Interestingly, recent advances in technology mean that autonomous finance may offer a means of leapfrogging those obstacles. Autonomous finance systems use advanced technologies such as machine learning, artificial intelligence (AI) and big data analytics to continuously learn, adapt, predict, and have the capability to operate on their own.
Up until quite recently, those technologies have been prohibitively expensive for most organisations and the skills to use them effectively have been rare and in extremely high demand. The advent of generative AI (GenAI) and the near simultaneous retrenchment in the tech sector has brought both the technology and the ability to use it within reach of just about all organisations, regardless of size.
Very importantly, low cost and no cost GenAI tools such as Microsoft Copilot can help to fill skills gaps in finance functions and accelerate automation efforts or restart stalled projects. Their natural language capabilities allow them to write the code for programmes and tools to carry out tasks and execute processes based on simple instructions from a human with little or no technology expertise.
This can be applied immediately to time consuming, recurring processes like month and year end close. In most cases, these are highly manual processes that deal with huge numbers of journal postings and have a high potential for human error. Automating them will both save time and effort and reduce costly errors.
Opportunities for embedding autonomous systems
Another powerful use case is vendor invoice processing. This is usually a labour-intensive activity that can require people to manually post information from paper invoices, create POs and payment approvals, pay the invoice, and create the journal. By combining GenAI with low cost and widely available optical character recognition (OCR) technology this process can be fully automated.
It can also be made partly or fully autonomous. Outputs can be screened by humans and over time and, as trust builds up, the machine can be relied upon more to make decisions on payment authorisations and so on. This relatively basic use case could be potentially transformative for organisations with high volumes of transactions.
In effect, any process with established routines and rules-based tasks and activities is extremely well suited to automation and ultimately to the application of autonomous systems.
Automation can ease growing burden of reporting
One key barrier to automation within the finance function has been a lack of buy-in at C-suite level. The return on investment from finance automation projects hasn’t always been readily apparent. The argument that it will free up time to spend on more value adding activity has never been contested, instead the question has centred around the nature of those activities and if they justify the investment.
The tide of new regulation coupled with new obligations being imposed by tax authorities has well and truly settled that debate. Already, Gender Pay Gap reporting has added to the burden on finance teams while Corporate Sustainability Reporting Directive (CSRD) is already in force for the first tranche of organisations that fall within its scope.
The scale of non-financial data to be reported by finance teams under the CSRD is simply vast. Layer on to that new reporting requirements under BEPs Pillar 2, new real time reporting and filing requirements from tax authorities, the introduction of auto-enrolment pensions, and upcoming regulations like the Corporate Sustainability Due Diligence Directive (CSDDD) and it is easy to see how the resources of finance functions will be stretched beyond breaking point unless action is taken now.
In addition, those new obligations will require even more time to be spent on analysis to uncover insights to enable the finance function to become more of a strategic business partner to the organisation.
We have crossed the Rubicon in terms of the value that the new AI toolsets can unlock. C-suites across sectors have started to adopt AI at pace and finance transformation is one of the core focus areas. GenAI’s ability to scan multiple large data sets, both structured and unstructured, to automate tasks and derive meaningful insights has the capacity to fundamentally reshape the finance function.
Seven-step roadmap to adoption
Finance automation is no longer an option; it is a necessity. That will also be the case for autonomous finance in the not-too-distant future. The pressure on finance functions will simply be too great to sustain without the support of automated and autonomous processes.
The only remaining question is how to progress the adoption and implementation journey.
There are seven steps to successfully embrace automated and autonomous finance:
Summary
Finance functions need to accelerate their automation journeys in the face of a rapidly increasing burden brought about by a combination of new regulations and increased demands from the business. GenAI and other new technologies have the power to support automation as well as assist in the adoption of high value adding autonomous finance processes.
