Skip to main content

Artificial intelligence (AI) and machine learning (ML) have played a key role in modernizing finance and accounting for years, but with the latest advances, particularly generative AI, the momentum has jumped into hyperdrive. For finance and accounting teams, the potential for greater efficiency, accuracy and performance has expanded exponentially, as has the challenge of keeping up.

It’s estimated that AI performance doubles every several months—making Moore’s Law seem relatively sluggish by comparison. That means in the six months since the first of our four-part series was published, a lot has already changed with the technologies, use cases and general acceptance. For example, OpenAI now allows companies to create their own custom GPTs. Microsoft integrated its ChatGPT into Excel and other Office applications. And Blackline rolled out the first AI-enabled intercompany accounting capabilities.

Enterprise adoption is also accelerating, and it tops finance leaders’ list of investment priorities for 2024. In our recent survey, 58% of respondents said they would invest new capital in digital transformation and AI if interest rates go down.

How to Stay Ahead of the AI/ML Curve

Staying ahead of the curve is next to impossible for today’s modern enterprise as things are moving too quickly. Artificial general intelligence (AGI)—where machines can reason like humans—is just one of the technical discoveries on the horizon and one aspect of the overall adoption puzzle. Legal and ethical concepts such as bias, plagiarism, privacy, and deep fakes create another complex level of challenges that will slow adoption rates in the enterprise.

Don’t become discouraged if it feels like the speed of adoption within your firm is lagging — almost everyone is experiencing similar challenges. Staying nimble, committed, and open to new and unexpected realities is critical.

Here, we recap highlights of our previous articles and outline four key steps for taking advantage of AI/ML in finance, accounting and internal audit.

➡️  How CFOs Can Use AI/ML to Streamline and Innovate Finance & Accounting

➡️  Top Use Cases Driving Adoption of AI/ML in Finance & Internal Audit

➡️  Want to Empower F&A Teams with AI/ML? Start with Your Data.

Although this series of articles has been geared toward the finance and accounting functions, the ideas here are cross-functional and can be applied to almost any corporate function, including supply chain, HR, sales, manufacturing, etc. Let’s jump right in and start building the plan.


Identify the Problems to Solve

Today’s AI/ML technologies are geared toward non-technical business users to make it easier to solve everyday problems. Identifying the problems to tackle first is a critical decision with several key variables at play. You may already have some ideas of what you want to tackle first. If not, there are a few common areas that are “target rich” with opportunities.

  • Risk and fraud: Consider recent audits where controls identified potential areas of risk or fraud such as high-volume transactions (payables, receivables, expenses, procurement, etc.). Using pattern recognition and other AI/ML techniques allows for complex pattern recognition and policy violations which would typically not be noticeable by humans. This is true where fraud occurs over multiple related transactions.
  • Manual tasks: Robotic process automation (RPA) has been around for a while and is very effective for automating routine, manual tasks. Many of the RPA tools utilize advanced AI/ML to do more than just repeat a single process, such as data entry, and allows them to use reasoning similar to human logic to make value-added decisions.
  • Forecasting: AI/ML is great at reading vast amounts of not only historical data but also combing external data to detect correlations that may have impacts on future results. Increasingly, all business leaders are asked to predict the future. This is an area where AI/ML can help.


Understand Enterprise Capabilities

Highly trained technicians such as statisticians and developers are still needed in some capacity, but many of today’s standard office tools such as Excel and cloud ERP empower traditional functional business operators. Before you embark on your AI/ML journey you need to understand what you have to work with in your department and within the broader enterprise.

  • Specialized AI/ML tools and infrastructure: Look to other departments at what they are doing with AI/ML and understand how they are accomplishing it. Meet with your IT group to learn about capabilities such as cloud computing, data management and governance capabilities, and specific AI/ML tools that are available to you.
  • Techniques and knowledge: Again, identify those in the organization already doing data science and AI/ML. Chances are they are using best, or at least accepted, practices and standards for modeling and overall delivery. Many IT organizations are employing functionality for ModelOps – which is the controlled way of building, deploying, and managing AI/ML solutions.
  • Cloud ERP and Office-native technologies: Excel is the most common, well-known data tool in the world. And now that Microsoft has rolled out Copilot, the common tools you are comfortable with now have highly advanced and powerful capabilities that can be fully used by simply using human-like conversational language. The same goes for modern cloud ERP systems — and it’s one of the key drivers for migrating to the cloud. For example, many standard AP modules come with AI/ML built in to automatically detect fraud — with no custom coding or expensive data scientist required.


Assess the Data

The saying “garbage in, garbage out” will be amplified by AI/ML. Fully understanding the input data is critical. AI/ML can also expose your data to security risks. Be sure you work closely with your enterprise security team and understand how sensitive data is being used and what impacts AI/ML may have on its control and dissemination.

  • Accuracy and completeness:  Review your input data to ensure it accurately represents a true picture of what is being measured. Data that is missing can sometimes be compensated for using traditional data science techniques.
  • Unstructured data: AI/ML is exceptionally good at consuming large amounts of unstructured data such as contracts, articles, and reports. Take full advantage of this to summarize contract language and create user-friendly chatbots for employees to get answers quickly.


Build and Govern

It does not matter what great discovery or value AI/ML has made; if not implemented carefully, it will be seen as a failure. For this reason, there are several things to think about.

  • Control the chaos: There is a fine balance between allowing team members to explore on their own and controlling the efforts. Users should be encouraged to learn, experiment, and succeed (or fail) without management intervention. However, as these grass-roots efforts start to develop real solutions for use in daily processes there needs to be a control mechanism to manage the deployment and management of these solutions. Again, this is where IT and ModelOps can be a big help. Knowledge needs to be documented and shared.
  • Manage expectations: Of everything I’ve written, this is probably the most important. Perception becomes reality all too often and otherwise successful programs are deemed failures due to one undesirable outcome. You will have undesirable outcomes in your AI/ML journey. This is guaranteed. Leadership must understand that, just like science, tools, techniques, and new discoveries happen almost daily. AI/ML is a technology that “learns” over time like a human. The model that doesn’t appear to work today can work better as it gets smarter. Expectations must be managed accordingly.

Accessing Specialized Skills for the AI/ML Journey

Even though we’re seeing more job descriptions that require data and analytics skills, it will take a while to proliferate throughout the modern enterprise. Fortunately, there are many qualified systems integrators and advisory firms that specialize in developing AI/ML strategies and operations.

There are many layers of complexity here, but the good news is that getting started doesn’t have to be complicated. A realistic, common-sense, grassroots departmental effort can bear many fruits without a lot of time and resources.

Don’t be afraid to start this journey today with what you already have. Encourage your team members to embrace new ideas, think outside the box, and explore without the fear of failure. Reward success, but also reward curiosity and initiative.

And, as always, please feel free to contact us if we can help.