01
What is native AI?
Native AI is AI capability built into software your business already uses. Because it sits close to the platform's data, permissions and workflow, it can be the lowest-friction route when it actually meets the requirement.
02
When is native AI the better choice?
Use native AI when it can perform the required job with acceptable quality, permissions, controls and economics. The benefit is often simpler ownership: fewer vendors, less data movement, fewer integrations and less maintenance.
03
When does native AI stop being enough?
A gap remains when the process crosses multiple systems, the built-in feature cannot access required context, the workflow needs deterministic routing or logging, or the business needs a capability that the platform does not provide.
04
When is custom AI justified?
Custom AI is justified when the capability gap is commercially material and cannot be solved adequately through configuration, integration or an existing product. A custom build should have a clear owner, measurable baseline, control model and maintenance plan before implementation starts.
05
How does GrowAILab decide?
GrowAILab uses five decision states so technology follows the business case rather than the other way around.
The existing software already solves the requirement adequately.
The capability exists but needs setup, rules, permissions, knowledge or process redesign.
The value lies in connecting systems, data and handoffs.
There is a genuine capability gap and the economics justify custom work.
The process, readiness, economics or risk do not justify intervention yet.
06
What should you verify before changing your software stack?
Confirm the current vendor's live capabilities, plan requirements, regional availability, permissions, data access and workflow limits. Product features change quickly, so GrowAILab treats vendor capability as a dated fact to verify rather than a permanent assumption.
QUESTIONS
Common questions
Is custom AI always more powerful?
No. More custom code can also mean more cost, maintenance and failure points. The right question is whether the additional capability creates enough business value to justify those trade-offs.
Should we replace software that already has AI?
Not automatically. First test whether the existing platform can meet the requirement through native AI, configuration or integration.
What if our software has AI but our team does not use it?
That may be an adoption, configuration, workflow or permissions problem rather than a software gap.