While 95% of AI Pilots Fail, These 5 Finance Use Cases Deliver Results

Most enterprise AI pilots fail. But the success stories show why back-office operations are a prime opportunity. This post outlines 5 finance use cases with specific guidance that will ensure your first AI project succeeds.

You see AI's potential, but the stakes are higher in finance so you haven’t made your first move. You're responsible for accurate and compliant execution. Moving too fast feels risky, but moving too slow will leave you behind. And even though your leadership is pushing you to "do something" with AI, it’s your reputation on the line.

A new MIT study revealed the stark reality: after analyzing $35-40 billion in AI investments, 95% of companies see zero return from their pilots. But buried in the headlines highlighting the failures is the blueprint for success, in which finance teams have the natural advantage.

Here's what the successful 5% figured out: more than half of generative AI budgets are devoted to sales and marketing tools, yet the study found the biggest ROI in back-office automation. For those early adopters, AI solutions are already eliminating business process outsourcing, cutting consulting costs, and streamlining operational procedures.

This is exactly where finance teams excel. While the company spotlights the high-risk high-reward customer-facing AI initiatives, you have the controlled, repeatable processes ripe for automation.

Imagine spending month-end on strategic analysis instead of reconciliations. Using your expertise to guide decisions instead of chasing missing data. Turning edge case handling queues into integrated feedback loops for agentic self-learning.

That's what AI unlocks when you focus on the workflows that matter.

The successful companies focus on one thing and do it well. "It's because they pick one pain point, execute well, and partner smartly with companies who use their tools." You don't need months-long ideation and planning cycles. You need quick wins that prove value without disrupting what works.

Here's how finance teams can join the successful 5%.

Short on time? Here’s a 30-second overview.

We’ll share how you can:

• Pinpoint the types of processes that benefit most from AI.
• Cut manual work without replacing existing systems.
• Deliver quick wins that get buy-in from leaders and teams.
• Use AI that works with messy, imperfect data.
• Move from pilot projects to AI-driven operations with confidence.

The fastest way forward is not another 18-month transformation project. It’s small, targeted AI projects that prove value quickly. These examples show where to start and how to build momentum.

Five proven AI use cases to transform your finance ops today

We know you don’t have time for AI projects that take months to show results. So, we’ve picked five use cases where we’ve seen finance teams make a big impact with AI, fast..

They cut hours of manual work, reduce the risks that come with error-prone processes, and give you the wins you need to take on bigger AI projects.

Here are the five places where AI is already delivering results in finance, what’s slowing those processes down today, and how AI fixes it:

Now, let’s walk through each one in detail so you can see what they all look like in practice…

Use Case 1: Invoice processing – Beyond rigid logic to intelligent exception handling

The traditional problem

Manual handling of invoice processing exceptions is costly and time consuming, and most “automated” systems don’t achieve the straight-through rates that were promised. Missing POs, formatting issues, price mismatches, or possible duplicates sends your team digging through emails, ERP screens, and shared drives. And desktop procedures only work so well, as humans tend to try to find shortcuts, which more often slows down handling and introduces more risk.

The AI solution

AI closes the gap between exception identification and resolution, handling most exceptions automatically and flagging only true outliers for manual handling. That means higher discount capture, more consistent (and auditable) decisions, and lower operational costs.

In our time spent with AP teams, we’ve learned that incomplete invoices and minor formatting errors can flag hundreds per month for manual review due to rigid business rules and validation logic. That drives up labor costs, increases cycle times, and creates late payment risks.

Intelligent invoice processing systems can:

• Extract invoice data reliably without weeks of “training” legacy OCR tools on every case
• Accurately execute 2- and 3-way matching, regardless of where PO and GR data lives
• Apply business rules consistently while automatically correcting minor data issues
• Flag only the exceptions that truly need human judgment

What makes this work

AI can reason with incomplete or messy data, understanding objectives and identifying reasonable, compliant remediation paths for exceptions. It can spot patterns within and across customers, learning from every processed invoice and improving performance over time.

Targeting a specific, common invoice processing exception or error lets you prove tangible value within weeks, not months or quarters. The key to success is picking one exception/error type, engineering your prompt iteratively with historical cases, and implementing it to run in parallel with (not in the critical path of) your existing processing pipeline.

Starting here provides a path to rapidly automating one of the most time-consuming, costly, and repetitive tasks in AP without introducing risk. It also builds early trust in AI because the wins are easy to measure, and the risk is low when you deploy a targeted agent within a broader system with robust guardrails.

Use case 2: Cash application – Automated remittance matching

The traditional problem

Lump-sum deposits, truncated transaction references, messy master data, and complex corporate structures make cash application processes slow and error-prone. Even “automated” solutions break when remittance formats or data structures change.

The result is many payments still requiring manual detective work.

The AI solution

AI learns customer payment patterns, understands messy remittance data, and resolves discrepancies automatically. This speeds allocation, cleans up records, and prevents reporting delays.

An AI-driven approach makes it possible to automatically:

• Parse remittance data instantly from documents, emails, or payment portals
• Match to transactions records even with inconsistent, partial, or non-existent references
• Allocate payments to the correct company and/or business unit, even for split allocations
• Create or update the appropriate records in your ERP
• Generate a rich audit trail for every processing step

By simply targeting a reduction in the number of payments that require manual matching (leaving the happy path process as-is), teams can prove value quickly

What makes this work

AI works with incomplete or inconsistent data and gets smarter with every run. It learns your partner-specific payment patterns, combines multiple data sources into one view, and applies matching logic consistently. Every step is logged so you can review the decisions at any time.

Our customers have realized rapid value from targeting the remittance data parsing and matching pieces of the process to start, building agentic capabilities that augment, but don’t replace, their existing processes. For parsing, that means simply leveraging an agent to replace manual data entry tasks. For matching, starting with just those unmatched transactions ensures you don’t have to break your existing process.

This is one of the lowest-effort ways to reduce manual work in AR (despite it taking 1-2 cycles to ensure accuracy and build confidence). It shortens the cash application cycle, improves accuracy, and frees your team to focus on higher-value tasks that drive the business forward.

Use case 3: Vendor onboarding – Automated compliance and risk assessment

The traditional problem

Risk and compliance requirements slow vendor onboarding down, and are often inconsistent across regions, vendor types, and business units. Vendor management teams must chase down and verify information provided by the vendor and trusted 3rd parties, resulting in a pile of documents, questionnaires, 3rd party reports, and reference calls that require manual compilation and review.

Delays slow down the business, while the fragmented, manual processes risk missing something important.

The AI solution

Agentic processes (or workflows) are systems that can take action to achieve goals without step-by-step instructions.

They accelerate approvals while ensuring compliance requirements are met, collecting, validating, and cross-checking data and documents so projects can start sooner.

AI-powered vendor onboarding can automatically:

• Compile information from questionnaires, forms, documents, certificates, etc.
• Communicate with vendors to flag issues or collect missing information
• Validate all provided and collected information against compliance policies
• Combine information with historical and 3rd party data for risk scoring
• Flag issues or long-tail risks to specialists with full context for fast handling

What makes this work

LLMs are exceptionally good at compiling information (across formats/languages/etc) and reasoning over it.

Combine that with deterministic business rules, the ability to communicate with suppliers, and the ability to check external sources to determine risk.

Agentic systems can then resolve incomplete submissions or flag non-compliance risks before they cause delays (and without requiring stakeholder input). They also keep a record of every decision for audit purposes.

Automating these checks takes the friction out of onboarding. Vendors get approved faster, risks are caught earlier, and your team can focus on building better supplier relationships.

Use case 4: Month-end close – Automated account reconciliation

The traditional problem

Reconciliation procedures typically involve exporting, cleaning, compiling, and validating data from multiple systems, processing it through a series of pivot tables, formulas, and macros, and then manually creating/updating records in the ERP.

Out-of-policy variances, missing remittance information, and unmatched transactions trigger long searches through files, data, and communications, ultimately delaying period close and trapping your cash.

It’s slow, inconsistent, and depends heavily on the people who know the process inside out.

The AI solution

Compile data, match transactions, investigate variances, and suggest adjustments so your team only has to handle true exceptions.

Agentic processes can automatically:

• Pull transaction and account data from any system
• Match transactions automatically using mapping files and historical patterns
• Flag exceptions and route them for review, providing full context for fast handling
• Suggest journal entries and standardized memos for adjustments
• Maintain a complete audit trail of every match and decision.

What makes this work

AI processes large, messy datasets quickly and applies matching rules consistently. It learns from historical reconciliation patterns, so recurring variances can be resolved faster. With rules-based exception handling, your team only sees the items that truly need their attention.

And you don’t need to replace your existing processes. You can start by just having an agent take the first pass to identify and investigate discrepancies, ultimately accelerating the manual work that’s already happening. From there, incrementally expand the scope as confidence builds.

Automating reconciliation takes one of the most time-intensive steps in the close and turns it into a predictable process. The close becomes faster, more accurate, and less dependent on manual work.

Use case 5: Financial reporting – intelligent data validation and quality assurance

The traditional problem

Reporting accuracy depends on hours of manual data validation, pulling data from multiple systems and applying complex formulas and rules. Inconsistent checks or bad source data mean errors can still slip through.

The AI solution

AI can validate data across systems, apply data quality rules consistently, and flag issues early to ensure downstream reporting is accurate and reliable.

With automated validation in place, teams can:

• Validate current data against business rules
• Flag anomalies, outliers, and missing values based on historical trends
• Compare data across systems to find mismatches
• Keep a full audit trail of every check

What makes this work

AI-powered data quality workflows not only check against explicit business logic, but they also learn and improve over time, making them more dynamic than manual or hard-coded checks. These workflows can process large datasets quickly, catch issues earlier in the cycle, and even provide potential root causes to speed up remediation work. Continuous monitoring replaces one-off checks, reducing the risk of last-minute surprises.

Automating validation means reports are ready faster, last-minute changes are rare, and leaders can present numbers with confidence.

Implementation Approach

These five examples show what’s possible when AI takes on the work that slows your team down the most. And the good news is that successful AI implementations follow the same basic approach that trades long, expensive scoping, alignment, and development cycles for lightweight, out-of-the-critical-path deployments designed explicitly for fast iteration cycles and continuous improvement.

The implementation approach we’ve proven with early adopters is:

1. Identify one manual task within a broader process, such as a specific exception type in your invoice processing pipeline.
2. Build an evaluation dataset with historical data to help train your AI solution (“context engineering”) and to validate accuracy.
3. Engineer your prompt iteratively, testing iterations against actual cases in isolation (this can even happen via ChatGPT/Claude/CoPilot chatbots).
4. Once you find a combination of the prompt and context that works, implement the solution as a helper to accelerate manual task completion. This ensures a human stays in the loop initially to correct any issues and provide feedback for improvement, and lets you build confidence and proof without the massive IT project.
5. Measure performance over time by tracking the reduction in time spent executing the task, and by tracking the number of cases that still require human input vs. cases that were 100% automated.
6. Once performance clears a reasonable bar, rinse and repeat this process for adjacent manual tasks within that process.

*(For a deeper dive into effective AI solution development, check out Building Reliable AI for Finance and Operations: A Tested Approach*)

Next, we’ll look at the capabilities behind these results and how they can work together to transform your finance operations.

The AI advantage: What makes these use cases work

Each of these use cases is powered by the same core AI capabilities, and the targeted implementations within existing processes ensures fast results and minimal risk.

The MIT study points to “process-specific customization” with “systems that integrate with existing processes and improve over time” as key ingredients to success, and that is exactly what these use cases exemplify.

And these aren’t theoretical use cases. They’re proven capabilities we’ve deployed into complex, high-volume finance environments in tight collaboration with finance operators.

The basic building blocks look the same across these (and many other) finance use cases.

Document intelligence

AI goes beyond simple OCR, reading invoices, remittances, and contracts with context in mind. It understands vendor communications, payment references, and regulatory language, even when the formatting is inconsistent.

Pattern recognition

AI spots trends, outliers, and anomalies across large datasets. This makes it easier to identify recurring exceptions, detect fraud patterns, and flag unusual transactions before they become problems.

Context-aware processing

Rules and policies are applied consistently across processes. AI understands how data points relate to each other, so it can follow business logic even when inputs are incomplete.

Flexible data integrations

AI agents work across systems without relying on rigid mappings or hard-coded logic. They can combine information from multiple sources, such as ERPs, CRMs, and data warehouses, to give a complete view of a process.

These capabilities are powerful on their own, but the real impact comes when they work together to move finance teams through a clear progression:

1. Assist - AI supports your team by taking on routine tasks and flagging exceptions.
2. Accelerate - As accuracy and trust grow, you expand automation into more complex areas.
3. Autonomize - AI handles entire workflows, with your team stepping in only when needed.

And the goal is not to replace human labor. The goal is to make labor more productive by scaling human judgement. That requires reducing time spent on tedious, repetitive tasks and focusing on higher impact, more fulfilling activities. But it requires the organization to make deliberate decisions to embrace AI, and that only happens when real business value is proven.

When we’ve helped finance teams make this shift, the gains aren’t just faster closes and cleaner data. The result is more time for process optimization, better decision support for leadership, and more effective, strategic partnerships across the business.

The question is how to get there without disrupting the accuracy and stability your team is responsible for.

Final thoughts: Building toward agentic finance operations

If you’re leading a finance transformation, we get that it can be hard to know where to start. You’re asked to modernize, but you also own the accuracy of the numbers. Moving too fast feels risky, but moving too slow leaves you behind.

When thinking about transforming finance operations, the safest place to start is with processes that are high-pain but low-risk, like the five in this guide. They prove value quickly without forcing your team to change everything at once.

At first, AI supports your team by taking on routine work and flagging exceptions. With the right orchestration, those quick wins turn into consistent, scalable results.

This approach lets you see the impact first hand, building the trust and confidence you need to take on more complex challenges. If you can build momentum without overwhelming your team, the finance function will shift from firefighting to providing strategic support to the business.

The advantage compounds over time. Early adopters build capabilities and data foundations that make every next project faster, easier, and more impactful. That is how finance moves from cost center to strategic enabler.

Pick one use case, deliver results, and build from there.

Ready to notch your first win with AI? CoPlane helps deliver quantifiable value within weeks. Book a strategy session or reach out today to get started. Founders@CoPlane.com