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.

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.

The learnings are based on our recent webinar, “Inside Reddit’s IT playbook for AI and scale”. Check out this session and other conversations with customers on-demand. When Reddit grew from fewer than 400 people before 2020 to roughly 4,000 globally distributed employees today, it wasn’t just their headcount that changed. The mandate for IT changed with it.

At Atlassian, on-call engineers live at the intersection of urgency and uncertainty. Floods of noisy alerts sap focus, energy, and productivity — especially when responders must decide what matters, what can wait, and what’s just noise. A single underlying issue can trigger dozens of near‑identical alerts in hours.

Since we first launched custom agents in May of 2024, we’ve seen teams use Rovo to build agents in Confluence that help them accomplish everything from turning customer feedback into PRDs to maintaining consistency across large sets of data and processes. And agents are getting even more popular, with over 5M invocations of agents every month. They take the foundation of knowledge that lives in Confluence, and act on it, saving customers over 200K hours in February alone.

Teams of all stripes have run billions of cross‑functional, multi‑tool workflows on Atlassian. After decades spent helping them plan, build, ship, and do, those same workflows are now lighting up with millions of agentic automations, up 7x in the last six months alone. All signals point to the rise of the AI‑native organization, where humans operate at critical junctures, deciding what matters and why, and agents do more of the execution.

We’ve all been there – toggling between six tabs, copying content from one tool into another, and wondering if anyone actually read the brief. The promise of AI was supposed to fix this. Instead, most teams got a chatbot bolted onto the side of their screen. We think AI should work the way a great teammate does: show up where the work happens, understand what’s going on, and actually move things forward. Not from a separate window. Not after a five-paragraph prompt.

AI is changing how engineering teams work faster than most organizations can adapt. Coding assistants are now part of the daily workflow, agents are starting to own tasks end-to-end, and the way we deliver software is being redefined in real time. With that shift, engineering leaders are facing a new set of questions. Are these tools actually improving outcomes? Where are they falling short? Which teams are seeing value, and which aren’t?

AI agents are only as good as what they know. Right now, most don’t know enough. Not because the AI is broken, but because the data is. Information is scattered across tools, siloed by department, stripped of the human context that makes it useful. Agents guess. They hallucinate. Teams splinter around different versions of the truth. Context isn’t a file or a ticket. It’s the space in between: why a decision was made, who owns it now, what broke last time.

Over the past few decades in the technology industry, some of the biggest constraints to building products have been about having enough engineers, time, or compute. For the first time, that era is ending. Tech teams are experiencing a revolution unlike anything they’ve seen before. The barriers to entry for building have all but disappeared. The constraint no longer comes from producing enough output, but from deciding what to build, and the restraint required to build something good.