Teams | Collaboration | Customer Service | Project Management

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.

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.

If you work in a fast-paced business, it’s not hard to see why real-time analytics might be valuable. Being able to analyze live (or near-live) data can help you and your team react faster, and make more informed decisions in the moment. Setting up analytics and reporting systems always has the potential to cause a headache – this is especially true when it comes to working with real-time data.

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.

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.

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.

Explore how incorporating workforce analytics into performance reviews reduces bias and ensures transparency. Learn how Insightful's remote employee productivity management tools empower managers with real-time data, boosting productivity and engagement across remote and in-office teams.

The EU parliament has approved its AI act, the world’s first recognized set of rules designed to regulate this technology. Under the act, AI solutions will be divided into risk categories, including an ‘unacceptable’ tier that will see models that pose systemic risk banned. The bill will come into force 12 months after it is made law and is subject to formal approval from ministers of EU member states.

Flexible work brings multiple benefits but demands managing time and efficiency for remote teams operating distributed across locations. With 51% of the remote workforce experiencing disengagement at work, organizations need to stay in sync with how teams work and what efficiency they gain.