What should management know – even if AI is handled operationally?

Introduction

In many organizations, AI is managed as an operational matter. Usage, adaptation, and daily follow-up rest with professional communities, administration, or IT – often far from top management.

At the same time, AI affects decision bases, working methods, and risk exposure in ways that are relevant at a strategic level. When this influence is not lifted up in the organization, a management gap occurs.

This article explains what management should know about AI usage – even when it is delegated operationally – and why upward reporting is a part of responsible management.

What is meant by operational AI management?

Operational AI management involves making decisions about usage, adjustments, and daily application of AI tools at a professional or operational level. This may include:

– selection of specific tools
– how AI is used in work processes
– training and support for users
– ongoing adjustments in practice

This is both natural and necessary. AI touches upon details in everyday work that are best understood close to practice. The challenge arises when operational management is not accompanied by sufficient information flow to management.

Why is this a management issue?

The management's responsibility is not related to daily use but to consequences, risk, and overall governance.

Even when AI is managed operationally, management is responsible for:

1. The organization's overall risk picture
   AI can affect quality, patient safety, reputation, and traceability – even without formal decisions.

2. Management structure and lines of responsibility
   When AI is part of operations, management must know who is responsible for what and where decision authority actually lies.

3. Compliance with expectations and requirements
   Supervisors, owners, and authorities relate to the organization as a whole, not to individual departments.

Without relevant information upwards, management's responsibility becomes formal but not real.

Common misconceptions

“As long as AI is not strategic, management doesn't need the details”

AI can be operational in form but strategic in effect. Small tools can change practices and risk without being seen as strategic initiatives.

“This is best handled at the lowest level of the organization”

Proximity to practice is important but does not eliminate the need for overarching oversight and governance.

“Management only needs to know about problems”

Governance requires insight before problems arise. Afterwards, the room for action is often limited.

“Reporting creates unnecessary bureaucracy”

Lack of information flow often leads to more informal practices, not less bureaucracy. Structured insight provides predictability.

What should be known at management level?

Management does not need technical details but should have a clear picture of how AI affects the organization.

Typically, the following should be known:

– Where AI is used in the organization
Not at the tool level but at the process and impact level.

– Which decisions and working methods are affected
Directly or indirectly, clinically or administratively.

– How responsibility is placed
Who owns the usage, who follows up changes, and who handles deviations.

– What risks have been identified
Overall, not technical – including cumulative and organizational effects.

– How management is kept informed over time
Not as a one-time information but as part of ongoing governance.

This provides a basis for real accountability without micromanaging operational practice.

What should be in place in practice?

To ensure upward reporting without unnecessarily burdening operations, the organization should establish a clear structure.

This includes, among other things:

– Clarified expectations for reporting
What should be reported to management, when and at what level.

– Firm responsibility for oversight
Someone must have the mandate to collect, assess, and convey relevant AI information.

– Distinction between operational use and governance information
Management needs insight into impact and risk, not daily usage.

– Connection to existing governance forums
AI should be incorporated into established structures for quality, risk, and internal control.

– Opportunity for escalation upon change
When usage changes in nature or scope, this should be automatically elevated.

Thus, information flow becomes part of governance – not an additional measure.

Actera's role in this

Actera is established to provide dental organizations with a structure around responsible AI usage.

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

Final consideration

AI can be managed operationally without being solely an operational responsibility. When technology affects practice, risk, and decision bases, it also becomes a management concern.

Good governance doesn't require detailed knowledge but the right information at the right level. When upward reporting is clarified, management can exercise its responsibility – even when AI is handled in the line.