As an accounting professional, you understand the significance of numbers. But in an era where chatGPT, machine learning, automation, and Big Data are transforming the industry, are you considering the new metrics that could forecast performance?
Why track KPIs?
How do you know where to go if you don’t know where you’ve been? And the better you know where you’ve been, say, a year ago, compared to today, can make an enormous difference in how you evaluate your performance and make decisions. Accurate data enables you to tweak, test, measure, and implement different aspects of your business. Peak performance is the goal; of course, it’s impossible to achieve it, considering our changing financial and technological landscape. But you MUST strive for it. Otherwise, you risk getting left behind.
Many accounting firms are rapidly integrating new technologies into their practices but are unsure of the exact impact on their businesses. So, here are a few new Key Performance Indicators you should implement to understand just how much time and money you’re saving with these new technologies and procedures.
AI Efficiency KPIs
Unless you’ve been living under a rock, you’re aware of the mind-numbing progress of Artificial Intelligence over the past year. A new use case seems to pop up daily, or a new performance record is smashed. As you’ve undoubtedly read in our previous article about AI, accounting is smack dab in the middle of the AI revolution. But if you want your accounting practice to stand out, you must put in KPIs directly related to AI. Here are a few to get you started:
Percentage of Tasks Automated
This KPI tracks the proportion of tasks that your firm has automated using AI, such as Invoice Processing, Transaction Categorization, and Expense Report Generation. A high percentage of automated tasks can prove an increased adoption of AI, but results must be cross-checked with Quality Assurance to ensure it’s worth it.
Time Saved Due to Automation
By automating repetitive and time-consuming tasks, AI can significantly reduce the time to complete them, thus increasing your firm’s productivity. Measure the time saved before and after implementing AI to quantify the benefits of automation. This metric will vary based on the task and can be used to prioritize the automation of tasks that yield the most time savings.
AI Training Time
This is the time it takes to train your AI tool to perform tasks to the desired level of accuracy. The more experience you gain with AI, the lower the time it should take to get an AI tool up and running, thereby speeding up the delivery of projects.
Error Rates in AI-Generated Reports
Even though AI can handle tasks more quickly than humans, it’s essential to measure the accuracy of its outputs. This KPI measures the percentage of errors found in reports or assignments completed by AI. A high error rate may indicate that the AI requires more training or that the AI’s complexity needs to be adjusted. It’s a delicate balance; increasing complexity can improve accuracy but may lead to overoptimization, which decreases the AI’s ability to generalize and adapt to new data, reducing the advantage of AI in the first place.
Keeping track of these AI KPIs will ensure your CPA firm correctly and efficiently implements and utilizes these new technologies better than your competitors who fail to do so. You can also better track its value and ROI to help you determine whether you should dedicate more resources to AI in the future.
Data Analytics KPIs
In today’s digital age, the volume of data accounting firms handle is vast and only growing. But what to do with it all? Figuring that out should be your first goal. Second, you must glean insights from using that data to identify trends and predict future outcomes to improve your decision-making process. Here are some KPIs that will help you:
Number of Data-Driven Decisions
You probably make important decisions every day – but how many of those are based on data? These decisions can be just about anything as long they are based on relevant and measurable information. For example, by analyzing financial data, a firm could make informed decisions about adjusting its pricing structure to maximize revenue while staying competitive. Alternatively, a firm could use data to determine where to open up a new office and the number of employees it should have.
Time to Insight (TTI)
You can have all the data in the world, but if it takes you a year and a day to use it, it’s worthless. In our fast-paced world, the faster you can act on your data, the more you can benefit your firm and your clients. So, how long does it take to analyze your data? You need to improve if your competitor analyzes it faster than you.
Predictive Accuracy
Unfortunately, your predictions and conclusions may be incorrect. Understanding how often your predictions are inaccurate using a particular method or technology compared to other methods enables you to make adjustments as necessary until they’re aligned.
Data Utilization Rate
How much of your data is actually being processed and utilized for insights? A higher utilization rate indicates that your firm is effectively harnessing the data available, maximizing your return on data investments. If it’s low, you’re either missing out on valuable insights. Alternatively, it may be just junk data if you are processing data and not garnering insights. You can try restructuring it to see if that helps or stop accepting that kind of data for now.
What’s the difference between AI and Data Analytics KPIs?
You may think it should be the AI’s job to analyze the data now. In many cases, AI and Data Analytics KPIs can be combined. However, there are distinctions between the two that can affect how they are implemented and utilized in an accounting firm.
While AI often relies on data analytics as part of its processes (for instance, machine learning algorithms use data analytics to learn from data), not all data analytics involves AI. Sometimes, data analytics involves traditional statistical methods or other quantitative techniques.
For example, consider an accounting firm that uses a software program to analyze past tax returns and identify opportunities for tax savings. If the software uses preprogrammed rules to identify these opportunities, it’s using data analytics. If the software can learn from past tax returns and improve its ability to identify tax savings over time, it’s using AI.
Regarding KPIs, some metrics are more relevant to AI (like the percentage of automated tasks and error rates in AI-generated reports). In contrast, others are more relevant to data analytics (like the number of data-driven decisions and predictive accuracy). Even though AI and data analytics can sometimes be used to achieve similar goals, they represent different capabilities, and their impact can be measured differently.
So, while there is some overlap, using separate sets of KPIs for AI efficiency and data insights can give you a more comprehensive view of how well your firm is leveraging technology.
In Conclusion
Adopting these new KPIs, along with your traditional metrics, such as Employee Productivity and Client Retention rates, will provide a comprehensive overview of your firm’s performance and future potential. Tracking AI efficiency and data analytics enables you to fine-tune your existing practices, enhance client services, and stay ahead of industry trends.
Always remember – what gets measured gets managed, and what gets managed gets improved.
