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Generative AI Business Use Cases

Large language models have taken the world by storm, and there are a lot of generative AI business use cases out there. But by chasing these ‘quick wins’ you risk implementing the technology haphazardly and creating more work for your teams. Instead, you should take a more holistic approach and make sure you include different voices in the implementation process.


In this article, we outline the most valuable generative AI enterprise use cases for different departments and focus on implementing them holistically to avoid silos and redundant efforts. We also mention some of the risks and limitations of the current generation of large language models, to help you make an informed decision.

Large language models (or LLMs) and generative AI solutions have taken center stage throughout the year. After OpenAI and Google launched their solutions, it seems like every day we hear about new models that will revolutionize how we live, work, and engage with others.

But to unlock the possibilities of this new technology, you need to look beyond tools and take a more holistic approach. Otherwise, you’ll be left with use cases that don’t drive value and expensive solutions that your people don’t use.

Based on our conversations with customers, we’ve highlighted various generative AI business use cases that can empower your organization. Towards the end, we will share some advice on taking your first steps to embedding generative AI into your processes and workflows.

Here is what we’ll cover:

Generative AI for Marketing

You can use generative AI solutions in infinite ways within your organization, but it is already clear that they will bring more value to certain departments.

According to research by McKinsey, marketing will significantly benefit from large language models – not surprising as your teams can use them to produce quality content in the blink of an eye.

Other than efficiency, the use of generative AI in marketing will help businesses scale much faster than before while adding to their personalization efforts and driving creativity.

The main generative AI business use cases for marketing are:

  • content generation
  • personalized campaigns and recommendations
  • automated outbound campaigns

Content Generation

Generative AI makes it easy to generate content ideas and drafts at scale – text, voice, images, or videos – all within your brand guidelines and tone of voice. In addition, you can repurpose your existing content into different formats – like breaking down a blog article into a series of social media posts.

However, you need to be aware of the limitations the current generation of large language models faces. The output quality drops as the quantity increases – making it more valuable for drafts or shorter pieces.

Personalized Campaigns and Recommendations

With the help of large language models, your team can adapt marketing campaigns and offers to specific audiences based on your customer demographic and past behavior. This also includes translating content quickly to other languages, which is crucial for centrally managed global campaigns.

These solutions can also boost your conversion rates by creating personalized product descriptions and images based on who is looking at the product, ensuring they resonate with the customer.

Automated Outbound Campaigns

Since large language models can mimic human speech, you can use these solutions to automate parts (or the whole) of your outbound workflows. By feeding the model with information on your target customers and market insights, the system can reach out to your potential customers through email or chat.

The model can also recognize when it’s the right time to include a human in the process – in case of challenging questions or high-risk situations.

Generative AI for Software Development

Software developers spend a lot of time on ‘grunt’ work – monotonous and repeatable. You can easily offload these to a generative AI solution, which is why software development is another function set to significantly benefit from advances in the field.

This way, your developers are free to focus on more complex problems that require human intuition and a high-level overview of the challenge – which are much more rewarding.

This is also reflected in the feedback so far. Even though generative AI solutions are still in their early days, McKinsey research shows that they can increase the productivity of software engineers from 20 to 45 percent. On the other hand, GitHub Copilot users report feeling 88% more productive.

However, even for grunt work, developers need to know how to prompt the model properly. This includes going through several iterations of the solution before reaching the desired outcome.

Consequently, to get the most out of these models, you need to choose the best solution for your context and train your developers to use it. This training includes helping them understand the risks and benefits of large language models and how to properly prompt the system for optimal results.

Generative AI for Customer Service

Customer service has already started to transform, thanks to previous advances in AI solutions. Here, we already saw the automation of simple tasks through simpler chatbots and self-service portals.

However, generative AI and LLMs still have a lot to offer. They take the automation one step further, allowing you to automate more complex tasks and helping your agents provide more comprehensive answers promptly.

Three main generative AI use cases for customer service are:

  • virtual agents
  • document summarization and agent assistance
  • customer feedback and sentiment analysis

Virtual Agents

Virtual assistants based on large language models can answer complex questions using simple conversational language. Since they can ‘remember’ past interactions, they can use this information to shape future conversations and personalize them.

However, we all know the high risks associated with customer service, so we don’t recommend letting these virtual agents loose without human oversight. This will ensure the answers your customers get are correct and result in a favorable resolution.

Document Summarization and Agent Assistance

The current generation of gen AI solutions cannot fully replace your agents. They are best utilized when augmenting customer service representatives – helping them respond to inquiries faster and providing more comprehensive and precise answers.

For example, an LLM-based solution can analyze incredible amounts of content (your how-to manuals, website pages, etc.) and provide insights to the agent while they are talking with the customer.

On the other hand, they can summarize customer conversations – turning each interaction into valuable learning materials for other customer service agents.

Customer Feedback and Sentiment Analysis

A more indirect way generative AI can improve customer service is by analyzing customer feedback and sentiment. Your agents can quickly extrapolate the most valuable insights from mountains of raw data and find new ways to improve customer interactions.

The solution can even go one step further and analyze the sentiment of the feedback, providing agents with a better picture of how customers react to certain products and services or their satisfaction with the support process.

Generative AI for Product Development

Developing a new product is always challenging since your engineers have to test many designs and options. Since they are limited in funds and time, going for one path usually means leaving others unexplored – often leading to suboptimal solutions.

Solutions based on large language models can help optimize the initial development stage and product manufacturing and maintenance.

The main generative AI use cases for product development are:

  • generation and evaluation of design options
  • identification of production errors and anomalies
  • predictive maintenance

Generation and Evaluation of Design Options

Large language models can save your engineers days of work when generating and evaluating design options. The models can test out different designs and materials by running thousands of simulations based on unstructured data.

These models can also reach completely non-intuitive solutions that your engineers might not consider when designing a product. They also refer to data, such as customer preference or market trends, to fine-tune the product and ensure it fits your customers’ needs perfectly.

As a result, you get the best possible solution with the least amount of material spent.

Identification of Production Errors and Anomalies

Generative AI solutions can analyze data coming from different sensors and cameras on the production line and compare them to descriptions and images of working products. Their success rate is much higher than older systems since they can learn over time and access product information in different formats.

Additionally, these solutions can identify anomalies or issues in the manufacturing process itself, further decreasing costs and optimizing the process.

Predictive Maintenance

AI solutions have been a part of predictive maintenance for a while, but large language models bring a whole new world of possibilities. Since they don’t require labeled data or predefined rules to function, they are easier to set up and more reliable, especially in the long run.

An excellent first step is using LLMs to augment the data you already have. They can understand the underlying data patterns and generate new synthetic data samples that follow that same pattern. This is especially valuable if you don’t have access to large data sets of labeled data or have sets that include rare anomalies or failures.

Another advantage of these models is that they can integrate different data sources (like sensor data, text manuals, inspection notes, etc.) into a multi-modal model. This model gives you a vastly more comprehensive image of the state of your machinery and provides faster and more accurate maintenance recommendations.

Generative AI for the Finance Department

Here, more than in any other part of your business, you need to properly plan out and implement generative AI models. You’ll need to include your technology and finance experts every step of the way to help balance out the benefits with the risks and limitations.

The primary use cases of generative AI in finance are:

  • automation of accounting functions
  • fraud detection
  • drafting of financial reports

Automation of Accounting Functions

Large language models can automate some of the basic accounting functions, letting your finance department focus on more strategic activities. The models are a great fit since this work usually includes going through mountains of text and takes a lot of time and attention.

For example, you can automate your accounts payable tasks by letting the system analyze and extract relevant information from documents such as invoices and receipts. You can also use it to automate some of the auditing work or let it draft or analyze legal documents for parts that might be problematic.

Fraud detection

Since generative AI can recognize underlying patterns in your data, these solutions can detect fraudulent or suspicious financial behavior. It analyzes vast volumes of your financial and business data to identify irregularities and flags them for further investigation.

If you already have an AI-powered fraud detection system, you can still benefit by augmenting it with generative AI. By synthetically creating training data, the gen AI solution helps train the algorithms that are used to recognize between legitimate and fraudulent patterns.

Drafting Financial Reports

Large language models can help your finance department by drafting standardized financial reports out of unstructured data.

These generative AI solutions can automatically create well-structured and informative reports by pulling finance data from different sources and departments and generating insights based on that data.

However, as mentioned at the start of this section, your finance team needs to be involved to make sure the reports are valid and reliable.

Generative AI for Data Analytics

The value of generative AI for data analytics is evident when we consider that LLMs can quickly analyze vast quantities of unstructured data (so-called big data) and provide conversational responses to queries.

Beyond simple dashboards, these solutions can provide context and create a story based on the analyzed data – crucial for data-driven businesses.

Two prominent use cases of generative AI for data analytics are:

  • data narration
  • data visualization

Data Narration

Data without context holds little value. A 20% jump in sales might sound great until we learned the competition doubled theirs. That’s where data narration (or data storytelling) comes into play.

Generative AI solutions can analyze your data and transform it into a coherent story that will move people toward specific actions. You can even personalize the story to your specific audience, ensuring it resonates with them.

The model continuously reads the data that is available, adapting the story to ensure it stays relevant and valuable.

Data Visualization

Data visualization is a synonym for the usual (and quite limiting) dashboards and charts. However, LLMs can help you go beyond those and find new and creative ways to visualize and present data. This way, you can generate insights or avenues that wouldn’t have been visible before.

You can also adapt the visualization using everyday language, making sure it fits your specific needs. To make things better, you can see the visualization transform before your eyes in real time in response to your instructions.

Why You Need a Holistic Approach to Generative AI

With generative AI solutions holding so much value, it’s easy for leaders to jump in headfirst to feel they are doing something. But this usually results in departments doing their own thing, creating inefficiencies and silos.

Our recommendation for a first step would be to create an inter-department team responsible for looking at this topic from different perspectives. It would be their job to raise awareness of generative AI within the organization and plan out the testing of initial generative AI business use cases.

As the first tests are nearing completion, the team should ensure that learnings and mistakes are shared with the whole company – so that others can learn from them and make the overall process more effective.


Authors and Contributors

Boban Djordjevic | Development Lead, NETCONOMY

As part of our Machine Learning team, Boban is responsible for handling initiatives around data and AI, supporting pre-sales activities by providing technical input, designing high-level architectures, and supporting customers in choosing the right solution approach.

Manuela Fritzl | Experience Management Consulting Lead, NETCONOMY

At NETCONOMY Manuela is responsible for the topic of experience management. This includes everything from researching trends, users, and topics as well as the planning and creation of experience management programs.

Nikola Pavlovic | Content Marketing Manager, NETCONOMY

Nikola is an experienced content and communication professional who believes that powerful storytelling is key for building brands, educating audiences, and designing marketing campaigns that deliver.