In today's fiercely competitive business world, attracting and retaining customers is no simple feat. Companies that fail to grasp the value of providing a smooth and efficient customer experience (CX) run the risk of losing out to rivals who understand that good service is defined by the customer and not the company. Nearly 66% of organizations that focus on customer experience report higher retention rates.

With the rise of remote and hybrid work, many of the things that were long held dear by companies, such as cohesiveness, loyalty, and a chain of command have all started coming apart, with massive long-term ramifications. This is because the traditional ethos of internal communication, which involved memos and emails, is no longer befitting this new age, bringing forth a string of new apps and solutions to pick up the slack “pun intended!” First up, why bother with communication tools at all?

Last week, Slack’s users realized that under the company’s terms of service, their private data could be used to train artificial-intelligence models. This came as a shock: chat messages convey sensitive company data, and LLMs(large language models, the category of AI models that includes ChatGPT) are known to leak the data they are trained on.

Effective communication between the police and the public is fundamental to maintaining community safety and fostering trust. However, traditional channels of communication, such as calls or in-person meetings, present challenges like long wait times and restricted accessibility. Considering these problems, law enforcement agencies have begun integrating modern means of communication, including live chat, to revolutionize communication with the public.

It’s not always obvious what kills your productivity or even goes beyond that to become a part of toxic productivity. According to a CareerBuilder survey, about 75 percent of employers say that more than two hours of efficient work are lost every day. It may get difficult to strike the balance between creating a collaborative environment and respecting the need for focus. Sometimes, the balance is disrupted so much that it leads employees toward toxic productivity.

Why should B2B companies care about customer engagement on their collaboration platforms? Well, the truth is that the efficiency of internal tools is directly influenced by how engaging they are for users. Simply put, a platform that's easy and enjoyable to use encourages frequent use and deepens user investment - all of which are essential for any B2B enterprise aiming to thrive.

In the fast-paced world of technological growth, where invention drives progress, the government and private sector organizations must collaborate. By collaborating, these industries have the potential to create a future where technology progress is easily matched with the needs of people, companies, and society at large. One such example of collaboration is Brazil.

Mattermost is at the forefront of providing a seamless platform for teams to connect and collaborate. Our AI assistant, Mattermost AI Copilot, has already proven invaluable, automating tasks and answering questions with conversational ease. But we’re envisioning a world where Copilot can do more than just chat. Today, we’re exploring an experimental fork of the Mattermost AI Copilot in a cloud demo environment.

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.

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.