Generative AI use cases for the enterprise

Generative AI-powered tools like ChatGPT, Google Gemini, Microsoft Copilot, Claude and Perplexity generate content including text (anything from emails to poetry), images and video. These tools can also code, analyze data, brainstorm ideas, support real-time communication, solve complex math problems and more. Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “strong AI.”

Computers have moved beyond mere number-crunching devices. They are now capable of natural language processing (NLP), grasping context and exhibiting elements of creativity. 

Thanks to Generative AI, organizations can use machines to: 

• Quickly find insights hidden within mountains of unstructured text, paving the way for more informed decision-making
  
• Automate tedious, repetitive tasks
  
• Streamline workflows with personalized content creation, tailored product descriptions and market-ready copy
  
• Design content, ad campaigns and innovative products that build better customer experiences

At the heart of generative AI lie massive databases and vast libraries of texts, images, code and other data types. Like diligent students, these large language models (LLMs) soak up information and identify patterns, structures and relationships between data points. They learn the grammar of poetry, artistic brushstrokes and musical melodies.

Generative AI uses advanced machine learning algorithms and neural networks to analyze these patterns and build statistical models. Imagine each data point as a glowing orb placed on a vast, multi-dimensional landscape. The model meticulously maps these orbs, calculating the relative heights, valleys, smooth slopes and jagged cliffs to create a probability map, a guidebook for predicting where the next orb (i.e., the generated content) should most likely land.

Now, when the user provides a prompt—a word, a sketch, a musical snippet or a line of code—the prompt acts like a beacon, drawing the model towards a specific region on that probability map; the model then navigates this landscape, probabilistically choosing the next element, the next and the next, guided by the patterns it learned and the nudge of the users’ prompt.

Each output is unique yet statistically tethered to the data the model learned from. It’s not just copying and pasting; it’s creatively building upon a foundation of knowledge fueled by probability and the guiding prompt.

While advanced models can handle diverse data types, some excel at specific tasks, like text generation, information summary or image creation. Additionally, the quality of outputs depends heavily on training data, adjusting the model’s parameters and prompt engineering, so responsible data sourcing and bias mitigation are crucial.

Imagine training a generative AI model on a dataset of only romance novels. The result will be unusable if a user prompts the model to write a factual news article. By incorporating diverse and accurate data sources, generative AI models can be trained to be more informative and objective. 

Generative AI is a potent tool, but how do organizations harness its power, effectively and affordably? The tool is shifting computing costs into high gear. The average cost of compute is rising sharply—and 70% of executives say generative AI is playing a key part in driving this increase.¹

On the other hand, Generative AI can stretch the computing budget. 73% of executives agree that gen AI can make their use of computing resources more efficient—and they’re already putting this theory into practice. For example, 67% of organizations are using gen AI to accelerate the development of new and more efficient models, algorithms and applications. And 65% of organizations are using gen AI to reduce required compute resources by automating tasks.¹

Not every application of gen AI is created equal. Each use case has its own compute, data and privacy requirements. Still, there are two paths most businesses are traveling to unlock the treasure trove of generative AI:

Ready-to-launch tools: The “AI for everyone” option: platforms like ChatGPT come pre-trained on vast datasets, allowing users to tap into their generative prowess without reinventing the wheel. Organizations can fine-tune these models with specific data, nudging them towards outputs tailored to specific business needs. User-friendly interfaces and integration tools make them accessible even for non-technical folks.

These public options offer limited control, less customization of model behavior and outputs and the potential for bias inherited from the pre-trained models.

Custom-trained models: Most organizations can’t produce or support artificial intelligence without a strong partnership. Innovators who want a custom AI can pick an AI foundation model like OpenAI’s GPT-4.5 or BERT and feed it their data. This personalized training sculpts the model into bespoke generative AI perfectly aligned with the business goals. The process demands high-level skills and resources, but the results are compliant, custom-tailored and business-specific.

The best option for an enterprise organization depends on its specific needs, resources and technical capabilities. If speed, affordability and ease of use are priorities, ready-to-launch tools might be the best choice. Custom-trained models might improve if customization, control and bias mitigation are critical.

Success in the application of generative AI lies in adopting a use-case-driven approach, focusing on your company’s problems and how generative AI can solve them. Key considerations include:

• Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing.
  
  
• Model matchmaking: Choose a suitable generative AI model for your specific needs.
  
  
• Teamwork: Choose a team and providers with expertise in AI, data science and your industry. This interdisciplinary team will ensure your generative AI is a success.
  
  
• Data: Garbage in, garbage out. High-quality, relevant data is the fuel that powers generative AI success. Invest in data hygiene and collection strategies to keep your engine running smoothly.

The use of generative AI has spread quickly throughout various industries and departments worldwide. Marketing and sales acted rapidly and are already infusing generative AI into their workflows. The speed and scale of generative AI’s ability to create new content and useful assets is impossible to pass up for any discipline that relies on producing high volumes of written or designed content.  

Software developers use generative AI to write, update and maintain code automate debugging and assist with app testing during app development. AI coding tools can also handle bug fixes and testing and provide the various documentation types a coder might need. This includes technical documentation, user manuals and other relevant materials that accompany software development.

Customer service has leapfrogged other functions to become CEOs’ #1 generative AI priority.² AI-powered chatbots and virtual agents access and process vast amounts of information to accurately answer customer and human agent queries. They can engage in natural conversations, providing around-the-clock support and delivering context-aware responses. These advanced assistants enhance user experience while reducing the need for human intervention. AI is also being used to analyze customer sentiment and improve service interactions. Generative AI also drafts follow-up emails, summarizes support tickets and creates knowledge base articles to improve self-service options.

AI-powered tutoring, content generation and automated grading are gaining traction. AI assists educators in developing personalized learning experiences, summarizing research materials and automating administrative tasks. However, concerns persist around data privacy, misinformation and academic integrity.

AI analyzes market trends, generates reports and automates financial forecasting for investors and analysts. AI-powered trading algorithms and personalized financial recommendations are becoming increasingly common.

To support fraud detection and risk management, generative AI can quickly scan and summarize large amounts of data to identify patterns or anomalies, aiding underwriters and claims adjusters in optimizing outcomes. It generates tailored reports and insights, streamlining decision-making. Generative AI helps prevent cyberthreats and fraudulent transactions, improving security and compliance in financial services.

AI-generated images and videos streamline content creation without requiring actors or equipment. Organizations use AI for localized video production and animation. AI tools can now generate high-quality video content, reducing production costs and enhancing creative possibilities. Users also use image generators like DALL to edit personal photos to create professional-looking business headshots for use on Slack or LinkedIn.

Generative AI is transforming life sciences by assisting with medical documentation, diagnostics, patient engagement and drug discovery. AI-powered tools summarize patient histories, lab results and medical records, allowing physicians to make faster, more informed decisions. Generative AI is increasingly used in medical imaging, analyzing X-rays, MRIs and CT scans to detect fractures and diseases. For the creation of new drugs, generative AI is modeling molecular structures, predicting the effectiveness of new compounds and accelerating the development of novel treatments. AI-powered virtual assistants help patients by answering health-related questions, scheduling appointments and providing medication reminders. Generative AI is also automating administrative tasks such as transcribing notes, processing insurance claims and billing. Strict regulations like HIPAA, along with concerns about data privacy, bias and ethics, remain major challenges.

Generative AI streamlines hiring, onboarding and employee development. It summarizes resumes, assists recruiters in screening candidates and automates interview scheduling. During onboarding, it personalizes training materials based on roles. For performance management, it generates structured review templates and career development insights. Conversational AI portals can provide employees with feedback and identify areas for improvement without involving management. Gen AI can also analyze workforce trends and predict turnover risk. 

Generative AI solutions are increasingly adopted for claims processing, fraud detection and risk assessment. AI tools analyze policies, automate underwriting and improve customer interactions, though regulatory compliance remains a key consideration.

AI summarizes contracts, legal documents and regulations, aiding professionals in research and compliance monitoring. AI tools help identify risks, generate reports and streamline due diligence processes in legal and regulatory environments.

Product designers increasingly use generative AI to optimize design concepts at scale. It assists in structural optimization which helps ensure strong, durable products that use minimal material, reducing costs and pricing. Generative design is most impactful when it’s integrated throughout the product development lifecycle, from the initial concept to manufacturing and procurement. Also, product managers use generative AI toconsolidate user feedback so products can be improved.

AI automates task and subtask generation, forecasts timelines and resource requirements, summarizes essential documents and assists with risk prediction. It allows project managers to focus on higher-level strategy rather than daily business management.

76% of CMOs say generative AI will change the way marketing operates—and 76% also say the failure to quickly adopt gen AI will significantly hurt their ability to stay competitive.³ Generative AI enables hyper-personalized marketing across channels. Well-developed prompts and inputs direct large language models to output creative content for emails, blogs, social media posts, product pages and websites. Customized language generators can be trained on an organization’s brand tone and voice to accurately match previous content, and existing content can be reimagined and edited. Gen AI provides deep analytics and metrics into customer behavior, can dynamically target and segment audiences and identify high-quality leads. 

89% of executives report that key investments in automation will include generative AI capabilities—and 19% say generative AI will be critically important to their supply chain automation futures.⁴ Generative AI is transforming supply chain management, workflows and operational efficiency in the automotive and other industries by improving logistics, inventory management and demand forecasting. Increased visibility and transparency help organizations respond to risks immediately rather than waiting for partners to report problems. Integrating clean and trusted data from across the supply chain makes it possible to power an LLM that people across the industry can tap for accurate, real-time information.

AI creates synthetic datasets for training models, testing products, and simulating real-world scenarios. This reduces reliance on sensitive or costly real-world data, accelerating development cycles and improving AI model performance.

While the potential of generative AI is awe-inspiring for many organizations, navigating this landscape requires a balancing act between progress and prudence.

• Protect sensitive data: Use only depersonalized and nonsensitive data to avoid exposing vulnerable information and comply with regulations.
  
  
• Stay informed: Follow industry news to identify reliable tools and avoid unethical AI practices.
  
  
• Develop an AI policy: Create guidelines for internal AI use and investments in third-party tools, drawing from available templates.
  
  
• Invest in upskilling: Investment in reskilling and upskilling programs is crucial, empowering workers to develop skills resistant to automation.

The rise of generative AI seems to have spiked interest in the broader set of AI capabilities. According to a survey from McKinsey⁵, AI adoption in respondents’ organizations hovered at about 50 percent for six years before jumping to 72 percent in 2024. As for the value of generative AI, that same survey found that organizations most often see meaningful cost reductions from gen AI use in human resources (HR). Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management.

Generative AI will continue transforming enterprise operations across various industries, much like the smartphone transformed business communication and productivity. From automating mundane tasks to fostering creativity in content creation and beyond, the potential of generative AI is vast and varied.

As these tools become more widespread in the workplace, they will inevitably bring changes to job roles and necessitate new skills. Alongside these developments invariably comes increased misuse of generative capabilities. Experts anticipate that bias will remain a persistent aspect of most generative AI models. As users gain the power to create diverse forms of content, including images, audio, text and video, the likelihood of malicious misuse is anticipated to rise. This scenario underscores the importance of developing robust mechanisms to mitigate such risks and ensure the responsible use of generative AI technologies.

Navigating ethical considerations, maximizing data security and adapting to evolving best practices are paramount. For enterprises ready to explore the full spectrum of possibilities that generative AI offers, guidance and insights are just a click away. Learn more about harnessing the power of generative AI for your business by exploring the IBM watsonx portfolio of AI products.