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Business-first AI transformation

I've engineered software for 25+ years. Now I'm testing firsthand how AI is changing that experience and writing about what is actually changing in practice.

I write about how businesses can use AI, software, data, and process redesign to create measurable value — starting with the business problem, not the technology.

Start with a guideAbout my background

I've been building software with a range of programming languages and technologies since 1998, designing reliable, scalable system architectures.

My perspective spans writing code, leading technology teams, and working hands-on across sales, marketing, and field operations.

I care less about what AI can do in theory than which processes and outcomes it can improve in a real business.

What you'll find here

Yes, I work on technical problems too. But I don't write about technical subjects for technology's sake. I write about the business problems AI, software, automation, data and process design solve in a real business—and what they change. The question at the top of my agenda lately is this: How does a company move from using AI simply to ask questions and get answers to integrating it into its processes, decisions and, when necessary, its business model?

If you have a particular question in mind, the guides explain the basics and the decisions involved, with links to relevant articles. You can find recent writing below and other guides and topics on the Explore page. Whether the intelligence is artificial or human, the question remains: How can we improve the quality of our work, become more efficient, innovate, work more professionally, and create more value?

Reading guides

What are you trying to work out?

Start with the guide that fits your question: assessing a project, changing how work gets done, or deciding who is responsible.

Explore all guides
Essays

Start here

Recent writing on AI, software, and business.

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Technical writing

Technical writing

Hands-on notes and tutorials on Go, Linux, Docker, web fundamentals, and more—with new technical writing added as I publish it.

A decision station connects a business workflow to either locally controlled or externally managed AI infrastructure

Choosing Between Open-Weight and Proprietary Models

Read →6 min
A blue speech token sits behind a divider beside a laptop displaying code-like lines.

When Should Claude Code Crack a Joke?

Read →4 min
A hand marks a decision card between a code proposal and the warehouse’s parcel and order records.

Who decides the business rules when AI writes the code?

Read →4 min
An internet request passes through an open gate into an AI agent workspace, where an API key and usage meter sit beside an isolated data vault.

Agent Security Does Not End with Model Choice: Lessons from METR’s Two Security Incidents

Read →9 min
Hands carry a stack of bound books with a blue bookmark toward an empty shelf.

Why I Retired Some of My Technical Articles

Read →4 min
A hand turns a blue dial on a shielded gate beside four layers of abstract data patterns.

Do You Need a Paid CMP for Google Consent Mode v2?

Read →19 min
A PHP elephant, inspection lens and blue packages sit along an assembly line.

CI/CD for PHP: A Comprehensive Guide

Read →8 min
A balance scale and precision gauges represent weighing a small model's operating constraints

PHP-ML in a PHP Application: A Bounded Decision Guide

Read →7 min
Browse technical writing →
Business-first AI transformation

How I evaluate a change

I first try to understand what needs to change. I choose the technology only after the problem, expected benefit, and cost are clear.

  1. 1

    Goal

    What should improve: cost, speed, capacity, quality, or revenue?

  2. 2

    Current state

    How does the work happen today, and where are time, money, or knowledge being lost?

  3. 3

    Looking ahead

    Even if I do not act today, I consider likely scenarios. A small preparation now can prevent far more work later.

  4. 4

    Priority

    Which change is most likely to produce a meaningful result?

  5. 5

    Benefit and cost

    Is the expected gain worth the cost, effort, and risk?

  6. 6

    Workflow

    What should be removed or changed before anything is automated?

  7. 7

    Approach

    Would AI, software, an existing product, or a process change be the best fit?

  8. 8

    Implementation and measurement

    Put the change into practice and measure what actually improves.

Business-first AI transformation

Field notes

Short accounts from systems built in the real world, with the details that made them useful.

Lead operations
100K annual leads

Medical Tourism Lead Management: How We Moved from Manual Routing to a Real-Time Sales System

A real-time decision-support and sales-management platform for routing, payments, and management visibility.

Read →
Patient conversations
One year of multichannel chat

What We Learned Building a Healthcare Chatbot for International Patients

What emerged from building and operating a chat system for patients across channels.

Read →
Small infrastructure
$5/month small VPS

Why I Run camiler.org on a Single VPS

A modern open-source infrastructure assembled with a small server and Cloudflare.

Read →

Open Publishing

Publishing, readership and monthly reports

Explore this blog’s publishing and visitor statistics, search visibility data and monthly reports in one place.

Explore Open Publishing