Noah Michael Gresham

More BFCM insights on incrementality tests run prior to the holiday: - Total efficiency improved by more than 75% when including the latent impact from BFCM. For example, if a brand saw a $1 iROAS before BFCM, it increased to $1.75 during the post-treatment window. - For experiments that started prior to BFCM and tracked the PTW lag effect into BFCM, 41% of the incremental value of media showed up in the post-treatment window. - In 44% of experiments, efficiency doubled (or better!) when measuring the BFCM lag. - Delayed effects of advertising during BFCM are nearly triple as much compared to evergreen PTWs throughout the year. Outside of BFCM, the delayed lift during the PTW for these same brands added only 26% more incremental value, meaning the delayed effects of advertising are especiallysignificant around BFCM. - Overall, spend before BFCM – including spend from previous months before November – continued to generate conversions well into November. In other words, if a brand cuts spend based on Day 1 ROAS, they’re cutting the revenue that materializes on Day 30.

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🚀 Unlocking Agentic AI: From MCP to A2A, ACP, ACNBP & Beyond Agent-based systems are no longer theoretical. They're here—and protocols are the glue that makes them collaborate, delegate, and scale. If you're building with AI agents, here’s a quick guide to the evolving communication stack that powers next-gen autonomy 👇 🔌 MCP (Model Context Protocol) Think of this as the USB-C port for agents. It standardizes agent-to-tool communication via JSON-RPC, letting agents access APIs, databases, or search—ideal for stateless tool use and injecting external knowledge. 🤝 A2A (Agent‑to‑Agent Protocol) Built for collaboration across boundaries. Developed by Google, now open under the Linux Foundation, A2A enables task delegation, capability discovery, and real-time coordination between agents via HTTP/SSE and JSON-RPC. 🧠 ACP (Agent Collaboration Protocol) Structured conversations between agents. ACP introduces intent-based messaging (like REQUEST, INFORM, PROPOSE), shared ontologies, error handling, and multi-modal payload support—ideal for high-trust, real-time interactions. 🔐 ACNBP (Agent Capability Negotiation & Binding Protocol) Enabling smart agent matchmaking. This newer protocol supports secure discovery, negotiation, and commitment exchange between agents—essential when agents need to select trustworthy collaborators on the fly. 🌐 ANP + AG‑UI (Emerging standards) ANP handles agent discovery in dynamic ecosystems. AG‑UI powers real-time agent-to-user streaming (SSE/WebSockets), making agents feel alive in UI environments. 🧩 How it all fits together Imagine an agent workflow: Agent uses ANP to discover peers Agents negotiate with ACNBP Tasks are delegated via A2A External tools are accessed over MCP Conversations flow using ACP UIs stream results in real-time using AG‑UI Each protocol plays a unique role—but together, they enable resilient, intelligent, multi-agent systems. 🎯 Why this matters ✅ Modular: Plug in/out tools and agents 🛠️ Secure: Discovery, binding, identity baked in 📡 Scalable: Works across local, cloud, and hybrid systems 💬 Multimodal: Text, code, audio, and visuals 📈 Enterprise-ready: Backed by industry adoption Are you exploring multi-agent architectures yet? Curious how MCP, A2A, or ACP could reshape your enterprise workflows? Let’s connect and compare notes—agentic design is moving fast, and collaboration protocols are the key to making it work. #AgenticAI #A2A #MCP #MultiAgentSystems #AIProtocols #AIArchitecture #AIEngineering #FutureOfWork #EnterpriseAI #LLMops #AutonomousAgents

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Generative AI startup ProRata.ai has raised $40 million in a Series B round and is launching a new search tool for publishers. ProRata.ai is creating a new AI business model where publishers are compensated when AI companies use their work. Its new product, Gist Answers, allows publishers to embed a custom AI search tool that provides summaries and recommendations based on their own content, with a 50/50 revenue-sharing model that ensures attribution and payment. The round was led by Touring Capital, with participation from existing investors Mayfield Fund, MVP Ventures, Revolution Ventures, SBI Investment, BOLD Capital, XPV-Exponential Ventures, and Idealab Studio. The funding will be used to expand its team and scale its sales and customer service operations to support its ethical search and revenue-sharing model. Congratulations to CEO Bill Gross and the ProRata.ai team. Axios: https://lnkd.in/dAdgUuNX #AI #GenerativeAI #Publishing #MediaTech #AdTech #SeriesB #Funding #VentureCapital

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New release from Packt Publishing… “Building Business-Ready Generative #AI Systems — Build Human-Centered Generative AI Systems with Context-Aware Agents, Memory, and LLMs for the Enterprise” See it at https://amzn.to/3Jdcio5 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🔵Build an adaptive, context-aware AI controller with advanced memory strategies 🟢Enhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT) 🟠Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fit

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An important article I wrote that differentiates LLM assistance and agentic AI. Please read. 🔗 https://lnkd.in/eb8mJyjJ A note for those applying to jobs: This matters for your career too. When you're in an interview and someone asks what you built, be precise. If you used an LLM to help you write code faster, say that, don't call it an agent. If you built a RAG pipeline, call it retrieval-augmented generation, don't call it agentic. If you built a chatbot with a system prompt, own it for what it is. Hiring managers who know the difference will respect your precision. Hiring managers who don't know the difference need you to teach them. Either way, you win by naming it correctly. Overselling what you built doesn't just hurt your credibility when someone digs deeper, it tells the room you don't actually understand the architecture you're describing. Know what you built. Know what it's called. Know why it matters at that level. That's what gets you hired. #AI #AgenticAI #LLM #NLP #SportsAnalytics #DataScience #CareerAdvice #MachineLearning

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The word "governance" is killing AI adoption. I keep seeing the same pattern at conferences and in customer conversations. Company wants to adopt AI agents. Someone raises the governance question. Meeting stalls. Six months of policy drafting begins. Meanwhile, competitors ship. Here's the thing: governance fundamentals are more important than ever. Well-documented, well-understood, high-quality data is exactly what agents need to perform well. The first principles haven't changed. But the word itself has become toxic. "Governance" sounds like committees, spreadsheets, and "eat your vegetables." Nobody gets excited about governance. And excitement is what drives adoption. Two suggestions. First: rebrand it. Call it DataOps. Call it Data Quality Engineering. Call it whatever you want. "X-Ops" is cool. "Governance" is a meeting nobody wants to attend. Second: don't make it a hard blocker for AI adoption. Your governance framework will never be "ready" before you start. Things move too fast. If you wait for perfect governance before deploying agents, you'll be waiting while everyone else learns by doing. Yes, your governance teams will play catch-up. That's OK. Better than the alternative. The companies winning at AI aren't the ones with the most governance. They're the ones who made governance an accelerator instead of a gate. What do you call it at your company? Curious if anyone's found a name that people actually rally around. #AI #DataGovernance #DataEngineering #Leadership

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One of the most requested features for dotDash + Flow is finally here: the ability to trigger agentic workflows directly from emails. Users can now enable inbound emails via a unique, per-workspace email address. By subscribing these addresses to the email.received event, you can trigger automated workflows instantly. Imagine CCing GenAI agents into your threads to classify messages, process attachments, or transform data before pushing it into your integration flows. #fluentdata #dotdashflow

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Great to see the administration focused on something really important for the country in the AI space, the AI Action Plan. https://lnkd.in/eSk2z_8h As our company experiences barriers every day in the government sector, some of these "removing red tape" focus areas will absolutely help speed adoption for new capabilities. Hopefully one of these focus areas become ATO processes to enable more rapid deployment of systems directly to end users and analysts on their networks and connected to the data they care about. National level compute, approved foundational models on network etc. are all critical to scale mission applications like Danti and many other application layer technologies. Also one of the paragraphs in how the United States Department of Defense can leverage this technology is particularly important. "Develop a streamlined process within DOD for classifying, evaluating, and optimizing workflows involved in its major operational and enabling functions, aiming to develop a list of priority workflows for automation with AI. When a workflow is successfully automated, DOD should strive to permanently transition that workflow to the AI-based implementation as quickly as practicable." This is the most common area we see related to our company and users where so many workflows today are needlessly done manually wasting our precious time resources where the simple stuff can now be left to the AI systems. Let our valuable resources, analysts, warfighters etc focus on the hard and complex stuff. Have been close with past Executive Orders, especially in the drone domain, lets hope this one gets off paper and into action soon! Bravo to Michael Kratsios and team!

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We’ve launched The Journal as an honest voice in an opaque art market. Our newest editorial, 'Beyond Data: Auction Results Tell Only Part of the Story,' examines how auction databases once reshaped the industry — yet platforms built solely on public auction records now offer only a partial view of value. Aggregated auction data once held real competitive advantage. Today, when individual auction prices can be surfaced instantly through search engines and increasingly synthesized by AI tools into compelling narratives, aggregation alone is no longer differentiation. Information is abundant. Context is scarce. Sign up to receive our content directly to your inbox. https://lnkd.in/eb65tkHh

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80% of AI shopping suggestions come from affiliates—not your site. 😞 Microsoft new MCP standard, NLWeb, will transform AI search as we know it. NLWeb creates a universal way for AI agents to interact with your brand's data via MCP, putting the brand back in the driver's seat. By exposing a "mystore.com/ask" endpoint, AI agents will be able to quickly access: - Product catalog, pricing, availability, offers - Educational content & FAQ's - Customer reviews and social proof - Brand story and differentiation points No more web scrapping. No more outdated information. This matters for a few reasons: 📝 Brands control their narratives, not some 3rd party affiliate site or competitor comparison pages 🎯 Accurate present your product info, differentiation, and offers 🤑 Craft AI-specific offers to boost conversion Given Microsoft's relationship with OpenAI, I wouldn't be surprised for ChatGPT to start adopting it this year. Early adopters already include Shopify, Tripadvisor, and Eventbrite—signaling this technology is moving toward industry consensus. The possibilities are infinite as this standard evolves. Aside from information retrieval, agents will have more autonomy to perform actions like email sign-up, loyalty program enrollment, and direct transactions.

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Walmart announced today a partnership with ChatGPT that allows customers to complete purchases from Walmart DIRECTLY within ChatGPT. This may be the most strategically important move Walmart has made in years. Three takeaways for brands: 🧭 1 - TRAFFIC *** This is a direct line into ChatGPT's MASSIVE traffic -- about the only thing that has a chance of trumping Amazon’s ecosystem. Walmart surly can't do this on its own, but with with 61% of customers changing their primary search engine to AI, this is accelrant Walmart has been looking for. Owning discovery in the next era of retail. 📌 SO WHAT: Customer volume to Walmart is forcast to rise ------ ------ ------ 🧭 2 - SEARCH CHANGES *** ChatGPT knows you. Your family make up, brand preferences, and where you travel. For decades, commerce has been reactive. The anoucement outlines: AI-first shopping ... plans and predicts helping customers anticipate their needs before they do. How will ChatGPT do this? Via matching contextual dimensions from brand pages to the individual user. "What" your product does isn't enough -- include who, when, where and why to be prioritized by the GPT. On average, brands have 48% of the dimensions Chat is looking for. 📌 SO WHAT: CONTEXT is critical. Include who, when, where, and why in your content (not just what). ------ ------ ------ 🧭 3 - FILL THE FIELDS *** We've optimized thousands of Walmart SKUS. It works even better than Amazon. We've learned Walmart HIGHLY values Item Data Quality (IDQ) and SEO value. Fill in every field correctly, and with highly searched customer terms. Post-optimization, we’ve seen up to +149% gains in keyword indexing, improving visibility across the shelf, and average 38% gains. ------ 📌 SO WHAT: IDQ with rich SEO is the baseline starting point, but fewer than 20% of brands have their content to this level. ------ ------ ------ Want an audit of your Walmart content to see if you're ready? Reach out. We're happy to help. #Walmart #OpenAI #Amazon #RetailStrategy #Ecommerce #ChatGPT #AI #Search #StructuredData #WalmartSEO #DigitalShelf #ContextualCommerce 👉 Read the announcement https://lnkd.in/g8WnGb2n

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