Most conversations about artificial intelligence focus on what the models can do. Write a poem. Generate an image. Answer a question. But inside large companies, the conversation is different. It is not about what AI can do. It is about whether AI can be trusted to do the same thing, the same way, every single time, at 3 a.m., without someone watching over its shoulder. Industry projections suggest that up to 50% of enterprise-built BI platforms will embed generative AI by 2028, compared to just 5% in 2023. That gap represents one of the hardest engineering transitions underway today.
Thirupathi Reddy Anneda has spent the last several years inside that gap. He is a Senior Technology Lead with more than 18 years in enterprise data engineering. His work on a Global Business Intelligence platform for a Fortune 500 technology company involves keeping thousands of databases, pipelines, and reporting systems running continuously. The platform spans Oracle, Snowflake, SAP HANA, SingleStore and MySQL. It supports streaming from Kafka, Spark and Flink. And it has to work every second of every day.
The problem is that traditional monitoring does not scale to this size. Human engineers watch dashboards, respond to alerts and investigate anomalies. That works when the system is small. When the system is large, the humans drown in noise.
“The real inefficiency is that we trained people to do work that machines should be doing,” Anneda says. “Pattern recognition, anomaly detection, root cause analysis. That is not a technology problem anymore. It is a process problem.”
Teaching Machines to Watch Themselves
His team architected and deployed Gen AI-powered observability automation using Claude AI and Endor. Endor is an internal generative AI platform for enterprise applications. It provides secure, compliant access to advanced language models. The AI reads logs, correlates timing across distributed systems, checks recent deployment changes, and writes a preliminary incident report. A human engineer reviews that report instead of starting from scratch.
Anneda learned this the hard way. Early versions flagged everything. A temporary network blip triggered a full incident report. Engineers stopped reading them. The team had to teach the AI to recognize normal system behavior and express uncertainty appropriately. It now assigns confidence scores to each conclusion.
“The AI handles first-pass triage,” Anneda says. “That means senior engineers stop being firefighters. They become architects again. They work on improvements instead of chasing alerts.”
The Release Problem Nobody Fixes
There is another kind of work that eats engineering budgets without anyone noticing. Upgrading databases. Patching middleware. Deploying new reports. These tasks are essential, repetitive and time consuming. 40% to 60% of engineering budgets go to maintenance and operational activities.
Anneda’s team engineered automated system upgrade orchestration frameworks for the GBI platform. The frameworks minimize production downtime and eliminate manual release effort. They handle dependency checking, sequencing, validation and rollback across the multi-database environment. The team built rollbacks before forward automation. If an upgrade fails, the system reverts automatically.
“You cannot upgrade Oracle the same way you upgrade Snowflake,” Anneda says. “A reporting database can handle brief slowness. A real-time pricing system cannot. Your automation has to know the difference.”
Before automation, releases required weekend work and late nights. After automation, releases became routine. Anneda considers routine the highest compliment. It means the platform is stable and the team can focus on improvements instead of emergencies.
The Customer Does Not See the Database
All of this internal engineering work exists for one reason. The customer.
Global product launches generate more than 10 million customer orders within minutes. Each order requires a precise, real-time shipping quote. That quote must account for where supply physically exists in the world, how much is available and how long delivery will take. Getting it wrong or slow damages trust.
Before Anneda’s work on the GBI platform, the shipping quote process was not built for global scale. Planners relied on manual spreadsheets, non-real-time data exports and human-driven updates. Legacy systems made it difficult to predict when quote changes would appear on the online store. Planners padded delivery estimates because they did not trust the system.
“Planners became conservative just to stay safe,” Anneda says. “That conservatism hurts customers. People saw longer delivery windows than were actually necessary.”
The new platform replaced all of that. It dynamically computes accurate shipping quotes at the moment of purchase, drawing on live supply pool data across a global logistics network, including supply not yet manufactured. These applications have saved billions of dollars with accurate planning, execution and reporting.
Harnessing Automation as Engineering Capacity
Beyond his production work, Anneda served as a Judge for the Globee Awards for Artificial Intelligence, evaluating enterprise technology projects across industries. He noticed a clear pattern. The projects that succeeded were not the ones with the most sophisticated AI. They were the ones where the team understood exactly what inefficiency they were removing.
Anneda has a simple rule for anyone building automation. Do not automate a broken process. Fix the manual process first. Map every step. Remove unnecessary approvals. Standardize everything. Then automate.
“The AI is not the hard part anymore,” Anneda says. “The hard part is knowing exactly what inefficiency you are removing and for whom. If you cannot answer those two questions, no amount of generative AI will save you.”
65% of consumers will halt a purchase if they experience even small digital disruptions, and internet disruptions cost companies up to $1 million per month. That is why this work matters. Not because the technology is interesting, but because the customer is waiting.
