Practical AI Tips and Insights for CFOs

Discover the transformative power of AI for finance in this article by Dayton Kellenberger, Vendavo’s CFO. Gain insights into harnessing AI’s potential and achieving harmonious collaboration between humans and machines.

Generative AI became hugely popular this year with the rise of ChatGPT but, despite its explosive success, the technology has yet to significantly impact the finance function. This is not to say that it can’t, or that it won’t. AI is instead on a slower growth curve as CFOs and finance teams need important answers before trusting the emerging technology for key financial strategy decisions. 

Generative AI Cautions 

Finance pros are generally more risk-averse than their counterparts in marketing or IT, and for good reason. Companies implement make or break strategy decisions based on financial reporting. They also take their accountability to stakeholders very seriously. For these reasons, there is no room for any AI hallucinations – inaccuracies formed by chatbots absorbing wrong, biased, or irrelevant information. 

Another critically important factor when considering whether to use generative AI in finance is that the emerging technology has yet to demonstrate strong data privacy measures. A company that is serious about protecting their own intellectual IP and their customers’ data has concerns over inputting data into a new public tool. It’s for this reason some say the future of generative AI lies in two applications – one for general productivity in the mass market and another for custom-built enterprise use cases, such as the one PricewaterhouseCoopers is building. 

Game-Changing Use Cases 

As researchers in the 2023 Accenture Tech Vision study point out, AI is reinventing work and every role has the potential for disruption. Rather than job displacement however, AI complements what many positions can deliver, and it fuels new opportunities. Economists at Goldman Sachs point out that more than 85% of employment growth over the last 80 years can be explained by the technology-driven creation of new positions.  

While most predict the widespread use of the technology within finance is likely a couple of years out, there are numerous potentials for early adoption.  

1. Data Analysis – To make thoughtful decisions on where to allocate resources, strategic CFOs spend much of their time measuring and quantifying an enormous amount of data. AI can analyze vast amounts of data quickly and accurately, removing the otherwise time-consuming, manual process of identifying and understanding patterns. Lack of customization capabilities can be an issue with ‘AI out of the box’ solutions however, given the nuance and human-powered expertise that is crucial. The key is to co-pilot with AI. 

2. Price Optimization – AI fuels price optimization efforts by quickly analyzing new or existing data and delivers timely pricing suggestions that maximize competitive edge. Relying on flexible pricing logic, prices can be scaled across all operations, geographies, channels, and catalogs. For more, see Vendavo Pricepoint.  

3. Alleviate Supply Chain Pressures – Walmart has found success in using AI to automate vendor negotiations for best prices on store necessities like shopping carts. (Not in-store goods yet, they say.) The process has cut negotiation time for each supplier deal from weeks or months to just days and they have reached an average savings of 3% on contracts handled by machine.  

These and many more possibilities help us see why Goldman Sachs says AI tools could drive a 7% increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period. 

When it comes to every business area and in finance in particular, responsible innovation needs to come at the intersection of AI technologies and human collaboration. Quickly bringing together data from disparate systems is enormously beneficial for sound decision making but a hybrid approach where humans and machines work together for the best possible outcome is required.