CIOs apply low-code lessons to agentic AI adoption

When they go high, we go low. Low-code, that is.

The rapid evolution of enterprise technology has led CIOs, or Chief Information Officers, to seek effective strategies for implementing artificial intelligence solutions across their organizations. The experiences gained from low-code platform adoption offer valuable insights for deploying agentic AI, which refers to AI systems that can autonomously perform tasks and make decisions.

The fundamentals: Successful AI implementation requires a delicate balance between empowering business users and maintaining proper IT governance and control.

• Employee adoption of generative AI tools increases significantly when the technology is customized to specific workflows and use cases
• The concept of “fusion teams,” which combine IT expertise with business domain knowledge, has proven effective in both low-code and AI implementations
• Organizations benefit from establishing centers of excellence that provide support, share best practices, and maintain standards

Implementation strategies: A structured approach to AI deployment mirrors successful low-code adoption patterns.

• Business users should be empowered as domain experts to create and modify AI-driven solutions within their areas of expertise
• IT departments need to establish clear governance frameworks while encouraging experimentation
• Regular hackathons and innovation sessions help identify champions and showcase successful use cases
• Training programs should be tailored to different roles and skill levels within the organization

Technical evolution: The convergence of low-code platforms and AI capabilities is creating new opportunities for business process automation.

• Low-code platforms are increasingly incorporating generative AI features to assist in application development
• Agentic AI implementations range from basic task automation to sophisticated autonomous workflows
• The integration of AI into low-code platforms makes advanced automation capabilities more accessible to non-technical users

Risk management considerations: Organizations must balance innovation with appropriate controls and oversight.

• Clear guidelines for AI usage and development help prevent security and compliance issues
• Regular monitoring and assessment of AI implementations ensure alignment with business objectives
• Documentation and sharing of successful patterns help scale beneficial use cases across departments

Future implications: The democratization of AI development through low-code approaches represents a significant shift in enterprise technology management.

• Organizations that successfully apply low-code lessons to AI implementation are likely to see faster adoption and better business outcomes
• The role of IT departments is evolving from direct development to enabling and governing business-led innovation
• Continuous learning and adaptation will be crucial as AI capabilities and use cases continue to expand