Mike Gibb at AccountsRecovery is hosting another great webinar, “What Smart Organizations Automate First: A Decision Framework for Choosing the Right Workflows.”Great topic, as usual – current and right on point. Kudos to Mike for pulling along the ARM industry with cutting edge, relevant best practice discussions.

This topic got me thinking. What comes first?

Automation is everywhere in collections today. Every conference presentation, vendor pitch, and industry roundtable pushes the same message: automate more, automate early, automate everywhere.

It’s true—automation can drive efficiency, reduce manual work, improve compliance, and free people for higher-value tasks.

But this raises a crucial question:

What foundation are we building all this automation on?

In the ARM industry, many organizations begin with the customer-facing front end—polished CRMs, sleek dashboards, dialer integrations, digital outreach tools—while postponing the deeper foundational work that ultimately determines whether automation will scale or simply add complexity.

And when the foundation isn’t strong, we must ask:

Are we accelerating growth, or accelerating the fragility already baked into our systems?

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How Does ROI Horizon Influence Where Automation Begins?

If leadership is evaluating ROI over the short term—this year and next—it’s inevitable that they will prioritize:

• tactical automations
• workflow shortcuts
• bolt-on tools
• scripts that solve narrow, local problems
• quick hits that bump RPCs this month

This isn’t mismanagement—it’s the reality of competitive pressure.

In the ARM industry, the fear of missing out is grounded in real consequences. If a competitor delivers a performance lift today—higher RPCs, better right-party contact rates, more promises-kept—clients notice immediately. Renewals and expansions can hinge on these quarterly results.

• You can’t grow a company if you’re losing market share.
• You can’t retain clients if you fall behind on near-term performance.
• So yes, tactical gains matter. They are necessary.

But here’s the strategic question leaders must ask:
Should the tools that deliver tactical ROI also dictate the design of the long-term enterprise architecture?

Short-term ROI and long-term scalability come from different kinds of decisions.

The tactical should not define the strategic.

The opportunity is to maintain the quick wins while also investing in foundational design that positions the business for long-term growth.

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What Happens When We Look at ARM Data Foundations More Closely?

To understand why the foundation matters, consider the state of ETL and data structures across the ARM industry.

Every client sends data in a different format.
Agencies respond by creating custom ETL scripts for each client.

And because CRM data models are often limited, teams repurpose fields—giving the same field different meanings depending on the client, portfolio, or placement.

This raises a critical question:
How scalable can automation be when your data does not mean the same thing across clients?

When a single field has three different meanings across three different clients, every automation rule built on top of that field becomes a liability.

Inconsistent data leads to inconsistent outcomes—and unpredictable behavior in workflows, compliance checks, segmentation logic, settlement offers, and AI models.

This isn’t a configuration challenge.
It’s a data design opportunity—one that determines whether automation is safe, scalable, and reliable.

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How Can Financial Architecture Become a Strategic Advantage?

If ETL is complicated, the financial model inside many ARM systems is even more restrictive.

Many systems still assume that:

“Each client placement carries one defined commission rate.”

But that assumption doesn’t hold up in practice—at all.

Most legacy ARM platforms were built around static pricing, even though pricing in our industry is inherently dynamic.

A placement may begin at a low commission rate when inventory is fresh, but as debt ages and the liquidation curve flattens, the work required increases—and so do the costs. Naturally, the commission rate needs to adjust.

And that’s only one dimension of change. Commission rates can shift:

• mid-stream, due to competitive pressure
• during a campaign, based on performance
• when clients reorganize portfolios, creating new pricing buckets
• after operational improvements or AI-driven efficiency gains, when clients want to share in those savings
• due to regulatory or compliance changes that alter permissible activities and associated costs

In Finance, a “price” is not final until the service is delivered.

Up until that point, pricing is a variable—it evolves with time, effort, liquidation expectations, and client-driven renegotiation.

This leads to a foundational question:
Does the database design actually support the way pricing behaves in the real world?

Does it support:

• time-based pricing, where rates shift as accounts age?
• event-based pricing, triggered by workflow steps or milestones?
• performance tiers, where compensation adjusts dynamically?
• mid-cycle changes, without corrupting historical results?
• client or portfolio overrides, applied immediately without code rewrites?
• AI-driven pricing models, where compensation aligns with efficiency gains?
• version control, so pricing tables evolve cleanly over time?

Most ARM CRMs cannot accomplish this without heavy custom coding—and some cannot do it at all.

But with a flexible, table-driven, version-aware architecture, pricing becomes a strategic advantage:

• onboarding becomes faster
• client reorganizations become manageable
• forecasting becomes clearer
• revenue recognition becomes more reliable
• automation becomes scalable across clients, not just within silos

Dynamic pricing isn’t a burden—it’s a competitive differentiator when the foundation supports it.

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Can AI Replace the Need for Good Architecture?

There’s a tempting belief circulating today:

“AI is so powerful it will work around our messy data.”

But here’s the real question:
Does AI actually reduce the need for structure—or does it increase it?

AI still depends on:

• clear field definitions
• consistent hierarchies
• stable logic
• unambiguous relationships
• reliable mappings

AI magnifies whatever foundation you give it.

• A strong foundation produces exponential gains.
• A weak foundation produces exponential inconsistencies.
• AI does not replace foundational architecture.
• AI makes foundational architecture more important than ever.

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If Not in the Middle or the End, Where Should System Redesign Begin?

When companies start redesigning systems, the usual first step is to gather requirements from:

• Operations
• Sales
• Marketing
• Client-facing teams

Those perspectives matter.
They surface workflow pain points and tactical opportunities.

But they don’t answer the foundational question:
Who understands how every decision flows through the entire business model—from client to portfolio to cash?

The answer (I’m biased of course!) is Finance.

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Why Should Finance Be Consulted First?

Finance is the group that experiences the full consequence of every upstream decision, long after the excitement of new features fades.

Finance is responsible for:

• reconciling hierarchy changes
• interpreting ETL exceptions
• normalizing inconsistent data
• applying pricing logic correctly
• ensuring compliance reporting accuracy
• producing KPIs clients depend on
• forecasting performance
• handling audit requirements
• rebuilding reporting when CRM logic fails

If you want to design a future-ready system, an essential question is:
Why not start with the team that is already stitching together today’s complexity into coherent reporting?

Finance understands:

• how client demands evolve
• how pricing structures change
• how reorganizations complicate hierarchy logic
• how data inconsistencies ripple downstream
• and how foundational design affects long-term scalability

Finance sees the system as a whole—not as a series of front-end features, but as an interconnected operational and financial ecosystem.

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What Becomes Possible When We Build the Foundation First?

Here is the opportunity:

A flexible, scalable, well-designed foundation turns automation from a tactical boost into a strategic engine.

With the right architecture, organizations can:

• onboard clients faster
• support more complex pricing models
• scale without rewriting code
• reduce compliance risk
• unify reporting
• implement AI safely and effectively
• shift from reactive fixes to proactive design

Foundational work isn’t glamorous. It doesn’t produce an immediate RPC spike. But it produces something far more valuable:

• a business that can grow without breaking.

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Final Question: What Future Are We Building Toward?

Every organization must answer this honestly:

Are we building systems for next quarter—or for the next decade?

You can automate tasks, workflows, and even decisions. But if the data model, hierarchy structure, and financial architecture don’t support scale, automation will always be tactical—not transformational.

When companies start with the foundation—and engage the teams who understand the full lifecycle of the data—they are not just modernizing.

They are creating the capacity to grow.

• Not for this month.
• Not for this year.
• But for the long-term future of the business.

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