What it takes to scale agentic AI in a regulated environment, according to CareSuper’s CTO

Agentic AI is creating new opportunities for efficiency and service improvement, but regulated organisations do not have the luxury of scaling it loosely.

Governance, trust, and accountability have to mature alongside the technology.

CareSuper CTO Simon Reiter talks about how the fund is balancing experimentation with regulatory obligations, internal adoption, and the controls needed to move AI safely into day to day operations.

Listen to the full episode on Apple Podcasts and Spotify.

Key takeaways:

• Governance has to come before scale, with clear policies, standards, and risk controls in place before AI moves into production.
• AI adoption depends as much on trust and change management as it does on the technology itself.
• Production ready AI needs strong foundations, especially clean data, connected systems, and clear identity controls.

Governance has to come before scale

Regulated organisations cannot treat AI as a tool first and a governance problem later.

The controls need to be built early so promising use cases can move into production safely.

Simon says CareSuper is running two streams in parallel, one focused on governance, ethics, standards, and regulatory alignment, and another focused on practical use cases from across the business.

That creates a clearer path from pilot to production, while filtering out ideas that are really process issues rather than AI opportunities.

AI adoption moves faster when people trust the change

Adoption does not come from rollout alone.

People need to understand where AI fits, what it improves, and why it is being introduced.

Simon says CareSuper invested heavily in change management before scaling usage across the organisation.

Executives, general managers, and technology teams went through training supported by internal sessions and practical examples, helping staff build confidence and see AI as a way to remove low value work while keeping humans in the loop.

Data, integration, and identity will decide what reaches production

The hard part is not running a pilot.

It is building the foundations that make AI safe and reliable once it starts acting across systems and workflows.

He points to three essentials: data quality, integration capability, and identity management.

Poor data can still produce convincing outputs, weak integration limits operational value, and unclear identity makes it harder to trace, audit, and govern agent activity.

That is why CareSuper has invested in stronger identity controls and a central asset register to link agents to accountable owners and ongoing review.