If there is one industry that is specifically suitable for AI agent implementation, it’s finance. It is data-intensive with a massive amount of structured and unstructured data for AI to thrive on.
The complex and constantly changing regulatory requirements are extremely tedious to track. Moreover, the operations involve routine tasks that can be easily automated.
Keeping all this in mind, as one of the leading AI agent development companies, we have identified 4 critical AI agent use cases for you to take a competitive lead. As an expert team, we have also shared the suggested tech stack based on popular ERPs.
Let’s start.
1. AI Agent for Procurement Contract Analysis
Procurement contract analysis is indeed a critical part of finance. It ensures that your vendors deliver what they have promised while avoiding surprise costs and reducing legal risks.
However, it is the most difficult one as well. There are multiple challenges to an effective contract analysis. First of all, the contracts are long, like really long. On top of it, they are complex and written in legal language. And if these contracts are stored across multiple systems, this will be a nightmare.
For this, we suggest an AI agent that scans contracts, pulls out metadata, highlights risks, and flags deviations. It will overcome the sluggish and error-prone manual review with high dependency on experts.
It will make tracking easy for renewal dates, risky clauses, and, more importantly, it will create standard templates automatically to compare contracts.
All the while ensuring that no contract is missed.
Suggested Tech Stack – Procurement Contract Analysis AI Agent
Data Integration (Contract Ingestion):
- NetSuite: Celigo
- Dynamics 365: KingswaySoft
- SAP S/4HANA: SAP Data Services
Document Management & Access:
- NetSuite: File Cabinet
- Dynamics 365: SharePoint / Dynamics Document Management
- SAP S/4HANA: SAP Document Management System (DMS)
AI & Machine Learning (Clause Extraction / Metadata Identification):
- NetSuite: Python with Scikit-learn
- Dynamics 365: Azure ML
- SAP S/4HANA: Python + TensorFlow
Natural Language Processing (Legal Language Processing):
- NetSuite: spaCy combined with ContractNLP libraries
- Dynamics 365: Azure Cognitive Services, including Text Analytics and Custom NLP
- SAP S/4HANA: SAP Conversational AI with NLP add-ons
Clause Deviation Detection / Risk Scoring:
- NetSuite: Custom Python Models
- Dynamics 365: Azure AI Contract Intelligence (Custom models)
- SAP S/4HANA: SAP AI Business Services, specifically Document Information Extraction
Supporting Documentation & Compliance Cross-Check:
- NetSuite: SuiteAnalytics
- Dynamics 365: Dynamics Compliance Manager
- SAP S/4HANA: SAP GRC (Governance, Risk, and Compliance)
User Interface / Dashboards (Insights & Alerts):
- NetSuite: Tableau
- Dynamics 365: Power BI
- SAP S/4HANA: SAP Fiori
Database / Storage:
- NetSuite: PostgreSQL
- Dynamics 365: Azure SQL Database
- SAP S/4HANA: SAP HANA Database
2. AI Agent for Monthly Reconciliation
Monthly reconciliations are important for early spotting of errors, fraud, or missing entries before they snowball into something unmanageable. However, at the same time, it is tedious and error-prone itself, given the complexities involved.
It is marred with tedious manual reviewing of data spread across sources. This involves the relevant ERP system, spreadsheets, shared drives, etc. Finance professionals check through the entries to find inaccuracies or mismatches. However, it is also a perfect spot to deploy AI agents. Here is how you can do it.
For this, we suggest an agent that can run across your system to compare entries, identify mismatches, and pull related documentation.
Here is what we suggest.
Suggested Tech Stack Based on Popular ERPs
Data Integration (ETL):
- For NetSuite, the common choice is Celigo.
- With Dynamics 365, businesses often use KingswaySoft.
- In the case of SAP S/4HANA, SAP Data Services is typically employed.
Automation Orchestration:
- UiPath is widely used alongside NetSuite.
- Microsoft Power Automate is a natural fit for Dynamics 365.
- SAP Intelligent RPA supports automation within SAP S/4HANA environments.
Artificial Intelligence & Machine Learning:
- NetSuite often integrates with Python and Scikit-learn.
- Dynamics 365 connects seamlessly with Azure ML.
- SAP S/4HANA relies on Python combined with TensorFlow for AI/ML capabilities.
Natural Language Processing (NLP):
- NetSuite setups make use of spaCy for NLP tasks.
- Dynamics 365 employs Azure Cognitive Services (Text Analytics).
- SAP S/4HANA includes SAP Conversational AI for NLP applications.
User Interface and Dashboards:
- Tableau is commonly paired with NetSuite.
- Power BI integrates closely with Dynamics 365.
- SAP Fiori provides dashboarding and UI within SAP systems.
Database and Storage:
- NetSuite supports PostgreSQL.
- Dynamics 365 runs on Azure SQL Database.
- SAP S/4HANA uses SAP HANA DB as its foundation.
3. AI Agent for Summarising Fraud Cases
Any fraud case generates a huge amount of data, and summarising it helps in quickly understanding what happened. Who is/was involved? And what evidence is available?
However, again, challenges exist. First, as is evident, the data volume is too much. There a lot of communication and transaction data involved, and it can be in different formats and in different sources. Plus, there is always a time pressure as the team needs to act fast and gather as much evidence as possible.
In such a scenario, a manual approach can defeat the purpose. Not to mention the chance of human bias.
We suggest an AI agent that reviews communication platforms, project management tools, and email threads to extract context. It will highlight inconsistencies and produce a structured summary, saving time, maintaining consistency and a sharp focus on evidence.
Data Integration (ETL)
- NetSuite-Compatible: Celigo
- Dynamics 365-Compatible: KingswaySoft
- SAP S/4HANA-Compatible: SAP Data Services
Automation Orchestration
- NetSuite-Compatible: UiPath
- Dynamics 365-Compatible: Microsoft Power Automate
- SAP S/4HANA-Compatible: SAP Intelligent RPA
AI & ML
- NetSuite-Compatible: Python + Scikit-learn / PyTorch
- Dynamics 365-Compatible: Azure ML
- SAP S/4HANA-Compatible: Python + TensorFlow
NLP (Natural Language Processing)
- NetSuite-Compatible: spaCy / Hugging Face Transformers
- Dynamics 365-Compatible: Azure Cognitive Services (Text Analytics & Language Understanding)
- SAP S/4HANA-Compatible: SAP Conversational AI
UI / Dashboard
- NetSuite-Compatible: Tableau
- Dynamics 365-Compatible: Power BI
- SAP S/4HANA-Compatible: SAP Fiori
Database / Storage
- NetSuite-Compatible: PostgreSQL
- Dynamics 365-Compatible: Azure SQL Database
- SAP S/4HANA-Compatible: SAP HANA DB
4. Agent 4: Budget Forecast Variance Explainer
Another critical area where finance professionals struggle is budget forecast variance - the difference between what you planned and what was the actual spend or revenue. It is a clear indicator of whether your finance team is planning realistically.
If done properly, it helps identify overspending or underperformance in earlier stages and guides better decision-making for future budgets.
However, it is affected by scattered data across systems, manual consolidation of numbers, lack of context and, most importantly, the time lag i.e. by the time variance is explained, the decisions are already delayed.
For this, we suggest an agent that can analyse spending/revenue fluctuations, find key drivers, and pull in supporting documents automatically. It will help in not only minimising (if not eliminating it completely) the time lag by providing context in time and reducing human error. Thus, providing the top management with better insights for robust decisions.
Suggested Tech Stack for Budget Forecast Variance AI Agent
Data Integration (ETL)
- NetSuite-Compatible: Celigo
- Dynamics 365-Compatible: KingswaySoft
- SAP S/4HANA-Compatible: SAP Data Services
Automation Orchestration
- NetSuite-Compatible: UiPath
- Dynamics 365-Compatible: Microsoft Power Automate
- SAP S/4HANA-Compatible: SAP Intelligent RPA
AI & ML
- NetSuite-Compatible: Python + Scikit-learn / PyTorch
- Dynamics 365-Compatible: Azure ML
- SAP S/4HANA-Compatible: Python + TensorFlow
NLP (Natural Language Processing)
- NetSuite-Compatible: spaCy / Hugging Face Transformers
- Dynamics 365-Compatible: Azure Cognitive Services (Text Analytics & Language Understanding)
- SAP S/4HANA-Compatible: SAP Conversational AI
UI / Dashboard
- NetSuite-Compatible: Tableau
- Dynamics 365-Compatible: Power BI
- SAP S/4HANA-Compatible: SAP Fiori
Database / Storage
- NetSuite-Compatible: PostgreSQL
- Dynamics 365-Compatible: Azure SQL Database
- SAP S/4HANA-Compatible: SAP HANA DB
Conclusion
That wraps up our discussion on these use cases. If implemented properly, they will help you overcome manual errors, gain operational speed, and thereby expedite market-differentiating decisions.
As a leading AI development company, we have worked with businesses and helped them identify the areas with high potential for agentic integration. This blog can be your starting point.
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