https://blog.itsdavidg.co/categories/ai-implementation/Recent content in AI Implementation on David Gomez - Technology & Business InsightsHugo -- 0.146.5en-usMon, 02 Mar 2026 08:00:00 -0500https://blog.itsdavidg.co/posts/chatgpt_enterprise/Mon, 02 Mar 2026 08:00:00 -0500https://blog.itsdavidg.co/posts/chatgpt_enterprise/<h2 id="introduction-the-generative-ai-transformation-has-arrived">Introduction: The Generative AI Transformation Has Arrived</h2> <p>Enterprise adoption of generative AI has accelerated at a pace unprecedented in technology history. According to McKinsey&rsquo;s 2025 State of AI Report, 72% of organizations have deployed generative AI in at least one business function, up from just 33% in 2023. More significantly, 42% report that generative AI has reduced costs, while 59% have measured revenue increases directly attributable to AI implementation.</p> <p>ChatGPT Enterprise, launched in late 2023 and continuously enhanced through 2025, has emerged as a leading platform for business generative AI deployment. With features including enterprise-grade security, admin controls, and advanced data analysis capabilities, the platform has been adopted by over 80% of Fortune 500 companies according to OpenAI&rsquo;s 2026 business update.</p>https://blog.itsdavidg.co/posts/mlops/Sun, 15 Feb 2026 08:00:00 -0500https://blog.itsdavidg.co/posts/mlops/<h2 id="introduction-bridging-the-gap-between-ml-research-and-production">Introduction: Bridging the Gap Between ML Research and Production</h2> <p>The machine learning landscape has reached an inflection point. According to Gartner&rsquo;s 2025 Hype Cycle for AI, while 85% of organizations have initiated ML projects, only 21% have successfully deployed models to production at scale. The gap between experimental success and operational deployment—often called the &ldquo;ML production gap&rdquo;—represents one of the most significant challenges facing AI-driven organizations.</p> <p>Machine Learning Operations (MLOps) has emerged as the discipline addressing this challenge. Drawing from DevOps principles while addressing ML-specific concerns like data versioning, model drift, and experiment tracking, MLOps provides the practices, tools, and cultural foundations for reliable ML systems in production.</p>https://blog.itsdavidg.co/posts/ai_automation/Tue, 10 Feb 2026 08:00:00 -0500https://blog.itsdavidg.co/posts/ai_automation/<h2 id="introduction-the-new-era-of-intelligent-automation">Introduction: The New Era of Intelligent Automation</h2> <p>The automation landscape has undergone a revolutionary transformation. What began as simple rule-based robotic process automation (RPA) has evolved into sophisticated AI-powered intelligent automation capable of understanding context, making decisions, and handling exceptions autonomously. According to Gartner&rsquo;s 2025 Strategic Technology Trends, 80% of organizations have adopted intelligent automation technologies, with 45% now using AI-augmented automation tools that combine RPA, machine learning, and natural language processing.</p>