AI in Finance: CFO Strategies for Successful AI Deployment

Only 10% to 30% of organizations report that they’ve realized significant financial benefit from artificial intelligence. Insufficient skills and employee acceptance are two of the top 3 leading causes for low returns on AI.

Leading CFOs look to the AI generation — data science talent who are developing, deploying or championing the first wave of AI solutions — to fill the roles that contribute to successful finance AI deployments. 

Identify members of the AI generation

Organizations that attract and develop talent from the AI generation will avoid significant challenges in productivity, team culture, employee engagement and retention. To identify members of the AI generation, look for these common traits: 

• They consider digital- and AI-enabled solutions first when solving problems.

• They view coding and analytics as basic life skills.

• They prioritize employment based on opportunities to further develop data science skills in support of meaningful work.

To attract this key talent, AI-forward CFOs adjust their recruitment strategies, develop new career paths and invest in data science technologies and development opportunities for current staff. These CFOs also adjust their hiring focus to create talent pipelines and develop trainings for candidates with nontraditional finance backgrounds. 

Rebrand finance to recruit the AI generation

Finance faces a branding problem with the AI generation. Many data science professionals still view finance as a necessary but uninteresting back-office function.

Successful CFOs build a strong employee value proposition for AI generation candidates by investing in the following activities:

• Promote meaningful work. Members of the AI generation are often motivated to solve the puzzles behind high-impact problems. Design workflows and projects structured as puzzles and pilot explorations, and develop a strategy for pilots to demonstrate commitment to employees and candidates.

AI’s human-like outputs may seem like an obvious benefit to a productivity-minded manager, but employees perceive artificial intelligence as an employment threat. Our research revealed that 70% of the active workforce believes AI can replace people — so it’s not surprising when new AI-driven solutions are rejected and fail to gain traction.

Blindly handing over responsibility to a machine is not just uncomfortable, it’s unadvisable. AI-supported processes must support a transparency that allows people to observe the process and freely take control when necessary.

CFOs looking to successfully deploy AI in finance should take the following actions:

Keep humans in the loop. AI algorithms do not match the full awareness and competency of people. The complexity of delivering unbiased and valid financials demands that people remain engaged in the automation loop. AI-forward finance functions design AI-driven processes so that automated steps and decisions are observable and that people can interrupt an automated process and supplement actions with human judgment. 

Position AI as a co-worker. Employees who perceive AI as a co-worker that helps them with their work feel more engaged and aren’t threatened by a technology some perceive as an adversary. Leading organizations emphasize AI solutions that improve personal productivity. They prioritize using artificial intelligence to help individuals do their jobs better rather than using AI to improve the productivity of departments or functions. These organizations are six times more likely to succeed with their AI initiatives, and their employees report a threefold level of job satisfaction. 

The recent entry of large, well-established companies into the generative AI market has kicked off a highly competitive race to see who can deliver revolutionary value first. The market is pushing the limits of what is possible. But in the rush to exploit this new capability, companies must consider the risks and impacts of using AI-driven technology to perform tasks that, until recently, were exclusively reserved for humans.

As the chief steward for an organization’s financial health, the CFO must balance the risks and rewards of tools like generative AI. Three distinct conversations across leadership circles will help CFOs establish reasonable expectations and ensure that the use of generative AI creates value without introducing unacceptable risks.

Conversation 1: Debunk the hype around generative AI

Despite its remarkable potential to help finance organizations navigate complex, high-volume data, generative AI’s limitations introduce real challenges that CFOs must raise when considering use of generative AI in finance and across the organization. Successful CFOs partner with senior technology leadership (e.g., the CIO, chief data officer, chief information security officer) to distinguish hype from reality, and then share the results of those conversations with other executive leadership team members.

Conversation 2: Define aligned, responsible, actionable use cases for generative AI 

With an understanding of generative AI’s limitations, CFOs can responsibly direct a conversation with operational management, executive leaders and representatives from the user community to define the following: 

• Actionable generative AI use cases that align with their organization’s overall strategy and risk tolerance

• Generative AI’s effect on current strategies, projects or investments

As with any artificial intelligence solution, the best use cases exploit a specific business’s strengths and defend its weaknesses. Aligning generative AI’s fundamental capabilities to your business’s unique strategies and objectives delivers a value that differentiates your company from its competitors.

Sixty-one percent of finance organizations we surveyed are not currently using AI. Either they are still in the planning phase for AI implementation, or they don’t have a plan at all. Only 9% are currently using and scaling AI. This places finance behind other administrative functions (i.e., HR, legal, real estate, IT and procurement). 

Finance leaders whose functions are not yet using AI cited four primary reasons: other priorities, lack of technical capabilities, low-quality data and insufficient use cases. 

The last three reasons — technical skills, data quality and insufficient use cases — are related to workflow and capability. But the most commonly cited reason for not using AI in finance is that finance leaders have “other priorities.” This speaks to an important aspect of finance leaders’ beliefs about AI — that it is a discrete project that would need to be added separately to their function’s transformation roadmap.