Generative AI use cases in enterprises
In the past couple of months, the ITRex innovation analysts have written several blog posts to educate our clients on generative AI and its use cases in business.
Currently, our Gen AI article series covers the following topics:
Therefore, in this article, we won’t delve into industry-specific use cases for generative artificial intelligence. Instead, we’ll tell you what processes and tasks this leading-edge technology can augment or fully automate.
Another important remark.
When exploring generative AI use cases for your business, you typically have two primary pathways:
The first is leveraging commercially available products like ChatGPT, Synthesia.io, or others, which can be fine-tuned using your specific datasets to cater to your unique business requirements. These platforms provide user-friendly interfaces and integration tools, making the adaptation process relatively straightforward even for those without an extensive background in AI.
The second option involves selecting an appropriate AI foundational model, such as GPT-3, BERT, or their successors, and training 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 option — i.e., building generative AI models from the ground up. We would not recommend going this route unless you’re a unicorn startup backed up by Microsoft, Google, and Tesla, and have the computing resources and technical expertise to feed 300 billion words to your system (that’s how much text data it reportedly took to train ChatGPT). The cost of developing fully custom AI solutions can be overwhelming, too.
Without further ado, let’s investigate generative AI applications in business.
Top 5 generative AI use cases
1. Automated customer support that maintains a human touch
One of the immediate generative AI use cases 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.
Generative AI applications in customer service also include hyper-personalization. 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 personalized 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 Gen AI-driven customer service automation, 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’s flight data to compare prices on a specific date with 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 the banking, insurance, capital markets, and 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 placing, there’s no digital marketing task that Gen AI cannot enhance.
The lion’s share of generative AI applications, however, revolves around 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 doesn’t come as a surprise that 25% of all digital content is already produced by Gen AI.
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 fast amounts of data that might be incomplete or erroneous.
Additionally, generative AI solutions like ChatGPT cannot access the Internet just yet, which prevents them from finding statistics, quotes, and other information for higher-value content.
The lack of real-time connectivity also limits generative AI applications in search engine optimization (SEO) to merely suggesting keyword ideas and content topics, despite the availability of specialized ChatGPT SEO plugins, such as SEO Core AI and Bramework.
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 vacancy 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:
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.
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.
Content editing. One of the key generative AI applications 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 one step further.
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 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.
According to McKinsey, using generative AI and other technologies 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 been advocating for data democratization — i.e., making information and data analytics insights accessible to all individuals within organizations, regardless of their technical expertise.
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, 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. Where self-service BI simplifies and automates data analysis for end users, generative AI can automate the generation of insights, predictions, and content based on your 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 can be proactive, offering solutions to real-world problems without explicit queries.
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. Out of the surveyed, 34% of the respondents have achieved substantial benefits, including increased competitiveness (52%) and enhanced 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.
It is the ethical and moral implications of artificial intelligence that cause resistance to change — i.e., the key barrier to digital transformation according to 49% of business executives.
With so many promising use cases for generative AI, it is only natural for your staff to be afraid of 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 has 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 makes a perfect generative AI use case.
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 use cases are just 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 surveyed expect the technology to play a significant role in their organizations in the future.