Teams | Collaboration | Customer Service | Project Management

Think about your to-do list for a given work week. From responding to a colleague’s quick message to building a team strategy, how do you prioritize how much time and energy to allocate to each task? Behavioral science tells us most of us don’t optimize; we satisfice — we find the first “good enough” option, and we move on. The term, a mash‑up of “satisfy” and “suffice,” was coined by Herbert A.

Discover how AI improves customer service with real use cases, benefits, and implementation tips in our comprehensive guide.

Starting today, Jira teams can assign work directly to Cursor, where a cloud agent will pick it up and begin working. You can steer agents directly from Jira, your IDE, or Cursor on the web. When Cursor needs input or is ready for review, it will notify you in Jira. When it opens a pull request, it will be automatically linked back to Jira.

Most companies in 2026 are already using AI somewhere in their hiring pipeline, their customer support queue, or their project management software. What fewer companies have figured out is how to use it well, in ways that actually improve working life rather than just adding surveillance overhead. This isn't a theoretical question. According to McKinsey's Superagency in the Workplace report, 92% of companies plan to increase their AI investments over the next three years.

I've spent the last year watching teams bolt AI onto their workflows like duct tape on a leaky pipe. A chatbot here, an auto-summary there, maybe a prompt library someone shared in Slack. Each experiment works fine in isolation. None of them talk to each other. The result? Fragmented productivity gains that never compound. In this guide, I'll walk you through a 6-step framework for genuine AI workflow integration. That means AI woven into the way your team actually delivers work.

There’s a paradox happening with AI: Usage and productivity are up, but bottom-line results aren’t always as obvious. It’s a familiar pattern you might be seeing in your organization: Leadership invests in AI, and employees say they’re getting more done: more code, more campaigns, more analysis.