Build, buy, extend, integrate, govern and assess. Written for leaders accountable for how technology changes the business.
Find your next argumentUse these questions to turn an argument into a reviewable decision.
Which part of the job needs to be distinctive?
Evidence to carry forward: One business outcome; credible alternatives.
Which constraints change the architecture?
Evidence to carry forward: Data, access and integration requirements.
Who carries the cost after launch?
Evidence to carry forward: An accountable owner and a revisit trigger.
An editorial framework. It is not evidence of a completed client system.
10 perspectives / Decisions
AI makes building software easier. That does not mean building everything is the intelligent choice. The strategic advantage lies in knowing which parts of the system should be distinctive, and which should simply work.
A useful vendor evaluation tests judgment, delivery and candor, not the number of AI tools on a services page.
The ability to edit source code is useful only when the codebase’s constraints and dependencies are understandable.
The right comparison is not a monthly software license versus a one-time custom build. It is the full operating economics of two ways to provide a business capability.
The AI platform, business process and governance team need one accountable route to production.
'We need our own AI' can describe four different requirements. The first project decision is to find out which one the business actually has.
“We need our own AI” is a perfectly reasonable business request. It is also an incomplete technical requirement. Ownership of a model, privacy of data and control of an application are three different questions.
Control may improve, but the organization becomes responsible for inference reliability and lifecycle management.
Software ownership is not one thing. The rights to code, data, model weights and cloud infrastructure belong to different contracts. Good engineering makes the boundaries understandable.
Custom RAG should be justified by an actual functional gap, not by the allure of building a private AI product.
Compare the alternatives before committing to a technology, a supplier or an operating burden.
A technology decision is not complete when a tool is selected. It needs a statement of the job, the options considered, the constraints that mattered, and the owner of its future operating costs. Our Decisions essays make that reasoning visible.
The questions worth asking are practical: what must be differentiated, what data cannot move, which integration is already available, what will happen when models change, and who can maintain the result? These decisions often have more influence on outcomes than the selection of an AI model.
Use the articles as thinking tools, not as product advertisements. Start with the question nearest your current decision, follow the cited sources and inspect the limitations. Each article should tell you what to do differently, when the recommendation changes, and which risks cannot be resolved by reading alone.
If the decision leads to a real engineering assignment, share the relevant requirements and constraints. Marmation can help determine whether a focused engagement is appropriate. Discuss a project.
A product idea, a broken workflow, scattered company knowledge or an integration others keep working around. Tell us what needs to change and what has already been tried.
Discuss a project