How Vertical AI Will Revolutionize Business Operations

AI pilots fail because data are not ready for AI, and contracts get signed before data and orgs are ready to succeed. People want the big splashy needle-moving programs and fall prey to forgetting the building blocks that build a real AI strategy. Intuition and experience. The antidote is simple: prototype small, learn fast. Find what’s broken in order to prepare the way for AI and build intuition and experience along the way. Here’s what that looks like: 1. Start with <$100 experiments. You don’t need six figures to know if an idea has legs. In fact, most pilots can be tested through 1–10 simple prototypes that cost less than $100. If you can’t demonstrate value at that scale, it’s unlikely to pay off at enterprise scale. From what I’ve seen these experiments more often point to the processes and data than AI technology. And having run practical experiments pays off when it’s time to talk about buying solutions. 2. Validate with one-prompt or ‘few-prompt’ prototypes. Sometimes the simplest test is the best. A single prompt, wired into the right workflow, can reveal whether a use case is worth pursuing. You don’t need custom infrastructure to get signal — you need creativity and curiosity. This could also be as simple as “What are current users doing with AI and how can we increase in that space?” 3. Build intuition, not just artifacts. The goal of prototyping isn’t the prototype itself. It’s the intuition teams gain about what works, what doesn’t, and where the real value lies. This is why frontline “prosumers” are so powerful: they can spin up quick tests, share lessons, and spot patterns that apply BETWEEN your data and AI. These prototypes are the building blocks of a coherent tool selection process and AI strategy. Don’t start conversations with vendors about big, transformative without understanding how your data and processes actually relate to your AI ambition. When it becomes apparent that data structure or quality are roadblocks for certain AI solutions, then Vendors will most likely say “Yes, IF..” when the right answer for your org may be: “NOT YET, because..” My take: In AI, small prototypes are such affordable and necessary filters. They separate the hype from the viable, the nice-to-have from the high-value. And they keep organizations from making the big mistakes.

The latest no-code AI builders can generate entire workflows from simple text descriptions. Here's what most businesses miss: The tool is only 30% of the solution. We recently analyzed a client's "AI-generated" workflow that looked perfect on the surface. Three critical issues: → Variables weren't properly mapped between systems → Error handling was completely missing → The AI agent had no actual tools configured The workflow "worked" in testing but failed immediately in production. At Zonder, we see this pattern repeatedly: businesses rush to deploy AI solutions without understanding the underlying logic. The result? Broken automations, frustrated teams, and wasted budgets. Our approach combines the speed of modern AI tools with deep technical validation: ✅ Every workflow undergoes systematic testing before deployment ✅ We build redundancy and error handling from day one ✅ Clients understand exactly how their AI systems operate The real value isn't in generating the initial workflow - it's in ensuring it actually works reliably at scale. What's been your experience with AI automation tools? Have you encountered the gap between "it works in demo" and "it works in production"?

The key to AI productivity isn't finding the perfect prompt, it's designing the right process. Anyone working with large language models quickly discovers this truth: the model itself isn't where the magic happens. It's in HOW you work with it. The numbers back this up. Economic and software development studies show productivity gains between 14% and 40% depending on the task. Even more impressive? Controlled experiments with code assistants measured acceleration of around 55%. But here's what matters: these gains don't come from discovering some mysterious "prompt formula." They come from a well-structured AI implementation framework: -Well-defined objectives that align with business outcomes -Detailed requirements that translate business needs into AI-ready tasks -Purpose-built technology configured for your specific workflows -Methodical validation ensuring consistent, high-quality outputs This structured approach mirrors exactly what we've built into our Lighthouse AI Journey™. Using our SEA Framework™ (Scope ► Explore ► Automate), we guide teams from identifying their highest-impact AI opportunities to implementing custom AI workspaces tailored to their specific context. Whether you're just beginning to explore AI's potential (INSIGHT), ready to implement practical solutions (IMPACT), or transforming into an AI-first organization (TRANSFORMATION), the difference between modest and transformative results isn't about the model, it's about the methodology. What process are you using to transform your processes with AI?

Why We Started Our AI Projects Over the past month, we built our first AI tools — one for customers, one for our team. I learned a lot along the way, and thought I’d share some of the lessons in this short series. We actually launched two projects side by side: - External AI Assistant: lets customers pull specs, compatibility, and product details instantly, instead of digging through websites or waiting for someone to reply. - Internal AI Tool: gives our sales and customer solutions team quick compatibility checks, margin scenarios, and draft quotes without bouncing between systems. We didn’t start this because “AI is hot.” We started because: - Customers want fast, accurate answers - Sales needs reliable insights in the moment - Operations need to scale without relying on in-house experts or bouncing between multiple systems Here’s a simple example. A customer can ask: “What battery fits this application?” and get a usable answer immediately. A rep can ask: “If I price this at $X, what margin does that give us?” without opening three different tools. The first thing I took away from this: the best AI projects don’t start with models. They start with business problems. Curious to hear from others — if you’ve built AI tools, what was the very first business case you tackled? Under the hood: We stood these up using a low/no-code setup. That let us test quickly and validate the concept before putting more resources behind it.

Don’t buy another AI tool until you’ve answered these 4 questions. The hype for new AI tools is crazy at the moment. Every day I see a new "revolutionary" tool that will make you a trillionaire. The truth is the majority of these are vibe coded GPTs that look nice, but barely do anything for your business. Yet, business owners are pushed by FOMO and jump onto the next "miracle" AI tool, wasting time and money. Here’s how to avoid wasting time, budget, and momentum by asking 4 simple questions: → 1. What’s the real bottleneck? Most teams try to automate the symptom, not the root cause. AI won't fix a broken strategy. Clarify the real problem first. → 2. Is this task repeatable? AI tools deliver the most ROI when used on high-volume, low-variability workflows. One-off tasks? Do them manually. → 3. Do I have SOPs? No playbook = no automation. You can’t automate what you haven’t documented. → 4. Will this integrate with my systems? Compatibility > capability. If it can’t connect with your stack, it’s not worth the cost, no matter how “smart” it is. If you want help building a clear AI roadmap before you invest in any tools... → I’m offering a few free AI strategy sessions this month. Grab a spot here: 👉 https://lnkd.in/dCJkeyP6 (We’ll map out where AI fits in your business - and what to avoid.)

Most people build Voice AI the wrong way. They skip the blueprint... ... they go straight to the hammer and nails. And that’s why it breaks. Here’s what typically happens: → Someone hears about Voice AI and gets excited. → They jump straight into building. → Start connecting tools. → Writing scripts. → Hooking up automations. But they forgot the most important step. The plan. ... What workflows will the AI follow? ... Where does the data go? ... What systems does it talk to, and when? ... Who takes over if the AI gets stuck? If you don’t map this out first, you’ll build a tangled mess. It’ll be clunky. It’ll cause problems later. And worst of all? It won’t convert. I’ve seen it happen with service businesses, who are already overwhelmed with leads they can’t follow up on. They need speed. They need clarity. They need systems that just work. That only happens when you design the workflow first, then build the tech around it. If you’re thinking about Voice AI to follow up leads, qualify jobs, or book appointments… Start with the blueprint. Then build the house. Want to see what a solid AI workflow plan looks like? Drop a “PLAN” in the comments and I’ll show you the exact framework we use. (Must be connected, sending 48h only)

Everyone is racing to automate with AI. They’re wrong. It’s a trap. You're told to connect APIs, build agents, and let the machines run the show. This is how you build a self-driving car with no brakes and aim it at your reputation. Blind automation doesn't create leverage. It creates liability. It amplifies your mistakes at the speed of light. It bakes mediocrity into your systems. It makes you look stupid, faster. The pros don't gamble. They build engines. My rule is non-negotiable: No Blind Automation. It's the final, critical step in my LUMEN Method for building proprietary AI systems. You don't automate a POSSIBILITY. You automate a CERTAINTY. Here’s the map. Before you even think about handing over the keys, you must be able to check these boxes: 1. Proven Manually: Has the process run end-to-end, by a human, at least 10 times with a stable, high-quality outcome? 2. Documented & Measured: Do you know what a "win" looks like? Is there a rubric? Are the inputs and outputs crystal clear? 3. Stable & Reliable: For the last 3 runs, did the process hit your quality threshold without major edits or exceptions? If the answer to any of these is "no," you have NO business automating it. You’re not building a system. You’re building a slot machine. Stop chasing the hype. Build a proven engine you can trust. P.S. If you'd like to learn more about this and you’re learning AI, you will love my weekly newsletter (it’s free). Make sure to subscribe to receive your letter.

👉 Evaluating AI Agents: Deterministic vs. Observability💥 🤔 One of the biggest challenges with AI agents is evaluation. Unlike deterministic software, agents can solve problems in multiple valid ways, and sometimes unpredictably. --------- ➡️ That’s why we’re seeing two complementary approaches emerge: 1️⃣ Deterministic Evaluation (ADK, etc.) ▫️Best for structured pass/fail tests before deployment. ▫️Define exact inputs, expected tool calls, and expected outputs. ▫️Great for CI/CD gates and regression testing. 2️⃣ Observability (AgentOps, LangSmith AI, Weights & Biases, etc.) ▫️Best for debugging, monitoring, and optimization in dev & prod. ▫️Capture full session traces: LLM prompts/completions, tool calls, costs, errors. ▫️Great for diagnosing why an evaluation failed. 🤷 When to use which? 🔸Use ADK-style tools when you need a strict ✅/❌ signal. 🔸Use AgentOps-style tools when you need to understand the run, not just judge it. The strongest workflows combine both: run deterministic tests → store results in an observability platform for full context. 3️⃣ Tools that do both Platforms like Maxim AI, Azure AI Foundry, and Sendbird are starting to blend structured testing + observability into one workflow. Expect this convergence to accelerate in 2025. 👉 Curious: how are you currently evaluating your agents—unit-test style, observability-first, or a hybrid? 📗 Deterministic article by Megan O'Keefe: https://lnkd.in/dsSzFUZS ------ ✨ The future of enterprise AI is here, and it’s agentic! 🌈 Join the AI Agent Ops Alliance™ (AOA)  Linkedin Group:   https://lnkd.in/dMDFZMJa 👍🏻 Like | 💬 Comment | ♻️ Repost

There’s a lot of debate on whether businesses need AI agents. Here’s what I am seeing. When companies start exploring AI, agents are often the first thing they hear about. They’re powerful, autonomous, and can handle complex tasks. I understand the excitement. But when clients ask if we’re building any for them, I tell the truth: we are, but only when the complexity of the task requires it. I’ll explain. Most problems don't need that level of autonomy. They can be solved with simple input-output workflows. Here’s what I’ve noticed while building these systems on the ground: 1) AI workflows can handle the majority of tasks Most company tasks follow the same steps every time. You don’t need agents for this. When inputs and outputs are clear, workflows are the best solution. They are fast, cheap and (if well built) reliable. 2) Agents help when workflows need to make choices They can decide which workflows to trigger, break them down, and execute them without you managing every step. They bring autonomy, but autonomy comes with risk (more on this below). 3) Apps help you see everything at once They orchestrate workflows and agents, track performance, and provide visibility to understand how the system supports the business use case. Using an agent for a simple task is just going to make things slower and increase costs. Yes, it can handle more complex work, but most tasks just don’t require it. Agents are useful, but identifying where to use them is the most important step to gain value from them. 

Frameworks are great but they're just mental models. Let's turn them into AI active systems with tools like Claude code Let me explain... Every framework you use, whether it's OKRs, PDCA, your own memory trick to remember the best way forward or even how you organize your inbox, is really just: A set of rules and decisions To help you go from inputs to outputs And rules + decisions = exactly what AI agents are built for. Everyone use frameworks, everyday. They use old ones that never changed, new ones that adapt to new work and take old ones to build there own out of it. They're memory hacks, decision trees, systematic ways of designing outputs from inputs. I'm sure though, they're poorly leveraged We can make a lot better out of them if we find the right way to do so with the latest AI agentic systems. We've operationalized so many processes in business Why not our thinking tools? Here's how it works: - Take your framework, the logic AND the templates - Gather your decision trees, your prioritization rules - List your evaluation criteria, whatever is part of the process - Decompose it up till you can explain it to an AI Now you're ready to use agentic systems like Claude Code, Gemini Cli, Deep Agents to make this into an active AI operating system: - They're built for SW engineers and you're not - They're designed to produce code and you won't - They're actively improved and you enjoy it - You'll use reusable prompts as your SOPs - You'll run programmed commands as workflows - You'll create subagents as you staff your projects - You'll make it customize it with md files - You'll leverage COMPUTE as a knowledge engine As always, I'm not talking about Automation here but rather a smart way to operationalize what we do best and make it better with Agentic systems The goal is to build intelligent systems around how we actually think and work, leveraging our frameworks as the operating logic. To theoretical? I'll show you a working example later during the week.