# Institutional knowledge is the data skill AI cannot replace - Published: 2026-11-10 - Author: [Vinit Rajguru](https://cooinsider.com/author/vinit-rajguru.md) - Topics: [Insights](https://cooinsider.com/insight.md) - Source: https://cooinsider.com/insight/institutional-knowledge-is-the-data-skill-ai-cannot-replace/ - Publication: COO Insider - Publisher: Connectively (Terkel, Inc.) - Editorial policy: https://cooinsider.com/editorial-policy/ One weekend I was sitting at my apartment playing with my kid, and I saw a video on TV saying artificial intelligence (AI) will replace everything and people are losing their jobs. The rumination started in my mind that I might also lose my job because of AI, and honestly I was scared and stressed at that time, even though I was in a good position at my current role. As always, the mind tries to protect you. Then I started remembering my early days, how I started my career and how I am using AI in my current role. When I got my hands dirty with it, I realized it will not take my job the way I was thinking. I have been with the same client for almost five years. When I started, I was shadowing someone and they told me to eyeball the data. Then after one month, that person left the company, and no one was there to teach me. I thought I couldn’t handle it because I didn’t know anything about it. I was hired as an ops person to help the organization with day-to-day operations. I honestly wanted to quit. No one was there to help me, and I was getting pressure from different teams including campaign ops, marketing ops, products and engineering. This incident happened when the team wanted to launch a campaign, and the goal was to send communications to users who don’t have a business account, but we ended up sending it to users who already had a business account. The reason was the type of identifiers (IDs) we were using to identify those accounts — no one knew what those IDs meant. So I started collecting evidence. Searching Slack to see if anyone knew about certain tables or columns. Digging through Confluence pages. Trying my luck everywhere. The customer data platform (CDP) also had its own reliability issues when data volumes were too large. The data pipeline was set up to run every two hours, falling back to the next cycle whenever a failure occurred. But there was a hard catch: a strict limit of 200 million records. Exceeding that volume broke the pipeline entirely, leaving us with only two paths forward: Either cut down the data volume or contact the CDP support team in advance to give them a heads-up that a specific audience would breach the threshold. Usually, whenever we received a request from marketing ops or campaign ops or the product team, we had to follow a few steps: Submit a ticket in our project management tool, wait for it to be assigned and then take it from there. Based on these requests, the average end-to-end turnaround time was approximately five days, which became a pain point for the marketing and campaign ops teams. While this timeline was generally acceptable for regular campaigns, ad hoc campaigns often required a faster turnaround. To optimize this workflow, and after many trials, I engineered an AI-assisted tool (Field Identifier) that lets campaign ops and marketing ops identify which fields they can use for their upcoming campaigns without reaching out to the data team every time. Did the changes actually make a difference? We let the data speak for itself. In the second half of 2025, our team of three completed 170 requests with an average five-day turnaround. After building and testing a prototype in January 2026, I launched it in February. Over the next six months, despite taking on a higher workload of 185 requests, we cut our turnaround time down to just two days. Field Identifier did a lot of the heavy work, but the speed boost came from a few moving parts working together — mainly automation, better Structured Query Language (SQL) queries and the team tweaking the workflow as we grew. This was our "aha" moment because the dashboard gave leadership visibility into the measurable improvements we had achieved. Currently, I am architecting an AI agent to fully automate query execution and segment building, so marketers don’t even have to interact with the tool. They simply make a request, and the work gets done. Their only responsibility is to review the result and make sure everything looks correct. The way I use AI is more like a conversation. I bring my problem, AI asks me clarifying questions, I answer and together we figure things out. But here is the thing: If I don’t know my own data, I can’t answer those questions. AI is only as good as what I bring to the conversation. Without years of embedded contextual knowledge, it is just guessing. Early in my career, when I told my colleague how lost I felt and overwhelmed I was, she gave it to me straight: I had been thrown into the deep ocean and had to sink or swim. Initially I thought, how can someone be so straight forward? Later I realized that most data environments are exactly like that. Nobody hands you a life jacket. The individuals who survive are those who master the waters and figure out how to swim — and then build the life jacket so the next person doesn’t have to. Through discussion with colleagues and friends who are interested in data analytics and data engineering, I have observed that some people are reluctant to enter into the domain thinking AI might limit the opportunities. My advice is to target overlooked areas. AI can definitely handle tasks, but you need to train it with the right knowledge first. That knowledge and those insights need to come from someone who is actively engaged in the domain, has hand-on experience with the day-to-day work and understands the why behind it. Be that practitioner. This article was first published on [Featured News](https://news.featured.com/institutional-knowledge-is-the-data-skill-ai-cannot-replace). ## Related - [Why leaders should get more tactical, not less, as they move up](https://cooinsider.com/insight/why-leaders-should-get-more-tactical-not-less-as-they-move-up.md) - [How business owners become the bottleneck, and how to step back](https://cooinsider.com/insight/how-business-owners-become-the-bottleneck-and-how-to-step-back.md) - [The Most Expensive Assumption a Leader Can Make: "We Know What Our Customers Want"](https://cooinsider.com/insight/the-most-expensive-assumption-a-leader-can-make-we-know-what-our-customers-want.md) - [Stop Automating Reports. Automate the Decision to Investigate](https://cooinsider.com/insight/stop-automating-reports-automate-the-decision-to-investigate.md)