Lanzko Insights

Organizations adopt AI and automation, but accountability for decisions becomes less clear.

Outputs are delivered quickly and confidently. People begin to treat them as guidance. Over time, AI influences decisions without a defined decision owner, a review step, or an escalation path.

In regulated environments, this creates a quiet risk. The issue is not only correctness. It is whether the organization can explain how a decision was made and who was responsible for making it.

Why it persists

Governance is often treated as a document problem instead of an operating design problem.

Teams write policies about acceptable use, but they do not define:

• where AI outputs appear in the workflow

• who must review them

• what decisions they can influence

• what happens when the output conflicts with human judgment

Without these details, governance is theoretical. The real operating system becomes informal and inconsistent.

The enabling approach

Governance starts by defining the decision boundary.

For any AI-enabled workflow, you should be able to answer:

• What is the decision being supported?

• Who owns the decision?

• What inputs are required before the decision?

• Where does AI provide assistance, and where does it stop?

• What triggers escalation?

• What gets documented for auditability?

Then design a simple control model:

• AI produces a draft or a structured input

• a human reviews and confirms

• exceptions route to escalation

• key actions are logged

This keeps the workflow fast while preserving accountability.

Practical example

Consider AI summarization for a claim file, legal matter, or audit record.

A safe design looks like this:

• AI creates a structured summary in a standard format

• the user confirms, edits, or rejects the summary

• the system logs user edits and the final version

• if the AI flags a high severity indicator, the workflow routes to a defined escalation queue

• settlement, authority, or exception decisions remain explicitly human

The value is not only the summary. It is the consistent decision process around it.

Governance and risk note

Governance fails when AI outputs are treated as system truth.

Controls that matter in practice:

• label AI outputs as draft unless confirmed

• require human approval for decisions and external communications

• maintain a clear record of what was reviewed and changed

• define escalation rules for conflicts and high risk flags

• ensure users can override AI without friction

These are workflow controls, not legal disclaimers.

The takeaway

The goal is not to prevent AI use. The goal is to prevent unclear decision making.

When AI is placed inside a workflow with defined ownership, review, and escalation, it reduces friction without reducing accountability.

That is what makes AI usable in regulated operations.