Engineering is stepping into a new chapter, where AI-powered tools are giving technical teams a double advantage: unprecedented productivity gains and the confidence to experiment more boldly. A recent MIT Economics working paper found developers using GitHub Copilot boosted weekly output by 26%, with the biggest gains among less-experienced engineers. Those gains aren't just about efficiency. They open the door to faster experimentation and validation, and the impact grows even greater as AI extends beyond engineering into product, design, and business functions.
The opportunity now is for leaders to guide their teams in weaving AI into routine workflows to break down barriers to experimentation and accelerate the path to innovation.
AI Tools of the Trade
AI begins to show its true impact when it's woven into everyday practice, transforming how teams build, test, and refine ideas. At Snowflake, our Toronto Engineering Hub has put tools like Cursor, Figma Make, and Glean into practice to accelerate how we work and allow us to shift towards a prototype first culture where we no longer need long product requirements or engineering design documents. For example, Cursor can now handle big portions of coding on its own, giving teams more time to build prototypes and test ideas faster. Figma Make allows engineers, especially those closest to the user experience, to create mock-ups and concepts themselves, and allows designers to iterate much faster which accelerates velocity dramatically. And Glean connects knowledge across the company, giving not just engineers but product, design, and business teams access to the information they need to keep ideas moving. The result is faster idea validation, shorter feedback loops, and a more agile cycle.
For less-experienced engineers, AI tools feel like having an on-demand mentor taking care of the repetitive work and offering best practices and instant feedback that builds confidence. Because the support is immediate, junior engineers are oftentimes eager to embrace these tools and help spread them across teams.
Tools that streamline coding, design, and knowledge-sharing give engineers a head start, but the real transformation happens when product managers, analysts, sales, marketing, senior leadership and other non-technical teams can tap into the same power to test ideas, extract insights, and improve decision-making. Tools like Snowflake Cortex make this possible by bringing AI directly to the data and enabling every team to collaborate on a shared foundation, making enterprise data and AI easy, connected and trusted.
Embedding AI into the fabric of daily workflows elevates experimentation from a team advantage to an organizational capability ready to scale.
Scaling AI in Engineering Without Disrupting Momentum
For engineering leaders, the real question is how to scale the adoption of AI tools without disruption. The answer starts small. Rather than reengineering entire processes, identify high-friction tasks, like code reviews, documentation, or early design exploration, where AI delivers immediate value. Rolling out tools in these focused areas helps teams see quick wins and builds trust in the technology. From there, leaders can roll out AI more gradually, layering new capabilities onto existing workflows.
Clear communication, training, and feedback loops are essential to ensure teams feel supported, not siloed. This approach makes AI integration seamless, where AI tools feel like a natural fit rather than a disruptive break from how teams already work.
The future of engineering, and every business function connected to it, will be defined by how quickly and confidently enterprises put AI tools into everyday practice. What matters now is leadership. By guiding teams to adopt AI tools thoughtfully and embedding them into the fabric of daily work, leaders can turn incremental improvements into a competitive advantage. In a landscape where speed and adaptability determine success, those who act now will set the pace for innovation tomorrow.
About the author:
Qaiser Habib currently serves as Head of Canada Engineering at Snowflake. Habib is a veteran engineering leader and a well-recognized expert in the Canadian market, currently focused on building and leading the Canadian engineering presence for Snowflake.
