From low-code to AI: Lessons in empowering enterprise users  

Enterprise artificial intelligence (AI) agents are emerging as the next wave of workplace productivity. These custom-built assistants streamline tasks, automate processes and personalize support for users across the organization. And much like the low-code movement of years past, this innovation isn’t coming from developers alone. 

The rise of low-code AI adoption is empowering power users, business analysts and frontline employees to create AI agents without writing a single line of code. With tools like Microsoft Copilot, agent-building has enabled employees to design and deploy AI-powered helpers using natural language, intuitive workflows and contextual prompts. 

This democratization of development presents a powerful opportunity and a familiar challenge. To scale responsibly, organizations must apply the lessons learned from the low-code era to ensure these agents are governed, supported and aligned to business priorities. 

Why it matters: AI agents are growing fast 

The demand for AI agent capabilities is accelerating rapidly. In a recent Cloudera1 survey, 96% of U.S. enterprises said they plan to expand AI agent deployments within the next 12 months. As the barrier to entry falls, the number of agents being created and the range of use cases is poised to multiply. 

These tools unlock innovation by enabling users closest to business challenges to design their own solutions. However, without the right frameworks in place, this growth can lead to redundancies, inconsistencies and risk. 

The low-code playbook still applies 

Many of the practices that helped organizations succeed with low-code platforms apply equally to enterprise AI agents: 

• Tiered environments