On an average day, a CFO can stroll into their office, spot stacks of reports on their desk meant to help the team make better decisions, but never get to them. Finance teams across the country are overloaded with legacy processes, which can turn office tasks into a nightmare.
Only 14% of paper invoices, which make up 80% of invoices today, are input into the AP system the same day they arrive. At the same time, manual processes cost 20x more than those with automated systems. Less than 1 out of 5 CFOs stated they could process month-close in five
days. Most departments take a month and a half.
At the same time, CFOs are struggling with the “glorified accountant” image. As the department that oversees an organization’s finances, accounting professionals should be strategic partners, not data entry workers. But that’s what ends up happening.
But imagine this: As a CFO, you walk into your office and log in to your dashboard. You have clean and concise data ready to review. AP and AR provide in-depth reports, with numbers untouched by redundancies.
You review the report, follow up with your team, and prepare for a board meeting with everything you need to make an eye-opening presentation. For CFOs who have implemented AI and machine learning (ML), that dream is a reality.
Integrating ML and AI Into the AP Workflow
Currently, about 60-73% of data remains unused. This is not just because there is an abundance of data, but also because poor capture, processing, and classification make it difficult to use data in any practical way.
As a subset of AI technology, machine learning provides the tools to optimize every aspect of the workflow – from data capture to analytics. Machine learning allows a program to better handle decision-making by learning from previous errors. While human oversight is still
mandatory, it takes significantly fewer personnel to manage and can handle more complex problems than early robotic process automation tools (RPA).
Through ML technology, AP teams can collect real-time data on how customers and vendors interact with purchase documents to fine-tune their process. At the same time, these programs can be customized for advanced classification and capture all data from an invoice, including
logos and descriptions.
This streamlines the entire AP process while providing you with more information. The most time-consuming aspect is simply the initial setup. After that, you can focus on optimization and reducing exceptions.
Discovering Value to Drive Revenue
A recent Gartner report states that AI will be the most important investment CFOs will make during this decade. CFOs can drive value through several avenues, including:
● Organizing data streams to reduce dirty data and increase transparency
● Plugging leakage as it’s revealed by clean data and automation
● Reducing manual workflows
● Providing a more user-friendly experience
How to Prevent Overspend
While it’s critical to shift to AI process and automation, overspend is a very real danger. Without proper planning, it is possible to purchase inefficient solutions or waste time fiddling with infrastructure. To reduce the likelihood you will overspend on redundancies:
1. Map out your systems and solutions
2. Understand the limitations of the technology and where you can automate
3. Communicate clearly with team members and other departments
You may also choose to purchase software for your ML needs, rather than invest in in-house platforms. In the long run, these programs are often expensive to maintain and may offset any savings accrued from the technology.
ML Is the Future
Through ML, CFOs can remove daily frustrations, disconnected data, and time-consuming processes from their department. This allows them to become a strategic powerhouse and reduce overall costs, freeing up time and money for critical business maneuvers. And for most
CFOs, this technology is bound to become an industry norm – so the sooner you transform your department, the sooner this technology becomes a key differentiator for your organization.
