Gaurav Vijaywargia
Frameworks, playbooks, and lessons from shipping AI at Work.
Your company spent six figures on an AI assistant that just translates English into API calls. If the task is that predictable, build a button. Here's how to tell if your AI is a switchboard operator or a senior analyst — and the three stages every enterprise AI goes through.
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Not a framework post. This is what it actually looks like to ship an AI-native platform inside a large enterprise — the spec-driven development approach, the agentic AI that replaced a 40-field form, the cloud surprises, and the 14 days I spent waiting for infrastructure tickets instead of writing code.
Salesforce, ServiceNow, Jira, Slack — everyone shipped a copilot. Each costs extra. Each only sees its own data. None talk to each other. You're paying $2,300/user/year for AI that can't cross tool boundaries. It's time to build a city, not more buildings.
Your AI demo was impressive. Then users found the edge cases and now you're playing whack-a-mole with prompts. The problem isn't your model — it's that you skipped evals. Here's how to build an eval system that actually compounds over time.
After shipping multiple AI assistants, here's what I can tell you: building one is the easy part. It demos beautifully, then real users and bad actors show up. Here's the four-layer guidance system that keeps your AI in orbit.
Everyone's investing in AI. Almost nobody can prove it's paying off. This four-pillar framework — Operation, Fit, Purpose, and Impact — gives you a structured scoreboard for answering the two questions that matter: "How are we performing?" and "Are we creating business value?"
For every AI tool IT knows about, analysts estimate there are five to ten more running under the radar. Nobody's being malicious — they just want to move fast. But the risks compound quicker than anyone expects.