The Drag
Inherited revenue systems quietly turn routine adjustments into high-risk decisions because the reasoning behind critical configuration has been lost, leaving operators accountable for outcomes tied to choices they never saw made.

The Pivot
AI can be used as a translation layer, not to automate decisions, but to surface the original intent embedded in workflows, formulas, and rules so operators can act with clarity instead of caution.

The Yield
Teams move faster without gambling outcomes, reduce hidden system risk, and establish revenue infrastructure that can be safely evolved rather than indefinitely preserved.

One day, you will inherit a system you did not build. It might be a Salesforce org. It might be HubSpot. It might be a shared drive full of Excel files that quietly run half the business. What they all have in common is this: the original builder is gone, and the logic left with them.

You open the backend and find the warning signs:

The danger isn't that the system is broken. You are worried that it might be working, and that touching the wrong thing will break something important. A CEO dashboard. A board report. A revenue number that suddenly stops reconciling.

You stop deleting. You stop simplifying. You postpone critical improvements because the "blast radius" of a change is unknown. This is how systems drift into paralysis. Not because the work is hard, but because you are operating without a map.

This problem rarely announces itself as "technical debt." It shows up as daily friction across your entire stack.

In Salesforce / CRM: You see automation layered over automation. Validation rules fail only on specific deal sizes. Required fields block urgent updates, with no record of the original business constraint.

In HubSpot / Marketing Ops: Lifecycle stages no longer match how Sales actually sells. Workflows silently overwrite manual corrections, and attribution data changes depending on which view is open.

In Excel / Finance: The danger is quieter but sharper. Forecast files contain hidden tabs. Manual overrides are hard-coded on top of formulas. Version drift turns "The Number" into a debate rather than a metric.

Most teams assume the risk lives in the configuration. It does not. The risk lives in lost intent.

When intent is missing, senior operators are forced to reverse-engineer logic before they can improve it. That work is mentally expensive, so teams avoid it. The system becomes untouchable.

Change slows, risk compounds, and leadership still expects results.

Every mature system contains logic that once made sense under constraints that no longer exist. Over time, that logic hardens into configuration. The work slows not because the system is complex, but because no one can safely explain why things behave the way they do.

Recovering lost intent follows a disciplined cycle: Extraction, Interpretation, and Documentation.

Do not rely on guesswork. Use these specialized "Extractor" tools to fetch the DNA of the system:

You are not outsourcing judgment. You are accelerating comprehension so judgment can be applied with confidence.

Raw logic describes what the system does, but not why. Nested formulas encode behavior, not intent. This is where experienced operators historically lose hours reconstructing context that no longer exists. AI compresses this step, if constrained correctly.

Do not ask the model to “explain the logic.” Ask it to reconstruct the operational rationale as if it were inheriting ownership of the system tomorrow. Paste the raw configuration into the LLM using the following Archaeology Prompt:

I am reviewing a legacy system mechanism I did not design. I am providing the raw configuration below (workflow logic, formula, flow, or script). Analyze it with the goal of restoring operational intent, not rewriting it.

Your output must follow this structure:

PURPOSE
In one or two sentences, explain what this logic does in plain English, as if briefing a senior operator with no technical background.

ORIGINAL CONSTRAINT (Inferred)
Identify the business or platform limitation this logic was likely designed to address at the time it was created.
Flag any hard-coded values that suggest a historical workaround.

CURRENT RISK / BLAST RADIUS
Describe what downstream processes, reports, or teams are affected.
If this were disabled or modified, what would most likely break or change?

STATUS ASSESSMENT (Advisory)
Based on modern platform capabilities and common operating patterns, assess whether this logic appears active and necessary, legacy but harmless, or deprecated and risky.
If this cannot be determined from the logic alone, state what additional context would be required.

DOCUMENTATION SUMMARY
Draft a concise description suitable for a system Description or Comment field that explains why this exists.

Do not assume intent.
Do not suggest changes unless clearly justified.
Explicitly flag any operational uncertainty.

Recovered intent only matters if it survives the next personnel change. If insight remains in a ticket or a Slack thread, decay resumes immediately.

Documentation must live where the decision executes. Its purpose is not to describe mechanics, but to preserve rationale, ownership, and risk. This is the point where information becomes infrastructure.

The Action Paste the AI's summary directly into the system (Description Field, Help Text, or Cell Comment) using the Undalis Manifest Protocol:

PURPOSE: One sentence on why this exists (not what it does). ORIGIN: Who requested it and when (e.g., "RevOps Request Q3 2019"). DEPENDENCY: What breaks if this is removed. STATUS: [Active / Legacy / Deprecated].

Example:

Once this structure is embedded in the system, intent is no longer tribal knowledge. The logic becomes inspectable, transferable, and governable. Only after this anchoring step should optimization or removal be considered.

You inherit a HubSpot portal with inconsistent lifecycle stages. Sales insists leads keep reverting. Marketing swears automation is correct. Attribution reports disagree depending on which dashboard is open.

When you investigate the Lifecycle Stage property, you find three active workflows writing to it. This matters because lifecycle is a single field with multiple authors, and HubSpot does not arbitrate intent. Whoever writes last wins, regardless of seniority, accuracy, or relevance.

Nothing is obviously “broken.” The system is simply behaving in ways no one can explain with confidence. On inspection, you discover three active workflows:

The reversion Sales sees is not random. It is deterministic. A lead books a demo and becomes SQL. Weeks later, the same lead re-enters a nurture flow and is silently rewritten by older logic. Attribution reports diverge because lifecycle timestamps are being rewritten after the fact. The system is behaving exactly as configured. The problem is that no one can explain the configuration as a whole.

HubSpot does not show this interaction in one place, so you must assemble it deliberately.

Using the “Used In” panel on the Lifecycle Stage property, you trace every workflow that touches it. You copy enrollment triggers, branch conditions, and update actions from all three workflows into a single document. This is not analysis yet. You are reconstructing the full decision surface so nothing remains implicit.

You paste the combined logic into the Undalis Archaeology Prompt.

The AI does not propose fixes. It does something more valuable:
It explains the behavior in plain language.

The output makes one thing clear: lifecycle ownership has drifted across teams over time. Marketing automation, sales activity, and campaign logic are all asserting authority over the same property, based on assumptions that were valid years ago but are no longer aligned.

What looked like randomness is revealed as overlapping intent. At this point, nothing has changed in HubSpot. And yet the system is no longer opaque.

You document the recovered intent directly inside HubSpot, in workflow descriptions and property notes. You record why each workflow exists, what original constraint it addressed, which team owns lifecycle decisions today, and which logic is now legacy.

The relief comes before the fix. Once behavior is explainable, it becomes governable.

AI does not fix systems by itself. People do, once they can see clearly enough to act. Its real value is not automation, but orientation: shortening the distance between “something here feels wrong” and a clear understanding of why the system behaves the way it does. That moment is where most operators stall, not because they lack skill, but because intent and risk are buried under years of accumulated decisions. By restoring visibility into those constraints, AI turns hesitation into informed judgment. It does not replace responsibility, it makes responsibility possible again.