AI Use Cases for Business Success

Over the last couple years, I’ve been working on incorporating AI tools into my personal workflow. One of the fun questions I enjoy asking other entrepreneurs is how they’ve integrated AI into their own lives and businesses. This has provided me with a variety of ideas and use cases to experiment with and explore. Today, I want to share the three most recent ways I’ve used AI. 1. Preparing for a Board Meeting Last week, I attended a board meeting with 30 other board members. Prior to the meeting, we received a PDF containing the board agenda, a list of attendees, and governance items. I uploaded this PDF to ChatGPT and Grok, asking each tool to extract all the names and companies listed, then search the web for two to three news items or recent events about each person or company. Within a couple of minutes, they generated a bullet-point list of all the attendees, their companies, and relevant recent news. As a result, I entered the board meeting much better prepared, with a variety of topics to discuss during our dinner session. 2. Analyzing a Potential Geographic Expansion One of our companies is researching the next city to expand into, based on our demographic profile and the most common types of users. I fed the details into ChatGPT and Grok, explaining the situation: “We’re considering City X. Our typical users are these types of people, in these types of industries, with these characteristics. Analyze the most prominent firms in those industries in that city, identify the neighborhoods where they’re based, and rank the top 10 neighborhoods based on these parameters.” A few minutes later, I received detailed reports with citations and a strong recommendation for the best geographic location. 3. Understanding Neighborhood Dynamics in South Downtown Atlanta We wanted to analyze the neighborhood dynamics around residential units and retail use in South Downtown Atlanta. I explained the context to the AI—our neighborhood redevelopment goals, our desire to create a large, innovation-focused district for entrepreneurs and startups, and how residential units might influence the area. I asked: “How would residential units for people living in the neighborhood impact demand for restaurants and retailers?” The AI instantly returned a retail demand equation, calculated based on the number of proposed residential units. It included a range of retail square footage demand and an estimated annual spend per resident at local establishments. In moments, I had a framework and example data to work with. These three recent examples really highlight the power of having a thoughtful, personal assistant that can perform deep dives almost instantly. The possibilities for use cases seem endless. I’m committed to further incorporating AI into my daily life and actively seeking out more applications. For me, these examples underscore the value of having instant research and analysis always on demand.

Sam Altman, the co-founder and CEO of OpenAI, made a provocative statement at a JP Morgan conference earlier this year. He believes a solo founder will soon reach a billion-dollar valuation without hiring a single employee. This one-person company would instead be powered by AI and “employ” dozens of AI agents to do the work. Not only do I believe this is entirely possible, but I think when it does happen, the company will be one of the fastest-growing unicorns ever. As I invest in AI-powered startups and teach my students how to use AI in their businesses, I have identified 5 general AI use cases that align with critical phases of the startup journey: 1. Research-Driven Ideation: The genesis of any successful startup is a deep understanding of market needs, pain points, and the competitive landscape. My colleague Scott Brady of Stanford calls this process Research-Driven Ideation (RDI). There are now AI-based tools for competitive analysts, automating competitive monitoring for senior managers—effectively Google Alerts on steroids, tracking personnel changes, marketing launches, traffic, and other publicly available data. 2. Customer Persona Development and Market Research: Understanding your target customer is crucial. Gen AI helps founders create multiple hyper-specific customer personas by analyzing customer data and building hyper-realistic, "living" customer personas to test key hypotheses quickly. 3. Experimentation and Validation: Gen AI facilitates rapid experimentation to validate key hypotheses such as CVP, GTM, and PF by enabling deeper business data insights and rapid prototyping. I have a founder friend who lost his technical cofounder and has been using ChatGPT to build his MVP. By learning to be more effective at writing prompts to generate the desired code output, he has been able to continue building as a solo founder. He told me, “The result is that my burn rate is incredibly low, and velocity has shot through the roof.” 4. Marketing and Customer Engagement: Founders will see major productivity boosts in marketing, community building, and sales prospecting. Flybridge has a portfolio company that builds super smart AI agents that can be used for just about anything. One of their customers trained their agent to automatically generate customized sales collateral and follow-up materials based on customer needs that a sales representative inputs into the system after a prospect call—and then the AI agent sends that tailored material to the customer. 5. Continuous Learning and Iteration: The path to PMF is iterative. Gen AI supports continuous learning by analyzing customer feedback and product usage data to improve their product, GTM, and onboarding processes quickly. How are you using AI to build your startup?

JPMorganChase has already deployed 400+ AI use cases, according to Jamie Dimon's latest shareholder letter. That shows the momentum many companies are still aiming for. AI has moved beyond testing. It’s now part of core operations and reshaping how top organizations run. In recent client conversations, I keep getting asked: “Where can I find more AI use cases for inspiration?” So I curated this list of 12 directories that features examples that help teams automate and solve real business problems across industries, from finance and healthcare to logistics and manufacturing. Together, they showcase more than 2,265 practical use cases, from legal ops and customer service to supply chains, compliance, sales and marketing. 📚 The list (in alphabetical order): 1) Amazon – GenAI Customer Stories (280+ use cases) https://lnkd.in/g-GzGaUD 2) Capgemini – Harnessing GenAI Potential (54) https://lnkd.in/gmCuy6i8 3) Deloitte – GenAI Dossier (73) https://lnkd.in/gzjwNz4F 4) EY – AI Use Cases Suite (15) https://lnkd.in/gn4eZnq8 5) Google – 601 Real-World GenAI Use Cases https://lnkd.in/g_Cx7eD3 6) IBM – The Most Valuable AI Use Cases (27) https://lnkd.in/ghy8bcNf 7) Intel Corporation – AI Applications Across Industries (35) https://lnkd.in/gm9hVV2f 8) McKinsey & Company – GenAI in TMT (63+) https://lnkd.in/gaBb6ydz 9) Microsoft – 700+ AI Customer Stories (use cases) https://lnkd.in/gFD2cUhf 10) Oracle – GenAI for Enterprise Apps (17) https://lnkd.in/gTBfEkZP 11) PwC – Applied AI Compass (200+) https://lnkd.in/gyUjZEtQ 12) SAP – AI Use Cases by Department (200) https://lnkd.in/g43WR--i How Tech Leaders Can Use These Directories: 1️⃣ Prioritize what’s proven: Start with repeatable use cases that have scaled in your industry. 2️⃣ Align use cases with business goals: Map examples to your OKRs, not just your tech roadmap. 3️⃣ Use them to shape your GenAI backlog. Turn inspiration into action by feeding use cases into your AI delivery pipeline. 💡 For startup founders: Use these directories to validate product ideas, identify whitespace, or benchmark against how enterprise teams are solving similar problems. If this sparked an idea, save it or pass it along. Appreciate a repost or tag when you share it! I will be sharing more on Agile AI adoption, automation blueprints, and use cases. Follow for the next drop. #GenAI #EnterpriseAI #Startups

This is a gem of a case study about how to apply AI across a business. Singapore Airlines is partnering with OpenAI to apply AI to its business in the following ways, reports A'bidah Zaid Shirbeeni in MARKETING-INTERACTIVE: 1. Personalize the airline’s virtual assistant to intuitively plan personalized travel and offer customers self-service options. Business Benefits:  ✅ Self-service delivers higher revenue impact than the flight recommendation chatbot ✅ Intuition (read: ChatGPT’s new memory) and personalization promote customer engagement 2. Create an internal AI assistant to guide employees on operations and automate routine tasks. Business Benefits:  ✅ Faster decision-making when time is critical ✅ The assistant applies learnings from past issue resolutions and support solves to answer current questions 3. Integrate ChatGPT with operations tools to crunch out complex workflows such as scheduling flight crews while referencing applicable regulatory guidelines. Business Benefits:  ✅ Optimizes planning ✅ Streamlines operations WHY THIS MATTERS: Singapore Airlines’ idea of an “AI-first customer journey” shifts the lens from thinking about AI-first companies toward using LLMs to build better customer experiences. That’s a powerful shift. This is applied AI at its finest - to build better customer experiences. What ideas spring to mind when you think about AI-first customer experiences at your company? ✨ Conversational AI imperatives from Chatbot Europe: https://lnkd.in/edxvM8d3 #ai #cx #ux #chatbot #appliedai #marketing Image credit: MARKETING-INTERACTIVE

The AI hype cycle is over. Now it’s time for real business value. Organizations spent the last year experimenting with AI tools, often with mixed results. Those who succeeded found that strategic integration is what drives ROI. Here's 11 ways top performers are achieving measurable ROI on their AI investment: 1. Process Automation Integration → Embed AI in existing workflows → 40-60% reduction in manual tasks → Focus on high-volume, repetitive processes Pro tip: Start with processes that have clear metrics and high error rates. 2. Customer Service Enhancement → AI-powered ticket routing and resolution → 30% reduction in response time → Improved customer satisfaction scores Pro tip: Train AI on your top performers' responses to maintain brand voice and solution quality. 3. Data Analytics Acceleration → Automated insight generation → Predictive modeling at scale → 50% faster decision-making cycles Pro tip: Build dashboards that translate AI insights into actionable recommendations for non-technical teams. 4. Revenue Generation → AI-enhanced lead scoring → Personalized customer journeys → 25% increase in conversion rates Pro tip: Use A/B testing to continuously refine AI models against actual sales outcomes. 5. Cost Optimization → Smart resource allocation → Predictive maintenance → 20-30% reduction in operational costs Pro tip: Create an AI savings tracker to document and communicate wins to stakeholders. 6. Product Development → AI-driven feature prioritization → Automated testing and QA → 40% faster time-to-market Pro tip: Implement AI feedback loops between customer support and product teams for continuous improvement. 7. Risk Management → Real-time fraud detection → Compliance monitoring → 65% reduction in false positives Pro tip: Regular model retraining with new fraud patterns keeps detection rates high. 8. Employee Productivity → AI-powered knowledge management → Automated routine tasks → 3-4 hours saved per employee weekly Pro tip: Create AI champions in each department to drive adoption and share best practices. 9. Supply Chain Optimization → Demand forecasting → Inventory management → 30% reduction in stockouts Pro tip: Combine internal data with external factors (weather, events, trends) for better predictions. 10. Content Creation → Automated first drafts → Multichannel optimization → 60% faster content production Pro tip: Build a prompt library of your best-performing content formats and styles. 11. Quality Control → Computer vision inspection → Defect prediction → 45% reduction in quality issues Pro tip: Start with human-in-the-loop systems before moving to full automation. The key? Integration. Success comes from embedding AI into core business processes, not treating it as a standalone solution. What's your organization's biggest AI ROI win? Share below 👇 ♻️ Repost if your network needs this AI implementation blueprint. Follow Carolyn Healey for more content like this.

AI isn’t the Future of Sales — it’s the New Competitive Battlefield. The playbook I used to finish as the #1 Enterprise AE at Salesforce just became MUCH faster and easier to execute using AI. Here are my top use cases: 1. Account Research: As an Enterprise AE, it's essential to understand what your customers do, how they make money, and learn about their top goals and priorities so you can align your solutions to their key initiatives. This has been, and still is, my most effective strategy to book meetings with Senior Executives. In the past I would have to manually find, read, and extract account details from various sources across the web, set up google alerts, read news reports, and comb through long financial documents like 10K's and proxy statements (DEF 14A) to find this information. This often took me hours upon hours, and was very tedious work. Now with the right prompts, I can use AI to quickly search for the top priorities and initiatives of my prospects, learn more about their business model and company structure, and import the content of key financial documents to extract data points that will help me develop a tailored, impactful POV. 2. Individual Research In additional to account research, I would spend extensive time researching the priorities of key executives. The gold was often buried in their keynote speeches, podcast interviews, and articles featuring their work. Now I can easily import the transcripts of these interviews to find key discussion points which I can directly support with my solutions. 3. POV development One of the key strategies I teach is linkage, which is the direct connection between their priorities and your solutions. Developing a strong POV which hits the mark with execs is extremely challenging and this "deep work" separates good sellers from elite sellers. With AI, you can marry the key priorities of a company with your solutions, and develop a compelling POV quickly. It's usually necessary to iterate a few times, so that the POV is very specific, addresses existing pain, and has a strong financial impact. As long as you know what a good POV looks like, AI can get you 75% of the way there and you can refine the rest 4. Executive Messaging Writing effective e-mails outlining your POV can often take a long time, especially if you're a perfectionist or have imposter syndrome. AI can help develop the 75% baseline of your message, and you can use your knowledge and skills to refine the other 25% so it sounds like you. These are a just a few of my favorites use cases for AI. If you want to learn how to use AI to drive massive sales growth in 2025, check out the AI-Led Growth conference next week. Register below: https://lnkd.in/gZCx8Qz9

It all begins with a question. I asked our Google Cloud Consulting team... 1.) How are they using AI to amplify their unique strengths and passions in their daily work, and 2.) ideas on what we could develop using AI to free them up to focus on the aspects of their jobs they are best equipped for—and truly enjoy doing. 🌟 The response has blown me away! 🤯 In just a few weeks, the team has shared over 100 ways they are already using Google AI to enhance their unique contributions, and close to 100 ideas on how we can develop AI solutions to remove repetitive or less fulfilling tasks, enabling them to double down on the work that excites them and delivers the most value to our customers. 🚀 Here are the top 10 use cases—curated with insights from Gemini, of course—that highlight how AI can help our team do more of what they love and excel at. Click the … in the upper right hand corner to save this post for your own teams AI journey. 1. Document Analysis and Generation: AI is heavily used for analyzing and generating various documents, including Statements of Work (SOWs), Product Requirements Documents (PRDs), contracts, proposals, and reports. 2. Meeting Enhancement and Summarization: Gemini is utilized to transcribe meetings, summarize discussions, extract action items, and generate structured reports from meeting content. 3. Code Generation and Review: AI assists in generating code snippets, skeleton scripts, and even complete applications based on provided requirements. 4. Customer Insights and Analysis: NotebookLM and other AI tools are used to analyze data, including earnings reports, public information, and meeting notes, to identify growth opportunities and assess customer health. 5. Knowledge Management and Retrieval: AI facilitates the organization and retrieval of information from various sources, such as internal documentation, playbooks, and research claims. 6. Proposal and RFP Response Automation: AI streamlines the process of responding to RFPs by generating proposals, scoping questions, and highlighting risks from provided inputs. 7. Content Creation and Communication: AI is used to generate various types of content, including blog posts, newsletters, learning materials, and presentations. 8. Learning and Development: AI supports learning and development by creating study guides, generating personalized learning paths, and providing answers to curriculum questions. 9. Automation of Repetitive Tasks: Tools like Botz are used to automate repetitive tasks, such as email drafting, report creation, and data analysis, improving overall efficiency. 10. Enhanced Data Analysis and Visualization: AI tools are used to generate custom dashboards, perform large-scale data analysis, and translate spreadsheet data into presentations. 📸 A glimpse into our North America Summit with our customer success team talking about AI4Us

Cutting through the AI noise - here are 5 use cases for using generative AI today in a law practice: 1) Having AI draft initial responses to standard discovery requests, pulling directly from client documents and past cases—turning 3 hours of document review into 20 minutes of attorney verification. 2) Using AI to analyze deposition transcripts and build detailed witness chronologies, flagging inconsistencies and potential credibility issues that could be crucial at trial. 3) Feeding settlement agreements from similar cases to AI to generate initial settlement terms, helping attorneys start negotiations with data-backed proposals rather than gut instinct. 4) Having AI review client intake forms and past matters to spot potential conflicts of interest—moving beyond simple name matching to identify subtle relationship patterns. 5) Using AI to draft routine motions and pleadings by learning from the firm's document history, maintaining consistent arguments while adapting to case-specific facts. The real value isn't replacing attorney judgment. It's eliminating the mechanical tasks that keep great lawyers from doing their best work. What specific AI applications are you seeing succeed (or fail) in your practice? #legaltech #innovation #law #business #learning

Last evening was one of those “this is why we do it” moments. We hosted a Happy Hour with over 20 business owners, partners, and friends. The energy in the room was honest, curious, and forward-looking. Thank you Bob Chiumento for putting this together. I shared practical AI tools that small businesses can start using right away. But the real value? I walked away with insights of my own. Some takeaways worth sharing:  ↳ Dr. Tina Scott, uses an AI chatbot that answers customer queries with clarity. She’s also experimenting with an AI Twin for her video content... making her expertise scalable and her website 'Smart'.  ↳ Karen Moffitt is using AI to brainstorm, research and test new recipes for her baking business. If you haven't yet, you got to try her awesome baked goods. Check out littlemissmoffittbaker.com.  ↳Arijit Banerjee, a serial entrepreneur and owner of multiple businesses, shared how AI helped him launch a new landscaping venture… streamlining setup, curating products, building inventory, and saving significantly on startup costs. What stood out: People aren’t waiting for AI to arrive. They’re already integrating it into their workflows and rethinking what’s possible. The pace of change is fast. The willingness to adapt is even faster. So I’ll leave you with this: How are you using AI in your business, or planning to in the months ahead? Would love to hear what’s working for you, and what you’re still figuring out. #AIforBusiness #Entrepreneurship #PracticalAI #SmallBusinessGrowth #LearningTogether #DigitalTools #BusinessConversations

Not surprisingly, at Mayfield Fund we are seeing a big wave of Gen AI applications; below are 5 use case themes emerging: 1. Content Generation: LLMs producing custom content for marketing, sales, and customer success, and also create multimedia for television, movies, games, and more. 2. Knowledge CoPilots: Offering on-demand expertise for better decision-making, LLMs act as the frontline for customer questions, aiding in knowledge navigation and synthesizing vast information swiftly. 3. Coding CoPilots: More than just interpretation, LLMs generate, refactor, and translate code. This optimizes tasks such as mainframe migration and comprehensive documentation drafting. 4. Coaching CoPilots: Real-time coaching ensuring decision accuracy, post-activity feedback from past interactions, and continuous actionable insights during tasks. 5. RPA Autopilots: LLM-driven robotic process automation that can automate entire job roles. What else are we missing?