As 97% of companies prepare to invest in Artificial Intelligence (AI) technologies, now is the time for middle-market and enterprise businesses to take action. Finance leaders who embrace AI are a step ahead of the competition—but it doesn’t have to be all or nothing.
CFOs and CTOs who feel overwhelmed at the thought of embarking on a company-wide AI plan: Don’t panic. Small and incremental AI integration can make a big impact on your business. Just deploying one AI use case can make your team more efficient.
Before jumping in…
We recommend establishing strong technology, security, and data governance processes before beginning an AI implementation. Starting from a solid financial foundation makes integrating new AI processes into existing workflows smoother. Plus, it helps mitigate security risks, reduce costs, and minimize disruption to your finance team.
If you’re not sure where to start, you’re not alone. We’ve compiled a high-level blueprint for incorporating AI into your business. With the help of an advisory and transformation partner, this strategic, phased approach can be personalized to your exact needs. Let’s explore the 4-step process!
Step One: Streamline Operations With RPA
Robotic Process Automation (RPA) is exactly what it sounds like–software robots, commonly called “bots,” automate repetitive processes. It’s a good entry point into AI because it’s relatively simple to set up and use. And let’s face it, every department has tasks that can be automated.
The Solution for Manual Tasks
Think of RPA bots as efficient assistants who offer an extra set of helping hands. They’re being used to complete straightforward tasks that take up valuable time.
For instance, here are just a few ways companies are using RPA:
- Invoice Management:
Pulling employee hours, entering invoice details into your payroll system, confirming the information, and processing payments takes a lot of time. With RPA, bots follow your team’s preferred steps to manage invoices and decrease errors. - Reviewing Expenses:
Instead of manually sifting through employee expense reports, verifying receipts, and submitting each expense for approval, bots can handle this process. - Creating Reports:
Creating useful financial reports often requires gathering and consolidating data from several systems. Bots can significantly reduce the time spent on this responsibility by tackling the hands-on parts and creating a concise report (with far fewer errors or inaccuracies).
You’ll love how quickly RPA can positively impact your team’s productivity, giving them more bandwidth to focus on work that requires critical thinking or devoting time to upskilling, which are both essential for the business’s success.
Step Two: Analyze Faster with ML
The next step in AI automation is Machine Learning (ML). If RPA bots are assistants, ML technology is your go-to number and pattern pro. ML uses algorithms to analyze information, learn from it, and make projections based on the information its given.
ML is a revolutionary historian, making it a great solution when companies need to review vast amounts of data. It takes human error completely out of the equation and, arguably, a lot of the headache.
Deep Insights Without the Long Hours
Businesses can use ML for:
- Forecasting Financial KPIs:
ML algorithms can review historical data and accurately predict future financial metrics. It can even collect data from other sources. And the best part is it’s an agile technology, so instead of starting over when your company has an unprecedentedly low earning month, the ML model can adjust its forecast to include that real-time information. - Running Risk Assessments:
ML is sophisticated enough to assess a credit applicant’s full financial history and predict their credit risk. It can even be used to identify fraudulent schemes and transactions and catch emerging patterns with its ability to spot trends. - Identifying Expansion Opportunities:
ML can examine a retail store’s client history, analyze each segment’s purchase histories, and identify customer segments the business can expand on. On the other hand, ML could pinpoint customer segments that are underperforming and help a store decide to retire or change certain products.
With ML, finance and accounting teams can review large quantities of data for various purposes in a fraction of the time it traditionally takes them.
Step Three: Improve Communication with LLMs
Language Learning Models (LLMs) are the communicator specialists of AI. LLMs, like ChatGPT and Copilot, were designed to understand human language. What sets LLM apart from RPA and ML is its interactive component. LLM can comprehend language’s contexts, sentiments, and nuances and respond like a human (for the most part—it’s not perfect).
A Dynamic Communication Solution
Here are a few ways LLMs can support businesses:
- Customer Service Chatbots:
Many websites use chatbots to interact with customers and provide support. Chatbots can act as gatekeepers, handling basic questions and passing more robust requests along to a person. - Text Analysis:
If you’ve ever had a large data report on your to-do list to slog through, LLMs may be your new best friend. It can quickly read information, analyze for key insights and trends, and find discrepancies. - Document Processing:
LLMs can extract text through Optical Character Recognition (OCR), making document processing faster and more accurate. For example, LLMs can read each receipt of a department’s invoice submissions, identify key data, like names and amounts, and then input it into accounting.
Besides improving your team’s efficiency, LLM can also enhance the quality of customer interactions and data analysis.
Step Four: Creatively Innovate with GenAI
The last stop on the AI roadmap is Generative AI (GenAI). This technology develops new content, like reports and presentations, based on patterns learned from your existing data. It’s intended to mimic human creation and think on its own. However, it’s important to note that GenAI cannot think critically like a human can.
Unlock Complex Creation Possibilities
Companies are using GenAI to:
- Create Complex Financial Narratives:
This type of AI can synthesize complex financial data and market trends into coherent narratives and insights, making it suitable for client reports, shareholder communications, and regulatory filings. - Perform Financial Forecasting with Uncertainty Analysis:
GenAI models can generate probabilistic forecasts that include uncertainty measures. It adds value by providing more nuanced and realistic predictions of financial outcomes. - Adhere to Ethical and Regulatory Compliance:
These models can be trained to follow ethical guidelines and industry-specific compliance standards. This includes detecting biases, ensuring fairness in decision-making, and complying with stringent financial regulations.
With technology simulating human’s ability to create, it’s important to ensure your data is accurate, secure, reliable, and compliant with internal and external regulations. We recommend a proactive data governance strategy to help your organization gain ownership of your master data and establish a single source of truth.
RPA vs. ML vs. LLM vs. GenAI: What’s the Difference?
| Step 1: Robotic Process Automation (RPA) | Step 2: Machine Learning (ML) | Step 3: Language Learning Models (LLMs) | Step 4: Generative AI (GenAI) | |
| Capabilities | Automates repetitive tasks using rule-based software | Uses algorithms to learn from data and make predictions or decisions | Understands and generates human language | Creates new content, designs, or solutions using AI models |
| How to Use It | Task automation, like data entry, invoice processing, and basic customer support | Data-driven insights and automation, like fraud detection, recommendation systems, predictive maintenance | Natural language processing and understanding, like chatbots, virtual assistants, content summarization | Innovative output creation, like synthesizing complex financial data and generating probabilistic forecasts |
| Benefits | Cost reduction, increased efficiency, error reduction | Improved decision-making, predictive analytics, automation of complex tasks | Enhanced customer interactions, improved content understanding | Innovative solutions, creative content, personalized experiences |
| Challenges | Limited to rule-based tasks, scalability issues | Requires large, high-quality datasets, complex to implement | Computationally expensive, requires large-scale training data | Ethical concerns, high computational costs, data dependency |
Navigate AI With SC&H’s Experts
Eager to tap into the power of AI to accelerate digital and finance transformation? SC&H can work alongside your team to craft a customized AI roadmap, translating your unique goals into actionable phases with a data-driven plan and appropriate budget. From there, we’re available to assist you throughout the entire implementation process so you can feel confident about achieving your financial objectives with AI at the helm.
SC&H can help your company navigate financial and digital transformation with a comprehensive plan, an appropriate budget, and strategic recommendations to address your unique challenges and meet your specific goals. Our AI Roadmap Service is a 6-week partnership in which we’ll work with your team to map out your company’s AI implementation plan and put it into phased, actionable steps. From there, we’re available to assist you throughout the entire implementation process so you can feel confident at every step.
You can count on us to:
- Meet with your company’s stakeholders to create a personalized AI action plan
- Advise you on the budget, time frame, involved parties, and new processes
- Help you select the right software and technology and assist your company so it’s a seamless integration
- Own change management and training
Ready to get started on the path to AI? Schedule a free chat with one of our transformation experts.
Not ready to go all-in but interested in learning more? Explore our advisory & transformation services.
