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3 Things to Consider When Starting to Embrace AI in Tax and Accounting

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Getting Started with AI in Tax: Understanding, Streamlining, and Preparing Data

Tax and accounting professionals are facing a new frontier in their industry with the integration of artificial intelligence (AI) into tax processes. The use of AI promises to enhance efficiency, provide deeper insights, and improve accuracy in tax and accounting work. However, many professionals may find the prospect of incorporating AI into their workflow daunting. To help tax professionals get started with AI in tax, there are three basic steps to consider: understanding AI and generative AI (GenAI), streamlining data processes, and preparing data for AI integration.

The first step in getting started with AI in tax is to understand what AI and GenAI can do for the tax profession. AI has already been used in tax departments for tasks such as automating data entry and classifying tax documents. With the introduction of GenAI, these capabilities can be expanded to include predicting tax liabilities and providing decision support for tax planning. Tax professionals should seek out resources tailored to the use of AI in finance or tax to gain a better understanding of how these technologies can benefit their work.

The second step involves assessing the quality of the data that tax professionals work with. Data from various sources within an organization, such as financial statements, invoices, and payroll records, must be managed and streamlined for effective AI implementation. By increasing automation and integrating systems like enterprise resource planning (ERP) programs, tax teams can ensure that data flows smoothly between systems without the need for manual intervention, reducing errors and saving time.

Finally, tax professionals must ensure that their data is AI-ready before integrating AI into their tax processes. Data cleaning practices should be implemented to remove errors and inconsistencies, standardize data formats, and establish data governance policies to maintain data quality over time. Data security is also crucial, especially when dealing with sensitive tax information, to comply with regulations such as the General Data Protection Regulation (GDPR) and the Sarbanes-Oxley Act (SOX).

By following these basic steps of understanding AI, streamlining data processes, and preparing data for AI integration, tax and accounting professionals can set the foundation for a successful AI implementation in their work. Embracing the evolving field of AI and GenAI and maintaining a culture of learning and adaptability will be essential for staying ahead in the game of tax and accounting in the age of AI.

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