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CorporateFinanceApril 02, 2024

Artificial intelligence in finance 101: how AI can direct better CPM outcomes

Let’s explore the basics of AI and how finance teams can use it to optimize decision making and efficiency. 

Artificial intelligence (AI) is rapidly transforming how we work — and finance teams are no exception.  

When AI is added to corporate performance management (CPM) processes, it can be a game-changer for finance professionals. That said, not all CPM software applies AI in the same way and not all AI has the same impact.  

Let’s explore the basics of AI and how finance teams can use it to optimize decision making and efficiency.  

This article will breakdown: 

  • The benefits of AI in finance 
  • What AI can do for CPM processes 
  • Examples of how finance can use AI in their CPM processes 
  • What to look for in AI-based CPM solutions

The benefits of AI in finance 

All the benefits and abilities that AI can add to CPM processes can be boiled down to serving two purposes: 

  1. Improved efficiency: AI can automate repetitive processes, which allows finance teams to work faster. 
  2. Improved decision-making: AI can make deep connections between diverse data points and provide finance with access to data trends that would be otherwise inaccessible. Finance can then use these insights to guide decision making when forming strategy and plans.  

In a nutshell, AI helps finance teams be more efficient while improving their access to performance trends that can inform decision-making. In doing so, AI has the power to transform the very fabric of finance and how teams operate from day-to-day.  

As Deloitte writes: 

“Artificial Intelligence is reshaping how finance operates, makes decisions, communicates, and drives enterprise value. Finance functions that embrace AI as a collaborator can enhance human capabilities and unlock untapped potential for growth, resilience, and innovation.” 

Types of AI 

AI is an umbrella term for a few technologies. 

Machine learning

Machine learning is an artificial intelligence that learns from data sets to recognize and apply patterns with minimal human intervention via algorithms and computational methods. In simpler terms, machine learning teaches computers to learn from experience. The remarkable thing about machine learning is that it not only recognizes and applies patterns, but it can create its own algorithms and can apply feedback to refine its algorithms.  

Generative AI

Generative AI is a type of artificial intelligence that uses algorithms to generate complex, creative content, like audio, images, videos, and text. For example, you could ask Generative AI a question about Q2 budget variance, and it will use sophisticated linguistic models to extract information from a large data set and prepare it as a graph, ready for you to analyze.  

Of all the different types of AI, Generative AI has the potential to elevate the way finance teams work. Deloitte writes, “We are on the cusp of an ‘iPhone moment’ — a major revolution in our personal and business lives. For corporations, GenAI has the potential to transform end-to-end value chains — from customer engagement and new revenue streams to exponential automation of back-office functions such as finance. 

Natural language processing

Natural language processing takes real-world input and translates it into a language computers can understand. Just as humans have ears, eyes, and a brain to understand the world, computers have programs to process audio, visual, and textual data to understand information.  

Finance and AI: What AI can do for CPM processes 

Now that we’ve covered different types of AI, let’s explore what AI does for CPM processes at a functional level.  

Completes repetitive tasks 

Repetitive tasks like data collection, anomaly detection, and transaction matching are relatively menial, but they consume the valuable time and brain space of finance teams. AI can automate these data-centered repetitive tasks. It can organize data from multiple sources, dimensions, and types for analysis, identify outliers in large datasets, and reconcile information on behalf of finance teams. Machines are far better at identifying errors in spreadsheets with thousands of cells than the hardworking teams that have been staring at those numbers all day. 

Data exploration

AI can mine strategic insights out of large bodies of information. By spotting unusual patterns and identifying correlating trends, AI can identify both risks and opportunities in performance data. 

What’s more, AI can handle volume in a way human methods cannot. Human-identified data trends tend to be linear and one-dimensional in scope. For example, you could determine the trajectory of sales in a quarter. AI-identified data trends go much deeper. AI can identify correlations between diverse data types at a much more sophisticated level of analysis. For example, the AI could tell you the trajectory of sales and identify the factors driving sales in that direction and show you how to change drivers to influence the trajectory of sales.  

Data quality

AI can spot anomalies in your data, bringing to your attention outliers and subtle human errors. This is incredibly valuable to leadership teams because AI can prevent mistakes and bad information from propagating into reports, plans, and decision-making. AI-scanned data tends to result in a more reliable foundation for analysis. 

Visualizing data and trends 

AI, specifically Generative AI, can generate complex, creative content, like music, images, videos, and text. Generative AI has advanced to the point where it can extend its creative power to data visualization, preparing the results of its data exploration in graphs, charts, and tables. 

Now, we’re seeing AI’s data exploration get so sophisticated, AI can use natural language processing to understand finance’s questions, via voice or text, and provide visual answers from within a dataset. Just like you can ask your Google Home for today’s weather, you can ask CPM AI to prepare a report on this week’s sales for a specific product.  

Predictive analytics

Using its deep data exploration capabilities, AI can accurately make predictions based on large, complex datasets. Using traditional methods, forecasts are straightforward. You can project figures, but you can’t account for curve balls, and you’re stuck within the limits of time-based financial data.  

With predictive analytics, however, your forecasts become more accurate because a wide variety of financial, non-financial, and market data is considered in every projection. You can get a prediction for a complex question like, what will the impact of a 5% employment rate be on revenue? What about impacts to EBITDA? And how can we counter these effects?  

Examples of AI-powered CPM processes

AI improves finance’s decision-making and efficiency, but what exactly does that look like in practice? Here are a few concrete examples of how AI can enrich CPM processes.  

Data collection

If your company acquired another organization, you’d need to add a new G/L source file to the group’s financial close process. AI can accelerate the G/L’s data mapping, ensuring that the data’s integrity is retained as it enters the group’s financial close system. In addition to mapping the G/L to the financial close system, the AI could also map data for ESG, tax, and lease reporting.  

Anomaly detection

Let’s say you were loading February actuals for your New York entity. AI could instantly read through the actuals and detect outliers in the February file. It could create a report that lists the potential outliers and the reason the figure was triggered as a deviation. You’d then be able to quickly vet the outliers to determine whether they are incorrect.  


It’s the beginning of Q2, and you need to create a plan for a product line in the EMEA. By analyzing the region’s data, the product line sales history, and market information, AI can determine the business drivers influencing sales so you can apply that insight to your sales plan and strategy for the coming quarter.  


What was the highest-performing marketing campaign in Q4 — and how can we make it even more impactful? AI can analyze demand, marketing, and sales data in context to determine the most successful marketing campaign and provide recommendations to maximize the impact of that campaign.  

Performance analysis

When leadership needs answers, they need them fast. Using Generative AI, you can ask your CPM a question, and it can generate an answer in the form of a direct response, report, or chart on a dashboard. For example, you could receive answers to questions like:  

  • Why did our overhead for New York increase year over year?
  • What was our highest profit center for 2023?
  • What was the line of business with the highest US sales last year?  

The AI would instantly pull results from your performance data and organize it into a report that is ready for analysis.  

What to look for in AI for finance 

While there are many different approaches to AI, there are three AI capabilities finance teams should ensure their CPM solution includes. 

1. Explainable outcomes 

How did the AI arrive at that prediction? What’s the proof that the driver the AI identified is actually influencing a KPI? Why did the AI flag a certain data point as an outlier? And, most importantly, does the CPM software give you access to these answers?  

You have to be able to trust your AI. Many AI-powered systems will provide you with predictions— but not insight into the AI’s logic. We call this “black box AI,” where the AI spits out an output, forcing you to trust its logic without any transparency.  A survey by the International Data Corporation found that a quarter of AI projects fail due to factors that include the black box phenomenon and interpretability challenges. 

We recommend looking for a solution that uses a “glass box approach,” where the finance teams can lift the hood on the outputs via explainable AI.  

The study Explainable AI: From Blackbox to Glassbox defines explainable AI as, “the class of systems that provide visibility into how an AI system makes decisions and predictions and executes its actions. Explainable AI explains the rationale for the decision-making process, surfaces the strengths and weaknesses of the process, and provides a sense of how the system will behave in the future.”  

Explainable AI is essential for finance teams that will use the AI’s outputs to make decisions, execute strategy, and build budgets. When you can see why the AI came to its conclusion, you can more confidently proceed to use its results in your decision making.  

2. Human control 

Building on the explainability factor, AI should keep finance teams in charge. AI should be a powerful tool that makes undiscernible information discernible. It should not be an autonomous decision-maker. EY writes that ultimately, finance teams need to see AI as a collaboration where AI can do the repetitive work and finance teams can do the strategic work.   

“While AI can process vast amounts of data at a rapid pace, it lacks the critical thinking and decision-making capabilities of people. The ability to identify and address bias in data and core skills such as knowing the right questions to ask stakeholders to understand their objectives means the finance professional has a significant role to play in this technological transformation.” 

While keeping humans at the wheel is true on a strategic level, it’s also true on a functional level. When the AI generates a prediction, identifies an outlier, or creates a report, finance teams should be equipped with the information to vet and check the AI’s work to ensure that the AI’s logic jives with the finance’s real-world understanding of the data.  

3. Data governance 

Data governance is a constant challenge for finance teams dealing with an influx of new requirements, including BEPS Pillar Two, ESG, and lease accounting. We recently wrote about how the scope of financial close and consolidation has expanded because of the growing data volume, data types, and reporting requirements. Mapping and formatting data across different sources so it’s apples to apples is a hefty task for finance teams to manage by hand. AI can ensure that data is streamlined and controlled.  

Acceleration Economy explains, “Today’s governance policies may call for a human to scan petabytes of this unstructured data, which would take years and be cost-prohibitive. But with AI models as part of the governance process, the task can be completed in a fraction of the time, by machines.” 

It's also important to remember that AI learns based on whatever data it receives. With that in mind, it’s important that finance teams control the data machine learning processes ingest to ensure the data is relevant and to avoid introducing biases into its analysis.  

AI can be an invaluable assistant to finance teams

By adding AI to your finance team, you’re giving them the ultimate helping hand. Not only can AI automate repetitive processes, but it can also provide finance teams with access to data trends and performance insights that would otherwise be inaccessible, buried under the enterprise’s mass of unstructured data.   

AI in CCH Tagetik runs platform-wide, augmenting the speed and accuracy of CPM processes and expanding data availability across your enterprise. Using a glass box approach, our explainable AI gives finance teams the authority to check, vet, and accept the AI’s work. You stay in control with the AI as your sidekick. Specifically, AI in CCH Tagetik can be used for data collection, anomaly detection, predictive planning, analytics, and driver-based planning.  

Learn more about the AI in CCH Tagetik and how it can support your finance team.  

Irene Rapp
Innovation Consultant at CCH Tagetik
Irene joined Wolters Kluwer CCH Tagetik in 2019. She is an Innovation Consultant focusing her expertise in the Artificial Intelligence domain. 
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