Why C-Level Executives Prefer In-House AI Projects

🧠 Fine-tuned agents are coming to your internal dashboards. Are you ready? Most companies are underutilizing their LLM capabilities by focusing only on external user-facing applications. Meanwhile, internal dashboards—from operations to analytics—are stuck relying on rote queries and button clicks. The game changes when we bring fine-tuned agents into the mix. I recently worked with a team that integrated role-specific LLM agents into their finance and supply chain tools. After minimal fine-tuning on internal terminology and workflows, the agents could auto-generate procurement reports and flag anomalies—entirely inside the dashboard. The result? A 40% reduction in time spent on routine data triage. To me, this is where GenAI shines: quietly improving institutional efficiency, not just making chatbots. The key is tight alignment with internal processes and teams that own the tools—not just throwing in a generic model. Anyone else exploring this space? Would love to hear what’s working

I woke up from a nightmare this morning. It felt so real I had to double-check the date. I was back in 2019. My team was 15 people strong. We needed 50 by January. The business was exploding. Support volume was doubling every month. I had almost no budget and absolutely no idea what I was doing. It was brutal. I was learning and drowning simultaneously. Here's what I'd do differently today: I'd use AI-powered customer support tools to scale in days, not months. I'd deploy AI agents to handle repetitive questions while keeping my best people on complex issues. And I'd use the budget savings to pay them significantly more for the higher-value work. The result? Faster scaling, happier teams, and better customer outcomes. If you're facing similar growth challenges, it doesn't have to be this hard. Tools like Kayako are designed exactly for this problem. You can scale your support team without sacrificing quality or burning out your people. If you are a Customer Support Leader and want to build a support system that handles more tickets, keeps customers happy, and hits your cost targets without adding more headcount, I created the 5-Step Guide to Scaling Support Without Scaling Headcount just for you. It gives you 5 proven steps to cut backlogs, lower costs, and raise CSAT using the resources you already have. Follow the link in the comments below to grab your complimentary copy 👇

𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐫𝐞𝐦𝐨𝐯𝐞 𝐜𝐡𝐚𝐨𝐬 — 𝐢𝐭 𝐫𝐞𝐯𝐞𝐚𝐥𝐬 𝐰𝐡𝐞𝐫𝐞 𝐲𝐨𝐮’𝐯𝐞 𝐛𝐞𝐞𝐧 𝐡𝐢𝐝𝐢𝐧𝐠 𝐢𝐭. Every system, in any business, acts like an X-ray: it doesn’t fix what’s broken — it just makes it visible. I learned that scaling both software and services. The patterns are identical — productization amplifies whatever’s unclear. When we built Hivemap, we thought we’d found scale —  AI software predicting delays in large infrastructure projects. Sounds productizable, right? Except every customer saw something different. Some wanted risk analysis, others scheduling optimization, and yet others dashboards. We weren’t selling a product; we were reinventing it for every client. Then I saw another team do the opposite — they solved one hated workflow for one specific persona in one type of company. Every buyer recognized it instantly. That’s what clarity looks like: one offer, one interpretation, instant adoption. Productization can be scalable — but only if the offer is so specific that every buyer sees the same thing. Without that, productization doesn’t create leverage; it codifies chaos.

I spoke with 20 founders last quarter working 70+ hours a week. Manually doing what AI could’ve done in minutes. Meanwhile Cursor hit $100M ARR with 20 people. Midjourney reached $200M ARR with 40 people. AI-native teams now generate $2-3M per employee. Traditional SaaS? $150–250K. Here are 6 workflows quietly killing early-stage founders (and what ai native teams do differently): Customer Support → AI agents resolve 80% of FAQs instantly. Daily Standups → Async updates save 100+ hours/person/year. Data Entry → Make and Zapier run 24/7 instead of you. Content Creation → AI drafts in minutes; founders only spend 20% of the time editing Scheduling → 7 email threads replaced by one Calendly link. Financial Reconciliation → Automated accounting + smart banking. What you can do today: Start small. Pick one workflow. Fix it. Repeat. Because the real question isn’t whether you can afford to automate It’s whether you can afford not to.

AI agents are changing the game for finance teams. Unlike traditional automation, they analyze data and take action. Imagine an agent that reviews invoices, flags issues, or approves payments automatically. Our blog breaks down what AI agents are, how they work, and how they can supercharge your finance operations. 👉 Read more: https://okt.to/ZUAzg8

Recently, I attended Kaggle’s Google AI Agent discussion and explored Google’s white paper on “Introduction to Agents” — which emphasizes how to take AI Agents to production in enterprises. Most of us are familiar with LLMs like GPT-4 or Gemini. When we ask a query, the model responds based on the data it was trained on. But since these models are pre-trained (with a cutoff date), they can’t fetch real-time data or perform calculations directly — often leading to hallucinations. User Prompt → LLM (ChatGPT/Gemini) → Response AI Agents mark the next revolution — they use an LLM as the brain and are equipped with tools (hands) to perform actions autonomously such as: -> Making decisions -> Performing calculations -> Querying APIs or enterprise databases -> Scheduling meetings or even making payments These tools let the agent interact with external systems — e.g., fetching current weather, reading enterprise data from a vector database, or checking your calendar. User Prompt → Agent → Thought → Action → Observation. Agents maintain memory and session state, enabling them to reuse previous data and minimize human input — for example, rescheduling a meeting intelligently based on your past calendar entries. ---- Google’s ADK supports multiple languages — Python, Go, and Java — and provides several agent types: * Base Agents – Extended into: 1)LLM-Based Agents: Use reasoning and planning to decide which tools to call (e.g., booking tickets, scheduling meetings). 2)Workflow Agents: 2.1)Sequential Agents – Output of one agent feeds into another (like an assembly line). 2.2)Parallel Agents – Work independently and merge results later (e.g., research across multiple topics). 3)Loop Agents – Execute repeatedly until a condition is met (e.g., iterative code generation and testing). Custom Agents: Build your own logic and flow. ---- *  Multi-Agent Collaboration Enterprises can have multiple agents developed by different teams. These agents can communicate to achieve complex goals. For sensitive tasks, humans-in-the-loop can provide final approval. Before deployment, agents should be evaluated (“LLM as Judge”) against sample datasets to ensure reliability. Once deployed, they must be continuously monitored for bugs, user feedback, and performance metrics. Reference - https://lnkd.in/g9_Fyecp Google for Developers, Kaggle #AIAgents #GoogleAI #Kaggle #LLM #DevOps #AIInnovation

What if a full day of work could be completed in just five minutes? That's what all the "AI" and automation tools out there are promising, right? Well, we want to show some proof. We're pretty anti-bots and surface-level AI around here, and this story is why...Recently, we had a client come to us because they were fed up with a bot they had installed that was supposed to make their lives easier. But what they got was a slow, error-prone bot that didn't work. We replaced that bot with a true database-level automation that runs faster and more accurately, clearing every item in the queue. By doing this, the client's coders can focus on meaningful work, errors are down, and the revenue cycle is running smoother than ever. Read the full story: https://lnkd.in/gyJSTsjK

Stop chasing meaningless technical metrics. 🙅 Ivo Bernardo explains why business outcomes, like reducing churn, matter more than a model's accuracy score. Learn to define and track metrics that deliver real value.

“Our goal has always been to give users confidence that their documents are accurately checked.”📄✅ Trade execution teams face a large volume of documents every day - from drafts and scans to mixed PDFs. Even with manual checks, overlooked errors can result in discrepancies under letters of credit or cash-against-documents presentations, leading to costly delays and penalties. That’s why we built QuickDocs. The platform automates document validation at scale, surfacing critical discrepancies with actionable AI insights, while keeping human oversight where it matters. Teams can process hundreds of documents in minutes, ensuring shipments and payments move smoothly. Designed specifically for commodity trade and fully integrated into the Covantis ecosystem, QuickDocs empowers teams to streamline global trade with speed and confidence. Read our Q&A with our Group Product Manager, Guillaume Perreau, to learn more: https://lnkd.in/eqePejSf