monday.com
monday.com Innovation & Technology Culture
monday.com Employee Perspectives
Pausing a product roadmap for an entire month to point 700 engineers at a single goal is a significant structural shift, but it transformed monday.com. Andrew sits down with VP of R&D Sergei Liakhovetsky to uncover how fixing core infrastructure and adopting a cell-based architecture paved the way for platform scale. Sergei details the exact framework his leadership team used during their 30-day pause to launch user solutions while maintaining a strict zero-bureaucracy policy. The conversation also explores the new realities of reliability as platforms transition from being CPU-bound to heavily GPU-bound under the weight of automated agents.
Seeking to move beyond top-down mandates for artificial intelligence, monday.com dedicated an entire month to an internal 'AI challenge' that invited employees from every department—not just engineering—to build their own tools. The result was a 'gusher' of productivity-boosting ideas, ranging from automated customer sentiment trackers to AI-driven resource allocators, proving that the most transformative software solutions often come from the people closest to the daily grind.
monday.com Employee Reviews


What People Are Saying About monday.com
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Product Innovation: In 2026 the company reframed its core as an AI Work Platform with native agent infrastructure, one‑click model connectors, and AI building blocks embedded across boards and automations. It also engineered mondayDB and expanded specialized products on a shared platform layer, signaling platform‑level advances rather than isolated features.
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Emerging Technology Adoption: AI is embedded directly into workflows (e.g., Sidekick, AI Blocks, AI Workflows) and connected to leading models like ChatGPT, Claude, and Microsoft 365 Copilot. Agentic capabilities and support for approaches like MCP illustrate rapid uptake of cutting‑edge human‑and‑agent collaboration.
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Experimentation Culture: The organization ran an internal AI challenge and paused the roadmap to concentrate a large engineering cohort on a single AI goal, producing tangible new tools and ideas. This structured approach indicates deliberate experimentation to accelerate discovery and delivery of AI capabilities.



















