With operational and compliance demands on finance departments continuing to scale, leaders are facing a productivity crunch. Many are turning towards automation and AI-supported workflow and reconciliation management to help break down high-volume tasks and analysis.
In fact, McKinsey reports that 44% of CFOs surveyed have used generative AI, in particular, to support finance functions in at least five use cases.
And yet, its research further states that a majority of companies have yet to embrace AI at scale across their businesses.
However, by harnessing automation and AI tools effectively, finance and accounting teams can expect boosts to efficiency, greater workflow visibility, stronger controls, and better preparedness for auditing.
The Productivity Challenge in Modern Finance
Finance professionals, including teams responsible for forecasting, FP&A, and forecasting, controllers, and CFOs, are facing productivity bottlenecks, largely due to process gaps, legacy software, and data complexity. Such productivity challenges include:
- Manual Processes: From data aggregation and balance checks to report building, manual processes reliant on Microsoft Excel, email chains, and individual data entry drive down efficiency, extending close cycles and delaying insight delivery. What’s more, manual tasks consume considerable human time and effort, reducing availability for analysis and strategy building.
- Data Silos: Large-scale data silos spread across multiple, disconnected ERPs, CRMs, and other systems don’t communicate in real time. Finance and accounting teams must assess and consolidate data manually, increasing cycle times and reducing team availability.
- Workflow Visibility Problems: Fragmented data and systems, coupled with version confusion from manual spreadsheets and email chains, lead to further confusion over task progression, task ownership, and cross-departmental communication. Legacy systems and processes that avoid automation effectively leave finance and accounting teams to piece together workflows and task statuses, consuming additional time and resources.
Many of these problems don’t simply arise from outdated systems or avoiding automation. Using the right technology can only do so much to support a process that no longer scales with its business.
However, research from Bain & Company shows that, by adopting AI to help manage financial services tasks, some companies have already seen a productivity boost of around 20%. Interest in leveraging automation technologies is cautiously increasing.
Leveraging Automation and AI to Boost Finance Productivity
Automating manual, high-volume tasks (e.g., repetitive invoice reconciliation, intercompany management, and currency conversion) both saves time and frees up human resources to be reinvested in other areas of financial and accounting management.
Financial orchestration tools with embedded automation features can also aggregate data from fragmented sources into a single, reliable source of truth, giving controllers, CFOs, and staff real-time insights into task progression. Data is automatically pulled, matched, and recorded as and when it is generated (e.g., when an invoice is produced or a bill is cleared).
This helps cut down on unnecessary fact-finding and intensive conversations near reporting deadlines, and ensures that everyone is on the same page. By establishing a rolling, automated reconciliation and recording system, finance and accounting departments no longer have to piece together details to satisfy auditing or leadership demands – the information they need is already there, and there’s a digital trail.
Programs that use glass-box AI (which can transparently explain its decisions) also help to produce stronger predictive insights, leading to more confident and effective decision-making. AI and automation processes thousands of verified records to support accurate analysis and uncover potentially overlooked trends.
What’s more, companies and finance teams that are resistant to AI, concerned about losing human oversight, will be reassured to know that there are platforms designed to give finance full ownership over tasks and processes.
The AI used in these platforms can raise exceptions and anomalies to human personnel for final review, meaning the risk of errors reaching the reporting stage is significantly reduced. Embedded approval controls, too, ensure that no work goes unverified.
However, AI and automation are only as effective as the process they augment. It’s important for CFOs and controllers to carefully assess process, skill, and delivery gaps in their current operations before applying AI to simply “fix” their problems.
Integrating Automation and AI in Finance Operations
Applying automation and AI to existing finance and accounting processes should be phased over several weeks to months, to ensure tools deliver the accurate, efficient outcomes that leaders expect.
Controllers must examine process areas that require the most manual support. Where will automation boost their efficiency and free up teams the most?
Once key areas for automation are identified, teams should test automation triggers and features on individual tasks – such as journal postings or intercompany matching – and take note of the outcomes. Over multiple tests, once it’s clear that handling times decrease and accuracy is maintained, it’s time to move to another task area.
Phasing automation integration allows for legacy systems and processes to effectively “catch up” and align with new expectations. It also gives human personnel time to learn more about how to manage automation features, where their guardrails lie, and how to handle any exceptions it raises.
A phased integration will also give fragmented teams time to adapt to new collaboration standards and workflows without causing unnecessary confusion or slowdown. What’s more, as research supports, a gradual approach to AI adoption allows time for adapting policies and employee training while carefully assessing individual impacts.
Conclusion
Choosing the right automation and AI tools can help finance teams improve departmental efficiency, gain visibility over task workflows, and better prepare them for auditing. Automation can improve data recording accuracy at scale, too, leading to more insightful and reliable analysis and reporting – giving leaders more confidence to make bolder decisions.
When automation augments finance productivity effectively, finance and accounting professionals have more time to analyze, strategize, and make impactful decisions on the wider business.
Provided that humans can still make judgment calls and that there is a robust process in place for AI to slide into, finance can expect faster cycles, more reliable workflows, and greater confidence in their data, processes, and decision-making.
Gurpreet Chaggar is an associate product marketing manager at Prophix. She joined the company in 2019 as an implementation consultant, where she developed a deep understanding of Prophix’s solutions and the impact Prophix has on helping clients optimize business outcomes.
