When AI Is No Longer ‘New’: Responsibility for Ongoing Oversight

Introduction

Most AI initiatives start as something new: a project, a pilot, or a limited effort. Attention is high, risks are assessed, and roles are clarified – at least in the initial phase.

Over time, this changes. AI becomes part of everyday life, integrated into routines, decision-making processes, and workflows. When the technology no longer feels new, the structured follow-up often fades.

This article explains why responsibility for AI does not cease after implementation but changes in character during long-term operation – and why ongoing oversight must be understood as a governance responsibility, not a project requirement.

What is meant by long-term AI use?

Long-term AI use refers to situations where AI tools have been in use for some time and have become an established part of the organization's practice. The focus shifts from ‘adopting’ to ‘using.’

Characteristics of such use often include:

– AI is part of daily work processes
– Its use is normalized and rarely discussed
– Training and follow-up occur less frequently
– Changes in tools or practices happen gradually
– Responsibility is perceived as implicit, not explicit

In this stage, it is easy to treat AI as stable infrastructure, even though the technology and usage are still evolving.

Where does the responsibility question arise?

The responsibility question arises when governance follows project logic, while usage follows operational logic.

This creates particular challenges in three areas:

1. Attention shifts away from risk
   When AI no longer feels new, awareness of limitations, misuse, and changes in practice often diminishes.

2. Changes occur without grounding
   AI tools are updated, used differently, or assume new roles in workflows – without these being assessed or documented.

3. Control is replaced by trust
   After a period of ‘problem-free’ use, control mechanisms can become informal or disappear, although consequences of failure persist.

In long-term operation, risk is rarely linked to one incident, but to an accumulated lack of follow-up.

Common misunderstandings

“When AI works, we need less control”

Stable use does not mean stable risk. Over time, both technology, data, and practice can change without this being visible.

“Control is most important at startup”

Startup is about intention. Operations are about actual use. It is in operations that informal adaptations and deviations occur.

“This is a technical maintenance issue”

Technical maintenance does not cover changes in how AI affects work, assessments, and decision-making processes.

“Responsibility is already clarified”

Responsibility must be exercised continuously. What was clear at implementation can become unclear as roles change and practices develop.

What should be in place in practice?

Ongoing oversight requires mechanisms other than project management. It is not about frequent measures but stable structures.

In practice, the organization should have:

– Steady ownership over time
Someone must be responsible for AI even after the implementation is “finished.”

– Periodic review of usage
Not to reevaluate everything, but to confirm that usage, purpose, and risk are still as assumed.

– Attention to changes in practice
Small adjustments in use should be noticed before they become informal standards.

– Maintenance of competence and understanding
Understanding of limitations and responsibility weakens over time without reinforcement.

– Documentation that remains alive
Descriptions of use, responsibility, and control should be updated in line with actual practice.

This makes oversight a part of regular operations, not a temporary measure.

Actera's role in this

Actera is established to provide dental businesses with a framework around responsible use of AI.

We do not work with technology development or clinical decisions, but with governance structure, lines of responsibility, and documentation – so AI can be used in a safe, predictable, and auditable manner.

Closing reflection

AI quickly ceases to be new but never stops affecting practice. When attention diminishes, the need for structure increases.

Ongoing oversight is not about mistrust of technology or professionals, but about acknowledging that responsibility in operations is different from responsibility in projects. When governance accompanies the technology throughout its lifecycle, AI can remain a stable and predictable tool – even over time.