monday.com Just Reported Earnings – And the AI Numbers Are the Most Interesting Part
The headline from monday.com’s Q1 2026 earnings (released yesterday, May 11) was solid: $351 million in revenue, up 24% year-over-year, record operating income of $49 million.
But here’s what I actually found interesting.
Their engineering team is 32% more productive. Product ships 38% faster to market. They didn’t grow their headcount proportionally. They used AI. And 10% of all net new ARR in the quarter came directly from AI features – customers are paying extra for AI, not just using it as a bundled freebie.
What They Actually Did
monday.com integrated AI into their core development workflow. Code assistance, automated review processes, AI-augmented sprint planning. The tools their engineers were already using – made meaningfully faster by AI at every step. They also launched what they’re calling an ‘AI Work Platform’ – native AI agents built directly into their product, not as a tab or add-on but woven into the core workflow. And they’re acquiring OneAI to add voice agent capabilities.
This is a company that’s using AI internally, monetizing it externally, and building future products around it simultaneously. That’s a rare three-layer AI story.
Why This Actually Worked
The 32% productivity number isn’t an estimate. It’s a reported metric from a public earnings call – which means it’s real enough that management was comfortable putting it in front of investors and analysts.
The key principle: AI productivity works when it removes friction from existing workflows, not when it asks people to adopt entirely new ones. monday.com didn’t ask developers to change how they work. They made the work they were already doing faster.
Second principle: The AI has to show up in the economics. monday.com monetized AI features separately – getting 10% of new ARR from AI means customers have decided it’s worth paying for. That’s the difference between a feature and a product.
I’m Mike Partners – entrepreneur, investor, and founder of VisionarySchool.com. I write these breakdowns because every business deserves access to the strategies that are reshaping entire industries. Here’s how to act on this one.
How to Apply This to Your Business
Identify your slowest recurring workflow. For most businesses it’s something in ops, sales follow-up, or content creation. That’s your first AI candidate – not your most complex process, your most repetitive one. From there, measure before you optimize. Take a baseline. How long does this process take today? How many outputs per week? You can’t prove improvement if you never measured the starting point. Finally, add AI to the step your team hates most. The easiest productivity wins come from the tasks people already procrastinate on – drafting, tagging, formatting, summarizing. AI handles that. Your team focuses on the judgment calls.
Frequently Asked Questions
What is monday.com’s approach to AI?
monday.com has taken a strategic, results-driven approach to AI deployment, focusing on measurable business outcomes rather than experimental technology. Their strategy emphasizes solving specific operational challenges where AI can deliver clear ROI, which is a model that businesses of any size can learn from.
How can small businesses apply these AI strategies?
Small businesses can adapt monday.com’s approach by identifying their most costly operational problems first, then finding AI tools that directly address those pain points. As Mike Partners explains, the same principles behind enterprise AI deployments can be scaled down and applied to businesses of any size – the key is starting with measurable problems rather than chasing trendy technology.
How is AI changing software development?
AI coding assistants and development platforms are dramatically increasing developer productivity by automating routine coding tasks, improving code quality through automated testing, and accelerating onboarding for new team members. These tools are becoming accessible to development teams of all sizes, not just enterprise organizations.
How do you measure the ROI of AI investments?
Measuring AI ROI requires setting clear baseline metrics before deployment. Track time spent, costs, error rates, and customer satisfaction scores for the processes you plan to improve with AI. After implementation, compare these same metrics to quantify the impact. This data-driven approach, which Mike Partners advocates at AiExpert.org, ensures that AI investments are delivering real business value.
What results has monday.com achieved with AI?
monday.com’s AI initiatives have delivered measurable improvements across multiple business functions. Their results demonstrate that AI works best when it is deployed strategically against well-defined problems with clear success metrics – a principle that applies whether you are a Fortune 500 company or a growing small business looking to gain a competitive edge.
