Abstract--The 3rd Generation Partnership Project (3GPP), the standards body for mobile networks, is in the final phase of Release 19 standardization and is beginning Release 20. Artificial Intelligence/ Machine Learning (AI/ML) has brought about a paradigm shift in technology and it is being adopted across industries and verticals. This paper focuses on the AI/ML related technological advancements and features introduced in Release 19 within the Service and System Aspects (SA) T echnical specifications group of 3GPP . The advancements relate to two paradigms: (i) enhancements that AI/ML brought to the 5G advanced system (AI for network), e.g. Artificial Intelligence (AI) and Machine Learning (ML) are transforming numerous industries and multiple aspects of modern life. From personalized recommendations on streaming platforms to real-time fraud detection in banking, AI/ML technologies are driving smarter decision-making across industries. In retail, they assist in inventory and supply chain management. In transportation, automotive vehicles rely on ML for object detection and navigation. As data continues to grow, these technologies are evolving rapidly, reshaping how we work, interact, and solve complex problems, making them central to innovation in today's world.

Artificial intelligence and machine learning already deliver plenty of practical value to enterprises, from fraud detection to chatbots to predictive analytics. But the audacious creative writing skills of ChatGPT have raised expectations for AI/ML to new heights. IT leaders can't help but wonder: Could AI/ML finally be ready to go beyond point solutions and address core enterprise problems? Take the biggest, oldest, most confounding IT problem of all: Managing and integrating data across the enterprise. Today, that endeavor cries out for help from AI/ML technologies, as the volume, variety, variability, and distribution of data across on-prem and cloud platforms climb an endless exponential curve.

Katie Dee is a graduate of SUNY Oneonta where she studied english with a heavy focus on writing and editing. Her passion for writing and interest in tech led her to become an Online and Social Media Editor for SD Times. She is also a lifelong dancer and was a member of the Oneonta State Kickline Team while in school.

Enterprises yearn for the competitive advantage that ML and AI can offer their business, but often prioritize technology strategically over people to unlock the value of their data. Why are AI and ML critical capabilities to your business and how will pushing their introduction or expanding their use impact your data strategy? Unfortunately, many leaders also misinterpret the desire for AI/ML capabilities as a proxy for "we need a better data strategy" and underestimate the effort required to take on this change. It's imperative that leaders define their data ambitions clearly and align them with the business outcomes sought. This is because the key to effectively unlocking the value of your data starts with aligning your people to this business outcome-driven data strategy.

Bizagi Catalyst 19: Bizagi recently announced that companies will now be able to instantly apply advanced artificial intelligence (AI) and machine learning (ML) to enhance customer experiences and outcomes with the latest release of the Bizagi process automation platform. The new out-of-box capabilities, which complement Bizagi's introduction of native process automation and AI/Cognitive Services in Azure Cloud, are being showcased at the Catalyst 19 event. The latest AI/ML capabilities enable companies to instantly analyze data and apply predictive analytics across business processes to enhance decision making and customer experience – without the need for data science modeling. These behavioral insights and next-best actions enable business users to acquire real-time advantages from new big data, accelerating process outcomes, and developing accuracy of crucial customer interactions across sales, marketing, support, and more. "AI is central to intelligent process automation, but organizations need those AI/ML capabilities in the hands of the people that set and manage business processes," said Bizagi CEO Gustavo Gomez.

Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower. We've been trying to get a handle on who has actually adopted AI/ML and to what extent. So we've been combing through these great new data sources from the good folks at McKinsey in their AI Adoption Survey, and Stanford's Human-Centered AI Institute 2018 AI Index which we used in our previous reporting. But one thing kept bothering me.