Build vs Buy
Build vs buy is the decision between custom AI software shaped around your process and an off-the-shelf product you adopt and configure, weighed on cost, fit, speed and control.
No jargon, no math
In plain words, what is it?
The mechanism is a scoring of one specific process against two paths. First you name the process precisely: not “use AI”, but “quote custom parts in ten minutes”. Then you test whether the process is core to your edge. If every competitor runs it the same way, a product already exists: you buy, and you are live in weeks. If the process is where you win, you check how much a product really fits. Covering most of the workflow means adopt and extend; covering little means build, which buys exact fit at the price of owning maintenance, data pipelines and upgrades forever. The hybrid is common: buy the model, build the workflow. Where it breaks: buying a generic tool for the process that makes your margin, or building what a mature product already does well because building feels serious.
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Name the process. Be specific: not “use AI”, but “quote custom parts in ten minutes”.
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Buy the generic. If competitors run it the same way, a product already exists and you are live in weeks.
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Check the fit. If a product covers most of the workflow, adopt it and extend.
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Build the core. When the process is your edge and nothing fits, custom is the honest answer.
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Own the outcome. Built or bought, someone inside must run, watch and update the system.
Terms that live nearby.
AI Strategy
An AI strategy is the plan that turns AI from scattered experiments into a coordinated capability: which processes you automate, in what order, with what data, budget and governance, and when you stop.
ReadAI ROI
AI ROI is the money an AI system returns compared with everything it costs to build and run it: savings and new revenue weighed against licences, integration, data work and human oversight.
ReadAI Readiness
AI readiness measures how prepared an organisation's data, processes, people and governance are to run AI in production, and it is assessed before any model is deployed.
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