Just a few years ago, businesses looking to build AI-powered products had little choice but to hire artificial intelligence engineers with deep expertise in machine learning, data science, model training, and deployment.
In 2026, things look very different.
Companies now have access to powerful foundation models, no-code AI platforms, AI agents, and tools that can generate code, automate workflows, and accelerate development. Some argue that these advances reduce the need for specialized AI talent, while others believe experienced AI engineers are more critical than ever for building reliable, scalable, and competitive products.
This raises an interesting question: As AI becomes easier to access, does that increase the value of AI engineers—or make them less essential for businesses building new products?
I’d love to hear perspectives from founders, CTOs, product leaders, and teams that are actively building AI-powered applications, automations, or enterprise solutions.
I don’t think the answer to your question is black or white.
I think that it is possible you could answer your own question. For example, if you are a CTO and you have an existing staff of software engineers, what would you do if you want to build new AI solutions into existing applications or build entirely new applications? Somebody has to do it, right? You could hire new AI IT staff, get your current staff trained, hire AI IT consultants, or a combination thereof. As time marches on, it is entirely possible that you would want to trim your IT staff. This is already happening.
As far as enterprises go, if CTOs are not already building AI solutions, then they are way behind the curve and probably need to be terminated.
It increases the need to train your current employees of various digital skills to use AI in their tasks. Of course, in a reasonable way. I don’t think it’s purely about AI engineers, but also (and I think first of all) works for designers, marketing teams, support, and literally any person from the digital area. They will need to orchestrate AI in their particular tasks.
From what I’ve seen, AI isn’t reducing the need for AI engineers, it’s changing what they’re expected to do.
Writing prompts is becoming easier.
Designing reliable AI systems, evaluating outputs, integrating models into products, and understanding business problems is becoming much more valuable.
The tools are getting smarter, but the bar for building production-ready AI is getting higher.
Just because AI is there doesn’t mean all is solved. Still need to know the environment, requirements, capabilities of the use cases. Integrating AI is just another part of the pipeline.