When people consider artificial intelligence (AI) in food logistics, they might initially think of use cases in warehouses and production floors. It’s easy to imagine the benefit that AI can bring to processes like quality assurance, procedure scheduling, or transportation and shipping, but you might be forgetting the back-office function that is equally as time consuming, resource intensive, and ripe for digital transformation.
Accounts payable (AP) processes are often laden with inefficiencies and saddled with a heavy document burden that demands full manual attention from staff, ultimately proving detrimental to customer experiences and revenue cycles. Still, as little as 6% of organizations worldwide have fully automated AP departments.
Navigating the current status quo of AI hype to find the right solution for your organization can be a daunting task, let alone implementing it into your processes.
Common pain points among organizations that have yet to undergo digital transformation of accounts payable include excessive manual data entry, frequent errors and typos, problematic layout inconsistencies, and an exhausting need to revisit the same piece of work multiple times.
Standardization at a global scale
In addition, many global food companies with AP teams spread across different countries often find that payments are processed differently in each region. This can create a problem: as the company grows, a lack of standardization inhibits the organization from keeping pace with the increasing volume of invoices.
Food manufacturers are posed with the challenge of scaling their AP departments to keep up with company growth.
The increasing volume of invoices can sometimes demand up to 50-75% more staff to accommodate manual processes, which would be detrimental to long-term efficiency and sustainability.
To streamline its AP process, some food manufacturers use an intelligent document processing (IDP) platform that leverages machine learning (ML) to normalize invoice data from over 2,000 distinct vendors to be seamlessly passed onto its enterprise resource planning (ERP) system. Machine learning enabled the training of AI models to understand the different document variations they received. The company rolled out invoice-to-pay solutions across 20 markets worldwide, representing 14 different languages.
The software means a brand-new invoice from a brand-new vendor can be accepted and the technology determines how much of the information could be extracted on a consistent basis, allowing smooth AI integration into the ERP system and robotic process automation (RPA) platform.
Enabling strategy and value in accounts payable
“Strategy” and “value” are words that aren’t often associated with accounts payable, as AP has a legacy connotated with the “most back-office” of back-office functions.
But the benefits of AP automation aren’t limited just to efficiency gains – it paves the way for new value-generating initiatives.
Automating invoice processing frees staff from “swivel-chair” work like data entry in favor of higher-value tasks like disputing transactions, enabling more fulfilling and judgement-based work to be completed.
For instance, a study conducted by Sapio Research on behalf of ABBYY on the benefits of automation showed respondents from the United States, UK, Germany, and France attributed it to an increase in higher value work (60%), happiness (62%), and employee innovation (59%). One-third of respondents reported there was improvement to work-life balance and almost half (47%) of executives saw a 2-times return on their investment.
Lessons learned for implementation
Implementing automation requires careful consideration and data-driven strategy.
For global organizations, regional accounting requirements can necessitate localized processes to be streamlined into a cohesive automation workflow. It’s important to partner with your digital finance team to create a core group that exemplifies how all AP processes should perform at a high level, considering these regional variations. The outcome should include thorough guidelines describing the inner workings of AP teams in their respective regions.
A key factor to success is keeping the business team in close proximity to AI implementations. It’s unadvisable to keep automation solutions in a silo isolated from the businesses’ core functions, as this approach is more likely to yield a confusing technical burden than a real solution. Using low-code AI tools that are designed to empower citizen developers helps enable business users and subject matter experts to be closely involved in the design of solutions, also allowing for long-term ownership of the automated workflows. Keeping business teams looped in ensures constructive feedback and continuous improvement.
While full automation is an ongoing journey for organizations, the benefits are measurable. According to PYMNTS.com, most mid-sized firms will be automating AP in the near future, and it’s easy to see why. Of those who have fully automated systems, an overwhelming 91% report increased savings, better cash flow and growth.
Overall, AI-powered automation solutions require hands-on experience to determine how well it works for an organization. Once you see how processes progress through it, you can begin to build a strong foundation, framework, and knowledge base. Making decisions without data can limit ROI.
