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Generative AI explained: Why GenAI is the (friendly!) future of financial performance analysis

In this article, we’ll explore GenAI, what it is, how it works, and ways finance teams can apply it to improve CPM processes. 

Today’s finance teams are well acquainted with the struggle of managing large data volumes. To say we are dealing with big data would be an understatement; enormous data would be a more accurate description.  

As we struggle to make business sense of data across our enterprises, we’re confronted with an ever-increasing number of reporting requirements—ESG, BEPS Pillar Two, lease accounting—that require the highest degree of corporate performance data mastery.  

How can we confidently comply with new and emerging disclosures when we still struggle to foster efficiency in processes as longstanding as the annual report? 

One answer comes via AI. And not just any AI. Generative AI (GenAI.) 

In this article, we’ll explore GenAI, what it is, how it works, and ways finance teams can apply it to improve CPM processes. Let’s dive in. 

What is generative AI?  

To answer this question, we went straight to the most well-known form of generative AI: Chat GPT. First, we asked Chat GPT for a concise explanation of generative AI. Here’s what it said: 

“Generative AI creates new content mimicking human creations by learning patterns from existing data. It's used in text, image, music, and video generation, with examples like GPT for text and GANs for images.”  

(Not bad, Chat GPT.)  

From this definition, you can see how finance, as a data-driven department that relies on text and visualization, could benefit from GenAI. GenAI is a handy tool for finance teams that must analyze performance data, manage performance data, and produce visualizations from that performance data, as GenAI can augment all three job functions. 

How does generative AI work? 

When you feed GenAI models historical data, the AI learns to recognize data patterns. It then uses those patterns to generate similar new data.  

For example, to train a GenAI model on a language, you feed it websites, books, and text until it learns the statistical likelihood of one word following the next. The process to generate images is similar. The AI ingests pictures, photos, and graphics. Eventually, it learns to reproduce visual patterns.    

Three ways finance can apply generative AI to CPM processes 

Since financial processes are marked by repetitive processes, data exploration, and the analysis of large datasets — the three things GenAI does best — CPM solutions that use GenAI can profoundly impact the daily lives of finance professionals. Here are three ways GenAI can assist finance teams.  

AI helps finance embrace a data deluge & connect the dots

The world's data is anticipated to reach 175 zettabytes, or 175 billion terabytes, by 2025. And there’s no end in sight.  

GenAI thrives on large volumes of data. It gets to the root of data to identify patterns and hidden correlations that would not be evident to the human eye (especially not after late nights crunching numbers.)  

GenAI doesn’t replace your critical thinking, but it can identify missing pieces that round out your thinking and make connections you might have missed. It can identify subtle patterns, correlations, and hidden factors influencing performance. Its ability to analyze everything leads to more informed and impactful decisions, helping you to move from the “what happened” to “why did that happen?” and “what will happen next?” 

AI is a helpful assistant  

According to the findings of the AI in Audit Survey released by KPMG, finance executives that use AI are already experiencing the following benefits: 

  • 51% increased efficiency and a reduced burden on employees using AI. 
  • 50% experienced greater data accuracy, reliability, and predictability using AI. 
  • 50% increased visibility into end-to-end processes and controls using AI. 

While GenAI can support finance tasks, it’s important to understand that it is more of a research assistant and less of a fortune teller. GenAI can provide insights based on historical data and trends, but the final decision-making should be on you, the human. 

When managing GenAI, it’s vital that finance teams can set parameters, choose the datasets AI will be trained on, and submit their own queries. While GenAI takes care of the labor of data-smithing, humans still need to vet and verify the GenAI’s work. It is efficient and intelligent, but it is not infallible.  

Tl;dr: Look at GenAI as an assistant that augments part of your intuition, refines your analysis, and boosts the accuracy of your decision-making.  

AI boosts productivity and analysis 

Research by CFO.com found that the average FP&A employee spends 75% of their time gathering data and administering the process, leaving just 25% to provide value-added analysis to the business. GenAI has the power to flip this statistic.  

GenAI specializes in making repetitive processes like data exploration and analysis almost instantaneous. Finance teams can reclaim their time on data exploration, driver-based analysis, creating charts, and crafting commentary for reports and instead focus on driving the business. Essentially, GenAI augments your decision making by providing intelligent insight into emerging trends.

Intelligent Analytics

Unlock insights. Instant visualization. Just Ask AI. 

The bottom line: GenAI is the helping hand finance teams need

GenAI can be a powerful extension of your expertise, helping you work faster, smarter, and with a deeper understanding of your financial data. 

Generative AI is packaged within the CCH Tagetik Intelligent Platform. With CCH Tagetik Ask AI, you get system navigation, data exploration, and visualization capabilities that leverage natural language processing (NLP), large language models (LLM), and GenAI to surface pertinent corporate performance information and make it available for analysis.  

Ask AI’s first use case provides a self-service analytics experience to our Intelligent Analytics customers.  

Here’s how Ask AI works: 

Ask AI generates insights — fast 

How many times have you asked, “Where can I find this information?” Now, you can simply Ask AI. Ask AI allows you to use your natural language to interact with CPM data. In other words, you Ask AI a question, and it answers by providing to you the best visual representation for the purpose of your analysis.  

Ask AI democratizes data 

There’s no need for complex queries or coding. Simply ask or write, "Tell me about the variances between sales and forecast for product X in all BUs in the last 12 months." Ask AI answers by building a chart, graph, or report for you. Anyone can use Ask AI and explore data approved for analysis — no tech skills are required.  

Ask AI facilitates data-driven decision-making 

Ask AI analyzes enterprise datasets at the deepest levels, generating results considering a comprehensive range of intersecting information. Its holistic analysis empowers CFOs to move from reactive analysis ("what happened?") to proactive forecasting ("what can happen and why?"). 

Learn more about CCH Tagetik Ask AI 

CCH Tagetik Ask AI instantly surfaces information finance professionals need. Ask AI enables our customers to bypass the need for functional and technical skills, reducing the time spent in data preparation, exploration, and navigation to zero.  

With Ask AI, we are equipping finance professionals with self-service analytics and augmenting reporting and analytics processes with GenAI starting now. The roadmap for our GenAI application is long: from analytics to data processing, system navigation to regulatory acceleration. There’s more to come. 

The (friendly!) future of financial performance analysis has arrived. To see the CCH Tagetik Ask AI in action, schedule a consultation here.

Claudia Busdraghi
Innovation Senior Consultant at Wolters Kluwer
Claudia has a bachelor’s degree in Computer Science from the University of Pisa and a master’s degree in Data Science and Business Informatics from the University of Pisa. During her university career, she had the opportunity to develop programming and informatics skills together with business expertise and to deepen her knowledge of data mining, machine learning, and advanced data analysis techniques.
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