TeamSupport is thrilled to announce its latest suite of AI-powered tools, built to increase agent efficiency and help your team take customer support to the next level. TeamSupport’s AI Assist incorporates the latest conversational AI technology, acting as a seasoned copilot to help agents close tickets faster, bringing faster, more satisfying resolutions to customers with less tedious manual work for agents.

Today’s business leaders agree that innovation is critical to survival. But fewer than 10% of companies report being satisfied with their innovation performance. If you want the advantage, you have to be speedy and agile. That’s why Miro and Microsoft are joining forces to give users the tools they need to streamline complex workflows and deliver innovative products and services to the global market.

We are thrilled to announce the launch of our latest innovation, the GPT Time Tracking Assistant by TrackingTime. This pioneering tool promises to redefine how you manage your workday. This state-of-the-art assistant employs cutting-edge AI technology to make managing time as simple as having a conversation. Whether you’re creating new tasks or filling out timesheets, this tool integrates seamlessly into your workflow, boosting your productivity effortlessly.

Editor’s note: This was originally published on Slack’s engineering blog. At Slack, we’ve long been conservative technologists. In other words, when we invest in leveraging a new category of infrastructure, we do it rigorously. We’ve done this since we debuted machine learning-powered features in 2016, and we’ve developed a robust process and skilled team in the space.

For non-developers, AI can seem like a black box. The concept of AI prompting sends a chill down our spines, especially when we’re bombarded with complicated examples and techniques. Multi-agent prompting? Prompt chaining? ReAct prompting? Sounds too much like the CompSci 101 course we failed in college. But according to Ethan DeWaal, Asana’s AI Program Manager, AI prompting doesn’t have to be complicated.

We recently heard from some in the Slack community that our published privacy principles weren’t clear enough and could create misunderstandings about how we use customer data in Slack. We value the feedback, and as we looked at the language on our website, we realised that they were right. We could have done a better job of explaining our approach, especially regarding the differences in how data is used for traditional machine learning (ML) models and in generative AI.

Outsourcing has been an important part of many big businesses’ plans since the 1990s. Many businesses have moved operations to places like Brazil, the Philippines, and India in the past to save money on labor costs. These countries now have inexpensive call centers and IT services. Because of this setup, which was made possible by fast data networks, work could be done almost as quickly as if it were done in person, but for a lot less money.

Artificial intelligence (AI) was the subject of an informative webinar titled “Navigating the CS Jungle,” hosted by Liam Martin, co-founder and chief innovation officer of Time Doctor, and featuring GQ Fu, CEO of LTVplus. AI is an essential part of modern customer service. As businesses strive to boost efficiency and enhance client experiences, artificial intelligence (AI) is becoming increasingly significant in the customer service industry.

AI’s starring role in workplace transformation is now undeniable. CEOs everywhere are asking senior leaders to create strategies for maximum AI impact, while frontline managers and their teams must navigate rolling out this new reality. AI feels like an imperative within all corners of the org chart. Eager to dig deeper, we turned to the latest research from Asana’s Work Innovation Lab, a think tank developing human-centered, cutting-edge research to help businesses evolve.