machine learning – BLOG ESKER UK https://blog.esker.co.uk Document Process Automation Tue, 23 Jun 2020 08:18:20 +0000 en-US hourly 1 https://wordpress.org/?v=6.0.9 https://blog.esker.co.uk/wp-content/uploads/2020/09/cropped-fav-32x32.png machine learning – BLOG ESKER UK https://blog.esker.co.uk 32 32 New Technologies for P2P & O2C Digital Transformation https://blog.esker.co.uk/new-technologies-for-p2p-o2c-digital-transformation/ Tue, 23 Jun 2020 09:00:00 +0000 https://blog.esker.co.uk/?p=1554 Technological buzzwords and acronyms are abundant in the digital age, with new tech and terminology emerging seemingly every week. But with so much to take on board, how can you determine what technologies are best for your business? Here at Esker we specialise in optimising the procure-to-pay (P2P) and order-to-cash (O2C) cycles through automating manual inefficiencies and low-value tasks. This post will take you through the key technologies we use, what they are, what they do, and how they can help streamline your business processes.

Process Automation
Esker’s solutions are based on a set of intelligent technologies (some of which are detailed below), that work together to automate the processing of documents in the P2P and O2C cycles. This improves the daily routines of Esker users and increases job satisfaction, as well as bringing benefits of efficiency, accuracy and cost saving to their businesses and improving customer and supplier relationships.

Robotic Process Automation (RPA)
RPA is technology that essentially acts as a robot – performing manual, repetitive tasks that would otherwise be time-consuming and labour intensive. Examples of tasks include:

  • Retrieving invoices or documents from customer or supplier portals
  • Processing transactions
  • Passing documents to other systems, e.g. sending invoices for approval or logging them in an ERP system

Artificial Intelligence (AI)
AI is the theory and development of computer systems to perform or enhance tasks normally requiring human intelligence. AI improves speed and accuracy within document processing by completing tasks such as:

  • Perceiving and interpreting documents
  • Matching PO invoices with corresponding PO lines and goods receipts
  • Identifying anomalies and predicting outcomes

Machine Learning
Based on input from users, a solution such as Esker’s learns how to process new document formats and expand its knowledge base. The more it learns, the more the recognition rate increases and the system can process documents of the same format without any input from users (touchless processing).

Deep Learning
Taking machine learning one step further, deep learning is a neural network using algorithmic software to train itself to perform tasks based on a large set of data. Put simply, the software analyses data to find patterns in the data so it knows what to expect and what to do with a new document when it comes in. Esker’s own deep learning technology, Synergy, has been trained by the millions of documents processed by its solutions over more than a decade, allowing businesses to benefit from high recognition rates from day one.

The Benefits
A digital solution such as Esker uses a combination of these technologies to automate manual inefficiencies and low-value tasks in the P2P and O2C cycles. This frees up teams to focus on more value-adding tasks, increases efficiency and reduces cycle times, benefiting not only your business, but your customer and suppliers too. Businesses set their own pace, choosing to automate one process at a time (Procurement, Accounts Payable, Order Management or Accounts Receivable), or automating entire cycles to unite their operations across a single, integrated platform.

Find out more
If all this technology talk has you buzzing to update your systems, take a look at the following resources for a more in-depth look into the benefits of process automation for the procure-to-pay and order-to-cash cycles:

AI & RPA: The Technology Powering P2P Digital Transformation

New Technologies for Order Management – Increase Your Competitiveness

Jennifer Ball

As Marketing Co-ordinator for Esker UK, Jennifer manages Esker UK's marketing campaigns and events for S2P solutions. She has been part of the Esker family since 2019.

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What’s the Difference? [RPA, AI and Machine Learning] https://blog.esker.co.uk/whats-the-difference-rpa-ai-and-machine-learning/ Thu, 16 Jan 2020 10:46:34 +0000 https://blog.esker.co.uk/?p=1314 Many organisations focus on maximising efficiencies of all processes in a business. Business executives, consultants and technical leaders are tasked to seek out technology advances to improve respective business processes. There are a few technology options in particular that get a lot of attention. Though they are often confused as competing technologies, they are all different and provide different benefits and outcomes.  The three main technology advances to help companies automate and streamline processes are robotic process automation (RPA), artificial intelligence (AI) and machine learning.

Let’s dig into what they are, how they can help and where processes fall short.

Robotic Process Automation

Often seen as a stand-alone solution, RPA is a “robot” that is programmed to push or pull information from one place to another. RPA has strong benefits for simple business processes because it can eliminate repetitive, manual steps. In short, if you can draw a virtual line from one point of a process to the other, RPA may be a good fit for it. An example of this would be: logging into a portal, downloading a document, then pushing the document to a folder for someone else to review. Sometimes, if the data is perfect, that data can be pushed reliably into a database/system of record after it has been downloaded by the RPA/robot.

There are even some cases where a robot can be programmed to read the data it’s pulling and make a simple determination about where that information goes. These are often times referred to as “if statements.” “If statements” are variables that determine where information should go but not necessarily validate if that information is correct. Changes occur on either end of the process where the RPA identifies any weakness. What happens if login information for a portal expires or the process changes on the other end? Consider the downstream effects of these changes and ask yourself, “Are there some areas in my business that would benefit from a simple and effective automation process?”

Artificial Intelligence

AI is considered a technological “brain.” Often construed as a technology for extracting data, AI is really just a technology that assists in handling data and information. In other words, it’s a group of technologies and algorithms that help solutions make accurate decisions based on trends and past decisions. But not all AI is the same — some AI technologies are complex and others are rather simple. Overall, if a technology is helping to make a decision — without a user forcing or programming it to — it can be classified as artificial intelligence.

So, what is AI most useful for? Most experiences suggest that AI “decisioning” technologies are best used to cut down on time-consuming, manual tasks. AI mimics decisions that otherwise take additional time for humans to make. The ROI calculation is a simple equation. Here’s an example: if one step in a process takes 10 seconds to complete and is repeated 100 times per day, AI intervention can save 16.6 minutes a day on that single step. It’s easy to see how ROI can grow to be substantial as these seconds can turn into minutes (or even hours) of time savings. Complex processes seem to benefit the most from this technology.

Machine Learning

In its truest form, machine learning is a variation of robotics in which the solution is manually told what to do. These are forced or programmed decisions based on what a user recommends. Despite all the promises that technologies like RPA and AI make, there are still situations where specifically telling a solution how to handle a piece of an example is necessary. Think of this as the technology that will handle the “details” of a business process.

Let’s say, for instance, you have a customer that consistently changes a material number when placing an order. As a CSR, you know what the customer is trying to order, but the change happens far too often for the cognitive functions of AI to intervene. In this example, machine learning can step in and force the system to defer to the correct change — just as a human would do given a manual process.

Think about your business process and ask, “Is my process complex or is it simple? Does the business process have variables that require constant change? If changes aren’t corrected in a timely manner, will it negatively affect my customer or supplier relationships?” In answering these questions, you may be able to determine what technology is best for your business process.

When it comes to customers and suppliers, leveraging all of these technologies has proven to produce the best outcomes. Solutions tied to these areas of focus should be flexible and retain the ability to grow and change with a business. Customers and suppliers are arguably the lifelines within a business and should be treated with the utmost care.

Written by Chris Wadley, Business Development Manager for Esker

Esker UK

Unlocking Positive-Sum Growth with AI-Driven Business Solutions for P2P & O2C Cycles

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