Introduction

AI is everywhere in finance news today. From predictive analytics to fraud detection, it is hard to find a finance conference or LinkedIn thread that does not mention artificial intelligence.

But while excitement is high, results are mixed. Many finance leaders rush to invest in AI tools, only to see little return. Budgets get burned. Teams grow frustrated. The tools end up shelved or underused.

This blog cuts through the hype. It focuses on where AI delivers real return on investment (ROI) in finance and what operations and IT leaders in finance must do to make those wins repeatable, explainable, and valuable over time.

The Problem: AI Investments Often Miss the Mark

AI has incredible potential. Yet, many finance teams are disappointed by their first investments.

Why?

• Tools are bought without a clear use case.
  Many companies jump into AI adoption because of market pressure or vendor hype. They buy tools with flashy demos but no plan for how those tools will solve specific operational problems. This leads to shelfware and expensive solutions that never get used meaningfully.
• Solutions are not integrated into existing systems.
  Even the best AI tool cannot deliver value if it works in isolation. Without deep integration into the finance stack, such as ERPs, procurement software or reporting tools, AI becomes just another dashboard that no one checks.
• The underlying data is poor or scattered.
  AI runs on data. If the data is incomplete, outdated, or stored across disconnected systems, the AI will deliver bad insights or none at all. Data quality and accessibility are often the biggest hidden blockers to AI success.
• Models work in theory but not in messy, real-world finance environments.
  Many AI solutions are built in controlled settings and tested on clean datasets. In real finance departments, data is messy, exceptions are common, and workflows vary. If the model cannot handle real-world complexity, it fails quickly.

A study by Gartner predicts 30% of Generative AI projects will be abandoned after proof of concept by end of 2025. And in finance, the stakes are higher. A failed AI project does not just waste money. It can compromise reporting accuracy, delay audits or introduce compliance risks.

Finance Can Benefit Deeply From AI

Used correctly, AI is a force multiplier for finance teams. It automates tedious work, surfaces insights faster and helps reduce risk. But value does not come from plugging in a tool and hoping for the best. It comes from using AI where it can deliver clear, measurable impact. Let us look at some numbers:

• McKinsey estimates that AI can deliver up to $1 trillion in annual value across global banking and finance sectors.
• Gartner reports that finance departments using AI have 30 percent faster close cycles on average.
• According to PwC, AI could contribute $15.7 trillion to the global economy by 2030, with significant impact in financial operations and risk management.

Where Does AI Deliver Clear ROI in Finance

AI can be valuable when used in the right places. Instead of broad, unfocused implementation, the most successful teams start with targeted applications that improve speed, accuracy and risk reduction.

Let us get specific. Here are the AI use cases in finance that have shown the highest return on investment:

1. Invoice Processing Automation
   • Automates accounts payable (AP) tasks
   • Reduces manual entry errors
   • Speeds up invoice approval and payment cycles
   • Tools like Stampli, Tipalti and Beanworks help finance teams cut AP processing costs by up to 80 percent
2. Real-Time Anomaly and Fraud Detection
   • Uses machine learning to detect out-of-pattern transactions
   • Flags suspicious vendor activity or internal misuse
   • Reduces reliance on manual checks
   • Mastercard uses AI to process more than 125 billion transactions and detect fraud with over 99% accuracy
3. Predictive Cash Flow Forecasting
   • Analyzes historical patterns
   • Anticipates future cash needs or risks
   • Supports better treasury planning
   • Platforms like Tesorio and Planful integrate AI models that improve forecasting accuracy compared to traditional spreadsheet models
4. Automated Reconciliation
   • Matches payments and invoices across multiple systems
   • Speeds up monthly or quarterly close
   • Reduces manual review hours
   • Finance teams using tools like BlackLine or Trintech report saving hundreds of hours per month on reconciliations
5. AI Assistants for Month-End Close and Compliance
   • Drafts documentation
   • Checks for compliance gaps
   • Creates reminders and status updates
   • These copilots can reduce end-of-month stress and improve audit readiness

Where AI Still Struggles (and Wastes Resources) in Finance

AI is not a silver bullet. It works best when tailored to specific workflows. Here are some areas where AI adoption often disappoints:

1. Generic or Poorly Trained Models

AI built on generic data may not understand finance-specific terminology, rules, or exceptions. This leads to irrelevant outputs or poor decisions.

2. Black-Box Tools Without Explainability

Finance leaders need to justify decisions to auditors, regulators and executives. If a model’s logic cannot be explained, trust and adoption fall apart.

3. Overbuilt Forecasting Tools

Some AI platforms attempt to model every variable at once. But if the result is too complex for finance teams to use or interpret, it creates more confusion than value.

4. One-Size-Fits-All Platforms

Enterprise AI solutions that promise to solve “everything” often require massive customization. That drives up cost and timeline without guaranteed ROI.

High vs. Low ROI AI Investments

Not all AI investments deliver the same value. Some tools solve specific, well-defined problems and show measurable returns within months. These include AP automation, reconciliation and fraud detection, areas where efficiency and accuracy can be quantified easily. On the other hand, low-ROI tools often promise sweeping transformation without a clear fit for day-to-day finance work. Overly complex forecasting platforms, black-box AI models that lack transparency, or generic solutions that require heavy customization tend to fall short. The difference lies in how well the AI is aligned with real-world finance workflows and how easy it is to integrate and govern.

Use Case	High ROI Indicators	Low ROI Indicators
Invoice Automation	Works with your ERP, supports audit trails	Needs full system replacement, limited reporting tools
Forecasting	Clear inputs, explainable models, real-time data	Overcomplicated setup, black-box logic
Fraud Detection	Learns from your data, sends real-time alerts	Generic rules, slow updates
Reconciliation	Auto-matching rules, cross-platform integration	Manual tweaking required, only works with one platform
Compliance AI Copilot	Tracks changes, links to policies and generates evidence	Hardcoded rules, limited documentation features

How Ops and IT Leaders in Finance Can Structure for Success

Building AI for finance is not just about buying tools. It is about setting the stage so that those tools work consistently, integrate smoothly and deliver measurable outcomes over time. Here is what high-performing teams do:

1. Prioritize High-Impact Use Cases First

Start small, but think smart. The goal is not to overhaul the entire system overnight. Instead, focus on use cases where AI can generate immediate value.

• AP automation reduces repetitive manual work and frees up team capacity.
• Expense auditing tools catch policy violations or unusual claims quickly.
• Forecasting solutions give CFOs data-backed confidence in strategic planning.
• Real-time alerts help spot financial risks before they escalate.

Note: Measure these initiatives using KPIs such as error reduction, processing time, report accuracy, or audit prep time. When results are visible, leadership support and user adoption grow faster.

2. Choose AI Tools That Integrate, Not Replace

Most finance environments run on a mix of systems. Tearing everything down is not just expensive, but it also introduces risk. The smarter approach is to layer AI tools on top of what already works. Look for tools that are API-ready, offer prebuilt connectors and play well with existing tech. Integration-first tools reduce downtime, improve adoption and deliver value faster.

• Stampli integrates smoothly with platforms like SAP and QuickBooks to automate invoice workflows.
• Tesorio plugs into systems like NetSuite and Xero to enhance cash flow visibility without disrupting the general ledger.

3. Build a Strong Data Foundation

AI is only as good as the data it runs on. If your finance systems are fragmented, outdated, or inconsistent, even the best AI will underperform. Creating a central data lake or warehouse can help, but governance is just as important. Define ownership for each data source and establish quality checks.

According to a 2025 Deloitte report, poor data quality is the leading cause of AI project failures in enterprise finance.

IT teams need to ensure:

• Clean, structured data pipelines
• Unified access controls and permissions
• Real-time sync across applications and departments

4. Facilitate Collaboration Across Teams

AI in finance is not an IT-only initiative. It touches finance, ops, audit and even compliance teams. Without cross-functional collaboration, efforts stall or miss key requirements.

Effective teams bring everyone into the process early. That means:

• Finance teams define pain points and expected outcomes
• IT maps technical feasibility and system dependencies
• Ops ensures that new workflows do not disrupt daily tasks

This kind of upfront coordination reduces rework, keeps expectations aligned and ensures tools are configured for real-world use, not just theoretical models.

5. Look for Vendors That Support Skill Building

AI is not just plug-and-play. Your team needs to understand how it works and how to use it confidently. Choose vendors that do more than hand over a dashboard.

A good vendor will:

• Offer workshops or onboarding tailored to your workflows
• Provide clear documentation and explain how models reach conclusions
• Help finance teams tweak and configure features independently

Partners like Tech Exactly, prioritize enablement from day one. They work closely with internal teams to demystify AI, guide real-world use and reduce long-term dependency. This investment pays off. Teams become more self-sufficient and less reliant on expensive consultants or external help. It also builds internal trust in the technology, which is essential for long-term adoption.

Closing Thoughts: AI Delivers When the Foundation Is Right

AI in finance is not about hype or complexity. It works best when it addresses real challenges, integrates with existing systems, and supports the way your team already works.

That is where thoughtful implementation makes the difference. At Tech Exactly, we help operations and finance leaders move past the noise. Our approach focuses on modular, practical solutions that slot neatly into your current tech stack. We automate where it counts, maintain transparency for audits, and ensure compliance is never an afterthought.

You do not need to reinvent your systems to unlock value from AI. With the right plan and the right partner, your finance team can see impact fast without disruption.