78% Still Manual: Why AI adoption in finance lags behind its promise

At the MAG Payments Conference 2025, we heard something that honestly shocked us: 78% of merchants told us they're still doing most of their monthly reconciliation manually. Not because they want to. Not because they don't know better options exist. They're just... stuck.

And that got us thinking about why.

Everyone Knows There's a Problem

Walk into any finance department and ask about their reconciliation process. You'll get the same tired look. Everyone knows it's inefficient. Everyone knows there are better ways. But when it comes to actually making the change? That's where things fall apart.

The tools exist. Platforms like Optimus can automate reconciliation end-to-end. But there's a massive gap between knowing what's possible and actually doing something about it.

The paradox of progress

After talking to dozens of CFOs and finance leaders, we've noticed some patterns:

• Legacy comfort zonesPeople trust what they know. Reconciliation workflows weren't designed—they just evolved over time. Teams have muscle memory for their spreadsheets, even when those spreadsheets are costing them weeks of work every month. Manual feels safe because it's familiar.
• Fragmented data ecosystemsData is everywhere and nowhere. Most merchants juggle 12+ data sources—different PSPs, multiple acquirers, various banks. Each one has its own format. Just thinking about consolidating all that into something an AI can read? Exhausting.
  
• Perceived implementation riskChange feels risky. Even when the ROI is obvious, finance leaders worry about disrupting workflows that currently (sort of) work. What if something breaks? What if we lose visibility during the transition? Better the devil you know.
  
• Skills and data literacy gapsTeams aren't ready for AI thinking. This isn't really about software. It's about a fundamental shift in how finance operates. A lot of teams don't feel confident interpreting
  

AI-driven insights or explaining them to auditors and executives.

The cost of staying manual

Let's be real about what this means in practice.

• Time drain: Large retailers are spending up to two weeks every month just reconciling POS data with bank statements. Two weeks! Half of that could be automated tomorrow. And it's not just time.
  
• Error exposure: Manual processes miss things. Duplicate fee debits slip through. Partial settlements go unnoticed. Small charges get misapplied. All those micro-leakages add up. But here's what really hurts.
  
• Opportunity Cost: Finance leaders are spending hours hunting down discrepancies instead of actually analyzing what the numbers mean. One merchant put it perfectly:
  

As one merchant at MAG PC 2025 put it:

"We've automated everything our customers see, but our finance back office still runs on muscle memory."

Why the AI promise fails in practice

Everyone's talking about AI in finance. But talking about it and actually using it are two different things.

1. Most data isn't AI-ready. AI needs clean, structured data. What finance teams actually have is messy, inconsistent, incomplete data spread across multiple systems. You can't automate what you can't organize.

2. Companies overcomplicate it. Instead of starting small, organizations try to roll out enterprise-wide AI transformations. Teams get overwhelmed before they see any results.

3. No one shows the actual ROI. CFOs need numbers they can point to—real savings, measured time reductions, specific errors caught. Without concrete proof, AI stays on the "someday" list.

From theory to execution: The Optimus 4-week PoV roadmap

To close the intent-to-execution gap, Optimus introduced a practical, structured approach to automation — a 4-Week Proof of Value (PoV) Roadmap, designed to demonstrate tangible outcomes quickly:

Week 1: Data onboarding and integration

Seamlessly ingest and normalize data across PSPs, acquirers, and banks — eliminating the structural barrier that blocks AI readiness.

Week 2: AI activation and auditing

Enable AI-driven analysis to identify hidden anomalies such as duplicate fees, delayed settlements, or unreconciled transactions.

Week 3: Reconciliation automation

Automate end-to-end reconciliation across sources — moving from manual checks to continuous, self-validating financial data flows.

Week 4: Insights and audit-ready reporting

Deliver transparent, audit-ready reports and dashboards, showing measurable ROI within 30 days.

This phased approach helps CFOs prove value before scaling — building confidence through outcomes, not promises.

The CFO’s new role: From controller to conductor

The CFO role is changing. It's less about controlling processes and more about orchestrating intelligent systems.

In 2025, financial leadership isn't about running manual processes really well. It's about designing systems that don't need constant manual intervention. When you embed automation into reconciliation and audit workflows, you shift from reactive reporting to proactive decision-making.

The goal isn't just a faster month-end close. It's trusted financial truth—data that's accurate, complete, and available in real-time.

Bottom line

AI in finance isn't failing because the technology isn't ready. It's failing because most organizations aren't ready for the change.

That 78% doing manual reconciliation? They're not unaware. They're not lazy. They're just missing a clear, structured path from where they are to where they need to be.

That's what Optimus does differently. We turn fragmented payment data into unified intelligence. We convert manual routines into automated workflows. Most importantly, we help finance teams experience automation as proof they can see and measure—not just another promise.

Because here's the thing: in today's environment, efficiency equals competitiveness. The question isn't "Should we automate?" anymore.

It's "What's stopping us?"