Streamline AI with Structured Outputs

AI is only as smart as the data you feed it. As RevOps teams move faster to adopt AI, one hard truth is becoming impossible to ignore: accuracy is now the foundation of modern revenue operations. In our latest blog, we unpack why AI-ready data enrichment is no longer a "nice to have", and what happens when AI is built on stale, incomplete, or misaligned data. We explore: - Why bad data doesn't just slow RevOps, it actively misleads AI - How enrichment accuracy directly impacts forecasting, routing, and GTM decisions - What "AI-ready" actually means for customer and account data today At EnrichIT!, we believe better decisions start with better data - not more dashboards, patches, or manual cleanup. Read the full post and see why accuracy is the real competitive advantage in the age of AI. https://lnkd.in/eEij5NwV #RevOps #DataEnrichment #AIReadyData #B2BGrowth #RevenueOperations #GoToMarket #EnrichIT

AI doesn't create clarity, data does. As executive accelerate AI adoption across RevOps, one reality matters more than any tool or model: accuracy. Without it, AI scales confusion instead of insight. This piece explains why AI-ready data enrichment is now a strategic requirement, and how leaders can avoid making high-confidence decisions on flawed data. A smart read for anyone accountable for growth, forecasting, or GTM execution. #ExecutiveLeadership #RevOps #AIinBusiness #DataQuality #B2BGrowth

I see this play out with RevOps teams all the time. AI isn't the problem, the data feeding it is. As AI gets embedded into forecasting, routing, and GTM workflows, accuracy stops being a backend issue and becomes a leadership one. Bad data doesn't just slow teams down, it quietly pushes them in the wrong direction. This post does a great job unpacking what AI-ready data actually means and why enrichment accuracy is foundational, not optional. Worth a read if you're building (or buying) AI into your RevOps stack. #RevOps #AIReadyData #RevenueOperations #DataEnrichment #GTM #B2BLeadership

🧠You probably don't need a multi-agent system. You need Agent Skills. Here's a pattern I see constantly in enterprise AI builds: A team wants to automate customer onboarding. So they build: → A compliance agent → A CRM agent → A comms drafting agent ...and then spend months debugging why the customer record loses fidelity between handoffs, why the compliance check contradicts the CRM state, and why the drafted email doesn't reflect what was agreed three steps earlier. The problem wasn't the ambition. It was the architecture. What if a single agent, loaded with the right skills at runtime, handled all of it? → Need compliance reasoning? Load the compliance skill. → Need to update the CRM? Load the CRM skill. → Need to draft the welcome comms? Load the comms skill. The agent *becomes* each expert contextually — with full memory of everything that came before. No handoffs. No context loss. No inter-agent telephone game. This is the power of Agent Skills as a design pattern. Agent Skills are dynamic capability modules injected into a single agent at inference time, based on what the task demands. Same outcomes as multi-agent. Far less overhead. The architectural wins: ✅ No inter-agent communication overhead ✅ Single failure boundary — dramatically easier to debug ✅ Unified context — customer state never degrades across steps ✅ Lower cost & latency ✅ One trace, one log stream — observability becomes simple And this isn't a fringe view. Anthropic, OpenAI, and Microsoft are all pointing the same direction: → Maximise your single agent first → Role separation doesn't require agent separation → Most multi-agent complexity is a tooling or retrieval problem in disguise So when does multi-agent ACTUALLY earn its place? → You need true parallelism across independent workstreams → You cross security or compliance boundaries requiring strict data isolation → Multiple teams own separate domains with independent release cycles Everything else? Probably an Agent Skills problem, not an architecture problem. The burden of proof should be on WHY you need multiple agents — not the other way around. My recommendation: → Build a single agent with a well-designed Agent Skills system → Measure where it genuinely breaks → Only then introduce multi-agent complexity where the evidence demands it 📖 Further reading: • Microsoft's AI Agent Decision Framework: https://lnkd.in/gP9at9Dj • Agent Architecture Survey (arxiv): https://lnkd.in/gFeMY8hh Are you using Agent Skills in production? Or have you gone multi-agent — and was it worth it? #AIArchitecture #AgentSkills #AgenticAI #EnterpriseAI #LLM #AIEngineering

There isn’t one universally agreed “best” AI agent of 2026, but the **most widely recognized** name is OpenAI’s Operator for general consumer-facing agent tasks, while Salesforce Agentforce is one of the most prominent enterprise agents. Industry roundups also split the “best” title by use case rather than crowning a single winner. ## Best-known contenders - **OpenAI Operator**: best known for browser-based tasks like booking and form filling, and often described as the big-name consumer agent. - **Salesforce Agentforce**: one of the most deployed enterprise agents, especially for CRM, sales, and support workflows. - **Claude Computer Use**: notable for desktop control and more technical workflows, but less mainstream with non-technical users. - **Microsoft Copilot Studio**: widely recognized in enterprise settings for workflow and business automation. ## Practical answer If you mean the **best-known overall**, I’d point to **OpenAI Operator** because it has the strongest name recognition as a standalone AI agent for everyday users. If you mean the **most established in business**, **Salesforce Agentforce** is the safer answer. ## Best by use case - **Everyday consumer tasks:** OpenAI Operator. - **Sales and customer support:** Salesforce Agentforce. - **Technical desktop automation:** Claude Computer Use. - **No-code workflow automation:** Lindy.

Most businesses today are using AI. Very few are building AI that actually fits their business. That’s where the real advantage lies. Custom AI models aren’t about complexity - they’re about relevance. Solving the right problems using your own data, workflows, and customer behavior. In this practical guide, we break down: • What custom AI really means for your business • When it makes sense to invest in it • A step-by-step framework to build, train, and deploy • How to integrate AI into real business systems • The kind of ROI you can actually expect If you're thinking beyond off-the-shelf tools, this is where to start. Read the full article here: https://lnkd.in/gZqcJMxU #ArtificialIntelligence #AIForBusiness #MachineLearning #DigitalTransformation #BusinessGrowth #Automation #TechStrategy #CommercePundit

You can't leverage what you don't understand. That's the line that keeps coming back to me — and it's why I recorded this. Most dealer groups sit at what I call Stage 3 on the Intelligence Pyramid — Functional Intelligence. The BDC has a chatbot. Sales has a pricing tool. Service has a scheduling system. Marketing has a content platform. Each one works. None of them talk to each other. And here's the part nobody tells you: buying more tools doesn't move you to Stage 4. It just makes Stage 3 wider. You accumulate more isolated intelligence. The ceiling stays exactly where it was. Crossing that ceiling requires architecture. Three layers, specifically: A Unified Data Lake — because your operational history is scattered across nine to fifteen separate systems. DMS, CRM, OEM portals, marketing platforms, service tools. Each one holds a piece of the picture. None holds the whole picture. Until you connect that data underneath, every AI tool you own is working with a partial view of your business. A Knowledge Hub — and this is the layer most groups have never seen. It takes your organizational knowledge — OEM policies, service bulletins, playbooks, brand standards, process documents — and vectorizes it. That means converting it into a format where machines understand conceptual relationships, not just keywords. When your service advisor asks a complex warranty question, the system understands the intent and pulls context from across the organization. With citations. Not opinions. Verified answers from your own documentation. An Agentic Layer — where coordinated AI agents observe, reason, and act across that unified foundation. Not chatbots waiting for prompts. Systems that identify patterns across departments, across locations, across brands — and surface intelligence that no individual would have time to assemble manually. With a human-in-the-loop protocol that keeps your people in control of every decision that matters. The result is what I call the compounding curve. An organization that builds this architecture today starts accumulating organizational intelligence immediately. Every insight from service informs sales. Every successful campaign at one location propagates across the group. Every pattern recognized makes the system smarter. A competitor who waits until 2028 starts at zero. They can buy the same technology. They cannot buy two years of compounded learning. That gap is competitive asymmetry — and it's permanent. This isn't a technology conversation. It's a literacy conversation. The executives making AI investment decisions right now need to understand the difference between reactive AI and agentic AI. Between a shared drive and a Knowledge Hub. Between tool accumulation and intelligence architecture. Between spending on AI and building with AI. Literacy precedes leverage. Architecture precedes intelligence. And the window to build it is open right now.

I've been thinking a lot about the future of systems of records. What happens to products like Salesforce and Zendesk? Do they exist in an AI world, and if so how? 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝟭: 𝗧𝗵𝗲 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘀𝘁𝗼𝗿𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗴𝗼 𝗮𝘄𝗮𝘆. A year ago a VC pitched me on his thesis that AI will become efficient enough to pull data directly from the 1st party data sources (e.g. Email, Slack, Calls, etc) and use it in real-time. Is this a really cool idea? Yes. Is this practical today? No. Is this practical long-term? I don't think so. AI, just like humans, should benefit from having data pulled out of 1st party data sources and pre-computed/structured to make it faster to use. Having to re-compute everything from scratch is REALLY inefficient. Simple things: → pulling lists (e.g. a ticket queue) → jumping between tickets → any type of reporting or analytics Even for one-off jobs, it's still helpful to have data pre-structured. As an example, if I want to know whether our customers are happy I could point Claude at every customer interaction, or pass it a pre-computed AI summary for each customer. 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝟮: 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗰𝗵𝗼𝗼𝘀𝗲 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘃𝘀 𝗯𝘂𝘆 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗿𝗲𝗰𝗼𝗿𝗱. Overtime we'll move from people buying SoRs, to agents buying them. "Go setup my support" is the eventual end state that you'll tell a general purpose agent like Claude Code, and it will go evaluate how to best do that. In that moment, it'll make a build vs buy decision just like a human would. Your product needs to pass the same build vs buy test a human would make. 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝟯: 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗰𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 "𝗔𝗜-𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆" 𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗿𝗲𝗰𝗼𝗿𝗱 If an agent is making the build vs buy decision, you should optimize for their use case. "AI friendly" or "AI accessible" will become the questions an agent will ask about your product. This mostly means increasingly making every action accessible via API and MCP. 𝗢𝘃𝗲𝗿𝗮𝗹𝗹 𝗺𝗲𝘁𝗮 𝘁𝗵𝗲𝗺𝗲: 𝗧𝗿𝗲𝗮𝘁 𝗔𝗜 𝗹𝗶𝗸𝗲 𝗵𝘂𝗺𝗮𝗻𝘀 AI will need a place to store and manipulate data, AI wants to be efficient. AI will also choose to build vs buy products. AI will want a nice UX within your product.

I run a personal AI assistant that manages my calendar, monitors my email, tracks tasks, analyses meeting transcripts, and maintains a CRM. Here is what I learned after weeks of hands-on use. *MCP vs Direct API* MCP is elegant. You describe a tool, the LLM discovers it, calls it, interprets the result. Beautiful architecture. However, in my setup, when I asked the agent to connect to the service via MCP, it replied: "I already know the REST API. Let me just call it directly. MCP would add tool discovery overhead, schema parsing, and response interpretation on every call. With direct API, I authenticate once, call the endpoint, and get structured data back. Same result, fraction of the cost." And it's a true of what happens under the hood, repeatedly: - The MCP tool description takes tokens for every call - The LLM needs to reason about which tool to use - The response comes back, and the LLM needs to interpret it Compare this to a direct API call where the agent already knows the endpoint, the auth method, and the response format. No tokens spent. No tool discovery. Just execute. MCP makes sense when you need flexibility across many unknown tools which are not repeatedly used. *Not Every Task Needs a Neural Network* I asked my agent to scan my email 4 times a day, looking for messages from specific domains. The agent dutifully connected to my inbox, read headers, analysed content, extracted entities, all goes through LLM inference. Then I realised sometimes instead of asking agent to do the job regularly, I can just ask an AI to automate it. So, I asked the AI agent to write a script that does the same job. The script runs on a schedule, and takes zero tokens for routine scanning. The LLM only activates when the script flags something that needs actual intelligence, like "new company found." *Layer 3: The Hybrid Architecture* The optimal AI agent architecture is a deliberate split: - SCRIPTS and DIRECT API: Known integrations (Calendar, Drive, CRM) - MCP: New/unknown tools, cross-agent discovery, dynamic toolchains - LLM REASONING (precious): Strategy, analysis, content generation, decisions: do not be greedy for this. Every task should be assigned to the cheapest layer that can handle it. Many put everything in the LLM layer and wonder why their AI costs skyrocked. *Real Numbers for Email Monitoring I measured the actual token consumption of my email scanning agent over two days, then rewrote it as a simple Python script. LLM Agent: - Per scan: ~6-7K tokens - Monthly: ~840K tokens Script: - Per scan: 0 tokens - LLM called occasionally: ~2-3K tokens - Monthly: ~90K tokens Result: 90% less tokens, 95% cost reduction, 20x faster execution. Same output quality. *So, the best AI agents are the ones that know when NOT to use AI.* And thanks to my AI for taking care of my pocket. #mcp #agent #integration #ai #llm #performance #xme

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