Generative AI Use Case Mapping: Identify, Prioritize & Apply AI Where It Fits Best

The recent rapid development of artificial intelligence means almost every business task can be automated or augmented by AI technology to some degree. When considering artificial intelligence for business purposes, it is important to understand the difference between traditional AI which analyzes historical data using machine learning and offers predictions or recognitions and generative AI, which is capable of creating original content such as text, images, code, and other media. A structured approach to use case mapping helps businesses distinguish where generative AI can be applied most effectively across these functions.

The reality, however, is that there is not always enough data or expertise available to choose the AI use cases most suitable for a business. Choosing the wrong generative AI use cases ends up damaging business processes or leading to poor customer experiences. During the implementation stage, if the wrong data or unclear frameworks are used by already limited teams, it is likely to have costly delays, wasted resources, and unhappy employees and stakeholders.

Properly identifying the right generative use case mapping for a business is a critical task to ensure that these technological advancements are meaningfully targeted at a company’s specific needs, well repurposed, and integrated with minimized disruptions to existing operations. Generative AI gives businesses the opportunity to automate tasks, improve their products and services, and better engage their customers while improving business performance and their bottom line.

In order to manage resource demands, upfront costs and the risks of poor execution, an incremental approach to building the foundations of generative AI is often the best route for most organizations. Doing so by focusing on high-value functions and processes and scaling outwards over time lets organizations quickly deliver ROI, build experience, refine processes, and create the organizational flywheel required for sustainable change. This adaptive AI process, when properly implemented, is key to maximizing both operational and financial performance.

What Is Generative AI Use Case Mapping?

Generative AI use case mapping systematically reviews, selects, and prioritizes specific AI applications to maximize an organization’s objectives. It achieves this by identifying relevant use cases based on organization strategy, data quality, and other strategic factors. The process involves business owners, data scientists, and people in technical roles working together to define the scope of the AI use case and develop a detailed mapping plan.

Even the best AI is plagued by data quality issues, organizational silos, a lack of time, and a shortage of expertise. Performance and precision across over 100 generative AI use cases are limited, according to data cited by business advisory firm Gartner. Adaptive AI is often unsuitable due to the high computational power requirements and the deep learning expertise needed, while cognitive AI systems require high-quality and large volumes of domain-specific training data to function effectively. These challenges make the mapping of AI use cases essential in order to ensure that AI projects match an organization’s larger objectives and operational requirements.

Generative AI use case mapping involves identifying potential AI use cases, assessing feasibility and impact, setting priorities, and defining the scope of the AI use case. By using a systematic approach to mapping, organizations can avoid common challenges associated with developing AI-based solutions and ensure that the resulting solution meets their needs and requirements.

Why Businesses Need a Use Case Mapping Framework

Businesses need a use case mapping framework because without clear processes and criteria for project selection and guidance, organizations frequently run into issues such as poor project fit. Poor process and project selection often manifest in three major ways.

The first is underperformance against a business objective such as increased revenue, cost reduction, or higher customer satisfaction. For instance, an insurance company that employs AI to automate claims processing may not experience meaningful cost reductions if the AI model lacks sufficient data or if the process itself is not well defined and only makes a small contribution to overall cost reduction.

A second manifestation of poor project fit is higher-than-expected costs. For instance, a retail brand that uses AI to personalize its marketing emails may find that the resources needed to train and maintain the AI solution outweigh the practical benefits it provides.

Finally, a third common result of poor fit is legal or compliance risks. For instance, a healthcare provider that uses Generative AI to create patient care plans may run into issues with compliance.

To avoid the pitfalls of poor project fit, organizations need solid AI project mapping frameworks because such frameworks can help in four major ways. First, AI project mapping frameworks assist organizations in establishing the processes and evaluation criteria necessary to address specific business objectives and limitations, allowing for proper project or process selection as technology evolves.

Second, project mapping frameworks help organizations increase the odds of success by facilitating the use of frameworks such as Cost Impact Return (CIR) and Mapping Grids, which ultimately lead to better project outcomes.

Third, AI project mapping frameworks offer organizations a systematic way to identify, prioritize, and implement AI Use Cases, as well as a means of reducing uncertainty surrounding AI initiatives.

Finally, AI project mapping frameworks help in reducing AI project and process-related costs by avoiding the implementation and maintenance of AI solutions that are inappropriate for specific AI Use Cases, thus saving time and resources.

Ultimately, an AI Use Case Mapping Framework helps enterprises identify, evaluate, and prioritize processes and projects. It helps businesses navigate the complexities of AI adoption by aligning AI solutions with business objectives. It also enables organizations to realize the full potential of Generative AI for business.

How to Identify Business Processes Suitable for GenAI by Using its Use Case Mapping

Identify which tasks have consistent input types and predictable output requirements without complex judgment or decision-making. Document the standard procedures, instructions, and rules that control these repetitive tasks. Evaluate whether these rules can be codified or represented in a way that a GenAI system can follow.

Signals that indicate that a process or task is ripe for automation with generative AI include:

• Cognitive AI: Cognitive AI is well-suited for repetitive tasks that require human-like decision-making, pattern recognition, or understanding natural language. Examples include contract review in legal services, medical diagnosis, and financial fraud detection. Signals that a process is ripe for automation with Cognitive AI include high error rates, slow processing times, and large volumes of unstructured data.
• Generative AI Examples: Examples of generative AI include chatbots, virtual assistants, and language translation tools. Processes that involve language translation, customer service, or content creation are all potentially ripe for automation with generative AI. Signals that a process is suitable for automation with generative AI include repetitive tasks that require human-like decision-making and communication, and the absence of domain-specific knowledge.
• AI Use Cases: AI use cases are a broad category that includes machine learning, natural language processing, and computer vision. Examples include predictive maintenance in manufacturing, fraud detection in banking, and demand forecasting in retail. Signals that a process is ripe for automation with AI include high data volumes, repetitive tasks, and the need for real-time insights.
• Generative AI Use Cases: Generative AI requires vast amounts of data to train its models, making it suitable for tasks that involve natural language processing, speech recognition, and image recognition. Examples include personalized product recommendations in e-commerce, speech recognition in customer service, and image recognition in healthcare. Signals that a process is suitable for automation with generative AI include large amounts of data, tasks that require human-like decision-making or content creation, and the need for personalization.
• Adaptive AI: Adaptive AI is designed for processes that require adaptation to changing conditions or new information. Signals that a process is suitable for automation with adaptive AI include rapidly changing environments, the need to adjust to new information quickly, and the need for constant updates.

Overall, signals that indicate that a process or task is ripe for automation with generative AI include high error rates, slow processing times, large volumes of unstructured data, repetitive tasks that require human-like decision-making or content creation, and the need for personalization or adaptation to changing conditions.

• Code generation: Repetitive and boilerplate coding tasks, such as creating database schema, data validation, and API endpoint generation, can be automated by GenAI.
• Customer support and service: Customer service operations, such as responding to frequently asked questions, handling simple inquiries, and directing customers to relevant resources, can be automated by GenAI.
• Education: Grading assignments, assessments, and quizzes can be tedious and time-consuming. GenAI can automatically grade student work, providing immediate feedback while reducing the workload on teachers.
• Financial services and investment analysis: Financial analysts and investment advisors often spend a significant amount of time collecting and analyzing data. GenAI can automate the process, providing quick and accurate investment recommendations based on real-time data.
• Fraud detection and risk management: Fraud detection and risk management require analyzing vast amounts of data to identify potential risks. GenAI can be used to automate this process by using predictive analytics to identify and mitigate risks before they occur, adapting to new types of threats over time.
• Graphic design and video: Graphic design and video creation tasks that involve the creation of basic graphics or templates can be repetitive and time-consuming. GenAI can be trained to automate the creation of basic graphics and templates, reducing the workload on designers.
• Healthcare: Healthcare providers often need to generate and process large volumes of data, such as electronic health records, insurance claims, and clinical notes. GenAI can be used to automate these tasks, allowing healthcare professionals to focus on providing quality patient care.
• Human resources (HR): HR services such as candidate screening and hiring, employee onboarding, and benefits enrollment often involve repetitive manual tasks that can be automated by GenAI.
• Insurance: Repetitive processes in insurance, such as claims processing, policy renewals, and underwriting, can be automated by GenAI solutions.
• Legal and compliance assistance: Repetitive tasks in legal and compliance, such as document review, contract management, and regulatory compliance, can be automated by GenAI solutions.
• Product development: Product development often requires repetitive tasks such as prototyping, testing, and quality assurance control. GenAI can be used to automate these tasks, by training the AI model to identify and flag any anomalies that may occur during the product development cycle.
• Project management and operations: Project management and operations often require creating and updating project plans, tracking progress, and monitoring budgets. GenAI applications can be trained to automate these repetitive tasks, reducing the administrative workload on project managers.
• Sales and marketing: Sales and marketing operations, such as lead generation and qualification, customer segmentation, and personalized messaging, can be automated by GenAI.
• Supply chain: Managing inventories is a highly repetitive task that requires tracking and monitoring large amounts of data. GenAI can automate the process, providing real-time insights into inventory levels, tracking inventory movement, and alerting supply chain managers to potential stockouts or overstock situations.
• Synthetic data generation: Synthetic data generation often requires the use of large and complex data sets. GenAI can automate the process by generating synthetic data that accurately mimics real-world data, reducing the cost and time required for data generation.

Before diving into real-world applications, it’s essential to understand what generative AI is and how it works. Our complete Generative AI guide covers foundational models, architecture, and emerging trends.

Manual and Repetitive Tasks

Manual and repetitive tasks refer to routine activities performed without significant critical thinking. Examples are data entry, report formatting, and feedback summarization. These are tasks where automation can free up workers to focus on higher-value tasks. Such activities are time-intensive, error-prone, and low-value, making them one of the best AI automation use cases.

McKinsey research shows that automation especially using robotic process automation and AI can reduce the time taken for manual and repetitive business tasks by approximately 50% to 70%. This includes improvements in cost efficiency (20%–35%) and straight-through process time reduction by 50% ~ 60%. Additionally, McKinsey’s 2018 Global Institute report estimates that AI technologies overall including automation could contribute up to $13 trillion in additional global economic output by 2030, reflecting an aggregate economic lift of about 1.2% per year. However, tasks requiring human judgment and creativity remain beyond full automation. In contrast, routine processes are already being transformed through automation to boost efficiency and free up human capacity for higher-value work.

At PayPal, processing customer feedback manually consumed an estimated 1,000 hours per month until they introduced an AI-powered sentiment analysis tool. This automation reduced processing time to minutes, delivering faster and more accurate insights into customer sentiment. AI enables such efficiency by analyzing large volumes of feedback with natural language processing rather than relying on human review. The key difference between Generative AI and Predictive AI lies in their outputs: Generative AI can create original content like text, images, or audio, whereas Predictive AI forecasts future outcomes using historical data such as predicting churn or demand. Both types also support workflow automation, but Generative AI excels at producing new artifacts while Predictive AI excels at estimation and forecasting stages. Meanwhile, the contrast between Generative AI and Discriminative AI is foundational in machine learning theory. Generative models learn the joint probability distribution P(X, Y) and can generate new data samples ideal for content creation. Discriminative models, on the other hand, focus on modeling P(Y | X), which makes them highly effective for classification tasks like fraud detection or sentiment classification.

Data-Driven Workflows (Analysis, Reporting, Segmentation)

Data-driven workflows are automated sequences of tasks powered by data to improve output quality and support decision-making. These workflows typically include stages like data collection, cleaning, transformation, analysis, classification, and reporting enabling organizations to extract actionable insights efficiently from raw inputs. Such workflows are used across industries. For example, enterprise content management platforms now integrate GenAI to automatically tag, summarize, and classify documents streamlining compliance and accelerating decision-making. In marketing, IDC reports that 79% of teams already leverage GenAI for content creation, and GenAI is expected to handle 42% of routine marketing tasks by 2026 freeing up creative teams to focus on high-level strategy David Gardner, CEO of VenueGen, notes that generative AI enables users without specialized artistic training to produce professional-looking content in-house reducing costs and improving efficiency to levels previously impossible without major investment.

While data-driven workflows have historically been considered back-office operations, modern automation enables them to function as intelligent digital assistants. According to Accenture’s 2021 Automation Maturity Assessment, AI-driven automation has already delivered cost savings of up to 50–60% in ticket incident volumes and IT costs, and up to 40–60% reductions in IT delivery costs demonstrating efficiency gains in the 30–50% range for many organizations.

AI automation algorithms have significantly improved and are particularly strong when they have access to good-quality data. They are easily able to adapt and learn from their mistakes leading to improved accuracy and efficiency in automated tasks. AI-powered solutions can automate existing complex tasks that involve information processing, as well as document classification and segmentation.

AI-powered automation can provide personalized insights and recommendations, improve customer engagement, and drive revenue growth. For instance, in marketing and advertising, data-driven workflows powered by AI can help businesses identify customer segments, understand customer behavior, and optimize their marketing campaigns. By automating these tasks, businesses can scale their marketing efforts. They can reduce costs and drive better marketing results.

AI also automates tasks, such as scheduling and assigning reports to appropriate teams, tracking progress, and providing real-time updates. This helps to streamline workflows and improve collaboration among teams.

According to Grand View Research, the global conversational AI market including NLP-based text generation, summarization, and smart search is projected to reach approximately USD 41.4 billion by 2030, growing at a 23.7% compound annual growth rate from 2025 to 2030. The report highlights strong adoption across industries such as healthcare, e-commerce, financial services, customer experience, legal, and media, where NLP and conversational AI are reshaping interactions and workflow automation.

Similarly, the broader NLP market encompassing voice recognition, sentiment analysis, and machine translation is expected to grow from around USD 27.7 billion in 2022 to over USD 213 billion by 2035, with a CAGR of 23.4%. This underscores the rapid integration of text and conversational technologies across sectors including healthcare, marketing, legal, e-commerce, and media.

Creative Content at Scale (Marketing, Email, Sales)

Creating content at scale refers to the process of producing a large volume of engaging and relevant marketing, sales, or communication materials quickly. Blogs, social media posts, product descriptions, and email newsletters are all examples. Scaling content production with a small marketing team is challenging and time-consuming, and this is where cognitive AI, particularly generative AI use cases, comes in.

Businesses can now produce a wide variety of engaging and relevant content rapidly because of a new generation of AI tools, including copy generators, email writers, and video generators. They employ NLP and ML algorithms to assess keyword density, topic relevance, and engagement metrics to determine particular content ideas and themes that will resonate with their audience. These instruments enable businesses to experiment with various content formats and styles and ensure that their communication materials align with their brand voice and messaging. Case studies show that AI-powered tools can drastically reduce content production time by up to 80% and cut costs by around 50% compared to traditional methods. For example, a marketing agency reported a 50% reduction in content creation time along with a 30% boost in engagement after integrating tools like Jasper (formerly Jarvis) and Canva. Similarly, video generation tools such as Synthesia helped LATAM Airlines accelerate training video production by over 80%, while Colossyan-enabled deployments achieved up to 70% cost savings. Boston Consulting Group also highlights that AI-supported marketing and creative processes can deliver up to 80% savings on agency costs and reduce campaign production time by up to 50–60%, with some clients reporting a 95% drop in SEO-related content costs and 40–60% increases in efficiency. By leveraging these AI systems, businesses not only maximize ROI but also reduce dependence on external agencies or contractors enabling in-house teams to produce high-quality creative output faster and more cost-effectively. By mapping use cases and implementing AI-driven content creation, organizations can maximize efficiency and consistency in their marketing efforts without significant resource expansion, providing clear generative AI examples and AI use cases for scalable content strategies.

Best AI Use Cases by Role and Industry

Across various industries, organizations are integrating artificial intelligence to streamline operations and enhance customer engagement. Many of these generative AI use cases driven by the technology’s ability to create original content and adapt its creative process autonomously.

Across all sectors, AI is being used to optimize internal processes by automating administrative tasks, thereby reducing costs and human errors while allowing teams to focus on more complex, patient-centric work. Key roles that benefit from AI integration include data scientists, AI project managers, software engineers, cybersecurity experts, cloud architects, and ethicists, with the overarching aim of aligning AI strategies with core business objectives and maintaining a competitive edge.

Harvard Business Review observes that many organizations are now prioritizing smaller-scale AI initiatives with clearly measurable returns, recognizing that large-scale AI deployments remain complex and unpredictable. As AI expert Andrew Ng advises, starting with a well-defined use case and organizational clarity is essential when businesses build AI applications focused on real problems, ROI typically improves over time, helping to justify continued investment. In most cases, generative AI-enabled automation delivers substantial benefits, particularly when it complements human expertise and scales thoughtfully.

Cognitive AI refers to a class of AI systems designed to mimic human cognitive processes, such as perception, reasoning, learning, problem-solving, and decision-making. Generative AI use case mapping has a significant influence on how companies continue integrating artificial intelligence into their everyday operations.

Some of the most developed sectors include the following.

• E-commerce: AI is frequently applied in marketing campaigns, creative content creation, and customer support. Gen AI models can create compelling advertising texts and blog posts. Likewise, Generative AI use cases can help reply to customer questions by combining information from customer databases.
• Healthcare: AI assists with medical image interpretation, medical research, and even mental health therapy. Generative AI can be used to analyze medical images such as x-rays and MRIs to provide more accurate diagnostics and create personalized treatment plans for patients based on their medical history, also in generating synthetic medical images for AI model training and improving diagnostic accuracy.
• Finance: On financial markets, AI-driven trading has been in use for decades now. Furthermore, Generative AI is now being leveraged to analyze financial documents and inform investment decisions for individuals and firms alike. And in that process generative AI use case mapping plays a great role.
• Energy: Smart data management solutions for production planning, maintenance activities, and inventory automation.
• Manufacturing: Adaptive AI and Cognitive AI combined with Internet of Things (IoT) data enables continuous process optimization and energy planning
• Entertainment: Generative AI is being used to create scripts and digital content for movies, TV shows, and video games, ranging from idea generation to storyboarding and production.

The following matrix highlights just some common Generative AI use cases by industry and role. These generative AI examples provide only a limited view of the thousands of areas where artificial intelligence systems are now integrated into numerous sectors.

Effectively mapping generative AI use cases isn’t just about identifying business needs it also requires understanding what the underlying models are capable of. Since most generative AI solutions are built on neural networks, a basic grasp of their architecture and behavior is essential. To explore how these networks function and evolve, check out our overview of Generative AI Neural Networks.

Sales Outreach, Copywriting, and CRM Automation

Sales outreach and copywriting include direct or indirect sales activities that deal with word-based communications such as building client relationships, writing persuasive emails, developing content, setting up meetings, product development, and after-sales service. CRM systems have long embraced automation but are becoming more intelligent with new AI capabilities.

The main challenge with CRM workflows is that Sales Reps often become distracted from customer-facing actions like outreach or calling. As documented in a Harvard Business Review article on automation in sales, “most of the time [sales reps] are focused on something else”, primarily routine admin tasks. Ultimately, this affects their productivity and effectiveness. When automated, client engagement can scale, reps can personalize outreach, and customer data is managed easily and securely using machine learning and deep learning.

Generative AI in business has become a game-changer for sales. Some of the benefits of automation include increased productivity, uniform processes, reduced human error, better data management, and a focus on more important customer-facing activities.

There are several generative AI business use cases. Practical utility is found in written content generation ranging from product descriptions, proposals, emails, and presentations which previously required considerable effort and time. Content customization or personalization is another significant function of this technology in sales. From customizing emails to personalized situations across geographies and cultures, different industries, roles, and personalities, GenAI can fine-tune communication. This level of content personalization is a game-changer for organizations.

Recommendation engines are a further common usage, especially in e-commerce and healthcare. While e-commerce uses it to recommend products to clients based on their past preferences and behaviors, generative AI use cases in healthcare are used to provide recommendations for better patient outcomes based on pre-existing historical data and treatment structures.

Generative AI systems can improve content quality over time through feedback loops and real-time updates. For instance, a GenAI chatbot answering “What Kind of Company is Tesla?” can refine its responses continually based on user feedback while ensuring compliance with content standards. Compliance monitoring tools then verify that updates adhere to guidelines, enabling automated systems to generate and review large volumes of content quickly and accurately.

In summary, automating sales and outreach-related processes and tasks enables businesses to enhance their competitiveness while making more efficient use of their resources. This allows them to allocate resources to areas within the organization that demand creative problem-solving skills, thus fostering the growth of their business.

AI in Customer Support & Chat Automation

Investment in generative AI for customer support and chat automation continues to rise as companies aim to enhance customer experience and efficiency. A Deloitte survey shows that 78% of organizational leaders plan to increase their AI spending in the coming year highlighting a growing strategic focus on GenAI for customer-facing functions. Generative AI assistants, such as ServiceNow’s AI agents, have already reduced resolution times for complex support cases by more than 50% freeing human agents to focus on more critical tasks. Meanwhile, Deloitte’s research indicates that 67% of respondents across industries are increasing their investment in GenAI due to clear business value being realized. This shift toward automation and generative AI empowers teams to deliver better service with fewer resource constraints.

Progressive organizations have successfully improved their customer service operations by leveraging automation and cognitive AI tools such as NLP, machine learning, and advanced analytics. AI-powered chatbots act as the first line of support, capable of interpreting customer inquiries, offering relevant information, and resolving common issues. These chatbots are powered by machine learning, NLP, and speech recognition. This enables them to interpret customer inquiries and offer relevant information and solutions.

Verloop’s Voice AI is a generative AI–powered customer service platform designed to improve response times and support efficiency especially during high load periods or when live agents are unavailable. Many customers leveraging Verloop report that voice automation handles over 80% of routine inbound and outbound requests, reducing operational costs by approximately 30% while boosting overall productivity and customer experience. The platform integrates advanced ASR (Automatic Speech Recognition) and NLU (Natural Language Understanding) to route customer queries intelligently, freeing human agents to tackle more complex issues. In industries like travel, e-commerce, and financial services, companies have seen Voice AI reduce per-call costs by up to 70% for example, reducing a ₹70 support ticket cost to around ₹20 per call thanks to Verloop’s scalable voice bot deployments.

Marketing Campaigns, Personalization, and Social Posts

Marketing campaigns, personalization, and social posts are among the best generative AI use cases thanks to their suitability for adapting brand messaging and offers to specific personas. They’re also well-suited for leveraging machine learning (ML) techniques for increased engagement and conversion rates. These efforts are often data-driven, requiring the analysis of customer behavior, preferences, and interactions to inform the content strategy and optimize the effectiveness of campaigns.

AI applications in marketing have unique creative strengths. These include generating ad variations, post scheduling, A/B captions, and social tone matching. Ad variations help test different creative options and messaging, providing insights into which elements are most effective in driving engagement and conversions. Post scheduling helps optimize the timing of social media posts, ensuring that they are published when the target audience is most active and likely to engage.

A/B captions allow for testing different messaging options to determine which resonates best with the audience. Social tone matching ensures that the brand messaging aligns with the values and personality of the target audience. Businesses can use generative AI to conduct marketing campaigns of branded content, unique product information, scenario-based product/service presentations, etc.

Here are a few of the many generative AI examples of marketing use cases.

• Berling Media: Social Media Content Generation
• Curie AI: Hyper Personalized Email Marketing
• Bunch AI: Content Writing for SEO
• PanelsAI: provides top-tier generative models (like the GPT series and Claude family) on a single platform
• Flick: AI-generated hashtag suggestions and auto-generated captions
• Outgrow: Personalized marketing messages depending on the user’s device location.

Marketing teams increasingly turn to generative AI for producing personalized blog posts, social content, and ad copy. This is more than automation—it’s strategic augmentation. Learn how companies apply these tools in our detailed report on AI-driven content creation for business impact.

Enhancing banking industry and Personalizing patient in healthcare Sector

Generative AI is transforming two highly regulated, high-stakes industries: banking/finance and healthcare. Its ability to rapidly analyze data, generate creative scenarios, and foresee future outcomes combines the strengths of cognitive AI and adaptive AI, opening up use cases with high value around mission-critical processes that in the past had no “technology short-cuts.”

In the last few years, artificial intelligence has permeated the financial services industry, from enhancing customer experience to optimizing financial processes via trading algorithms and risk management assessment. Generative artificial intelligence (GAI) is a subsector of AI that analyzes data, finds existing patterns and structures, and then uses those findings to create new material that is analogous to the original.

Personalization in customer communication is a fundamental use of generative AI in banking that is already widely implemented. Chatbots and virtual assistants powered by generative AI models provide personalized responses to customer queries, improving customer engagement and satisfaction. In addition, banks use generative AI to send emails and messages based on each customer’s specific characteristics and behaviors, resulting in more effective marketing campaigns.

Generating new fraudulent patterns is one of the most critical use cases of generative AI in the banking sector. By analyzing massive swathes of historical transaction data, generative AI can identify patterns that are indicative of fraudulent activities. These patterns are then used to create new fraudulent scenarios, which are then tested against the existing fraud detection models. The table below demonstrates how much such advancements can save banks.

Generative AI can be used to generate financial reports, saving banks time and resources. By training generative AI models on historical financial data, banks can generate accurate and comprehensive financial reports with minimal human intervention. This improves the accuracy and consistency of financial reporting while also reducing the risk of errors.

In the healthcare sector, generative AI can transform diagnostics, patient content generation, and personalized treatment planning. One use case seeing rapid implementation is automating the generation of clinical documentation such as discharge summaries, diagnostic findings, treatment plans, and patient instructions. By analyzing trends in a patient’s electronic health record (EHR), AI can pre-populate key sections of documentation with accurate and consistent terminology, reducing the risk of errors.

With natural language processing capabilities, AI-generated reports can be tailored to match the reading level and comprehension of the end audience, including patients and their families. The scripts used by hospital front-office chatbots can be matched to callers’ skill levels. Using generative AI, patients can have access to the medical information they need at their fingertips.

Ecommerce Product Descriptions & Recommendations

Description: Ecommerce product descriptions and recommendations refer to the written content and personalized suggestions provided to customers on online retail platforms. Product descriptions are detailed, informative, and engaging texts that highlight the features, benefits, and unique selling points of individual products.

Typical Benefits: To create personalized product suggestions based on customer preferences, behavior, and historical data, businesses can use generative AI. These suggestions can include related products, complementary items, or recently viewed products. By providing personalized recommendations, businesses enhance customer satisfaction and loyalty while boosting sales and revenue.

Market projections indicate substantial growth in global e-commerce. Shopify forecasts that global online sales will reach approximately USD 7.6 trillion by 2027 up from around USD 6.6 trillion in 2025 reflecting a compound annual growth rate (CAGR) of about 7 ~ 8% between 2022 and 2027. According to Statista, global e-commerce sales reached roughly USD 4.9 trillion in 2021, and are expected to more than double to approximately USD 7.4 trillion by 2025. Within retail categories, McKinsey reports that global luxury goods saw robust growth in recent years fashion and leather reached about 5% CAGR between 2019 and 2023, while luxury watch and jewelry segments showed steady expansion. However, specific projections such as “global shoe sector growing by 22% in 2021” or “fine jewelry increasing by 10% in 2022” were not found in McKinsey’s public reports.

Main Challenges: The main challenge is learning how to use generative AI to automate product descriptions and SEO product pages effectively to ensure quality and relevant output, data privacy and security, managing technical costs and complexity, and ensuring human oversight to mitigate bias and inaccuracies.

Key metrics: Key metrics to monitor throughout the development process include Views of Product Detail Pages (PDP), Click-through Rate (CTR), and Add-To-Cart Rate (ATCR).

Tools and Implementation: Integrate generative AI tools such as Jasper, Copy.ai, or OpenAI’s into their e-commerce platforms. Train the AI models with relevant product data and customer information using machine learning frameworks like TensorFlow or PyTorch. Develop a clear content strategy, gather and process relevant data, train AI models for e-commerce applications, and ensure compliance with privacy regulations to use generative AI for e-commerce product descriptions and recommendations.

Portfolio & Project Documentation Automation

Portfolio and project documentation frequently necessitate significant time and effort due to their repetitive nature. Modern freelancers benefit from automation, but can be challenging to set up as each project has a unique requirement.

Auto-document projects may be made easier and project management more efficient by leveraging generative AI tools, reducing time and effort spent by automating the labor-intensive process. AI-based full-stack website development platforms such as Wix Studio aids in portfolio management, and may even assess client feedback to generate fresh ideas for designing the client’s website based on their recommendations. Implementing knowledge management systems within such companies allows frequent updates to their training materials, blogs, and portfolios, boosting their visibility and eliminating the necessity to create fresh content from scratch.

This technique offers adaptability, personalization, and integration with numerous project management systems, utilizing pre-existing templates and metadata to simplify document generation. Generative AI tools provide a cost-effective alternative to outsourcing or manual creation of project documentation and portfolios.

Evaluating Use Case Fit: A Checklist Approach

Evaluating use case fit for generative AI involves considering various factors to determine whether the deployment of AI technology in a specific scenario is appropriate, efficient, and likely to achieve desired outcomes.

Organizations should consider several key criteria when evaluating AI use cases, including the potential for improving customer experiences, process efficiency, and reducing costs. Organizations should also consider the technical feasibility of implementing AI, the availability and quality of data, and the compliance and security implications of using AI.

Inputs for evaluating generative AI use case typically include a deep understanding of the specific scenario or process being considered for automation, including the goals, objectives, and expected outcomes. Ultimately, evaluating use case fit for generative AI enables organizations to make informed decisions about whether to invest in a specific scenario or process, ensuring that resources are allocated efficiently and effectively.

The cost-to-deploy and value-generated framework is an approach used to evaluate the potential benefits and costs associated with implementing new technologies or processes. The cost-to-deploy refers to the resources required to implement and maintain the new system, such as the cost of purchasing and integrating new technology, training employees, and potentially changing or modifying existing systems.

Value-generated refers to the benefits that the new technology or process is expected to deliver, such as cost savings, increased efficiency, improved customer satisfaction, or competitive advantage. When evaluating cost-to-deploy vs. value-generated, organizations should consider both the short- and long-term costs and benefits, as well as any potential risks or uncertainties associated with the new system.

Gen AI use cases ultimately, the goal of this approach is to ensure that the potential benefits of implementing new technologies or processes outweigh the costs and that resources are allocated in a manner that supports organizational goals and objectives.

Technical complexity vs. payoff is a tradeoff that organizations must consider when evaluating the potential benefits of implementing new technologies or processes. Technical complexity refers to the difficulty or complexity associated with developing, implementing, and maintaining the new system, including factors such as integration with existing systems, data management, security, and scalability.

Payoff refers to the potential benefits that the new technology or process is expected to deliver, such as cost savings, increased efficiency, improved customer satisfaction, or competitive advantage. When evaluating technical complexity vs. payoff, organizations should consider the potential risks and challenges associated with implementing the new system, such as the availability of skilled resources, project management challenges, and potential disruptions to existing systems and processes.

Additionally, organizations should consider whether the potential payoff justifies the level of technical complexity involved in the implementation. Ultimately, the goal of this approach is to ensure that the potential benefits of implementing new technologies or processes justify the level of technical complexity, and that the implementation is feasible given the organization’s technical capabilities and resources.

Matching business goals to the right AI stack requires careful assessment. Review our platform evaluation matrix for insight into feature alignment, integrations, and pricing models.

Feasibility, Cost, Accuracy, Impact, Repeatability

Generally, generative AI solution performance areas can be focused around feasibility, cost, accuracy, impact, and repeatability. For feasibility, factors include a variety of items such as whether the problem the team will focus on addressing is too complex for AI to be able to solve or whether there will be any technology-related hurdles.

Cost refers to data, infrastructure, operational, personnel, and maintenance costs, which the business must always factor in. Accuracy must ensure consistency, compliance, alignment with organizational and legal guidelines, and data security. The impact should measure factors such as company-wide usability and productivity gains.

For repeatability, we have simplicity and predictability as the two most important factors. Simplicity is based on whether the input and output are simple or complex; the simpler the better. Predictability is handy since AI thrives on structured and standardized data.

Minimal compliance efforts are an essential part of accuracy as well as overall solution performance. Accuracy is also contingent on parameter and model fine-tuning. Cost (ROI analysis and/or costs associated with tool customization), accuracy, and impact can all be improved with low setup and customization requirements, where applicable.

Building a Use Case Priority Map

Use Case Priority Maps (UCPMs) help teams decide how to prioritize GenAI use case implementation by providing a score based on feasibility and strategic importance. The notion was developed by SAP Business Transformation Consulting (SAP BTC), one of the leaders in process and data consulting.

To transition from evaluation to mapping, they provide 10 steps to identify the highest-priority projects.

• Step 1: Assemble a cross-functional mapping team. An AI implementation project team requires expertise from four areas.
• Business development to guarantee strategic fit
• Operational managers who best understand daily workflows
• Technical experts who understand how potential AI projects can integrate with current systems
• Legal experts ensuring that projects are carried out in compliance with relevant regulations and organizational policies, including current ethical standards, crucial for identifying and mitigating potential legal and regulatory risks while proactively shaping policies and procedures
• Step 2: Brainstorm AI use cases. Gather a list of i use cases from various business units or departments such as customer support, marketing, finance, and operations. Encourage team members to think creatively about potential AI applications and to leverage their domain expertise to identify relevant use cases.
• Step 3: Establish evaluation criteria. Establish a thorough set of evaluation criteria considering feasibility, cost, accuracy, quality, impact, repeatability, unit volume, and potential competitive advantages. These are the metrics that will guide use case scoring.
• Step 4: Score each business use case using evaluation criteria. This is the step where a Use Case Priority Map comes to life. Applying the evaluation criteria established in Step 3 determines which use case for generative AI solution implementation is most suitable. This is where the use of checklists comes into play, and using a scoring rubric helps teams think more deeply about each project. Teams should seek to identify at least two use cases for generative ai. Each of the five key criteria mentioned previously – Feasibility, Cost, Accuracy, Impact, and Repeatability – should carry a weight of 20% in the overall score to reflect their equal importance to both business and technical concerns.
• Step 5: Generate detailed reports. It is critical to produce detailed reports that summarize the team’s findings about how to use generative AI, the scoring criteria, and a matrix of likely generative AI use cases and their score against key criteria. These reports help teams communicate, measure, and iterate on their decisions as implementation continues.
• Step 6: Select the highest-priority AI use case. Consensus rarely comes from a single round of scoring and discussion, so SAP recommends teams reach an agreement on a high-priority project by a two-step voting system. Each team member first votes for two use cases for generative ai that they consider top priorities. Narrow the remaining list down to a single use case for generative ai by holding a subsequent vote. This enables the team to prioritize focus and resources effectively while ensuring transparency and alignment throughout the decision-making process.
• Step 7: Develop a visual mapping and scoring system. SAP notes that teams should ensure they use a “visual mapping or scoring system” to help prioritize GenAI use case implementation. Italics because the term UI/UX doesn’t come up in the guide. Yet many teams find they benefit from a good mapping of potential use cases.
• Step 8: Create a roadmap for GenAI implementation. Teams must develop a roadmap that outlines key milestones, timelines, roles and responsibilities, and success metrics as with any team-based project.
• Step 9: Communicate findings and achieve stakeholder buy-in. Conduct any design thinking workshops required to get input from involved stakeholders, including executives, managers, and end-users, and identify potential challenges or barriers upfront.
• Step 10: Iterate and refine the process. As teams complete the project, they repeat the process with other use case candidates.

An AI Use Case Priority Map should become a dashboard that decision-makers can refer to easily to read out scores for candidate projects. SAP even recommends tracking progress towards milestones visually on the map so that progress can be seen at a glance.

Once the right AI use cases are identified and prioritized, the next challenge is execution. This is where infrastructure choices particularly around API architecture play a critical role. A well-designed centralized API system ensures that the selected generative AI solutions are not just theoretically sound but also practically scalable across departments and workflows. To learn how API centralization improves integration, access control, and AI deployment efficiency, explore our guide on Centralized API System Benefits.

How do I know if GenAI fits my business need?

Generative AI fits your business needs if the use case features high volumes of repetitive tasks and meaningful data on a structured input. If you are thinking of mapping GenAI into your business, there are lots of generative AI examples where it can help. Deloitte suggests businesses look for these bottlenecks or inefficiencies and see if GenAI is capable of handling them. Businesses should also consider the expertise of their workforce to understand if GenAI fits their needs.

For example, If your work, business, or daily workflow involves regular use of different generative AI chat models, you’ll naturally start exploring platforms that bring all those tools together in one place. You’ll also start thinking about cost-efficiency looking for tools that don’t lock you into expensive monthly subscriptions but instead offer a pay-as-you-use pricing model. Through this kind of market research, you’re likely to search for a solution that gives you access to multiple leading AI models such as the GPT series or Claude family without the hassle of switching platforms or overpaying.This is exactly where a tool like PanelsAI “for only $1” and “only pay for what you use”.

What makes a process good for automation?

A highly repetitive process with a low error tolerance that is clearly structured and rules-based makes a process suitable for automation. According to the Digital Marketing Institute, processes that require little or no human intervention and are time-consuming, repetitive, and involve large amounts of data are suitable for automation.

Automating these processes can eliminate inefficient bottlenecks and cut costs. Other processes suitable for automation include data collection and cleansing, document processing, compliance and risk management, and supply chain and logistics.

Automation frees up employees’ time for more value-added, creative tasks, resulting in more productive output per hour worked. Automation can eliminate human error from repetitive manual tasks, which can help increase output quality and consistency, according to the Harvard Business Review.

What are the most common use cases of generative AI?

Generative AI, just a few short years after seeing its first mass-market introduction, has many common use cases, and a few notable ones are worth special attention.

• Writing: GenAI can produce a wide range of outputs, including emails, blog articles, advertisements, promotional slogans, and even short stories. Some of the best adaptive AI use cases are project documentation,software development, policy drafting, and report writing.
• Summarizing: A generative AI model may be trained to recognize key ideas and distill long material into short summaries. This program can summarize meeting notes, legal documents, technical manuals, and scientific research papers, among other things.
• Analyzing: Data may be utilized by GenAI to produce findings and results in a variety of sectors. For instance, the technology can parse large chunks of legal text and contract stipulations to identify risky clauses, or it can analyze chunks of Python code to find errors.
• Replying: One of the best advantages of generative AI is that it may be customized to provide unique responses depending on user inputs. As a result, the technology is a better fit than adaptive AI for chatbots, assistants, and customer service applications.
• Visualizing: When working with artistic tasks, one of the best cognitive AI use cases is the ability to produce visual representations based on a prompt. This can include things like concept photos, storyboards, and advertising visuals. It can also bring together disparate images such as house blueprints and partial photographs combine them into a complete rendering.

Other generative AI examples include music and video creation, as well as speech synthesis and translation. These applications demonstrate the versatility of GenAI and its potential to transform various industries. Ultimately, choosing the right executor human or artificial intelligence comes down to a potent blend of mechanical quality (overall effectiveness), financial impact (cost-effectiveness), and confidence (acceptance among humans).

What role does data quality play in AI adoption?

Data quality is a vital part of AI adoption. It impacts everything from project risk to business value. Quality data sets enable greater operational efficiency, compliance with data governance and regulatory requirements, better competitive positioning, and discovery of new revenue streams. In addition, greater accuracy, reliability, and economic viability of outcome improves consumer and stakeholder trust.

In 2022, Tata Consultancy Services highlighted data preparedness as a key metric for data quality but the specific figures you mentioned weren’t found. Their publications emphasize that data readiness remains a significant barrier to AI adoption. While no exact percentage (like “45%”) was publicly reported, TCS warns that poor data practices can undermine trust in AI models and increase bias if not properly managed.

Using generative AI examples ranging from staff time management to customer engagement, organizations need to analyze a multitude of data points. Such maneuvering requires the appropriate data infrastructure to be designed and set in place, adequate and relevant data collection, training data curation, and user-friendly interfaces to maximize data usability and accessibility.

Data quality can be measured and enhanced by leveraging surveys on user-friendliness, data documentation, and data maintenance, modeling tools to assess formats, and hone metadata and metadata pattern identification. Such measures ensure structured, labeled, and accessible data enhances the quality and success rates of generative AI use cases.

How are government agencies using generative AI?

Government agencies use generative AI in a growing array of ways market by market, but fairly consistently for three connected drivers: to improve citizen engagement, to boost in-house operational efficiency, and to achieve cost-saving. Several use cases within those three strategic imperatives have emerged in the past year, including the creation of citizen chatbot services, automating document summarization, simplifying communication through multiple languages, AI-driven analysis of policies, risk-scenario simulations, etc.

Cognitive AI and generative AI have played a useful role in augmenting citizen services in countries at the cutting edge of smart government. Semantic search and retrieval, knowledge base enrichment, and context-aware group assistants are useful counterparts to established chatbot, service bot, augmented customer service agent, search, and knowledge base products.

Positive generative AI examples in chatbots that provide information on government services, answer questions, and direct citizens to useful resources abound. A tool called Smarty, has been developed by the Swedish government’s job portal (Arbetsförmedlingen) using Open AI’s GPT-3.5 Turbo and GPT-4. The tool is designed to simplify the process of understanding job posts, especially for individuals with weak language and reading skills. SaaS solutions from Coveo, Ada, and Babel Street, among others, have delivered live talk experiences that can interact with and adapt to hundreds of real-time conversations in a range of languages.

Document processing and summarization often form the core of a use case for AI in government. Through a combination of natural language processing and machine learning, such systems parse and summarize extensive documentation, whether on immigration, housing, travel protocols, or public health. The EU AI Act will provide a useful framework for risk-based selection of such AI use cases.

Language translation and multilingual communication are key use cases for AI in government services. AI-powered translation tools help simplify official communications such as policy updates, forms, and notifications making them accessible across regional languages with speed and accuracy. In the United States, agencies like the Department of Homeland Security and the U.S. Citizenship and Immigration Services have begun experimenting with AI-powered language tools to support multilingual service delivery, especially for immigrant communities. These tools assist in translating forms, legal notices, and support content into Spanish, Chinese, and Arabic helping reduce miscommunication and administrative delays. In the private sector, Thomson Reuters has introduced a generative AI–powered translation tool supporting English, French, and Portuguese designed for legal professionals with additional languages expected soon.On analyzing policy data and performing risk-scenario simulations, centralized sources at chambers of commerce or government agencies have often posted policy briefs, analyzing the implications of new regulations, trade agreements, regional investment incentives, and similar topics. Generative AI use cases point to the need for summarization of dense regulatory documents with key points and impacts highlighted.

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