- Beyond RPA and into AI and ML– Technology to Drive P2P Efficiencies
Beyond RPA and into AI and ML– Technology to Drive P2P Efficiencies
When it comes to driving procure-to-pay (P2P) efficiencies and reducing risk, robotic process automation (RPA) has allowed organisations to thrive through automating repetitive tasks and minimising errors. However, other innovations such as machine learning (ML), advanced automation, and artificial intelligence (AI) can make the P2P process not only more useable but strategic. But how should and can each be used?
What is Robotic Process Automation (RPA)?
Robotic process automation (RPA) is one of the most useful technologies for procure-to-pay (P2P) – but it’s only one piece of the innovation puzzle in an advancing technological landscape. RPA is a set of configurations that automate manual and repetitive tasks. Known as a virtual workforce, RPA can integrate with software and replicate desktop actions, meaning that it can perform simple tasks like maintain a vendor database, acknowledge receipt of goods, resolve price discrepancies, or issue checks.
However, RPA isn’t the only technology organisations use to drive efficiencies in their procure to pay (P2P) operations. Artificial intelligence (AI), natural language processing (NLP), and predictive analysis also provide ample support to procurement departments and add that strategic value.
Why RPA isn’t enough
In the Gartner report “When and Where to Use Robotic Process Automation in Finance and Accounting,” it’s estimated that 40% of enterprises will have buyer’s remorse regarding RPA by 2021. This is likely due to the fact that RPA is less of a business process and more of a labor replacement for laborious, manual tasks. And as companies upgrade their legacy systems, the RPA tools that exist to make older systems more efficient will be dated.
The future of efficient P2P tools
P2P efficiency today relies on smart technology. Smart being the operative word.
Technologies like artificial intelligence (AI), natural language processing (NLP), and predictive analytics do more than just automate manual tasks. They also provide more accurate data and human-like interactions while improving their processing abilities and the scope of their skills over time. Thus providing that analytical overlay.
AI and Machine Learning
It’s essential to know the difference in mindset to understand how to use RPA and AI effectively. While RPA is process-driven and focuses on mimicking repetitive actions, AI uses data to imitate human intelligence. AI combines automation with machine learning, analysis, and other technologies to offer insights and analytics and apply and advance learnings over time.
When it comes to P2P technology, AI can review and aggregate your past transactions and use these to make suggestions based on trends. Product mapping and catalog searches are just two ways AI can be used to solve challenges organisations face every day. Let’s look at product mapping. Here, AI can be used to automatically organise products to a buyer’s category once a supplier uploads their catalog.
NLP Virtual Assistants
Natural language processing allows technology to process and analyse language data. Today, we see NLP at work with chatbots and devices like Amazon Echo. These self-service portals eliminate many manual tasks in customer service and finance, thus removing the need for RPA. When combined with AI, NLP is a powerful tool that can offer a human-like experience for users.
Our Basware Assistant is one example of NLP at work. Instead of navigating through several screens to find what they are looking for, users can communicate with the assistant to streamline their search. In this way, they can efficiently search for orders and purchase requests by using vendor and item names, and even ID and document numbers.
NLP doesn’t just make this process more comfortable, but it’s also more efficient. Instead of meandering through a website or cluttered catalog, now users can reorder supplies or look up invoices just by asking a chatbot.
A derivative of AI-powered insights, predictive analytics analyses patterns from transactional information, and anticipates potential outcomes. This ability helps management to be more proactive about decision-making, as well as boost efficiency. For example, predictive analytics can highlight what invoices might be paid late or capture early payment discounts.
As your data accumulates, it becomes easier to see what you can do to prevent bottlenecks, late payments, and more risks. With predictive analytics, you can better analyse supplier performance, exceptions, and invoice cycle times and progress against a goal, as well as trends for controlled and uncontrolled spend.
Learn more about the future of P2P tech
Stay on top of the latest technology advancements for P2P with this in-depth SolutionMap. Each SolutionMap offers procurement technology ratings, customer and analyst input, and highlights different organisational needs. Download it here.