An article by Karl Crowther, VP of MEA at Alteryx

In the digital era, the role of Chief Financial Officers (CFOs) has evolved beyond traditional financial stewardship. Today, CFOs have transformed into future-facing navigators – capable of harnessing real-time, data-driven insights to steer their organisations while sidestepping risks. Twenty years ago, CFOs didn’t have the tools needed to look ahead in any sophisticated way. Systems would record transactions, and CFOs would then report on the business afterwards. The standard measurement points were monthly – if not quarterly, and reporting would often take a week or more after month (or quarter) ended.

Digital transformation impact

Driven by the rapid pace of digital transformation and an accelerated need to see around corners – to mitigate risk before it turns into crisis, and to seize opportunity before it fizzles out, CFOs are now future-facing navigators. This is evidenced in Deloitte’s Middle East CFO 2022 survey, which revealed that 53% of CFOs’ main priority in the region is to establish a strategy for financial performance and growth. 

Continued pressures around digital transformation and big data challenges are driving more organisations to adopt innovative AI technologies such as generative AI to fuel finance's future vision, and automated decision-making capabilities. The Middle East alone is expected to accrue $320 billion of the benefits of AI by 2030, according to PwC. 

Data-driven decision-making and navigating uncertainty

If business leaders can recognise and act on trends sooner - with more information at their fingertips - they can adapt and respond more effectively to change as it happens. From a CFO pursuing a reduction in the time to close the quarter, or run a forecast report, the ability to automate the data pipeline and analysis processes to predict and uncover patterns from large quantities of disparate data accelerates the steps to success.

Modern CFOs, equipped with analytic tools and generative AI, are transforming their roles from data interpreters to strategic decision-makers. This shift is highlighted by the same Deloitte survey, where over 80% of CFOs are either implementing or have implemented digital strategies in the region, with 41% in the UAE and 23% in Saudi Arabia completing this transition. The integration of analytics into decision-making processes is helping businesses understand market dynamics in real-time. Those businesses able to analyse vast datasets and identify patterns indicative of risk are navigating economic uncertainty in real-time so they can forecast volatility and plan effectively to stay ahead of these scenarios. 

But data is dirty, it’s everywhere, and it's growing in volume. The rapid increase in data volume and velocity also necessitates a shift from traditional data processing methods to more dynamic, automated analytics. This need to extract meaningful insights from data at scale is in part driving 78% of CFOs to prioritise finance digitalisation, as noted in PwC’s Global Digital CFO 2022 Survey. However, the key lies in the realisation that every department needs to enable and empower the substantial reservoir of untapped data talent poised and ready to unlock its full potential. Only the combined approach of ensuring data is accessible along with democratising self-service analytics, empowers those with the unique office of finance skill sets to create models to anticipate trends and identify the key performance indicators in their business. Furthermore, it allows them to react faster, and make better decisions.

Growing role in governance and cybersecurity management

While the use of AI and Large Language Model (LLM) technologies within finance has lowered the barrier of entry for delivering data-driven insights and intelligence at scale, they also present new data privacy risks and compliance challenges. Ultimately, the key challenge is that AI usually follows a “garbage-in, garbage-out” principle, where outputs are only as good as the data input. 

Hence, it is critical to establish stringent governance processes for data validation and use - emphasising the need for high-quality, unbiased data sets to train AI models effectively. Without clear governance frameworks, finance specialists are less likely to feel confident in the accuracy and compliance of insights derived from an internal LLM chatbot, for example.

As with any journey, data governance requires upskilling, training, and education so everyone can understand why good governance matters and how to go about it. AI systems, especially those based on unsupervised learning models, can produce unpredictable results. Ensuring transparency in AI-driven decisions and periodically checking the underlying assumptions of these models are critical steps in mitigating potential risks associated with any AI deployment. 

Cross-functional collaboration and skill evolution

In the past, CFOs depended on data science experts to provide them with data insights. Now, armed with self-service analytics and accessible AI, finance teams have the capability to independently analyse data and generate insights. Despite these automation capabilities, AI systems still rely on skilled human oversight to manage and interpret complex data sets. CFOs must ensure their teams are upskilled to handle AI systems responsibly, aligning AI strategies with the organisation's broader financial and ethical standards.

Today, self-service analytic tools like Alteryx have transformed data literacy and analytic skills into user-friendly drag-and-drop interfaces, enabling finance and business professionals to deliver value in their roles without needing expertise in coding. With access to comprehensive and governed data, these innovative ways of working act as a force modifier for the digitally enabled office of finance, needing to see around corners to drive business success at scale and speed. This data-driven decision making has allowed CFOs to transform their functions, drive growth, and lead their organisations confidently into the future.