Teams | Collaboration | Customer Service | Project Management

One remote worker might spend 3 productive hours working non-stop without ever touching their keyboard, while another might trigger hundreds of thousands of mouse movements with no meaningful output; that is the problem with monitoring remote work through surveillance: activity and productivity are not always synonymous.

I've seen this advice recycled for years, and it rarely fixes the real continuity problem. If you search how to retain agency clients, you get the same playlist: be proactive, run a quarterly business review, send a note, report ROI. Useful. Incomplete. The longest-held accounts I worked on shared one trait: clients rarely had to re-explain the brief, rarely felt the story reset when people changed, and rarely got surprised by the commercial story late in the year.

Most search results for PSA integration still assume you run an MSP service desk next to remote monitoring. That is useful if that is your business. It is less relevant if you are trying to keep agency retainers, consultancy fixed fees, and multi-role rate cards aligned with finance. What protects margin is clear ownership, not just adding another Zap. Two systems both believing they own the client, the rate, or the invoice status is the real risk.

What I keep seeing across services teams is adjacent tracking without a true client contribution view. You already watch utilisation, project budgets, or billable mix, yet a loud account can still look healthy while it taxes the firm. This guide shows how to measure client profitability, triage the book, and act while delivery is live.

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.