Lanzko Insights

The problem

Organizations adopt AI and automation while the underlying workflow remains unclear, inconsistent, or unmanaged.

When that happens, teams automate the mess. Output increases, but outcomes do not improve. In regulated environments, the added risk is that unclear workflows also weaken oversight and defensibility.

The result is frustration. Leadership does not see ROI, operators lose trust, and oversight functions get pulled into cleanup.

Why it persists

Technology decisions are easier than workflow decisions.

Workflow work requires clear ownership, agreement on decision points, agreement on standards, and behavior change.

AI projects often start as tool selection because it feels faster. Vendors can demo value in minutes. Meanwhile the real blockers, inconsistent intake, unclear escalation rules, uneven documentation, and conflicting expectations across teams, remain.

AI then becomes a proxy for “fix the process,” even though it cannot do that on its own.

The enabling approach

Start by treating the workflow as the product.

Before AI is introduced, define:

• where work enters and how it is triaged

• what the decision points are

• what information is required at each decision point

• who owns decisions, escalations, and approvals

• what “good” looks like in documentation and file notes

Once that is clear, AI becomes easier to place, such as summarization at intake, consistency checks before approvals, issue flags for escalation review, and structured drafting to reduce repetitive writing.

Practical example

If the organization cannot answer when escalation is required, what the minimum documentation standard is, who must approve authority or exceptions, and how follow ups are tracked, then no AI tool will reliably improve outcomes.

Once those decision points are defined, AI can remove friction by pre filling intake packets, summarizing key documents into a standard structure, identifying missing required elements, and drafting standardized updates that still require review.

The workflow becomes faster because the process is clearer, not because the model is smarter.

Governance and risk note

The main risk here is automating inconsistency.

If different teams follow different standards, AI will reinforce that variability unless you define a single expected structure and decision rule.

Governance starts with workflow standards, what must be true before a decision is made, what must be documented, and what must be escalated and to whom. AI should support these standards, not bypass them.

The takeaway

If your workflow is unclear, AI will not fix it. It will amplify it.

The fastest path to measurable improvement is:

1) define the workflow and decision points

2) define documentation and escalation standards

3) add AI only where it reduces friction in those defined steps

This is how you get outcomes, not activity.