How forward-thinking CFOs are leading the AI charge

There are varying differences behind each CFO’s strategic plan. In my experience, short-term CFOs focus on cost-cutting to save money and manage the bottom line. However, forward-looking CFOs manage not only the bottom line but also the top line. They care about growing market share, revenue and the long-term financial health of the business, as well as competitive positioning. Enter the age of artificial intelligence It’s no secret that in the last five years, many businesses have invested in machine learning and artificial intelligence initiatives without much return on that investment. According to leading industry analysts, only 20 per cent of AI projects will move from pilot to production. Companies that found themselves in that position did so not because machine learning or AI isn’t a powerful transformative force, but because there was not a clear business case or reason for that investment, and a financial lens was not applied. Investment in AI is no longer optional, though, and CFOs can help lead the way by looking at how best to become an AI-first company. While many financial officers may shudder, thinking this means they need to invest dozens of highly paid engineers and millions of dollars, this isn’t the case. CFOs can keep costs down while still finding ROI by focusing on solving a specific, core business problem. From here, the path to scaling throughout the organisation becomes easier, and starting second and third use-cases can be a fast follow. Far too many AI projects kick off without a clearly articulated financial model and business case for what the objectives are. Here is where the CFO can be a critical partner to teams tasked with building AI. The best officers will look to invest in AI but reviewing it with the same scrutiny and granularity as expense reports are by invoice processing teams. That is, with close attention to detail around investment and outcomes. Managing AI projects with the same care as expense reports can yield a much higher return on investment. To do this, it’s critical to start with understanding the broader context of market forces and business positioning. A skilled financial team can quickly point to large cost centres and business inefficiencies that make for good places to find the right business problems for AI projects. Often the finances of the business are rich with customer touchpoints and transaction data that sit in the front and middle of the house. These areas can be used to identify the best opportunities for AI pilots. Because of this, an excellent first hire is perhaps not intuitive – you don’t need a data scientist to start – but rather a skilled product manager, who is well versed in AI, to assess business needs and identify candidates. A CFO can work with the AI team and set spending guidelines to specify the appropriate places to focus and invest in AI from the beginning. Those categories are largely reducing the cost of customer acquisition, or the cost to service a customer, and delivering new products or services. As an example, CFOs may target reducing call centre costs and improving customer experience to improve retention. A specific call centre might handle 7,000 calls per day with an average call cost of $9, which equals $65,000 a day or $23 million a year. Of those calls, 15 per cent are about tracking down the serial number for the account, which the customer lost or misplaced, which takes on average five minutes to handle after the customer has waited on hold for 15 minutes. A call-centre assistant can be targeted to first identify if the caller is calling about finding a serial number, and if they are, to then locate the client’s number without engaging an agent. This is handled in half the time, and maintains or improves customer satisfaction. While looking at a success metric of controlling 80 per cent of those calls without an agent, the call centre can expect a cost reduction of $1.38 million annually. The project goals for success are reasonable and well within the bounds of what is possible and achievable with a machine learning-based solution. The project doesn’t aim to solve every call centre problem – nor automate $22 million in cost – but it provides a framework for investing in the project and realistic expectations and the ability to start small and scale up as success is proven. Machine learning applications can provide measurable business growth, and CFOs can direct the initiatives by showing how the investments are translating into value. Aside from productivity, AI is being used in many aspects of business, from improving the use of customer data to detecting bad debt. CFOs have an opportunity to adopt AI to build programmes that will be successful and bring value to the entire business.

For examples of AI projects that provide measurable business growth, view Appen’s on-demand webinar, "Making AI Work in the Real World". In it, Alyssa Simpson Rochwerger shares stories of real companies getting real value with AI. By Alyssa Simpson Rochwerger