Bodaty Blog

  Why a Fortune 500 Company Killed Their AI Project 3 Weeks Before Launch Last month, a Fortune 500 company killed an AI project three weeks before launch. The AI worked great. Models were accurate, pipeline was fast, demos impressed everyone. They killed it because Legal couldn't answer one question: "If this AI makes a mistake, who's accountable?" The Pattern I Keep Seeing - Month 1-6: Build the AI system. Governance is "we'll figure that out later." - Month 7: Demo to stakeholders. Everyone's excited. - Month 8: Legal review. Compliance review. Security review. - Month 9: "Where's the audit trail?" "Who approved this model?" "What happens if it's wrong?" - Month 10: Project delayed indefinitely. According to Deloitte, 62% of enterprise AI projects experience significant delays during compliance review. Average delay: 4.3 months. The Five Pillars Governance means answering these questions: 1. Explainability - W...

** Why "Just Add an Approval Step" Is Harder Than It Sounds Every AI demo follows the same script: 1. "Watch this AI agent analyze your data..." 2. "Now it's generating recommendations..." 3. "And here it executes automatically!" Then someone asks: "What if the recommendation is wrong?" The presenter pauses. "Well, you could add an approval step." That's where the demo ends. Because in practice, "add an approval step" means: - Building a custom notification system - Creating a UI for reviewing AI decisions - Preserving context so reviewers understand what they're approving - Handling timeouts, escalations, and edge cases - Maintaining audit trails for compliance Gartner found organizations spend 40% of their AI budget on "integration and operationalization"—which includes human oversight mechanisms. **The Three Problems** In my experience, human-in-the-loop fails for three reasons: 1. **Context ...

  Anthropic has MCP. Google has A2A. OpenAI has their Agents SDK. The biggest players in AI are racing to define how agents communicate with each other. After spending nine years building AI systems—including several that resulted in patents—I keep noticing a gap in all these protocols: humans. **The Current Landscape** - **MCP (Model Context Protocol)** - Anthropic's approach to standardizing how AI models share context - **A2A (Agent-to-Agent)** - Google's protocol for autonomous agent coordination - **OpenAI Agents SDK** - A production framework for multi-agent systems Each solves real technical problems. But they all focus on AI-to-AI communication. **The Missing Question** When Agent A hands off to Agent B, what if a human should have been Agent B? From my experience building AI in healthcare, finance, and logistics: the hardest part isn't AI talking to AI. It's AI knowing when to talk to humans—and doing that handoff well. The protocol that figures this out wins. ...

  Introduction: In the early days, working with AI felt like commanding a robot. You gave it an instruction, and you got a result—sometimes great, sometimes… not so much. But lately, it’s started to feel different. More like a conversation. More like working with someone. In this piece, I reflect on that shift: how AI is becoming a co-creator, what makes those interactions successful, and why emotion, context, and feedback loops might be just as important as computing power. 1. Working with AI Feels Like Working with a Really Smart (But Literal) Partner One of the biggest misconceptions about AI is that it just “knows” what you want. The truth? You often need to guide it. A lot. I’ve found myself correcting it mid-way: “Hmm, that’s not what I meant. Can you focus more on X and less on Y?” And when it responds with “You’re absolutely right,” and pivots—there’s a real moment of collaboration there. That kind of responsiveness is powerful. It reminds me that AI doesn’t ne...

AI is evolving—but so are we. After nearly a decade of building patented AI systems that span memoir writing and ecommerce logistics, one thing is clear: the most powerful systems don’t replace humans. They collaborate with us. Over the past nine years, I’ve been on an immersive journey—researching, developing, and deploying AI-powered systems that span personal storytelling to hyperlocal ecommerce logistics. This work led to multiple patents and innovations in areas that combine human emotion, logistical efficiency, and machine intelligence. It all began with building operational systems for hyperlocal ecommerce and memoir writing. These early products were designed to solve very real human problems—how to get groceries delivered faster, or how to record and preserve the memories of a loved one in a meaningful way. But as the products matured, I realized the potential to deeply enhance them with artificial intelligence. From machine learning models for ecommerce recommendations to con...

While we're on the topic of our AI patents. I'm thrilled to share that we've filed a patent for the next evolution of our AI-powered memoir creation system! This invention underscores a significant advancement in human-AI interaction, showcasing how artificial intelligence can collaborate creatively with individuals to document life's meaningful moments in a deeply personal and interactive way. The patent application describes an artificial intelligence-based system and method that automates the creation of personalized memoir events by analyzing user-provided information, voice inputs, and multimedia content, generating memoir narratives enhanced with emotional insights, voice cloning, and virtual reality for an immersive, personalized user experience. This significantly enhances the depth and authenticity of personal narratives. Looking forward to exploring the profound potential of AI in capturing and sharing our human stories! hashtag # AI hashtag # Innovation has...