Custom AI, off-the-shelf software or ChatGPT: what to choose?

Off-the-shelf software (SaaS) is a standard product, the same for everyone: fast to activate and priced as a subscription, but you adapt to its features. ChatGPT and generic LLMs are versatile assistants you use by hand, great for drafts and occasional tasks, but not integrated with your systems and with your data leaving to an external service. Custom AI is built on your process: it integrates with your management system and archive, follows your rules and keeps data under your control.

The choice depends on three questions: is the process standard or specific? Is the volume high and recurring or occasional? Are the data sensitive and to be integrated with your systems? The more the answers lean toward specific, high-volume and integrated data, the more custom is worth it.

It is worth it when the need is standard and already well covered by the market: invoicing, email marketing, appointment scheduling, accounting. If a ready-made product does what you need and your processes can adapt to how it works, it is the fastest and cheapest choice: no development, you start right away.

It becomes a limit when your process is unusual, when you need integrations the product does not provide, or when you end up paying for (and using) only a small part of a big package. There, adapting to the tool costs more than it seems.

It is enough for occasional, non-critical tasks: writing a draft, summarizing a text, getting a starting point. It is versatile, immediate and low-cost. For one person's personal or sporadic use it is often more than enough.

It is not enough when the task is recurring and high-volume, when integration with your data and systems is needed, or when data is sensitive: used by hand it does not connect to your systems, does not apply your rules reliably, and sends information to an external service. For a critical business process, these limits weigh.

It is worth it when the process is specific, recurring and high-volume, when it must integrate with your ERP, CRM or archive, and when data must stay under your control. In these cases the tool adapts to you — it reads and writes where you already work, follows your rules, keeps data with zero-training and an isolated environment on request — and the return comes from the time it frees up continuously.

It is not worth it where volumes are low or a ready-made product already covers the need well: forcing a custom project there is waste. That is why the initial diagnosis is honest and can conclude that, in your case, the right route is off-the-shelf software or a targeted use of a generic LLM.