Beyond ChatGPT: 10 Future-Forward Generative AI Use Cases for Enterprises

Dmytro Ivanov
MACHINE LEARNING ENGINEER
Daria Iaskova
COMMUNICATIONS MANAGER

If we examine the evolution of artificial intelligence, a significant milestone distinctly separates the process into two phases: before and after. And this milestone is marked by the advent of generative AI. Over the past few years, this technology has permeated a myriad of industries and business domains, yielding a variety of practical and impactful generative AI use cases. 

Despite generative AI carrying substantial doubts related to security and ethics, as of 2023, three-quarters of global enterprise CEOs believe that employing advanced generative AI is a go-to way to gain a strong competitive edge in the market.

In this article, we’ll review the most promising yet hands-on use cases for generative AI and explain how enterprises can benefit from applying the technology to business and operations.

What is generative AI in a nutshell

Generative AI is the subset of artificial intelligence that uses unsupervised and semi-supervised machine learning to generate new content based on existing data. This field of AI represents a system of neural networks, embracing Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer architectures, working together to perform specific tasks. In this ecosystem, large language models (LLMs) represent a part of the transformer architecture, understanding inputs in human language and using them to create new content.

Simply put, generative AI is all about training machines to interpret human language and generate entirely novel and valuable output derived from the training data. This is exactly what has made technology so popular and widely used in everyday life.  

ChatGTP and other solutions that brought GenAI to the world

Below, we’ll mention some generative AI use cases that in the past few years became increasingly common or anticipated with regular users.

  • ChatGPT, an undeniable game-changer and the biggest technology newsmaker of 2022-2023 is a conversational AI tool developed by OpenAI that allows users to interact with a language model and get human-like text responses. Available as a web application and an API, in some sense, it replaced the Google search engine and became an intelligent assistant for students, content creators, managers in charge of business correspondence, and billions of global users.  
  • Bard, a challenger to ChatGPT and another generative artificial intelligence chatbot developed by Google which has already proved to be highly efficient for conversational tasks like identifying market trends, understanding customer needs, and ​​generating personalized sales pitches. 
  • Copilot, a cloud-based AI assistant that works with Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) providing code suggestions to software engineers based on the project's context and style conventions. The tool is reported to be used by thousands of programmers including individual contributors and employees of top-tier global companies.
  • DALL-E, an interactive web interface that lets users experiment with image generation capabilities by using textual descriptions and providing a suitable visual representation based on the input given. The tool is increasingly used by designers and digital creators and assists in crafting educational materials, advertising, and even visualizing physical products.
  • Gemini, Google's latest and long-awaited multimodal generative AI chatbot expected to mimic human-style conversations and become the first of its kind publically available artificial general intelligence (AGI) application. It is designed to adeptly interpret and understand images, proficiently code, drive data analytics, and serve as a valuable tool for developers in creating innovative AI applications and APIs.
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While by now most regular digital users have interacted with at least one generative AI tool, employing its business potential is a whole new thing from both the technical and strategic perspective. 

LLMs standing at the core of generative AI use cases

The majority of popular generative AI tools are built upon LLMs, statistical language models trained on vast datasets to proficiently generate and translate text and other content, handling a spectrum of Natural Language Processing (NLP) tasks. Just like that, ChatGPT uses the GPT model, Bard uses PaLM, Copilot uses Codex, and so on. 

This means that when a company decides to get started with generative AI, their next steps would be one of the following:

  • getting a dedicated API and using one of the popular LLMs listed above
  • choosing from the available and smaller open-source models (for example, LLaMA, Mistral, and Falcon)

What happens next is deploying and training the model using a previously prepared dataset.

How to get your very own LLM?
Learn to choose and train wisely

By now companies can make use of generative AI by collecting a required amount of relevant data, using it to train a selected machine learning model, and applying the solution to specific business cases. Sounds intricate, isn’t it? 

To understand the practical value of generative AI for enterprises, let’s overview the benefits organizations can get from similar solutions and explore how far businesses have moved with its adoption.

State of GenAI: limitations and benefits for enterprises

Describing the potential of generative AI and its adoption among enterprises as promising would probably be an understatement.

  • The global GenAI market is expected to grow at a compound annual growth rate (CAGR) of 27.02% and will reach $118.06 billion by 2032.
  • Statista predicts that by 2025 large-scale adoption of AI is expected to reach 46%.
  • According to Gartner, by 2025, 30% of outbound marketing messages from large organizations will be crafted with the help of generative AI.

Yet, adopting the technology at an enterprise level comes with a handful of challenges and limitations. The majority of them are related to security concerns, the presence of technical barriers, and industry-specific constraints.

GENERATIVE AI: CHALLENGES AND LIMITATIONS FOR ENTERPRISES

Security

AI implementations come with severe threats to data security. When it comes to enterprises, it’s often about the data under NDA to be shared with GenAI providers.

Lack of data

To successfully fine-tune a selected LLM, organizations have to previously collect sufficient data. Whereas, only 13% of companies in reality have a hands-on data strategy in place.

Low data quality

In practice, the collected data often appears incomplete, low-quality, or irrelevant to ongoing business objectives, which prevents enterprises from successful GenAI implementation.

GPU infrastructure barrier

Many generative AI models are resource-hungry and running them requires much computing power and GPU enablement, which are sometimes unaffordable even for enterprises.

Lack of qualified ML engineers

Implementing AI requires input from qualified ML engineers and Solution Architects who can integrate the technology into the company’s information architecture and tailor it to specific business needs.

Need practitioners’ input to company-wide generative AI adoption?

Still, with more enterprise leaders reporting on implementing generative AI and the technology itself evolving in line with the growing business requirements, the benefits companies are getting with its adoption significantly overshadow possible pitfalls and make companies find ways to overcome existing limitations.

For example, a recent survey by IBM has revealed that global CEOs are taking actionable steps on AI adoption, bringing to life even more business use cases of generative AI.

As we observe GenAI's rapid evolution and its adoption for strategic initiatives, we can now state that the technology is gaining a lot of traction among enterprises and has the perspective to fuel critical operational and business decisions. This, in, turn requires mastering the technical side of generative AI implementation.

Being a trusted digital product partner to world-renowned enterprises, Trinetix stands at the cutting edge of GenAI development and adoption within market leaders. Our data analysts and ML engineers keep on researching and implementing the technology’s practical potential to solve very specific and complex business tasks. Some of them we share further in this article.

Real-life generative AI use cases for enterprises

As of 2023, the largest share of generative AI use cases falls under marketing and advertising, technology, and consulting. This is justified by the nature of tasks that typically comprise day-to-day operations in each of the domains mentioned—they are either creative or related to processing and analyzing huge amounts of data.

At the same time, our experience in implementing intelligent automation for A-list international companies proves that generative AI can reinvent enterprise processes and streamline the efficiency of business units and operational structures in the areas where it’s applied.

#1 Reinforcing workplace productivity

Implementing generative AI can help companies elevate operational efficiency and improve employee productivity at scale. 

Considering that LLMs learn from unique business knowledge, organizations can use the technology to enable intelligent assistance to employees whose jobs require dealing with corporate and customer information. What is more, such tools can be helpful to account executives and anyone in charge of processing invoices, tax reports, insurance quotes, and other industry-specific data.

For example, integrating one of the popular LLMs into the internal knowledge management infrastructure allowed our Fortune 500 client to create a ChatGPT-like virtual assistant trained on the company data, saving a considerable share of operational budgets spent on processing and managing disorganized enterprise-wide data. Similar solutions can be part of claim processing, fraud detection, policy creation, and other Generative AI insurance use cases.

Learn more about GPT-enabled data management for enterprises

#2 Streamlining document management and reducing legacy burdens

The quantity and variety of enterprise documents not only tend to frustrate the employees in charge of administrative tasks but also become a cornerstone to business scaling. 

From our experience, 9 in 10 large-scale businesses’ transformation plans risk smashing against poor data management strategies. This often happens due to the vast amounts of enterprise data stored in legacy systems and formats, making it challenging to retrieve, manage, and align information with actual business objectives. 

Generative AI paired with computer vision can help organizations process and analyze different types of historical data to discover critical dependencies and streamline the way enterprise employees interact with this data, leading to faster issue resolution and business continuity.

Combined with optical character recognition (OCR), machine learning can help companies extract data from legacy and distributed documents stored in PDF, CSV, PNG, and JPEG formats. This way, generative AI allows enterprises to generate new common-format data sources, building company-wide document management processes integrity.

#3 Facilitating strategic planning and business enablement

With generative AI capabilities growing literally day after day, adopting the technology can now assist enterprises with more complex tasks like decision-making, problem-solving, and strategic planning. This, in turn, leads to ensuring end-to-end business enablement and allows market leaders to develop and preserve their competitive advantage.

One of such cases is the story of our Fortune 500 client that aimed to tame dynamic flows of incoming data to stay informed about potential clients' strategic movements. Incorporating refined data classification mechanisms and experimenting with generative AI allowed the company to focus on driving valuable business insights instead of focusing on tedious manual work that didn’t bring the desired results.

Discover the secret of Fortune 500’s success with AI and machine learning

#4 Empowering personalized customer service

Although the customer service domain was among the early GenAI adopters, today the use of technology has already become a proven savior for the majority of widely-known enterprises and mid-sized companies.

AI-powered chatbots and virtual assistants handle customer inquiries, reply to highly specific questions, understand and properly react to customer emotions, and provide comprehensive and personalized answers. In addition, they analyze given feedback, learn from their own mistakes, and self-improve with minimal human interactions.

Over the past few years, such chatbots have become increasingly popular in retail, hospitality, and healthcare. In the banking sector, the value of AI chatbots is supposed to touch $6.83 billion by 2030. For the latter, the use cases for generative AI are not limited to automating bank-customer interactions, as intelligent chatbots have already proved to be efficient in providing personalized investment recommendations, calculating credit scores, detecting and preventing fraud, and improving overall financial literacy among digital users.

#5 Stimulating data-driven demand forecasting

Generative AI has a huge impact on global logistics and supply chains. LLMs can analyze historical data related to inventory, transportation, and warehouse management to predict future demand, address loading challenges, and optimize stock replenishment processes. 

Considering that GenAI models can continuously learn from dynamic datasets, companies from retail to manufacturing can use them to build accurate demand forecasts, predict future market trends, and analyze consumers’ behavior to drive improved business performance and guarantee ultimate customer satisfaction.

Maersk, a global logistics and shipment leader, has implemented the technology to streamline container logistics operations by predicting demand, and optimizing vessel schedules, thus improving the efficiency of its container shipping.

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#6 Delivering streamlined risk prediction

GenAI's ability to analyze large datasets and accurately predict trends makes the technology almost industry-agnostic. Thus integrating generative AI models into the core of enterprise operations can enhance risk management in a variety of use cases:

  • generating solvency and liquidity risks in trading and investments to help financial professionals evaluate the resilience of their strategies and portfolios
  • analyzing patient data to predict health risks, aid in disease prevention, and optimize treatment plans
  • identifying and mitigating cybersecurity risks by analyzing patterns and anomalies in network traffic and system behavior
  • inspecting geographical and meteorological data to predict natural disasters, enabling proactive measures for disaster preparedness and risk reduction
  • predicting equipment failures and maintenance needs in manufacturing to minimize downtime and optimize resource allocation
  • foreseeing and managing risks related to energy grid stability, optimizing resource allocation, and preventing disruptions
  • analyzing data on road conditions, vehicle performance, and driver behavior to enhance safety measures and reduce road accidents

#7 Bolstering research and development (R&D) activities

Generative AI accelerates innovation by analyzing vast datasets, identifying emerging trends, and providing valuable insights that guide strategic decision-making, ensuring organizations stay at the forefront of technological advancements and maintain a competitive edge.

Applied to R&D activities, the technology is now reinventing the process of product exploration and discovery in a few meaningful ways:

  • Generative AI fuels predictive modeling and helps create potential outcomes and trends, enabling R&D teams to make informed decisions about product development, market positioning, and technological investments.
  • The technology assists in hypothesis testing by simulating various scenarios based on historical data. This iterative process helps R&D teams refine their hypotheses, identify optimal strategies, and minimize risks associated with new ventures.
  • By automating routine and time-consuming tasks, such as data preprocessing and analysis, GenAI allows R&D professionals to focus on more complex and strategic aspects of their work. This accelerates the overall pace of research.
  • LLMs are now trained to analyze customer preferences, market demands, and historical product performance to guide personalized product development. This ensures that R&D efforts align closely with consumer needs, leading to more successful product launches.
  • GenAI models assist in brainstorming and ideation processes by analyzing vast sources of information and suggesting innovative ideas, potential research directions, and areas for exploration.
  • Through predictive analytics, GenAI helps optimize resource allocation by identifying areas with the highest potential for success. This ensures that R&D budgets and efforts are directed toward projects with the greatest likelihood of positive outcomes.

#8 Providing game-changing assistance in software development

A recent research by McKinsey revealed that engineers complete coding tasks up to twice as fast with generative AI. Although the results are primarily dependent on task complexity and programmers' experience, the use of the technology is already proved in practice by the fact that 70% of developers see a benefit to using AI coding tools at work.

Despite the use cases of generative AI for software engineering varying from using tools like Copilot and ChatGPT to deploying and consistently improving in-house GenAI-based code assistants, the technology is considered efficient primarily for tasks like code documentation, generation, refactoring, and high-complexity tasks.

# 9 Reinventing recruiting and HR processes

Another Generative AI use case is related to human operations and recruiting processes. Applied to this business domain, GenAI can streamline talent acquisition, reinforce day-to-day work routines, and contribute to employees getting better experiences. As a result, companies drive improved brand loyalty and can significantly boost enterprise-wide operational efficiency.

Applied at an enterprise scale, the technology can enhance every stage of talent acquisition and workforce management, revolutionizing how organizations attract, assess, and develop their human capital.

  • Automated candidate sourcing that includes analyzing resumes, social profiles, and professional networks and identifying potential candidates whose skills and experiences align with job requirements.
  • Improved resume screening processes that encompass extracting relevant information and ranking applicants based on their qualifications, improving the efficiency of candidate shortlisting.
  • Personalized candidate engagement leveraged through analysis of historical data and candidate interactions and further tailoring communication, feedback, and engagement strategies, creating a more positive and individualized experience for candidates.
  • Diversity and inclusion (DEI) enhancement by identifying potential biases in recruitment processes and allowing HR professionals to design fairer and more inclusive hiring strategies, fostering a diverse workforce.
  • Skills gap analysis that includes assessing current employee skill sets against future organizational needs and helps HR teams identify training and development opportunities to bridge gaps and upskill the workforce.
  • Automated onboarding processes and providing new hires with personalized information, training modules, and resources to enable faster assimilation of new employees into the company culture.
  • Continuous learning and development that includes analyzing employee performance and skill evolution, and facilitates the creation of personalized learning and development plans.

#10 Driving sustainability and ensuring regulatory compliance

With risk mitigation and improved decision-making being strong competitive advantages of GenAI, one more use case of Generative AI is helping businesses achieve regulatory compliance and build sustainable policies and operations. And the variety and diversity of industry-specific standards and requirements make it a fit for a number of niches and domains.

  • Finance and banking

Generative AI is used to analyze transaction data and detect fraudulent activities to ensure compliance with anti-money laundering (AML) regulations. It helps to prevent financial crimes and promote ethical practices.

  • Healthcare

The technology examines healthcare data to optimize treatment protocols and reduce medical waste. This helps organizations ensure compliance with healthcare regulations while driving sustainability through better quality patient care.

  • Manufacturing

GenAI algorithms analyze production processes, energy consumption, and waste generation to optimize resource utilization and minimize environmental impact while enhancing overall sustainability.

  • Energy sector

GenAI models predict energy demand, optimize distribution networks, and identify areas for renewable energy integration, promoting sustainability by reducing carbon footprints and ensuring compliance with energy efficiency standards and regulations.

  • Agriculture

Advanced ML algorithms assess soil quality, weather patterns, and crop data to optimize agricultural practices and ensure compliance with environmental regulations while promoting sustainable farming techniques that minimize the use of pesticides and fertilizers.

  • Government and public sector

Generative AI aids government agencies in analyzing data related to public services, optimizing resource allocation, and ensuring compliance with regulatory standards related to social and public governance.

What else stands behind enterprise sustainability compliance?
The majority of enterprises leveraging generative AI are not using it as a standalone technology. As a rule, dedicated practical solutions are still based on machine learning algorithms and associated with the use of conventional neural networks. Here GenAI usually helps to achieve precision, boost efficiency, and create a competitive advantage.

How to get started with generative AI?

The most important and challenging part of enterprise-scale GenAI implementation is the practical and strategic understanding of its deployment and integration into the existing digital ecosystem. 

Working with market leaders, we spent much of our time understanding the best practices of pioneering technology adopters. Our practice has shown that the key to getting valuable outcomes from GenAI implementation lies in starting with fundamental business analysis and thorough preparation.

Identifying specific business use cases
Initiating cross-company stakeholder collaboration
Developing a holistic implementation strategy
Deploying the solution and integrating it into the operational infrastructure
Providing employee training to ensure enterprise-wide adoption

What does it mean in practice?

Deloitte, a leading global professional service provider, has recently shared its hands-on vision of GenAI implementation that includes proactive collaboration with a variety of stakeholders and involves analyzing their perspectives on technology adoption.

Being focused on building the whole GenAI implementation process around specific domains’ challenges and objectives, this approach resonates with our innovation enablement philosophy. And below we’d like to share our in-house execution flow that will help enterprises understand the steps they need to take to get started.

  1. Define objectives and form the product vision.
  2. Collect enterprise data needed to achieve these objectives.
  3. Enable secure cloud storage and data management.
  4. Evaluate service providers and choose an appropriate generative AI model.
  5. Adapt and fine-tune the model according to the objectives.
  6. Test and deploy the model in production. 
  7. Measure the results using industry benchmarks.

Wrapping up, we’d like to underline that the majority of successful generative AI use cases started with the development of a comprehensive and technically grounded implementation roadmap that encompassed the following steps.

The above flow paired with the proficiency of your AI implementation partner can help you master the changing GenAI landscape and drive improved strategic outcomes. Yet, finding such a partner in today’s competitive market can be a challenge indeed.

At Trinetix, we help enterprises turn the excitement of innovation adoption into an actionable roadmap that prioritizes current business objectives and sets the bar for success measurement. If you want to make cutting-edge tech work for your organization, let's chat about creating more impactful generative AI use cases together!

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