Unlock AI's Potential: A Practical Guide to Identifying and Scaling Use Cases

Successfully integrating AI into an organization hinges on three fundamental principles. Firstly, AI initiatives should be driven and encouraged by leadership, signaling its strategic importance. Secondly, while complex use cases might seem impressive, they can often slow down progress. Empowering employees to identify the most practical and beneficial use cases for their specific roles and teams is a more effective path to rapid success. Catalysts for adoption, such as hackathons, use case workshops, and peer-led learning sessions, have proven crucial for many clients. Following these principles provides a solid foundation for systematically gathering and implementing AI use cases.

Once a framework for identifying new AI opportunities is established, the next crucial step is to train teams on the fundamental methods of leveraging AI. Analysis of over 600 customer use cases reveals that most fall into one of six 'foundational archetypes' – universal types of use cases applicable across all departments and domains. These foundational elements serve as shortcuts for employees to quickly identify the most promising business use cases, representing hundreds of scenarios observed across various industries, roles, and workflows. Each archetype offers a direct path to scalable value. We will now explore each of these foundational elements, beginning with content creation.

AI is extensively used for research across industries. This ranges from quickly learning new concepts like AI adoption or design thinking, to searching for articles or competitor information online, and undertaking more comprehensive multi-step research projects that scan articles, data points, and insights from the web. Teams also upload lengthy internal documents to quickly extract key insights. A major advantage of using AI for research is the ability to specify the format and structure of the analysis results, such as tables, bullet points, summarized paragraphs, or cross-references. AI's attention to detail and ability to follow instructions make it an excellent research assistant. Research use cases to get started with include: * **Sales & Marketing:** Exploring new industries, gaining deeper insights into competitors, and researching new audiences. * **Finance:** Searching for public companies, M&A targets, or articles and guidance on accounting standards. * **Product:** Evaluating new market sizes, researching competitors, identifying trends, and analyzing user feedback. * **Sales Teams:** Searching the web for new suppliers and evaluating the pros and cons of their products. * **Software Engineering:** Reviewing API endpoints and external documentation. Introducing Deep Research, a new intelligent agent feature in ChatGPT, allows for multi-step research on the internet independently. With a prompt, ChatGPT can search, analyze, and synthesize hundreds of online sources to generate a comprehensive report at the level of a research analyst.

AI empowers anyone to consolidate data from various sources, uncover insights and trends, and process complex spreadsheet data without advanced Excel, SQL, or Python skills. You can provide AI with multiple spreadsheets or dashboard screenshots for rapid analysis. It can interpret spreadsheet data, understand visualizations, and even assist in formatting outputs for reports. You can also guide the presentation of results, specifying preferred chart types, summary formats, or comparison logic. Entry-level data analysis use cases include: * **Marketing:** Uploading webinar attendee data and quickly visualizing it; summarizing key trends from dashboard screenshots. * **Product:** Analyzing trends, social media feedback, or uploading feature requests from CRM data to identify new opportunities. * **Sales:** Reviewing your account list to identify top-performing accounts; mapping prospects to accounts and scoring them based on intent signals. * **Finance:** Quickly analyzing spending data and identifying trends, or consolidating data from different spreadsheets and databases. Poshmark uses ChatGPT to generate Python code for analyzing millions of spreadsheet rows for business performance analysis. They then use AI to generate weekly performance reports and accounting memos for executives, saving hours of manual work each week. As Rodrigo Brumana, CFO of Poshmark, states, 'We’ve drastically reduced manual work and increased speed, accuracy, communication, and insight. I see everyone’s work improving.'

Many use cases involve partially automating tasks. Clients identify repetitive, routine tasks and design methods for AI to handle them. Automation processes can be simple, such as generating weekly competitor updates, or more complex, like creating financial reports for weekly executive briefings that are available for human review. Memory and custom instructions are key to automating these types of processes, and custom GPTs are a way to share them. By establishing a standard set of instructions, uploading the same documents, and specifying the same outputs each time, teams can offload lower-value tasks. While these automated processes are often standalone tasks today, with the introduction of products like Deep Research and Action Agents, we are moving towards a future where AI can undertake multi-step tasks independently and on a schedule. Automation use cases to get started with include: * **Marketing:** Creating standard reports and visualizations for quick webinar takeaways; or generating Slack updates from meeting notes or transcripts. * **Product:** Building a release update summarizer; or summarizing and sharing weekly customer insights; organizing meeting notes into Slack posts for executives, summarizing dependencies and next actions. * **Finance:** Translating weekly financial data into executive summaries and providing alerts for changes that require attention. * **IT:** Providing your software architecture as uploaded screenshots and requesting an analysis of key dependencies, risks, and opportunities for optimization. BBVA has automated parts of its credit analysis work with its Credit Analysis Pro GPT, which helps credit risk analysts extract unstructured data from various sources like annual reports, ESG assessments, and press releases, accelerating their evaluations.

As teams become adept at identifying problems and potential AI solutions, the volume of use cases often grows rapidly. The challenge then shifts from exploration to prioritization: which use cases can scale to impact all employees? Which are most likely to yield cost efficiencies now? Which might lead to new products or revenue streams? The Impact/Effort framework, a simple quadrant that scores use cases based on their value to the company and the effort required, is a valuable tool for this. Categories within this framework include: * **High ROI Focus (High Impact, Low Effort):** These are quick wins and excellent starting points for building momentum. * **Self-Service (Low Impact, Low Effort):** Individual users can create solutions for specific tasks, often starting as individual solutions but growing into valuable assets for teams. * **High Value/High Effort (Transformative):** These are often transformative (e.g., Moderna's Dose GPT or Klarna's customer assistant) but require significant time, planning, and resources. Many teams start with quick wins to build momentum before investing in these. * **High Effort/Low Impact:** These can be safely set aside for now but may become easier to build and deploy with new products and features. Evaluating and prioritizing AI use cases this way helps accelerate significant outcomes and further fuels interest and investment. Jeret Shuck from Softbank shared how he uses this simple yet powerful tool. Notably, 62% of AI's value is realized within core business functions.

AI is not traditional software or a cloud application; leveraging its advantages requires a new mindset. However, experience with clients shows that people across all fields can quickly learn this mindset and begin discovering high-impact use cases in their work. To initiate this process, organizations must help their teams take three key steps: 1. **Understand Where AI Creates Value:** Identify the areas within your business that can immediately benefit from AI. 2. **Teach Foundational Use Cases:** Help teams explore basic use cases and begin building their own. 3. **Prioritize What to Scale:** Use the Impact/Effort framework to focus on high-impact, low-effort opportunities. The more individuals who redesign their tasks and processes with AI, the more opportunities will be uncovered. This guide aims to provide a clear starting point for your team. OpenAI is committed to supporting you as you transform ideas into outcomes. As Stéphane Bancel, CEO of Moderna, states, 'We are looking at every business process—from legal, R&D, manufacturing, to commercial—and thinking about how AI can redesign those processes.' If you're interested in bringing AI into your business, explore how OpenAI can help build scalable and responsible AI strategies.