Estimated reading time: 35 minutes
I. Introduction
We all heard it—AI has the potential to create business growth. But do organisations actually know what AI can do for them? According to Devoteam’s survey, 36% of interviewed businesses believe that identifying concrete use cases is the biggest barrier to benefiting from AI in their organisation. The difficulty lies in translating the general potential of AI into concrete applications that address specific business needs and offer measurable value.
In this article, we present Devoteam’s approach to identifying and evaluating AI use cases for business. Moreover, we compiled a list of 100 common general and industry-specific Gen AI use cases that can improve efficiency, innovation, and customer satisfaction. Finally, we will look at game-changers – the use cases that have the potential to transform businesses. Read on if you want to not only start but thrive with AI, and don’t forget: make sure your AI solutions are at your service.
II. How to Identify Gen AI Use Cases
To identify AI use cases, our experts use a structured, advisory-led process that guides clients from their initial challenges to concrete, high-impact AI implementations. The use cases identified for businesses have a good balance of effort and impact, taking into account current challenges, strategic vision, and available data.
Here is a breakdown of the key steps of the process:
- Initial Engagement and Strategy Alignment: The process begins with workshops for leadership and business units. The goal is to align strategic business priorities and identify challenges and needs that can be addressed with Generative AI solutions.
- Use Case Identification: It’s time to combine challenges identified with different business units with current technological possibilities and what is feasible for the specific organisation. The focus is on identifying “game-changers” – use cases with high potential impact on the client’s business. Throughout the identification process, it’s crucial to consider the availability and maturity of the data needed for each use case.
- Prioritisation: Considering factors such as effort and impact we chose Gen AI use cases that can deliver significant value while being feasible to implement. Additionally, we can look at factors such as the need for accuracy, strategic risk, and ability to communicate to identify the use cases that should be prioritised. The order of implementation is also often determined by the data availability. The outcome of this step is the identification of priority use cases.
- Roadmap: In the last step, we plan the roadmap to the successful implementation of priority use cases. The approach is often iterative, with a focus on continuous improvement and scaling.
The first two steps of the process are workshops, and they depend highly on the specific business. However, during the Prioritisation step, we use a very concrete evaluation method that is applicable to all businesses. Let’s look closer at the methodology in the next chapter.
III. How to Evaluate Gen AI Use Cases
1. Effort vs Impact
When deciding which use cases to choose, we start by assessing what GenAI can do for a specific business business. There are three main ways to apply generative AI to the business: knowledge access, integration, and automation. Each of these reflects basic attributes for effort and impact.
1. Knowledge access: AI solutions allow people to gain access to more content and more data that answer their questions rather than point them at potential sources. Generative AI can create secure access to knowledge with minimal effort, but the expansion of access will have a high impact on the organisation’s innovation, productivity, internal and external perceptions, and potential for growth.
Examples: Enterprise search, personalised financial advice, customer support chatbots
2. Integration: Existing business processes can benefit from integration with Generative AI by replacing repetitive tasks, using predictive algorithms to anticipate needs/problems, and making workflows more efficient. Integration of generative AI into existing business processes requires more effort, but it results in an immediate impact on costs and quality, and removes low-value tasks, leaving more time for value-added work.
Examples: Demand forecasting, ad spend optimisation, supply chain optimisation, adaptive security
3. Automation: Automating existing business processes or creating new automated business processes will generate the most long-term returns. However, based on technical obstacles and change management concerns, it will take more effort initially. Over time, generative AI may be able to model and create new business processes independently, making the effort to automate them negligible.
Examples: Anomaly detection, automated content moderation, automated appointment scheduling for patients
The following diagram illustrates how these three categories fit against the dimensions of effort and impact:
2. Evaluating accuracy, strategic risk, and ability to communicate
In addition to the criteria of impact and effort, Gen AI use cases should be examined to determine how robust they need to be. This is done by examining their need for accuracy, strategic risk, and ability to communicate.
In order to assess the 3 components, ask the following questions about each use case:
- How accurate must the answer given by GenAI be? Does exact precision matter, such as in a purchasing or management recommendation, or can it include some inaccuracies, such as when generating a brainstormed list of potential project tasks?
- How strategic is the risk associated with a use case? Will an inaccurate answer put lives, significant budget, production lines, revenue, or other factors at risk? Or is the risk minor, meaning that if something goes wrong, it will be easy to identify and inexpensive to fix?
- How well does the AI need to communicate? Does the response need to be clearly expressed in fluent prose (in whatever the target language may be)? Or is it okay to be more cryptic, for instance, evaluating financials in a way that only accountants might understand or when communicating data related to a business decision?
Not all applications will require high performance across all these areas. Transforming data, or writing code, might not include a “need to communicate” element as the goal is not a natural language response. If, however, the application uses natural language to communicate with customers, it will need to be accurate and easy to understand.
IV. 100 Gen AI Use Cases To Enhance Your Business
There are multiple ways we can organise and categorise Gen AI use cases, focusing on industries, business functions, problems each case solves or technology used. At Devoteam, we look at 3 types of use cases:
- General use cases: These are general applications that can be used in various sectors, such as sales, product development, strategy, procurement, marketing, data management, customer support, and customer experience. As AI becomes more integrated into standard business applications, these general use cases are likely to become commodities in the near future.
- Use cases specific to an industry: These are tailored to the unique needs of a particular industry or a specific role within an industry. Examples include credit risk assessment in banking or research and development in the pharmaceutical or health industries.
- Unique “game-changers”: These are the use cases developed for specific clients. They are highly customised solutions designed to address client’s specific challenges and provide a competitive advantage.
1. General Use Cases
- Business performance reporting & analytics: Generative AI automates report creation and deepens data analysis, providing real-time, personalised insights.
- Content development: Gen AI can help to produce high-quality and diverse content for marketing, advertising, entertainment, education, and other domains. It can for example generate images, write catchy headlines and captions, create podcasts based on text or generate video scripts.
- Customer experience: Generative AI allows businesses to personalise customer experiences by analysing customer data, including past interactions, preferences, and account history. For example, AI can personalise a website’s content in real time, showcasing products that align with users’ interests.
- Customer support: AI is revolutionising customer support by predicting users’ needs, automating routine tasks, and delivering proactive and personalised omnichannel support in real time. AI Chatbots can handle simple inquiries, freeing up human agents to focus on complex issues. AI can also proactively identify potential problems, such as a delayed shipment, and notify customers. See an example project in finance.
- Data augmentation: Businesses can enhance their existing data sets with AI by generating new data points that are similar but not identical to the original ones. This can improve the performance and robustness of machine learning models and algorithms, especially with limited or imbalanced datasets, and protect customer privacy.
- Data anonymisation: Generative AI enhances privacy and security by creating synthetic data that mimics real data without revealing sensitive information. This allows businesses to share and analyse data ethically and legally, for example, by generating synthetic faces, medical records, or text that preserves key characteristics without disclosing personal details.
- Human resources: AI can streamline talent acquisition by analysing candidate profiles and generating job descriptions, interview questions, and onboarding materials. AI can also help upskilling the workforce, identifying skill gaps and recommending training, while AI-powered chatbots can answer employee HR questions.
- Knowledge access: Internal knowledge becomes much more accessible with generative AI tools like enterprise search. Gen AI knowledge access tools analyse internal content in order to precisely answer questions rather than just return documents.
- Information technology: Generative AI can assist in code generation, testing, quality assurance, creating documentation, app development, legacy code conversion, and creating immersive VR/AR environments.
- Marketing: Generative AI helps marketers conduct customer research, analyse data and prepare reports. It can also generate and optimise content, from written to visual and audio content.
- Intake and onboarding: Generative AI can help organisations create a more efficient, engaging, and personalised onboarding experience, leading to improved employee satisfaction, faster time to productivity, and increased retention. It can also help source and engage the right candidates.
- Procurement: Procurement departments can use generative AI to develop vendor negotiation playbooks and contract reviews.
- Product development: Generative AI can revolutionise procurement by synthesising unstructured data (like past supplier interactions) to automate flagging and summarising discussions. It can capture and summarise external data (e.g., supplier news), generate procurement documents (SOWs, RFPs, POs), and improve data processing. Furthermore, AI can automate vendor communication and mitigate risk by analysing contracts and regulatory information.
- Sales: Gen AI can automate lead scoring and prospect research, summarise sales calls, identify key conversation moments, and analyse sales campaign’s effectiveness.
- Scheduling assistance: Generative AI can streamline scheduling by automatically suggesting meeting times based on participant availability and preferences. It can also generate personalised calendar invites and reminders, minimising scheduling conflicts and maximising attendance.
2. Industry-specific Gen AI Use Cases
Generative AI has many potential applications in specific sectors. These examples focus on industry-specific industries, but all of these industries will benefit from the common use cases as well.
Aerospace
- Generative Design: Optimising aircraft component design for weight, aerodynamics, and fuel efficiency.
- Predictive Maintenance: Forecasting equipment failures and scheduling maintenance proactively.
- Simulation & Training: Creating realistic flight simulations and training scenarios.
- Automated Documentation: Generating maintenance manuals and technical reports automatically.
- Route & Schedule Optimisation: Analysing flight data, weather, and traffic to create fuel-efficient routes and schedules.
- Predictive Maintenance: Predicting potential aircraft failures and optimising maintenance.
- Testing with Digital Twins: Digital twins let them test designs and drive performance more quickly, without the need to produce physical models. Discover how you can build Digital Twins with Nvidia.
Education
- Personalised Learning: Creating bespoke learning plans and educational content.
- AI Tutors: Providing individualised feedback and support to students.
- Content Generation: Developing educational materials, quizzes, and assessments.
- Administrative Automation: Automating tasks such as grading and report generation.
Energy:
- Grid Optimisation: Improving energy distribution and minimising waste.
- Predictive Maintenance: Forecasting equipment failures in power plants and grids.
- Resource Exploration: Analysing geological data to identify potential energy sources.
- Renewable Energy Forecasting: Predicting solar and wind energy generation.
- Virtual Power Plants (VPPs) Optimisation: Optimising how VPPs operate, coordinating distributed energy resources like solar panels, batteries, and electric vehicles
Financial Services:
- Fraud Detection: Identifying fraudulent transactions and patterns.
- Credit Scoring: Assigning credit scores to customers based on their financial situations.
- Algorithmic Trading: Automating trading decisions based on market data.
- Risk Management: Assessing and managing financial risks.
- Personalised Financial Advice: Providing tailored investment recommendations.
Healthcare:
- Drug Discovery: Accelerating the development of new drugs and therapies.
- Personalised Medicine: Tailoring treatments to individual patients. Read more on how AI is transforming the medicine of tomorrow.
- Predicting Treatment Response: Predicting how a patient will respond to a particular treatment based on analysing existing data.
- Medical Imaging Analysis: Analysing medical images to detect diseases.
- Chatbots for Patients: Chatbots can improve communication between patients and healthcare providers, making it easier for patients to get the information they need.
- Patient Care Automation: Automating tasks such as appointment scheduling.
Hospitality
- Personalised Travel Recommendations: Providing bespoke recommendations to guests.
- Chatbots: Answering guest queries and providing customer support.
- Dynamic Pricing: Optimising pricing based on demand and occupancy.
- Operational Efficiency: Automating tasks such as check-in/check-out or identity verification.
Logistics
- Route Optimisation: Improving delivery routes and reducing costs.
- Warehouse Management: Automating warehouse operations and inventory management.
- Supply Chain Optimisation: Predicting demand and optimising supply chain logistics.
- Autonomous Vehicles: Developing self-driving trucks and delivery vehicles.
- Personalised Customer Service: Chatbots can provide personalised customer service, answering queries, tracking shipments, and resolving issues efficiently. Read more on AI in customer service here.
Media & Entertainment
- Content Creation: Generating scripts, music, and visual content and podcasts.
- Personalised Recommendations: Recommending films, music, and other content.
- Content Moderation: Automating the moderation of user-generated content.
- Game Development: Creating realistic game environments and characters.
- AI Non-Player Characters (NPCs): Creating more realistic and dynamic NPCs that react to player actions, enhancing the immersion
- Accessibility: Making media more accessible to people with disabilities, for example by generating audio descriptions..
Telecom
- Network Optimisation: Improving network performance and reliability.
- Customer Service: Providing AI-powered chatbots for customer support.
- Adaptive Security: Adapting to evolving security threats and automatically adjusting security measures to protect the network and customer data.
- Predictive Maintenance: Forecasting equipment failures in telecom networks.
- Real-time Fraud Detection: Analysing network traffic and user behaviour to detect fraudulent activities in real-time, such as call hijacking, or identity theft
- Resource Management: Optimising the use of resources, like energy consumption or network bandwidth.
Professional Services
Customer Service & Experience
- AI Chatbots for Customer Service: Provide 24/7 support, answer customer queries, and resolve issues.
- Agent Assist: Provide support agents with relevant information to resolve customer cases more quickly.
- Personalised Customer Interactions: Tailor interactions based on individual customer data and preferences.
- Vocal Customer Support: Enhance customer service by providing vocal assistance.
- Customer Feedback Analysis: Use GenAI to analyse customer feedback to identify areas for improvement.
- Sentiment Analysis: Understand customer emotions in interactions to improve service quality.
- Multilingual Support: Provide customer service in multiple languages using GenAI translation.
- Summarise Customer Interactions: Condense lengthy customer interactions for easier review and follow-up.
Sales & Marketing
- Dynamic Ad Generation: Generate personalised ads based on user behaviour.
- Ad Spend Optimisation: Use ML models to ensure the correct customers see the correct ads at the right time.
- Personalised Product Recommendations: Recommend products to customers based on their preferences and purchase history.
- Product Description Generation: Automatically create product descriptions for websites and marketing materials.
- Marketing Content Creation: Generate various marketing materials like blog posts, social media content, and email campaigns.
- Email Personalisation: Generate personalised email content to improve engagement and conversion rates. For a deeper dive into the methodology and advantages of creating these personalised journeys, read our guide to AI hyper-personalisation.
- Pricing Optimisation: Generate pricing strategies based on market data and customer behaviour.
- Lead Generation: Generate leads based on AI-driven analysis of customer data.
- Sales Script Generation: Create tailored sales scripts for different customer segments
Finance & Legal
- Fraud Detection: Identify fraudulent transactions based on transaction properties.
- Credit Scoring: Assign credit scores to customers based on their financial situations.
- Risk Assessment: Generate risk assessments for various business activities.
- Compliance Monitoring: Use AI to ensure compliance with financial regulations.
- Contract Review and Analysis: Automating the review and analysis of legal contracts.
- Predictive Analytics: Predict the outcome of legal cases based on historical data.
- Legal Document Generation: Automate the creation of legal documents such as contracts and pleadings.
- Due Diligence: Automating the due diligence process for mergers and acquisitions.
Human Resources
- Employee Onboarding: Generate personalised onboarding materials for new employees. Read our guide on AI hyper personalisation.
- Talent Acquisition: Improve the recruitment process by analysing candidate profiles. Create AI-based drafts of job descriptions, suggestions for interview questions, and onboarding material.
- Employee Upskilling: Identify skills gaps and recommend relevant training.
- Enterprise Search: Turn your knowledge base into a chatbot for employees to answer all their HR-related questions.
Public Sector
- Citizen Services: Providing AI-powered chatbots for citizen enquiries or tools for managing and services.
- Fraud Detection: Identifying fraudulent claims and transactions.
- Infrastructure Management: Optimising infrastructure maintenance and operations.
- Automating Administrative Tasks: Automating repetitive administrative tasks, such as data entry, document processing, and report generation.
- Accessibility: Generating different formats of information (text, audio, video), improving accessibility, and designing accessibility policies.
- Policy Analysis: Analysing data to inform policy decisions.
- Public Consultation and Feedback: Analysing public feedback from surveys, social media, and other channels to understand citizen needs.
Retail
- Personalised Recommendations: Recommending products to customers.
- Virtual Try-On: Allowing customers to visualise how products would look on them without physically trying it on.
- Inventory Management: Optimising inventory levels and reducing stockouts.
- Customer Service: Providing AI-powered chatbots for customer support.
- Supply Chain Optimization: Analysing data from various sources to identify bottlenecks and inefficiencies in the supply chain.
- Marketing Automation: Automating marketing campaigns and promotions.
- Product Development: Analysing market trends and customer preferences to generate new product ideas and accelerate product development.
Technology
- Code Generation & Completion: Generating code in various programming languages based on natural language descriptions or prompts.
- Quality Assurance: Generate test cases, identify potential bugs and suggest fixes in code.
- API Design and Documentation: Generatie API specifications and documentation.
- UI/UX Design: Generate UI mockups and prototypes based on natural language descriptions.
- Hardware Design: Generate designs for electronic circuits, chips, and other hardware components.
- Network Optimization: Optimise network performance and security.
- Cybersecurity: Detect and respond to cyber threats and vulnerabilities.
- Data Augmentation & Synthesis: Generating synthetic data to augment existing datasets, improving the performance of ML models and preserving data privacy.
- Infrastructure Optimisation and Management: Analyse infrastructure usage patterns and suggest optimisations for performance, cost, and efficiency.
3. Game-changers
Game-changer use cases are a specific category of AI applications tailored to a client’s unique challenges and needs, offering the potential for significant business impact. These use cases are distinguished from more general, cross-industry applications or those specific to a particular industry or role.
Here’s a breakdown of their characteristics and importance:
- High-Impact Focus: Game-changer use cases are identified for their potential to create substantial positive change within a specific organisation
- Client-Specific: These use cases are not generic solutions; instead, they are custom-made to address the particular problems and opportunities of an individual client.
- Differentiated Solutions: Game-changer use cases aim to provide a competitive advantage. They are designed to be unique and hard for competitors to replicate, setting a company apart in the marketplace.
- Beyond Standard Solutions: Game-changers address very specific problems that might not be covered by off-the-shelf solutions.
Examples of game-changers use cases:
- Generative AI model that helps fight animal diseases: Devoteam helped a client to create robust surveillance and monitoring systems to detect disease outbreaks early, allowing for prompt intervention and control measures.
- Optimising meal planning for a food service company: An AI solution helped a company providing meals to hospitals and schools better manage food expiration dates. By digitising invoices and other data, the AI system optimised meal planning to prioritise ingredients nearing their expiration dates, reducing waste and improving efficiency.
- Summarising data from hotels for Revenue Managers: Devoteam developed a pilot project for a Generative AI solution that uses available past data about the hotel to track performance, identify trends, and make strategic decisions.
V. Not a DIY: Why Do You Need Experts to Identify Gen AI Use Cases
Coming back to the results of Devoteam’s survey, where 36% of businesses stated identifying use cases is a roadblock, we can see the need for expert guidance in the process. AI use case identification is not a DIY process for several key reasons, primarily due to its complexity and a lack of internal AI skills and experience.
Let’s look at some of the most important reasons why businesses need guidance:
- Navigating Complexity: Identifying impactful Gen AI use cases is not straightforward. Many clients struggle to translate their business challenges into concrete AI applications. Expert guidance is needed to navigate the various options and determine which AI solutions align with their specific needs and goals.
- Lack of AI Skills: A significant obstacle is the lack of AI skills within client organisations. This skills gap is not only on the technical side but also in understanding the business implications of different AI applications. Experts can provide the necessary knowledge and experience to bridge this gap.
- Prioritisation and Focus: There are numerous potential Gen AI use cases, but not all will be relevant or impactful for each client. Experts help clients focus on a few high-impact “game-changer” use cases, rather than trying to implement many solutions at once.
- Data Readiness: Experts understand that AI projects are closely linked to data availability and maturity. They assess whether clients have the necessary data for specific use cases and guide them on how to prepare their data if needed.
- Advisory Role: Experts provide an advisory role that involves guiding companies through the entire process, from identifying problems to tracking the impact of implemented solutions.
- Staying Ahead of the Curve: The field of AI is rapidly evolving, with new applications and technologies emerging constantly. Experts stay informed on these advancements, allowing them to advise clients on the latest and most effective solutions. This includes helping them understand the difference between general, “commodity” AI use cases and more tailored, high-impact solutions.
Let Devoteam Light Up Your AI Journey!
Devoteam offers a structured approach, guiding clients from initial challenges to concrete implementations. The methodology is rooted in understanding each client’s specific business context and industry, engaging with board members and analysing the industry landscape to align AI initiatives with strategic priorities. Moreover, with over 1,000 AI consultants and 300+ successful AI projects, Devoteam has the experience and knowledge to deliver impactful results.
Do you want to find tailored, high-impact Gen AI use cases that will change your business? Contact us and start your AI journey!
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