
What Can AI Actually Automate in CRO Research?
Can you have AI watch Clarity recordings and produce a ready-made optimization report? The limits of data access, automation, and human judgment in CRO research.
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.
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.
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?
The pieces below are ordered from newest to oldest. If the first few feel too basic or too technical, keep exploring, because in each piece we look for answers to questions at different depths. Whether the intelligence is artificial or human, the question we need to answer is this: How can we do our work better—with higher quality, greater efficiency, more innovation and greater professionalism—and create more value?
These curated paths bring related essays together around concrete questions companies need to answer.
A practical guide to the decisions that shape an AI system: the problem, information, authority, evaluation, and the role of models and routing.
Explore the guide →A critical guide to measuring AI visibility, interpreting scores, and deciding what the evidence really supports.
Explore the guide →A reading guide on turning AI use into business value, redesigned work and durable organisational capability.
Explore the guide →A reading guide on AI regulation, accountability, human oversight, data boundaries and deployment risk.
Explore the guide →A developing guide to quality, verification, maintenance and delivery choices in AI-assisted software work.
Explore the guide →A developing guide to company knowledge, internal archives and preserving organisational memory through AI change.
Explore the guide →Recent writing on AI, software, and business.

Can you have AI watch Clarity recordings and produce a ready-made optimization report? The limits of data access, automation, and human judgment in CRO research.

You may use the same AI as your competitor. The competitive difference emerges when the model changes the promise made to a customer and informs the next decision.

Trusting AI advice, accepting it, and making a sound decision are not the same. Human approval alone cannot tell them apart.












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








What matters is not the model, but the decision, workflow, or economics it changes.
Explore AI writing →02Processes, handoffs, information, and decisions are where operational friction becomes visible.
Explore business writing →03Architecture, infrastructure, and implementation determine whether an idea becomes dependable.
Explore engineering writing →I first try to understand what needs to change. I choose the technology only after the problem, expected benefit, and cost are clear.
What should improve: cost, speed, capacity, quality, or revenue?
How does the work happen today, and where are time, money, or knowledge being lost?
Even if I do not act today, I consider likely scenarios. A small preparation now can prevent far more work later.
Which change is most likely to produce a meaningful result?
Is the expected gain worth the cost, effort, and risk?
What should be removed or changed before anything is automated?
Would AI, software, an existing product, or a process change be the best fit?
Put the change into practice and measure what actually improves.
Short accounts from systems built in the real world, with the details that made them useful.
A real-time decision-support and sales-management platform for routing, payments, and management visibility.
Read →What emerged from building and operating a chat system for patients across channels.
Read →A modern open-source infrastructure assembled with a small server and Cloudflare.
Read →This site is where I share notes on AI, software, the products I build, and the problems I encounter at work.