Cleo, the pioneer and global leader of the world's Ecosystem Integration software category and provider of the Cleo Integration Cloud (CIC) platform, announced it has significantly enhanced its Procure-to-Pay business process automation solution by expanding integration capabilities for manufacturers whose ecosystem suppliers lack EDI capability. "With CIC PAVE we are advancing the modernization efforts of manufacturers by facilitating frictionless interactions with their non-EDI suppliers and vendors. Prioritizing automation is now more important than ever, and Cleo is making it easier than it's ever been before." The new solution, called CIC PAVE (for Procurement Automation and Vendor Enablement), is a strategic add-on to the company's extensively deployed B2B integration platform which is already in use at more than 4,100 companies worldwide. In a nutshell, the new capability standardizes and centralizes supplier interactions via an online portal, helping manufacturers extend their supply chain digitalization efforts to suppliers and vendors who are not EDI-capable nor API-ready, particularly small to medium-sized businesses.

Esker, a global cloud platform and leader in AI-driven process automation solutions for Finance and Customer Service functions, announced a strategic partnership with Ecuador-based BPONE: The Best Professional Outsourcing, a global company specializing in outsourcing and consulting services. With BPONE's deep understanding of the local market and Esker's industry-leading technology, the partnership is poised to drive significant growth and impact for both companies in Latin America as they seek to advance digitization throughout the region. "Many Latin American businesses continue to rely on manual processes to handle back-office tasks rather than applying technology-forward advancements to boost productivity" Since 2016, BPONE has maintained a growth mindset at the forefront of its operations. The company successfully expanded from Ecuador to open new offices in Colombia, Peru, the U.S., Spain and more to further establish its global footprint. As its reach grew, company leadership recognized the need for a sophisticated automation intelligence system to offer to its clients that could handle an influx of invoices and streamline efficiencies while decreasing mistakes caused by manual tasks.

Despite a global rush toward enterprise digital transformation, the document remains at the heart of most businesses, and unfortunately, managing them still remains a distinctly manual process. Despite its structured nature, the flexibility of a document makes it hard to automate business processes, and taking data from multiple line-of-business applications to insert it in a document is a matter of cut-and-paste, from screen to document and often back again once a document is received. Launched at Ignite in October 2022, Microsoft Syntex is here to solve some of these tediously manual issues, adding document processing tools to SharePoint. The solution uses machine learning to help construct and parse documents, turning a manual process into one where humans guide and check software, and where legal, regulatory and contractual requirements are still met. In this in-depth look at Syntex, learn more about content AI and some of the current use cases for this release.

The world produces quintessentially vast amounts of data--more than it can consume. Statista report dated September 08, 2022, states that the total amounts of data created, captured, copied, and consumed globally had reached 64.2 zettabytes in 2020. Furthermore, data creation is projected to grow by more than 180 zettabytes by 2025. Companies need to collect huge volumes of data produced to extract valuable insights via data analysis to survive, let alone thrive in the competitive marketplace. It is the lifeblood of most business decisions, functions, and processes.

This is part of Solutions Review's Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, dotData Founder and CEO Ryohei Fujimaki offers commentary on data quality strategies to get your data machine learning-ready. As the world embraces machine learning (ML) and Artificial Intelligence (AI), data leaders are adjusting and perfecting data quality management frameworks. Traditionally, there are two stages in data quality: raw unprofiled data and cleansed data, free of common errors and commonly used for business intelligence (BI). But, companies at the forefront of data-driven decision-making have realized that data quality needs to level up--and this is where ML-ready data comes in.

Correctly labelling training data for AI models is vital to avoid serious problems, as is using sufficiently large datasets. However, manually labelling massive amounts of data is time-consuming and laborious. Using pre-labelled datasets can be problematic, as evidenced by MIT having to pull its 80 Million Tiny Images datasets. For those unaware, the popular dataset was found to contain thousands of racist and misogynistic labels that could have been used to train AI models. AI News caught up with Devang Sachdev, VP of Marketing at Snorkel AI, to find out how the company is easing the laborious process of labelling data in a safe and effective way. AI News: How is Snorkel helping to ease the laborious process of labelling data?

This article is for people who don't know a thing about MLOps or want to refresh their memory. You've probably been hearing about MLOps while you're scrolling through LinkedIn, reading blogs, looking at AI conferences, etc. MLOps stands for machine learning Operations and is a combination of machine learning, DevOps, and Data Engineering. For the point of this article, I will define each. Data Engineering focuses on the design and building of pipelines that can transform and transport data into a format so that it can be reached by other tech experts such as Data Scientists or other end users. These 3 come together in order to deploy and maintain machine learning systems in a reliable and efficient way.

What differentiates human beings from robots is their ability to think. Robots can manage rule-based tasks for us but will not be able to analyze or strategize for us. Let's evaluate what RPA can help us achieve and what it can completely replace. Robotic Process Automation, more commonly referred to as RPA, involves programming software to automate manual processes across applications. The goal is to decrease the burden of repetitive and redundant tasks on employees.

Just about every electronic contraption you care to think of contains at least one printed circuit board (PCB), which serves to house and connect the various components that allow the device to function as a whole. While circuit boards are mostly invisible to end-users, they are foundational to the world they inhabit, powering smartphones, automobiles, microwave ovens, garage doors, and the entire connected world. Thus, the global PCB market is big business, expected to grow from a $60 billion industry in 2020 to $75 billion by 2027. And it's this sector that Germany-based Celus wants to capitalize on, with an automated platform spanning the whole circuit board design process from ideation to printed PCB. To accelerate its mission to "automate electronics design," Celus today announced it has raised €25 million ($25.6 million) in a series A round of funding.

CFO Journal talked to Kristin Reinke, vice president and head of finance at Google, about those new technologies and how they accelerate the quarterly close, the use of spreadsheets in finance and the things that cannot be automated. This is the fourth part of a series that focuses on how chief financial officers and other executives digitize their finance operations. WSJ: What are the core parts of your digitization strategy? Kristin Reinke: We try to focus on the most important things: Automation and [how] we can improve our processes, being better partners to the business and then [reinvesting] the time we save into the next business challenge. WSJ: Which tools are you using?