Many finance teams are transformation-weary. Employees have long been promised that technology will make delivering their work product easier, but that newfound efficiency rarely comes to fruition. Every finance transformation has an element of “we can do more or achieve the same level of productivity with fewer people,” but retained talent is often the recipient of increased workload, courtesy of shortfalls in the implementation and adoption of technology.
There’s a history of optimism bias among those predicting how new technology will impact the way people work. Aiming to maximize cost reduction through finance transformation makes for an accessible minimum viable product (MVP), while the real challenge has been in effecting sufficient transformation in the ways of working to redirect resources to value-adding work that drives the bottom line. Yet, the desired increase in efficiency and effectiveness is elusive and often hasn’t been fully realized before another new transformation is put into motion. Finance leaders have a responsibility to pause and reflect on our track record with technology and lessons learned even as we look ahead to the possibilities of emerging technology and innovation like artificial intelligence (AI) in the much-anticipated Fourth Industrial Revolution.
Why are finance transformations difficult?
It’s worthwhile to pause and reflect on what’s been the roadblock to realizing the full potential of existing technology on the way to this new frontier. A core challenge has been an aversion to implementing change. Interest in adopting technology that fits the organization’s business models and goals but hesitancy to change processes leads to perpetuating inefficiencies in the new ecosystem. Ideally, when you introduce new technology, it’s time to simultaneously introduce a fresh perspective to existing processes and capabilities. The more one modifies and customizes a new product to fit the existing way of working, the greater the risk that advertised benefits are compromised and ultimately undelivered. Challenge the status quo and explore the art of what’s possible, but choose to work with new technology and processes as they were designed to function.
A common focus is often shifting the balance between the time and effort finance teams spend on manual, low-value tasks versus the time they’re able to dedicate to higher-value tasks like drawing insights, making recommendations, and influencing decision-making at a broader level. When efficiency gains fall short due to low adoption, design flaws, system interface issues, data quality issues, etc., the outcome is overworked teams struggling to deliver a return on investment, the business case, and valuable insights promised to stakeholders and business partners.
That said, AI has game-changing potential, much discussed but not yet fully understood, when it comes to realizing greater efficiency and shifting focus to those high-value processes.
Will AI be different?
The generative learning nature of AI is transformative in a way that previous technology has not been. Prior innovations in the finance space have focused on big data, data mining, automation, and streamlining analysis with the objective of reducing the time spent handling data. Most finance transformations have been predicated in part on the presumption of efficiencies from technology, leading to roles being promptly eliminated. However, the anticipated productivity is often not fully realized or evident to the retained teams. As MIT economist Bob Solow mused, “You can see the computer age everywhere but in the productivity statistics.” With machine learning, cloud computing, robotic process automation (RPA), and blockchain already widely available today, history suggests the productivity impact of emerging technology is muted relative to the step-change potential of the technology.
Many finance teams today are juggling more demand and deliverables than capacity, but AI has the potential to tilt the balance, reset, and redefine the future of work with reasonable workloads and transformative insights in an instant. By enabling the effective automation of repetitive workflows, AI and machine learning (ML) can significantly eliminate the constraints of manual processes and shorten the time needed to deliver unbiased insights for decision-making. Not surprisingly, the near-term concern is technology replacing humans, but it’s important not to lose sight of the immediate opportunity to maximize and amplify human productivity for a very stretched workforce.
Where will AI take the finance workforce?
Technology has previously promised transformation in the finance industry, with mixed results. AI has significant potential to actually deliver. However, while prior innovations have primarily focused on workforce reduction, AI should instead be seen as a way to augment employee contributions in the first instance rather than replacing human capital. Unlike innovations of the past, AI broadens the range of impacted activity from skills-based to knowledge-based workloads. Reskilling and upskilling finance professionals to leverage powerful, generative tools that fast-track information consumption and insights is a unique opportunity to reimagine the way a long-standing function adds value to a business. AI has the potential to raise the bar for effectiveness, productivity, and innovation of insights, maximizing the value of finance professionals as even stronger business partners.
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