contact Search
Search

AI opportunities in finance

Hadley Baldwin

The use of AI within finance functions is not a new phenomenon, with the automation of transactional tasks (such as invoice matching) a well-established use case. However, research has shown that the adoption of AI in finance has, to date, lagged behind other functions such as HR and legal.

This looks set to change, with finance functions predicted to spend upwards of $40 billion on AI software each year by 2027, ranking it as a business function with one of the highest levels of investment in AI.

Evidently, finance functions which do not start to embrace this trend now will run the risk of being left behind by their competition. How can you determine which AI use cases are the most promising for your organization?

AI implementation

Our recommended approach

Step 1

Define the AI vision and guiding principles

Step 2

Identify AI use cases and define a strategic blueprint

Step 3

Prioritize pilots and define an AI roadmap

Identifying potential AI use cases within finance

AI should never be implemented blindly. Clearly defining the relevant use cases for a specific function is key to success. Once use cases have been identified, they require careful consideration to understand how difficult they are to implement and the potential return on investment that they offer.

We believe organizations should select a small number of use cases most relevant to their specific circumstances and focus on getting them right. 

Evaluating feasibility and implementation risks

Some AI use cases are more straightforward to implement than others, and getting the balance right between business benefit and ease of use is critical to ensuring success. For example, while AI-powered, data-driven decision-making is already a reality, it cannot yet compete with accountants’ human intuition and judgment, which enables complex decision-making based on more subjective factors.

Some of the technical feasibility challenges include:

  • Access to good quality data (error-strewn data will certainly lead to the generation of poor forecasts and incorrect insights)
  • Complexity involved in tailoring general AI solutions to your specific business needs
  • Risk of biases when using AI to analyze data sets
  • Selecting the right AI model – if the model assessment doesn’t consider the availability of data and computational requirements needed then performance and scalability could be severely limited
  • Integration with existing systems – organizations must consider how AI will integrate with the as-is technology landscape. Using AI add-ons for existing systems is one way to mitigate this challenge.

Organizational feasibility factors include:

  • Clearly defining the problem – if the organizational problem can’t be clearly articulated, then it will be challenging to appropriately tailor the AI solution
  • Change management capability to implement AI within your organization
  • Developing the internal technical skills required to run, or make changes to, your AI initiative after implementation
  • Strategic alignment to wider business goals
  • Potential need for a new operating model to take advantage of AI benefits.

Leaders implementing AI initiatives should also be aware of external factors, such as rapidly evolving legislation, which could put the brakes on prospective projects.

Depending on which elements of an organization’s finance function AI is deployed within, there may also be several ethical considerations to think about. These are particularly relevant when using AI to make decisions and include:

  • Ensuring that systems are designed to avoid unfair bias
  • Using algorithms which are transparent and easily interpretable
  • Making sure that there is human accountability within the organization for any AI-driven decision.

Calculating the return on finance investment in AI

Finance functions have typically invested less in AI than other departments, with CFOs often citing other priorities for short-term investment. Once you have identified the relevant AI use cases for your organization, building a compelling business case is a key step in helping your planned initiative compete for funding.

This case for change needs to carefully weigh the potential benefits against the risks and costs, just as we would recommend for any other project.

Key finance benefits for an AI business case

A pragmatic approach for AI in finance

Any organisation aiming to roll out AI within its finance function needs to take a pragmatic approach to its implementation. 

Finance holds inherent risk for most organizations, and stakeholders may be concerned about deploying relatively new technology in an area of the business that is critical to its operations. However, this criticality means eliminating human error and automating decision-making or processing could deliver significant benefits. 

When it comes to implementing AI within finance functions, the most important piece of advice is to treat the investment as you would any other significant change initiative. Carefully weigh up the benefits against the costs and risks, potentially trial initiatives before committing, and ultimately ensure your AI initiatives align with the organization’s overall business strategy.

Whether your organization decides to start with small trials, or fully commit to a full roll-out, ensure that team members understand the drivers behind implementing AI and bring them along for the change journey from the start.

As AI continues to evolve, so too will its potential use cases within finance departments. By staying informed and adapting to these advancements, organizations can ensure they maintain a competitive edge and remain poised to capitalise on the significant potential that AI innovation brings.