Blog, page 2
Amit's notes
My personal views only.
You are at phase zero, and the deck you were sold starts at phase three
Every enterprise AI maturity model starts a rung above where most companies stand and skips the one that holds the rest up: getting the tool safely into people hands. Your team already has Claude. If IT cannot produce the tenant policy, the egress allowlist, the tool allowlist, and the audit log, you are at phase zero, whatever the deck says.
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Good-enough AI will eat the premium-model business
Good-enough AI is driving commoditization from below. Stanford HAI clocked a 280-fold drop in the cost of running a GPT-3.5-level model. Once a cheaper model clears the bar for a job, the frontier model stops earning its premium for that job.
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Revenue per employee is the only number that survives AI
Most operating metrics get noisy or gamed once AI absorbs the task work. Revenue per employee stays hard to fake. When Facebook bought WhatsApp for about 19 billion dollars, the company had 55 people. That ratio, output per head, is the acid test of whether AI bought you a real gain in output.
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The consultant who fought to keep his client off AI
Some advisors resist letting a company connect AI to its own systems, dressed up as too risky. The Everlaw survey found 90% of legal professionals expect AI to change billing within two years. The real driver is an AI consultant protecting the gatekeeper role.
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The dashboard delusion
A dashboard is a decision you have stopped making. Goodhart law corrupts the metric the moment it becomes a target, and watching a number feels like managing it. Name the decision each dashboard should trigger and the one person who owns it, or delete the dashboard.
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Your AI context layer is only half a brain
An AI context layer feeds every model one governed source of company truth, and DataHub and Atlan will sell you that read half today. The half that notices when a person did not get what they wanted, the re-ask nobody logged, is what turns a knowledge store into a brain.
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A report and a semantic model are not the same thing, and your AI agent only cares about one of them
Most people treat a Power BI report and its semantic model as one object. They are two files doing two jobs. When you point an AI agent at your data, the report is the cheap half and the semantic model is the part that took three years to get right.
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BI only ever saw half your company. AI can see the other half
Business intelligence was always the quantitative side: rows, numbers, things that fit in a column. The qualitative half, the calls and emails and tickets where the why actually lives, was invisible to it. That half is most of your data, and it is where AI adds value BI never could.
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Managed AI agents and the cost crossover nobody calculates
Anthropic managed agents bill $0.08 per session-hour, and everyone races to compare that to a cheap VM. The comparison misses the point. Runtime is a rounding error next to tokens, and the operations bill decides the rest. Here is where self-hosting an AI agent actually starts to pay, with the real 2026 numbers.
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One-time question or a permanent dashboard? AI just changed the answer
Every BI team has quietly run the same triage for years: is this worth a dashboard, or is it a one-off? Building a dashboard was the only durable option, so the long tail of one-time questions mostly went unasked. AI collapses the cost of the one-off, and that reshapes the whole portfolio.
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Should you build your agents in Copilot Studio? The demo is not the question
Low-code agent builders like Copilot Studio get you to a working demo in an afternoon. That is real, and it is also the trap. The question is not whether it demos well. It is what you give up the day you need control, and whether you will need control.
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Stop building "the ERP agent." The value is in skills that cross functions
Companies build AI agents shaped like their org chart: an ERP agent, an HR agent, a finance agent. Each one is a silo with a chat box. The real payoff shows up when skills compose across functions, because data exists to tell a story or trigger an action, not to sit in one department.
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The hard part of analytics AI is not the answer, it is figuring out the question
Everyone obsesses over whether the model reasons well. The real failure in AI over business data happens earlier, at the moment the agent decides what you meant. A confident answer to the wrong question is worse than no answer at all.
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Your old dashboards are the answer key for your new AI
Teams building analytics AI keep starting from a blank page. Meanwhile the most validated business logic they own is sitting in the dashboards they already shipped. Those reports are years of distilled definitions and a ready-made test set. Mine them.
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The Claude Certified Architect path and the four Academy courses
The Claude Certified Architect credential sits on a free, four-course learning path: Agent Skills, the Claude API, the Model Context Protocol, and Claude Code in Action. The courses carry the value. The exam is the paperwork. What each course covers and who on your team should take it.
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How the Claude Partner Network tiers actually work
The Claude Partner Network is free to join, so membership on its own tells a buyer nothing. The real structure is four tiers, each earned by three published numbers: certified people, customers in production, and public references. What every tier asks for, and what comes back at each rung.
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Your AI has no whoami
Every enterprise AI platform resolves what you can access through SSO and SCIM. None of them load your team instructions from who you are. Claude gives admins one 3,000-character field for everyone. Microsoft Copilot reads your permissions but not your team playbook. Here is the gap and what works today.
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The AI committee always arrives second
Companies form an AI committee after employees already use AI daily. University of Melbourne research covering 48,000 workers in 47 countries found 58% use AI at work and 57% hide it. The committee exists to catch up, and that changes who sits on it and what it does first.
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How to host a small app and database on a $4 DigitalOcean droplet
Cloudflare Pages hosts a static site for free, but it cannot run code or store data. A $4 DigitalOcean droplet runs a real Node app with a SQLite database behind automatic HTTPS. Here is the exact setup captured from a live box, plus when to reach for something bigger.
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Why your good content still does not rank
Your content is clean, correct, and on-keyword, and it still sits on page four. After Google December 2025 core update folded helpful-content into core ranking and pushed E-E-A-T past health and finance, the only edge left is proof of real expertise you cannot fake.
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How I run my whole consulting practice with Claude
I run Blue Sheen, the firm I lead with Pravina, through Claude and Claude Code. The practice lives in a version-controlled folder that Claude reads at the start of every session, with Close CRM as the source of truth. This is the real workflow stage by stage: prospecting, proposals, delivery, and the judgment a human still has to own.
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When to use a dynamic workflow
A dynamic workflow in Claude Code runs up to sixteen subagents at once and a thousand across a job. That power is wasted on most tasks. This is the decision I use before reaching for one: when a single agent wins, when a dynamic workflow earns its cost, and when the answer is to not automate at all.
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AI does tasks. It does not do jobs.
Ten years building Tallyfy, and a year pointing AI agents at it, taught me one blunt thing. A job is a chain of tasks, and AI reliability multiplies down that chain until the whole thing is a coin flip. The fix is not a smarter model.
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Claude Team vs Enterprise: when 50 seats is not a forced upgrade
The 50 seat number that scares Anthropic Team admins is the sales-assisted Enterprise minimum, not a forced upgrade. Claude Team runs to 150 seats. The real Team to Enterprise decision is about governance features like custom roles and the Compliance API, not headcount.
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Dynamic workflows: parallel verification at scale
Dynamic workflows in Claude Code run tens to hundreds of subagents that check each other before anything reaches you. The parallelism is not the interesting part. The verification is. Here is how I am using one to re-verify around 300 posts on this site, and when it earns its cost.
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Why the Microsoft Store keeps opening after you install Claude on Windows
The Microsoft Store opening by itself days after a Claude install almost always traces back to a broken claude:// protocol handler. In split-account Windows setups the MSIX registers under the admin profile, so your session cannot resolve the link and Windows offers the Store. Here is the cause and the fix.
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You probably do not need a transfer agent: how we self-manage our cap table with AI
Most early-stage startups are not legally required to use a stock transfer agent. Delaware law lets a company keep its own electronic stock ledger. Here is how we run our cap table at Tallyfy as a version-controlled JSON file, with AI doing the reconciliation and reports, plus what the law (DGCL 219, DGCL 224, Section 12(g)) really requires.
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Claude Code to Claude for Chrome: the handoff pattern
Claude Code lives in your terminal. Claude for Chrome lives in your browser. They do not share context. So your Code session writes a self-contained prompt your for-Chrome session can run, and the browser job gets done. Plus how the native paths work on macOS, Windows, and Edge today.
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CLAUDE.md hierarchy: lock at two levels, split the libraries, audit the rest
CLAUDE.md hierarchy looks tidy in a personal repo. Push it across departments and it splits into a tree most users cannot reason about. Lock at two levels. Split read-only governance from read-write working content. Run a seven-check audit on every new file. Anything deeper is a vanity hierarchy that breaks in weeks.
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How to log every Claude API call for compliance - native logs into your SIEM
Wire-level AI inspectors price at six figures and infer what audit logs capture exactly. Claude Enterprise exposes three native log surfaces - Audit Logs, Analytics, and OpenTelemetry. Here is how to ship them to Splunk, Datadog, Elastic, Sumo Logic, Microsoft Sentinel, Arctic Wolf, or a roll-your-own pipeline, with the architecture, the credentials, and what the auditor wants to see.
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