Teams | Collaboration | Customer Service | Project Management

As a cloud architect, you’re going to have to embrace AI as part of your job — unless handcrafted artisan cloud becomes a niche in the future. But this doesn’t mean that you’re going to be left by the wayside. Anybody who has used AI coding tools knows that interesting things can happen when decisions are left up to them. So, that means decision-making should be left up to the people and the code writing to the AI bots … right?

Endava is a leading provider of next-generation technology services with 11,000 full-time employees serving enterprise clients across multiple industries. At a time when many organizations are testing AI in pockets — a chatbot here, a model there — Endava recognized the business imperative of a holistic AI transformation strategy at a company-wide level.

AI code-generation tools promised a revolution in software development. And in many ways, they’ve delivered. Code that once took hours to generate now takes minutes. But the goal isn’t just to code faster; it’s to code well. Your teams need to feed AI coding tools the right context to generate code that works with your existing systems, meets your business requirements, and integrates without expensive rework.

We’ve reached a point where almost anybody can create their own AI agent or automation. That’s great for experimentation, but a major headache for IT and security leaders. The question CISOs are grappling with is how to give people the tools to explore agents and agentic workflows while making sure they don’t inadvertently break the law or breach data processing agreements. It’s a problem we ran into at Miro.

AI adoption doesn’t stall because teams lack access to the technology. It stalls because people don’t know where the boundaries are. Collective Next — a consultancy that co-designes solutions with Fortune 500 and nonprofit clients — saw this firsthand, both internally and with their clients.

There’s a lot of ground to cover between a plan and a shipped product. You know how it goes: context drops off between handoffs, work gets redone in a dozen tools and formats, and the stuff your team already figured out doesn’t always carry forward. This month’s updates bring your thinking closer to the finish line. Specs and PRDs become working code. Prototypes move into production. Goals break down into tasks, and tried-and-true workflows run on repeat.

We’ve kicked off 2026 with an amazing collection of 185 new templates! Explore these fresh workflows, including updated calendars, planning templates, goal-setting activities, and AI-powered frameworks with Flows that will do the busy work for you — and so much more. If diving into AI is one of your goals this year, be sure to check out our new collection of AI-Accelerated templates for you and your team to get great done, faster.

For the past couple of years, AI in product development has largely been an individual sport. A designer generates mockups. An engineer autocompletes code. A PM drafts a brief. Useful — but isolated. The collaborative muscle that makes great products hasn’t been getting the workout it needs. That’s starting to change. And analyst firm Constellation Research just put a stake in the ground about why it matters.

Two recent data points sum up the story of AI deployment so far. First: Enterprise AI spend is predicted to hit almost $115B in 2026, signalling the urgency with which the technology is being adopted. Second: Only 15% of AI decision-makers reported a revenue lift for their organization in the past 12 months, meaning the reality is yet to match the hype.