Teams | Collaboration | Customer Service | Project Management

Here’s a stat that’s been rattling around in my head. In our 2026 AI SDLC study, 94% of engineering leaders told us they’re using AI, but only 6% have the systems to actually scale it across their whole software lifecycle. Almost everyone is playing with agents, yet almost no one can let agents run at scale without things breaking.

This morning I shared with the Miro team that we’ve signed a definitive agreement to join Bending Spoons. What I said to them is what I’d say to anyone who has followed this company over the past fifteen years, so I’m publishing the note here with only the internal logistics removed. You can also read more about the details of the agreement in our press release. Team, I have big news to share: Today, we signed a definitive agreement for Miro to be acquired by Bending Spoons.

Most product teams outgrow their first roadmap tool before they realize it. A spreadsheet works fine when you have one product and three stakeholders. Once you’re managing multiple workstreams, collecting feature requests from customers, and trying to connect strategic goals to what engineering is actually building, the cracks appear fast. This guide covers what to evaluate when you’re ready to move to a purpose-built product roadmap tool.

Most product teams carry two versions of the truth. The roadmap lives in a slide deck, a spreadsheet, or a dedicated roadmap tool. The actual work lives in Jira. Product managers update one, engineers update the other, and neither stays in sync for long. The cost is real. When a feature slips in Jira but the roadmap still shows it shipping next sprint, stakeholders get false confidence. When engineering adds scope in Jira without it appearing on the roadmap, the product strategy quietly breaks.

Product feedback management is the process of collecting, organizing, and acting on input from customers, internal teams, and stakeholders about your product. That input might be a feature request from a key account, a bug report from a power user, or a pattern of complaints surfacing in support tickets. The goal is not just to gather that input – it is to turn it into decisions. Which requests get built? Which get deprioritized? Which reveal a deeper problem the team has not yet named?

Most vendors sell "AI work management" as a prettier board with a chat box. That framing is too small for anyone running paid client delivery. AI work management is the operating layer that decides who does what, when you can safely promise dates, and what the work actually costs. It also decides which parts a supervised AI agent can take without wrecking trust. Before I joined Teamwork.com, I saw the spreadsheet version of that job repeatedly across agency environments.

AI financial insights for professional services leaders are not a chatbot bolted onto last month's export. At Teamwork.com, we see leaders need margin, utilisation, and forecast signals while work is still movable. For example, a delivery lead can change staffing mid-project before an overrun hits the P&L; I've watched firms mistake a prettier dashboard for insight.

A quiet assumption is buried in the phrase “quality care”: that care is something delivered to a patient, like a package left on a porch. In practice, care only works as a two-way transaction. A treatment plan a patient doesn’t understand, doesn’t trust, or doesn’t follow is not a lesser version of good care – it’s not care at all. Medicine has always run on consent and cooperation, not compliance in the disciplinary sense.