Discussions

As Generative AI continues to mature, many companies are asking: Is it worth investing in generative AI development now, or should we wait until the technology stabilizes further?

Generative AI offers huge potential — from auto-generating content and automating creative workflows to synthesizing data and speeding up prototyping. Businesses can use it to draft marketing copy, generate images, build report templates, create test data, or even prototype new products — all with much less human effort.

However, this promise comes with real challenges and risks. Models can produce inaccurate or misleading outputs; they often require large, quality data sets; deployment can demand significant infrastructure; and ethical/legal issues (copyright, bias, privacy) remain serious concerns.

So the question becomes: when investing in generative AI development services:

How do we balance innovation potential against cost, risk and reliability?

What standards or safeguards should we put in place before using AI-generated outputs?

Where is generative AI truly mature enough to replace manual workflows — and where does it still need human oversight?

I’d love to hear what this community thinks: Have you experimented with generative AI development? What worked well, what failed — and would you build on it again?