The First Steps to Leveraging AI in Finance Operations 

Want to use AI in finance operations? But are you building the right foundation for it to work? 

As accuracy, speed, and efficiency have become key differentiators, many businesses are turning to AI tools and AI-driven processes to navigate ever-growing data streams and complexities. 

AI systems can process large datasets and perform mathematical computations at lightning speed. Applications with exceptional intelligent automation, analytical, and predictive capabilities unlock unprecedented levels of operational efficiency and financial intelligence. 

The game-changing evolution continues with extended capabilities like optical character recognition (OCR), natural language processing (NLP), conversational AI, and more.     

So, if you’re managing your financial operations without AI you’re missing out! 

But here’s the thing: technology is no magic. You need to build the right foundation to leverage its full potential and maximize ROI. 

AI in Finance Departments vs. Consumer-Facing Processes 

Financial processes within finance departments and customer-facing financial service processes (e.g. banking, insurance, or retail) are quite different. This means you need AI tailored to specific needs. 

For instance, a company may want to automate routine accounting, payroll, and tax filing tasks. They may also seek advanced data analytics and predictive AI for project finance management, budgeting, and risk evaluation. 

AI adoption in finance operations is growing rapidly.  According to a Gartner survey, 58% of finance functions were using AI in 2024. Four main use cases that stood out include:  

• Intelligent process automation for information processing  

• Anomaly and error detection in large-scale data such as internal claims, expenses, and invoices  

• Analytics for improved financial forecasts and results analysis  

• Operational assistance and augmentation, primarily with GenAI  

In contrast, customer-facing financial processes focus on service delivery and customer experience. Some common applications include interactive chatbots, payment reminders, and tools such as EMI and loan calculators. 

AI also excels at data anomaly detection, identity verification, and real-time tracking. It can quickly identify data errors and unauthorized access, helping to prevent incorrect payments and unsanctioned transactions.  

Notably, these capabilities are useful for both types of financial functions.

Critical First Steps for Seamless AI Implementation in Finance 

Clearly, the nature of your business and the financial process itself will determine the type of AI solution to be implemented. However, you must prepare the groundwork for successful deployment.  

Here are the 10 most important foundational steps you need to take:  

1. Choose Financial Tasks to Integrate AI

Understanding your precise needs and challenges is crucial for implementing AI in your financial processes. Identify the areas where AI can deliver the most benefit and value. Is it accounting automation, investor reporting, personalized recommendations, or financial forecasting?  

Consider quanitiative factors like potential investment and ongoing costs, and ROI, along with qualitative aspects such as better accuracy, time savings, and compliance, which indirectly benefit your bottom line and output. 

You can speak to experts (like us at Centelli) to help you identify the lowest hanging fruit – click here to book a call! 

2. Decide the Level of AI Sophistication

The right level of sophistication ensures that your AI solutions are both effective and efficient. Basic automation tools will suffice for rule-driven and repetitive tasks like data entry, report creation, or bill processing. AI’s amazing pattern recognition capabilities can create powerful identity theft and fraud prevention applications for you.  

Operational scale is yet another barometer. For example, intelligent automation with AI and RPA may suffice for small business workflows, but a large enterprise or an e-commerce platform may benefit from advanced predictive analytics and deep learning algorithms.

3. Get Your AI Technology Stack Right 

AI models for finance operations are designed using a mix of technologies, including programming languages, big data, ML/DL/NLP, visual recognition, generative AI, and more. Different combinations are meant to fulfill different purposes. 

 
For example, big data and machine learning can boost accounts receivable and optimize trade credit decisions by analyzing supplier payment patterns. RPA, OCR, and NLP, working together, can automate data entry from handwritten and printed documents while understanding the context of the information extracted from them. 

4. Create IT Setup for AI-led Finance Workflows

Make sure you have adequate IT capability to host your AI solution(s). Evaluate server capacity, data storage, and network bandwidth and optimize the existing set-up if required. You may need to upgrade in some cases. 

Also consider what’s more achievable and practical—a cloud platform or an on-premises solution. Weigh both options in terms of cost, security, and scalability.  

A robust infrastructure is vital for processing large datasets efficiently and ensuring smooth AI-driven finance workflows.  

5. Gather Technical Expertise and User Skills 

 Rich expertise in data science, machine learning, and related technologies is a prerequisite for crafting AI applications for businesses.  You may also need to train in-house finance teams to use these tools and solutions effectively.

However, AI-enabled consumer-facing solutions are typically user-friendly and highly adaptable.

For example, interactive chatbots or robo-financial advisors require only basic smartphone skills. This allows human agents to focus on more complex and value-added work, while virtual agents manage routine and repetitive queries.

6. Assess Data Quality for AI Model Efficacy 

AI models are trained with pre-existing data. Evaluate your existing data sources for accuracy, completeness, and relevance. Make sure all data is cleansed and validated to feed the AI system you will deploy. Do you use multiple platforms to enter, store, and retrieve data? It’s better to create a centralized database for consistency and evaluation.  

Download our free guide ‘First Steps to Automation & AI in Finance Teams,’ to learn about common use cases & solutions we’ve successfully delivered! 

With poor data quality, you compromise AI performance and the outcomes for the financial processes it drives.  

7. Outline Data Governance Mechanism 

While financial data is valuable, it is also sensitive. Whether it’s company or customer data, you must create a blueprint for managing data within your systems and ensuring compliance with relevant data privacy regulations (e.g., GDPR, CCPA). This will help prevent accidental data leaks, malicious breaches, and unauthorized access and sharing.  

Failure to comply can attract serious fines and penalties. In 2024, regulators across EU and US came down heavily over data protection violations! 

8. Align AI Investments with Process Goals 

Technology investments can drive significant returns with proper planning and expert implementation. However, it’s essential to set realistic expectations and consider both tangible and intangible advantages to evaluate the value AI brings to your financial operations and ultimately to your business.  

Hence, define KPIs for AI performance, focusing on direct benefits such as increased accuracy, speed, and output, as well as indirect benefits like time savings and resource optimization. 

9. Plan for Post-Deployment Monitoring  

Beyond deployment, you also need to implement an effective monitoring system to assess the outcomes and impact of AI-driven financial workflows. Select performance tracking and reporting tools, and establish feedback loops to enhance the system based on monitoring results.  

This will help identify issues and areas for improvement. You may need to retrain your AI model as needs evolve, ensuring it remains effective and aligned with operational objectives. 

10. Address Finance Team Concerns 

Navigating the changes that come with AI adoption may not be easy for finance teams. Employees can become insecure or find it difficult to work with new systems. As such, you may need programs to provide training and education on AI concepts and tools while addressing concerns and biases.  

Furthermore, you also need to ensure collaboration between the finance and IT teams for best results. 

The Two Pathways to AI-powered Finance Functions    

If you are looking to leverage AI and intelligent automation for your finance operations, you have two pathways to choose from: manage everything in-house or partner with experts like us. 

The first option requires significant resources to build the necessary capabilities and plan effectively, which isn’t practical for most businesses—and probably not for you either. 

We save you time and effort by guiding you to the ideal solution, customizing it to your needs, and helping build a strategic roadmap for a smooth transition. Plus, our ongoing maintenance and scaling services ensure long-term value from your AI initiatives.  

Feel free to reach out if you have any questions or need more info. Book your call now!