BEGINNER DATABRICKS TUTORIAL Databricks Genie gives business users a familiar chat interface for asking questions about company data. Instead of starting with SQL, they can ask a question such as “What were gross sales last quarter?” and receive a written answer, result table, chart, and the SQL used to calculate it. If you have used
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PRACTICAL AI EXPERIMENT Asking an AI tool a business question can feel deceptively simple. The user types “What were sales last quarter?” and expects one dependable number. Behind that question are decisions about order status, discounts, returns, reporting dates and the company’s own definition of sales. I wanted to see how much those definitions matter
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PRACTICAL AI EXPERIMENT Inventory reports have a habit of looking simple until you actually have to decide what deserves attention. A product can be below its reorder point and still be fine because stock is already arriving. Another can look healthy today while a long supplier lead time means it will run out before replacement
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PRACTICAL AI EXPERIMENT Vendor contract review can involve a surprising amount of repetitive comparison work. A reviewer reads a clause, checks the company’s contract playbook, decides whether the position is acceptable, and then moves on to the next clause. Multiply that across a long agreement and a steady stream of vendors, and a fairly straightforward
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PRACTICAL AI EXPERIMENT Accounts payable has a lot of work that is individually quite simple and collectively quite annoying. A vendor sends an invoice, someone finds the purchase order, and then the checking begins: quantity, unit price, freight, payment terms, total, and whatever special instructions happened to be written into the PO. I wanted to
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A Data Driven Culture Framework That Actually Changes Behavior A data driven culture framework is not a dashboard, a data catalog, or a training program. It is the set of habits that determines whether people actually use data before important decisions. Most companies that say they have a data culture have plenty of technology to
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How to Pilot AI Without Setting Anything on Fire At 2:43 AM on a Tuesday, the alert notification lit up multiple phones: “Critical: Demand forecasting system recommending 847% increase in orders for winter products. Human review required immediately.” The AI system had been running smoothly for eight months. No major incidents. Consistent business value. Users
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AI Change Management: What to Do When the Technology Works but People Don’t Change AI change management gets difficult at the exact point where most project plans think the hard work is over: the system works. The model is accurate. The integration is complete. The dashboards are live. Then people quietly keep doing the job
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Designing AI Products People Actually Use The demo went perfectly. Clean visualizations showing predicted demand for the top 100 products. Confidence intervals indicating forecast reliability. Historical accuracy metrics proving the AI was working better than previous methods. The technical team was proud. The executives were impressed. Then the CEO asked the question that made everyone
The AI Adoption Playbook: How Enterprises Turn Pilots Into Business Results The AI system was working perfectly. Model accuracy at 89%. Zero downtime. Response times under 200 milliseconds. By every technical metric, the deployment was a resounding success. There was just one problem: only 34% of the team was actually using it. This is the
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