• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, the finance team is already behind, trying to close the books for the previous period while also preparing for the upcoming budget review, and the category manager is comparing last quarter's spend to this quarter's, trying to identify areas where costs can be optimised, all while the shift supervisor is checking the maintenance log to ensure that equipment is running smoothly and not incurring unnecessary expenses.

The difference between time saved and time redeployed

The introduction of AI productivity tools in the enterprise, particularly in finance, often leads to discussions about time savings, but what does this really mean? Time saved is not the same as time redeployed, and this distinction is crucial when evaluating the impact of such tools. When time is saved, it means that a task is completed more quickly, but this does not necessarily translate to increased productivity or efficiency. Time redeployed, on the other hand, refers to the ability to allocate saved time to more strategic or value-added activities, which can have a more significant impact on the organisation.

For instance, if a financial analyst uses an AI tool to automate the process of data extraction and reporting, they may save several hours per week, but if this saved time is not redeployed to more critical tasks, such as financial modelling or forecasting, the overall impact on productivity may be limited. The category manager, for example, could use the saved time to negotiate better contracts with suppliers or to identify new cost-saving opportunities.

Why self-reported time savings overstate the effect

Self-reported time savings are often unreliable and can overstate the actual impact of AI productivity tools. This is because individuals may not accurately track or report the time they save, or they may not consider the time spent on other tasks that are not directly related to the tool. Moreover, self-reported time savings do not account for the potential time spent on training, maintenance, or troubleshooting the tool, which can offset some of the saved time.

A more accurate assessment of time savings would involve tracking the time spent on specific tasks before and after the introduction of the AI tool, as well as considering the time spent on related activities. This would provide a more comprehensive understanding of the tool's impact on productivity and help identify areas where time can be redeployed to more strategic activities. The shift supervisor, for example, could use a more accurate assessment of time savings to identify areas where maintenance schedules can be optimised, reducing downtime and increasing overall productivity.

The finance data this depends on

The effective use of AI productivity tools in finance depends on access to accurate and timely data, including general ledger, budget, open commitments and purchase orders, receivables and terms. This data is critical for tasks such as financial reporting, forecasting, and planning, and AI tools can help automate and streamline these processes. For instance, an AI tool can help the financial analyst to quickly identify trends and anomalies in the general ledger, or to forecast future revenue based on historical data and industry trends.

The category manager, for example, can use AI tools to analyse purchase orders and identify areas where costs can be optimised, or to negotiate better contracts with suppliers based on data-driven insights. The finance team can also use AI tools to automate the process of reconciling accounts and identifying discrepancies, freeing up time for more strategic activities. To learn more about how AI can be applied to finance, visit our finance page.

What it changes about variance explanations and budgets

The use of AI productivity tools in finance can significantly impact variance explanations and budgets. Variance explanations, which are typically provided after the period, can be automated and generated in real-time, allowing for more timely and accurate analysis of financial performance. This can help identify areas where costs are not aligned with budget, and enable more effective corrective actions.

Budgets that appear healthy on the surface may actually be overcommitted, and AI tools can help identify these potential issues before they become major problems. By analysing data on open commitments and purchase orders, AI tools can provide early warnings of potential budget overruns, enabling the finance team to take proactive measures to mitigate these risks. The shift supervisor, for example, can use AI tools to identify areas where maintenance costs are likely to exceed budget, and take steps to optimise maintenance schedules and reduce costs.

Measured with variance to budget, commitment coverage, and days sales outstanding

The impact of AI productivity tools in finance can be measured using a range of key performance indicators (KPIs), including variance to budget, commitment coverage, and days sales outstanding. These KPIs provide insights into the effectiveness of financial planning, budgeting, and forecasting, and can help identify areas where AI tools can have the greatest impact.

For instance, a reduction in variance to budget may indicate that AI tools are helping to improve financial forecasting and planning, while an increase in commitment coverage may suggest that AI tools are enabling more effective management of open commitments and purchase orders. Days sales outstanding, which measures the time it takes to collect accounts receivable, can also be impacted by AI tools, which can help automate and streamline the invoicing and collections process. To discuss how AI can be applied to your specific use case, contact us.

What this does not do

While AI productivity tools can have a significant impact on finance, they are not a panacea for all financial challenges. They do not replace the need for skilled financial analysts, category managers, and shift supervisors, who are essential for interpreting and acting on the insights generated by AI tools. Moreover, AI tools are not a substitute for effective financial planning, budgeting, and forecasting, which require a deep understanding of the organisation's financial situation and goals.

AI productivity tools are also not a quick fix for underlying financial issues, such as inadequate financial systems, poor data quality, or ineffective financial processes. These issues must be addressed through more fundamental changes to the organisation's financial management practices, rather than relying solely on AI tools to overcome them. By understanding the limitations of AI productivity tools, organisations can use them more effectively to support their financial goals and objectives.