By the end of 2022, generative artificial intelligence (AI) had fully sparked public imagination about the future. Consumers and enterprises alike have been using artificial intelligence for years: speaking to voice assistants like Siri or Alexa, automating routine tasks, using algorithms to recognize patterns and correlations in data. But applications that could produce original text or digital art that quickly became household names, heralded a new age where AI can mimic human creative processes.

How much will generative AI impact our lives? That remains to be seen; however, research has suggested that breakthroughs in generative AI could increase global GDP by 7%—nearly $7 trillion—and boost productivity growth by 1.5 percentage points.¹

Generative AI might start by producing concise and coherent summaries of text (e.g., meeting minutes), converting existing content to new modes (e.g., text to visual charts), or generating impact analyses from, say, new regulations. Producing novel content represents a definitive shift in the capabilities of AI, moving it from an enabler of our work to a potential co-pilot. It might augment human capability and then advance it.

CFOs and Finance leaders should start to develop strategies today for how generative AI will affect both their functions and their businesses tomorrow.

Getting to know generative AI

To consider the potential impact of generative AI in Finance, it’s helpful to explore what generative AI is and how it operates.

Generative AI:

• Creates original content such as text, images, audio, code, and video. Up until now, these types of content required solely human skill and expertise to create.
• Can add contextual awareness and human-like decision-making to enterprise and finance workflows, potentially dramatically changing how work is conducted.
• Is powered by foundation models, which run on deep-learning algorithms modeled on the organization of neurons in the human brain.

What does this mean for Finance?

A lot. Generative AI is powered by data. And since Finance draws upon enormous amounts of data, it’s a natural fit to take advantage of generative AI. 

Here we consider areas within Finance primed for generative AI-enabled transformation:

Generative AI has the potential to transform Finance, and business, as we know it. It will take upfront investment in time and money. According to a Gartner study, 80% of CFOs surveyed in 2022 expected to spend more on AI in the coming two years.² With that investment, however, around two-thirds think their function will reach an autonomous state within six years.

To make sound decisions, it will be crucial that leaders consider the use of generative AI from an enterprise-wide approach with a clear understanding of where this technology will have an impact on operating expenditures, capital expenditures, market capitalization, and a lot more. CFOs and Finance leaders can play a pivotal role in driving strategic collaboration among key C-suite leaders to enable greater success—and return on investment—of AI deployment and adoption. The journey should begin with a sound strategy and a few use cases to test and learn with well-governed and accessible data. It doesn’t have to be perfect, but it should be controlled.

While the future looks promising, generative AI has some current limitations that Finance professionals should consider. 

Implications on Finance talent

For all its tantalizing potential to automate and augment processes, generative AI will still require human talent.

But workers are skeptical. In a recent Harris Poll of workers, about half do not trust the technology.³ Finance leaders should consider change management carefully, leaning into the idea that generative AI can support our lives, transforming from an enabler of our work to a potential co-pilot.

Certainly, roles and responsibilities will change, and new types of workers may be needed. But enduring human capabilities like curiosity, empathy, critical thinking, teaming, and more will still be needed. Finance professionals may need to develop new generative AI fluency and enhance skills such as:

• How to engineer prompts (i.e., ask good questions) to get desired results.
• How to recognize potential bias.
• How to confirm the quality and validity of generated output and monitor performance of models over time.

Generative AI has quickly cemented its foothold in the public consciousness. It has sparked excitement around productivity increases and cost savings but also warrants caution.

Yet, generative AI may completely transform the Finance function. Leading organizations have launched pilot programs and are scaling fast.

In the short term, generative AI will allow for further automation of financial analysis and reporting, enhancement of risk mitigation efforts, and optimization of financial operations. The impact is unlikely to stop there, though. With its ability to process vast amounts of data and quickly produce novel content, generative AI holds a promise for progressive disruptions we cannot yet anticipate.