Finance Transformation: The AI Dilemma

Former US Secretary of State Henry Kissinger famously said, “If you don’t know where you are going, every road will get you nowhere.” This is so true and apt when it comes to AI hype and how Finance functions are scrambling to find meaningful use cases. No conversation related to Digital Transformation is complete today, without references to Generative AI and Predictive analytics being part of it.

AI provides a huge potential to transform the ways of working, redefine business processes and enable massive productivity. One can find numerous successful case studies where organisations have generated real efficiencies and cost savings using AI.

For instance, a Global microchip manufacturer saved more than 5,000 hours annually just by deploying AI for their Accounts Payable Invoice processing. However, the dilemma lies in today’s predominantly ‘use-case driven’ approach.

Finance FOMO?

CFOs and Finance Leaders, in fear of missing out, rush to treat AI as a one-size-fits-all solution for Finance Transformation. This addresses some automation needs in the short-term but eventually results in a set of disparate AI deployments aggravating the Organizational ‘Technical Debt’. Gartner predicts that by 2024, half of Finance AI deployments will be either delayed or cancelled due to scale-up issues!

The majority of business processes today including Finance came into existence as a direct business need during previous industrial revolutions which were dominated by mass manufacturing. In a way, the Finance processes today mimic a typical production floor’s assembly line. A systematic ‘handover’ of the output from one sub-process to the next…in other words a chain of interdependent sub-processes. One cannot start the Monthly Forecasting process before Month-end books are closed and books cannot close until all the cash receipts from customers are applied to invoices, for instance.

Now these processes did serve well in the previous industrial revolution. However, since the 4th Industrial Revolution, Organizations have been quick in digitally evolving. An accessible and inexpensive Cloud Computing, coupled with AI, IoT and Big data, has re-invented the entire business model. In fact, new industries (e.g. ridesharing, SAAS etc.) came into existence that never existed a few decades ago.

Unfortunately, the Finance and accounting processes have not been able to keep pace with this rapid technological and business evolution happening around it. Numerous recent studies suggest that up to 60-70% of Finance processes are still manual.

So, the question arises; “How does Finance deploy AI and use the technology to transform its ways of working” ? The answer lies in a paradox! Instead of asking how AI will transform the Finance ways of working, we need to think about how we transform our existing ways of working to be able to deploy AI. This approach will not only promise scalability in the long term but will also ensure delivery of tangible productivity.

CFOs and other Finance leaders should consider an ‘inside out’ approach to deploying AI.

Here are the key considerations in adopting such an approach and ‘How’ you can start today with this approach:

Start inside out

Ironically, the most important step to automation does not start with technology. It starts with something more basic. Review the existing complexity and interactions within finance processes and the operating models supporting these processes. Simplify, streamline, and optimize these inter-dependencies. Automating complex processes only increases further complexity and hence results in most AI deployments becoming part of legacy systems.

How you can start today: Create a Finance organization-wide “Simplification Campaign”. Create a professionally competitive environment between teams on who is driving the most simplification processes. Clearly measure the results in terms of hours saved or cost reduced. Reward the teams to encourage the right behaviours.

Bring an outside-in perspective

In addition to addressing the internal processes, Finance needs to bring a simplification or transformation perspective from its key business partner functions. For instance, we can simplify a process to record and pay media invoices, we also need to factor input from Marketing function on simplifying end-to-end process of vendor selection till receipt of services and payment.

How you can start today: Involve key business partners on simplification journey right from the beginning. Sometimes, the biggest complexity lies at the edge of Finance rather than within the function. Deploying AI in Finance to automate the processing of invoices will end up in diminishing the returns over time as the linked processes outside finance are still complex and manual.

Standardize & Scale

AI from its evolutionary standpoint is still at, what Yuval Noah Harari recently referred to as an “amoeba of Artificial Intelligence”. Effectively what this means is, that AI is still primitive and only at the stage of doing specific tasks. The major difference between AI in the 1950s and today is the cheap computing power and access to huge amounts of data.

In other words, we must understand that AI does not have a consciousness or ability to make decisions of its own (…not yet at least). Therefore, the success of an AI within the organization depends upon how standardized the processes are! The more your processes are streamlined and standard across different functions and business units, AI scalability becomes easier and easier.

How you can start today: Create a core team of Process and transformation experts within the Finance teams who act as a Centre of Excellence. Share best practices across different business units or functions to ensure scalability. Consider setting up micro-digital factories consisting of a combination of ‘dedicated’ data analysts and ‘revolving’ functional experts.

AI is a fascinating technology and the strides we are making today is changing the world as we know it. However, the rush to implement an AI-first strategy needs to be tackled in a careful way. Holistically evaluating technology requires a responsible and informed decision making process.

Treat AI as a ‘Digital Employee’. Similar to how we would carry out an appropriate due diligence before hiring a physical worker, the digital worker requires the same…if not more! The true benefits of AI lies just beyond the edge of its hype and indeed…in its ability to be your co-pilot.