Guides, templates, and tools to plan, build, and scale AI—no fluff.
A practical, fillable playbook designed to help small and medium-sized business owners identify, document, and automate repetitive processes using AI. It provides structured sections for defining goals, mapping steps, and analyzing automation opportunities—empowering leaders to build efficiency into every workflow.
A compelling case study detailing how Levi Strauss & Co. modernized its global supply chain using AI and advanced analytics. It showcases measurable results—doubling forecasting accuracy, reducing inventory costs, and improving stock availability—while emphasizing leadership, data quality, and cultural alignment. A must-read for business owners seeking to streamline operations and boost profitability through smart automation.
Discover how Sport Clips Haircuts used AI to revolutionize workforce scheduling and customer experience across 1,800 locations. This case study shows how predictive staffing and data-driven forecasting cut wait times by 27% and reduced labor cost variance by 11%, proving that AI can deliver scalable consistency and profitability for service-based businesses.
Explore how Georgia Tech’s Online Master of Science in Computer Science program transformed student support with “Jill Watson,” an AI-powered teaching assistant. Trained on 40,000 forum posts, Jill Watson provided 24/7 student assistance, achieved up to 97% accuracy, and enhanced teaching presence while freeing human TAs for higher-value interactions. This case study demonstrates how AI can scale education without sacrificing quality or human connection.
This playbook from Conefer, Inc. reveals how AI is transforming education by reducing administrative burden, accelerating feedback, and increasing engagement. Learn how teachers, administrators, and education entrepreneurs can reclaim 40% of their time through automation workflows for lesson planning, grading, student engagement, and professional development — without losing the human connection that defines great teaching.
This Conefer, Inc. playbook reveals how law firms, accountants, consultants, and other professional practices can save up to 40% of their time through automation. Discover proven workflows for document preparation, client intake, billing, and compliance that increase accuracy, improve throughput, and reduce risk — all while preserving the professional judgment and client trust that define top-tier service.
This Conefer, Inc. playbook helps retail and e-commerce leaders harness AI to streamline operations and elevate the customer experience. Learn how automation can reduce restock delays, optimize pricing, personalize marketing, and improve service response times — all while protecting margins and freeing your team to focus on high-value sales and customer relationships.
This Conefer, Inc. playbook helps local service providers — from HVAC and plumbing to cleaning, landscaping, and salons — streamline their operations with AI-powered systems. Discover how automation can reduce missed appointments, optimize scheduling, improve billing, and build stronger customer relationships. Learn step-by-step workflows that transform daily chaos into predictable, scalable systems that boost efficiency by up to 40%.
This case study showcases how professionals across industries are using ChatGPT to boost creativity, productivity, and efficiency — without needing technical expertise. From automating emails to writing marketing content and managing daily operations, these 20 real-world examples reveal practical ways business owners can use AI to save time and get better results. It’s an ideal introduction for small business owners exploring how AI can fit into their workflows.
This case study explores Amazon’s journey from traditional recommendation systems like collaborative filtering and random-walk models to neural embedding-based techniques. Using real Amazon co-purchase data, the research demonstrates how neural embeddings capture product relationships more effectively in sparse datasets — leading to smarter, more relevant product suggestions. The analysis reveals that while traditional models struggle with scalability and sparse data, neural embeddings excel by encoding hidden relationships between products. This approach offers valuable insight into how AI can enhance personalization and drive e-commerce success.
This case study examines how Amazon leverages big data to understand consumer behavior and create personalized experiences. By analyzing billions of data points — including purchase history, browsing habits, and search trends — Amazon identifies what customers want and when they want it. The study illustrates how data-driven decision-making enables precise recommendations, optimized marketing strategies, and improved customer retention. It’s a clear example of how businesses of any size can apply data insights to anticipate customer needs and improve performance.
This case study explores the story of IBM Watson Health — an ambitious effort to revolutionize healthcare through artificial intelligence. It covers Watson’s early hype, the technical and organizational obstacles that led to its decline, and its subsequent strategic restructuring. The lessons highlight the importance of aligning AI innovation with realistic expectations, data readiness, and human expertise. For business leaders, it’s a compelling look at how to recover from overreach and rebuild trust in AI-driven transformation.
This case study explores how AI-driven recommendation systems, such as Amazon’s, shape what consumers see and buy online. Researchers tested how search patterns influenced the results users received — finding that once algorithms learned specific keyword behaviors, they showed increasingly narrow product suggestions. This “anchor effect” demonstrates how personalization can both speed up decision-making and unintentionally limit consumer choice. For business owners, it’s a valuable look at how recommendation systems work and how transparency can build consumer trust.
This case study explores how retailers are using artificial intelligence to deliver highly personalized customer experiences. By analyzing consumer data — from browsing behavior to purchase history — AI systems tailor recommendations, promotions, and product assortments in real time. The results include increased engagement, improved conversion rates, and more efficient inventory management. For business owners, it offers a clear example of how AI-driven personalization can turn customer data into actionable insights and competitive advantage.
This case study highlights how a global manufacturing leader leveraged AI to modernize its operations and culture. Faced with data silos, outdated materials, and resistance to change, the company used machine learning to compare materials, improve product design, and predict equipment maintenance needs. By shifting from preventive to predictive maintenance, it reduced waste and unlocked new efficiency and revenue opportunities. The real success came from training leaders to understand AI’s business potential and starting with small, high-impact wins that built organizational momentum.
This case study features a major U.S. convenience retail chain that used AI-powered computer vision to transform customer experience. By analyzing real-time camera data with tools like TensorFlow, OpenCV, and Amazon SageMaker, the company gained deep insights into shopper behavior and store traffic. These insights informed store layouts, optimized product placement, and personalized loyalty programs — boosting customer satisfaction by 40% and increasing revenue per location by 28%. The project demonstrates how retail businesses can use AI to create smarter, more responsive shopping environments.
This collection of case studies illustrates how companies in various sectors are using artificial intelligence and generative AI to drive measurable transformation. From automating marketing workflows to optimizing supply chains and enhancing customer experience, each example demonstrates practical results and lessons learned. For business owners, it offers an inspiring yet realistic view of how AI can unlock efficiency, creativity, and competitive advantage — no coding expertise required.
This case study explores how Walmart leveraged artificial intelligence and advanced data analytics to revolutionize its operations. By using predictive models and real-time analytics, Walmart improved supply chain accuracy, optimized inventory, and reduced waste. The company also enhanced customer experience through dynamic pricing and personalized recommendations. For business owners, it demonstrates how data-driven decisions and automation can create competitive advantage and operational resilience at any scale.
This case study examines how Microsoft Copilot was designed and evaluated to support everyday work tasks through AI assistance. It explores how integrating generative AI into familiar tools streamlines writing, analysis, and decision-making. The study highlights improvements in user satisfaction, task completion speed, and overall workflow efficiency. For business owners, it provides insight into how AI copilots can augment staff performance, reduce routine workload, and free up time for higher-value tasks.
This case study explores how no-code platforms empower teams to prototype AI-driven products quickly while maintaining a human-centered focus. It outlines methods for balancing usability, ethical design, and business impact in AI development. By combining empathy-driven research with accessible no-code tools, organizations can reduce barriers to experimentation and shorten time-to-value. For business owners, this demonstrates how to start integrating AI into products or services without heavy technical investment.
This paper outlines how no-code machine learning platforms enable users to develop and deploy AI models without programming expertise. It covers key concepts in automation, data preparation, and model evaluation while demonstrating real-world applications across industries. For business leaders, it showcases how no-code ML tools reduce the barrier to entry for AI, allowing faster experimentation and better decision-making without relying on dedicated data science teams.
This paper explores how low-code and no-code development platforms are evolving with the integration of generative AI. It highlights how these technologies simplify app creation, automate repetitive tasks, and enable faster experimentation without traditional coding skills. The combination of AI and no-code tools empowers businesses to prototype ideas, streamline operations, and respond quickly to market demands. For business owners, it illustrates how innovation is becoming more accessible — allowing teams to focus on strategy and creativity rather than technical complexity.
This case study details the journey of a London-based startup that partnered with experts to build a no-code AI platform enabling business users to create and deploy AI models effortlessly. Through strategic collaboration, agile development, and thoughtful UX design, the idea rapidly evolved into a minimum viable product (MVP) that secured funding and went live within weeks. The platform now allows organizations to harness AI without technical expertise, demonstrating how partnership and accessible technology can accelerate innovation and unlock business potential
This document outlines the State of New Mexico’s official AI governance framework, focusing on transparency, ethical standards, data protection, and human oversight. It serves as a practical example for business leaders seeking to understand how responsible AI principles can guide their own adoption strategies. The guidance promotes building AI systems that are fair, explainable, and trustworthy — offering a real-world roadmap for ethical innovation that small and growing businesses can model.
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