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From Artificial Intelligence to Financial Intelligence: Leveraging AI to Strategically Transform Finance Teams

by Laurent Charpentier on 09.4.2018


Some call it the fourth industrial revolution. AI fascinates us, raises many questions, excites us, even scares us a little. (I’m still not sure I want to be on the same roads as self-driving cars!) But there is no denying that AI makes our daily lives so much easier than it was a few years ago, and much more than imagined. In this three-part blog series, we’ll explore the positive impact of AI on finance teams.


When we talk about AI, we immediately think about what we use every day, such as virtual assistants or chatbots (i.e., Siri or Cortana), smartphones that identify us through fingerprint or facial recognition, cars that are able to detect pedestrians and to park themselves (often better than humans do). We also think about computers that recognize and analyze documents automatically.


AI is also widely present in the business environment. There is evidence of this in:

  • HR departments that more efficiently manage a selection process.
  • Quality assurance departments anticipating and even preventing problems before they may occur.
  • Marketers who predict customers’ needs and optimize interactions between brands and consumers.

And AI has made its way into finance departments. As a finance leader or accounting professional, you may ask,

How do these technologies impact finance functions and workflows? What transformations can be predicted? How will this technology shape tomorrow’s finance department?

A recent report by CFO, an Argyle company, explores the positive implications of AI on finance teams citing that AI and associated natural language interfaces have the potential to change the way that the finance team and other stakeholders interact with data. “As true partners in business development and identifying opportunities for growth and expansion, the CFO plays a key role in bringing emerging technologies such as AI to the business.”1


The concept of automating accounts payable processes first surfaced about twenty years ago. While earlier solutions had nothing to do with current approaches, especially in terms of performance and reliability, they did have the same objective: Automate a tedious and repetitive process to make AP personnel lives easier and optimize the efficiency of finance processes.


Before AI, accounting teams manually created and processed invoices, purchase orders, or delivery orders on paper documents. Those documents were then manually entered in computer systems, coded, and finally transmitted to the managers for approval and payment. In fact, manual processes are still prevalent today, even though, thanks to AI, there can be no more manual processes! The AP workflow process is automated by software which analyzes, recognizes, directs, and exports data into a company’s ERP/financial system.


Another of many pain points that comes with manual processes is that suppliers have little to no insight into payment timing details. By automating the AP workflow, you can have full access to this information in real time.


The use of AI in accounts payable solutions makes a significant positive impact on the finance department. Even better news, because of the maturity, reliability and industrialization of today’s intelligent AP automation solutions that are leveraging AI, business models have been created that are now accessible to and affordable for the small and mid-sized markets—exciting, because previously these solutions were only available to enterprise firms on-premise.


In addition,

  • Algorithms have become more and more reliable, flexible, and adaptable, permitting solutions to automatically manage a variety of document types, such as invoices. As a result, data is automatically recognized in an exhaustive and reliable way, with no prior configuration.
  • SaaS (software-as-a-service) cloud solutions are available to millions of users, which results in constant technological enhancements. This contrasts to older on-premise solutions that limited usage.
  • The self-learning—machine learning—capabilities of cloud-based software solutions are constantly improving. These solutions essentially “learn” from their mistakes and do not make them again once humans correct them.

Here at Yooz, we’re leveraging all of these technological advancements powered by AI. And we’ve made our motto: Easy. Powerful. Smart.


The use of AI in financial and accounting systems is leading to real profits. AI-driven AP automation solutions are able to learn as fast and as accurately as an experienced human to:

  • Identify and interact with suppliers
  • Automatically intake, code, process and route invoices, using OCR (optical character recognition). Read our article, Debunking the Myths of OCR, for a comprehensive account about what it is, what it isn’t, how it works, and why it matters.
  • Denote payment deadlines, approval workflows, and the approvers

All leading to dramatic reduction in processing cycle time and a corresponding savings in costs at every level.


PayStream’s, Guide to Payables Automation 2018 illustrates the different thresholds organizations typically reach based on their automation maturity, illustrating that significant improvements in processing times and cost savings follow technology implementation.2


  Novice Mainstream Innovator
Average processing time from invoice receipt to approval 45 days 23 days 5 days
Average processing cost per invoice (combination of paper and electronic) $15.00 $6.70 $2.36
Percentage of invoices received electronically 3% 9% 32%
Percentage of invoice terms discounts captured 18% 40% 75%


“What used to take weeks now only takes about 2 ½ days,” says Bryan Schmidt, controller, UNITE HERE HEALTH and Yooz client and champion. “The improvements are due to capturing, automatically coding and storing invoices instead of handling paper or sending around PDF files. The system observes and learns from clerks’ keystrokes, continuously improves GL coding, and reduces errors.”


Finance teams will notice a positive transition from a task-driven approach to one of empowerment in which systems driven by AI are now in charge of low-value repetitive tasks—data entry, verifications, referrals, and fraud detection—and employees are freed to produce real added value with time for analysis, strategy, creative thinking, and decision-making.

In view of the spectacular progress of AI, this new world will be more familiar to you long before a self-driving mail truck will drop the last paper invoices in your office.