical price trends and track fluctuations. This information allows travelers to determine the optimal time to book and earn rewards.
The use of generative AI solutions for customer support can thus help your company reduce wait times, improve satisfaction, and cut down on customer service costs. According to Accenture’s A New Era of Generative AI for Everyone report, the technology’s potential for task automation and augmentation is particularly high in banking, insurance, capital markets, energy, and utilities. Overall, the adoption of conversational and generative AI for customer service will allow companies to reduce the associated expenses by up to 30%.
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.
Are there any successful generative AI examples in content marketing then?
Here at ITRex, we’ve been using Gen AI-powered tools for content creation for almost a year. We’ve tested the technology on various tasks, from editing job descriptions for the HR team to writing technology articles.
By exploring generative AI use cases in content marketing, we’ve made our writers at least 30% more productive, meaning they can now devote more time to competitor and client research and interactions with subject-matter experts.
The improvements are noticeable across various tasks, including:
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Initial research. Gen AI tools help writers wrap their heads around complex technology topics, such as automated data collection or using machine learning in bioinformatics, and guide further research.
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Content drafting. Gen AI-produced copy could serve as an early draft for articles and parts thereof. Our content team enriches such drafts with statistical data, references to reputable research papers, input from technical experts, and relevant case studies.
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Content editing. One of the key generative AI use cases at ITRex includes running human-written content through smart algorithms to detect grammatical errors and style inconsistencies, break overly long sentences into smaller ones, and even edit articles in the style of popular online publications.
Your company could take the experiment a step further, maximizing the value of generative AI business applications.
By training commercially available tools or retraining foundation LLMs on your data, you could create highly personalized and effective content that ranks well on search engines, attracts relevant traffic to your website, and converts website visitors into leads.
3. Business process automation that brings value
The business process automation (BPA) landscape has long been dominated by robotic process (RPA) and intelligent process automation (IPA) solutions. To learn how these technologies stack up against each other, check out our BPA vs. RPA vs. IPA article.
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:
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Performing managerial activities, such as prioritizing tasks in project management applications, scheduling meetings, and organizing emails
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Searching for accurate information across your IT infrastructure and summarizing content through a conversational interface
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Creating standard or custom documents and reports automatically
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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.
According to McKinsey, using generative AI in business strategically can automate up to 70% of tasks that take up your employees’ time. This can lead to a notable increase in productivity, with a yearly improvement rate of 3.3%.
4. Data analytics that is accessible to anyone
The ITRex team has long advocated for data democratization—that is, making information and data analytics insights available to all employees within organizations, regardless of technical expertise.
We’ve been creating self-service business intelligence (BI) solutions and AI-based augmented analytics tools for the world’s largest retail, healthcare, and media and entertainment companies.
Thanks to properly performed enterprise application integration (EAI), expert data management, AI analytics, and effective user interface design, we’ve helped our customers improve asset management and maintenance operations, pinpoint areas for cost reduction, and boost productivity.
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:
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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.
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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.
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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.
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Scenario modeling. Generative AI can assist users in making complex decisions by simulating possible outcomes or generating data-driven proposals.
Recent studies indicate that 32% of organizations have already tapped into analytics-related generative AI use cases. 34% of those polled reported significant benefits, including increased competitiveness (52%), and improved functionality or performance of their products (45%).
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.
How do Gen AI pioneers address this problem?
The answer lies in effective employee education and onboarding.
Just recently, Asana interviewed over 300 marketing professionals to learn how their companies integrate AI into business processes. It turns out only 15% of organizations provide formal AI education and learning management programs for marketing employees! However, 55% of the participants whose employers do offer such programs are confident that they’ll reach their AI implementation goals within 12 months, compared to just 23% of specialists lacking access to AI training.
Employee education is an ideal use case for generative AI in business.
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.
Frequently Asked Questions (FAQs)
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What is generative AI?
Generative AI refers to artificial intelligence technologies capable of generating content, such as text, images, videos, and music, that is similar to what a human could produce. These technologies learn from vast amounts of data to recognize patterns, structures, and features, which they then use to create new, original content. The unmatched content creation capabilities make generative AI a lucrative technology for enterprises and pave the way for numerous generative AI business applications.
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Can generative AI replace human workers?
While generative AI business applications can automate and enhance various tasks, the whole concept of using generative AI for business is not about replacing humans but augmenting their capabilities. In areas like customer support, content creation, and data analytics, generative AI can handle routine tasks, allowing humans to focus on more complex and creative aspects of their work.
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How can businesses start using generative AI?
Organizations can start leveraging generative AI for business by experimenting with commercially available platforms like ChatGPT or by customizing foundational AI models with their data for more specific needs. The choice depends on the company’s technical expertise, resources, and desired level of customization. By collaborating with reputable AI consultants, your company can investigate potent generative AI business applications and select those with the greatest transformation potential.
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Are there ethical considerations when using generative AI in business?
Yes, ethical considerations include ensuring the accuracy of generated content, protecting user privacy, avoiding bias in AI models, and transparently using AI to augment rather than replace human interactions, especially in sensitive areas like healthcare or financial advice.
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Can generative AI be used in any industry?
Absolutely! Generative AI business applications range across various industries, including but not limited to healthcare, retail, finance, education, and entertainment. Each industry can leverage generative AI for unique use cases, such as personalized customer experiences, content creation, data analysis, and scenario modeling.
