By Scott, Director, Tides Digital

For most of the last two years, the conversation around AI hiring has been driven by experimentation. Companies wanted to understand what was possible. They built small teams, ran proofs of concept and explored where large language models or machine learning could add value.

What I am seeing now is a move into a very different phase.

AI is no longer a side project. It is becoming part of the production environment. It is tied to real users, real data and real commercial outcomes. That changes the type of organisation you need to build around it.

The early hiring focus was on research capability and model development. The current focus is on reliability, integration and behaviour over time. Leaders are asking who will own these systems once they are live, how they will be monitored and how they will evolve alongside the product.

That shift is changing the profile of the most valuable hires.

The engineers making the biggest impact are the ones who can operate across boundaries. They understand data pipelines, infrastructure, APIs and product context. They are comfortable with models, though they are equally focused on latency, cost, observability and failure modes. They treat AI as part of a wider system rather than a standalone component.

This is also where a lot of hiring processes are slowing down.

Companies are not only hiring for technical skill. They are hiring for judgement. They want people who can make decisions when there is no established pattern to follow. That takes longer to assess and it requires a clearer definition of what success looks like in the role.

From a leadership perspective, this is less about AI and more about organisational design.

When AI moves into production it forces alignment between teams that previously operated separately. Data, platform, product and engineering need shared ownership of outcomes. The businesses that recognise this early are building roles that reflect it. The ones that do not are trying to solve a system problem with an individual hire.

Candidates have become more selective in response. The strongest engineers in this space are looking for environments where AI has a clear purpose, where leadership understands the long-term commitment and where there is real authority to shape how systems are built. They are less interested in joining a team that is still deciding what AI means to the business.

What makes this particularly interesting is that it has very little to do with hype.

This is a delivery conversation. It is about how you run software that includes probabilistic systems, how you measure success when outputs are not always deterministic and how you build teams that can support that over time.

In the conversations I am having with CTOs, the question is no longer whether they should invest in AI capability. It is how they structure their teams so that capability produces consistent outcomes.

Hiring is where that strategy becomes visible.

The companies moving most effectively are the ones that have accepted that AI in production is an infrastructure question as much as it is a research question. They are hiring people who can take responsibility for the full lifecycle of these systems and they are designing roles that give those people the scope to succeed.

That is where the next phase of technical leadership sits.