Enabling non-technical domain experts to use automation by employing simple low-code/no-code interfaces has always been considered key to driving adoption of technology for improving business value. In the emerging era of agentic artificial intelligence (AI), where intelligent systems act autonomously to achieve defined goals, this empowerment takes on renewed significance. Organisations worldwide are now seeking not only to equip employees with AI-driven tools but also to enable them to build their own intelligent agents that enhance productivity and innovation. This evolution marks a new phase of human–machine collaboration: humans contribute deep, often tacit domain knowledge and intuition, while AI systems provide analytical depth and adaptive decision making. As low-code/no-code tools democratise application creation, non-technical business users are emerging as key co-architects of autonomous, goal-driven AI agents. Success in this space, however, is not automatic. Businesses need to identify the right use cases, choose flexible and secure platforms, and put in place strong governance models to manage risk and ensure trust. This paper introduces a reference capability model to implement a citizen development approach that offers a structured framework for organisations to evaluate their current maturity, identify the capabilities required for scaling and reinforce areas of existing strengths. The resulting assessment serves as a strategic roadmap, guiding organisations towards successful and sustainable deployment of agentic AI. Like every transformative technology before it, citizen development powered by AI is inevitable; it is not a question of if, but when. Early, deliberate and well-governed adoption will position organisations to achieve sustainable success in this rapidly evolving technological era. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Building similarity graph...
Analyzing shared references across papers
Loading...
A. S. DIXIT
Journal of AI, robotics & workplace automation.
Accenture (Switzerland)
Building similarity graph...
Analyzing shared references across papers
Loading...
A. S. DIXIT (Sun,) studied this question.
www.synapsesocial.com/papers/69cb6526e6a8c024954b92f1 — DOI: https://doi.org/10.69554/tqzh1870