AI Agents Tackle Finance’s Last Frontier: Unstructured Tasks -- Lumina Data
By Lane F. Cooper, Editorial Director, BizTechReports
While most business technology has focused on automating structured processes, a growing share of inefficiencies in finance operations remain tied to tasks that don’t follow neat rules or formats. According to executives from Lumina Data (https://www.luminadata.ai/), artificial intelligence (AI) is now poised to address these challenges—starting with unstructured tasks in finance departments.
In a recent BizTechReports vidcast interview, Lumina Data co-founders Afrozy Ara and Deepti Chafekar, discussed how goal-based AI agents can work alongside humans to streamline processes, reduce error rates, and enable faster financial operations without requiring companies to replace their existing systems.
Scaling Finance Operations
For mid-market companies—typically defined as organizations with 150 to 2,000 employees—finance operations often sit at a crossroads between ambition and capacity. These companies are large enough to face complex reporting, compliance, and reconciliation demands, but not always resourced like enterprise-scale counterparts to fully automate every function.
Despite adopting enterprise resource planning (ERP) systems, SaaS-based accounting tools, and reporting platforms, many mid-market firms still rely heavily on manual workarounds and spreadsheets to bridge the gap between system capabilities and real-world business complexity. That’s because traditional automation is built around structured data and well-defined workflows—leaving exception management and nuanced, ad hoc tasks largely untouched.
“Everyone talks about digital transformation,” said Ara. “But in the mid-market, many finance teams are still doing work the old-fashioned way—not because they want to, but because structured tools simply don’t account for the unpredictable, fragmented nature of daily operations.”
These constraints leave finance leaders with a strategic dilemma: either over-engineer their tech stack at great cost and disruption, or continue compensating with manual interventions that sap productivity and introduce risk.
But it doesn’t have to be this way. The opportunity, she suggests, lies in reimagining automation not as a system overhaul, but as a shift in how teams approach task execution—with AI agents supporting unstructured processes without disrupting existing workflows or systems of record.
When Standard Processes Fall Short
Even with capable financial software in place, most mid-market firms hit operational snags when processes don’t go as planned—something that occurs frequently during month-end close cycles, quarterly reporting, or internal audits.
These problems typically show up in two forms:
Incomplete system integrations — where newer tools, acquisitions, or departmental systems don’t fully align with the ERP or core financial database, forcing teams to reconcile data manually.
Exception handling — where transactions, entries, or datasets fall outside the bounds of what the system can process automatically, triggering additional manual investigation.
This reliance on informal processes—executed most commonly on Excel spreadsheets, email attachments, or Slack threads—becomes a kind of operational safety net. However, it also introduces inconsistency, creates knowledge silos, and leaves teams stretched thin during peak reporting periods.
“Exception handling is the Achilles’ heel of most mid-market finance processes,” said Chafekar. “Systems are great when everything lines up perfectly. But when there’s a missing value, a timing mismatch, or a formatting difference—everything grinds to a halt, and someone has to fix it by hand.”
This is where goal-based AI agents offer a new path forward. Rather than requiring structured inputs and linear workflows, a new generation of AI agents—enabling agentic AI strategies—are designed to observe what people are doing, learn the intent behind the task, and collaborate with their human counterparts (or managers) in a dynamic, interactive way.
Here is how it works. If a finance team spends three hours each month manually reconciling transactions between two systems with mismatched formats, an AI agent can learn the reconciliation logic and replicate it with increasing accuracy. Over time, it eventually reduces what was previously defined as a set of unstructured tasks to a one-click process with human oversight.
“It’s not about replacing people,” Ara noted. “It’s about using AI agentic templates to make exception handling repeatable and intelligent, instead of purely reactive and manual.”
The long-term benefit is greater operational resilience. Teams become less dependent on tribal knowledge and more capable of managing high-volume workloads without error-prone manual steps.
Hidden Costs of Manual Finance Workflows
While the operational burden is clear to finance teams, the financial cost of unstructured task management is often hidden in budget lines—categorized as overtime, error corrections, audit delays, or even employee churn.
In mid-market environments, CFOs are under constant pressure to maintain lean teams and reduce overhead, yet still deliver high-quality, accurate reporting to executive leadership, investors, and regulators. The irony is that much of the team’s time is spent on tasks beneath their skill level—copying and pasting data, comparing spreadsheets, or formatting reports for compliance.
Leaders may not see the cost of inefficiency on a balance sheet, but they will feel it in missed deadlines, inaccurate forecasting, and team burnout. Manual processes also introduce compliance risks, especially in industries where reconciliation accuracy and audit readiness are critical. Even small discrepancies can lead to costly rework, external audit findings, or regulatory scrutiny.
Lumina Data’s executives argue that AI agents offer a path to measurable ROI—without the need for massive software investments or consulting engagements. The value shows up in several ways:
Labor cost reduction from automating low-value, repetitive tasks;
Fewer manual errors, preventing duplicate payments, reporting inaccuracies, or audit flags;
Increased productivity per finance team member, enabling more strategic analysis and business partnering; and
Faster time to close and report, improving decision-making velocity.
“The finance team shouldn’t be a bottleneck for growth,” Ara said. “With AI, they can become a force multiplier instead.”
In this context, goal-based AI agents become more than automation; they’re a capacity-building investment, helping mid-market companies scale intelligently.
Integration Fatigue and AI-First Design
From a technology standpoint, mid-market firms face a unique conundrum. Many already have multiple systems in place—ERP, CRM, BI, reconciliation tools—but they struggle to extract value from them because of poor integration, siloed data, and limited flexibility.
Too often, even newer platforms are designed around rigid, structured input requirements. As a result, organizations wind up using only a fraction of the tools’ capabilities, while compensating with manual workarounds for the rest.