Qlik’s new agentic experience aims to bridge the gap between data, decisions, and outcomes, with a goal of helping customers overcome a lack of seamless data access, slow time to insight, and limited user adoption. Much of agentic experience highlights Qlik’s deep understanding of the market need for intelligent automation and self-service analytics. And Qlik is looking to achieve this through natural language interactions. By extending the chat interface of Qlik Answers to not only empower organizations to analyze both structured and unstructured data, which is an excellent capability on its own, but also to serve as the entry point to agentic experience, the ability to interact with data through natural language, combined with automated action capabilities, will significantly lower the barrier to entry for all employees.

The success of agentic experience hinges on several critical factors. The quality and reliability of the underlying Qlik-provided AI agents are paramount. It’s not just about accuracy and relevancy of answers, which Qlik customers can attest is already delivered via Qlik Answers, but explaining the reasoning in a clear and understandable way will go a long way in delivering greater trust. This explains why Qlik is emphasizing the "trusted answers" aspect so heavily. For organizations to trust actions taken by underlying agents, providing more information about agent guardrails and how these guardrails will be extended to customers should be prioritized by Qlik. Additionally, Qlik should be focused on delivering additional specialized agents that will enhance how organizations can interact with and act upon data and insights.

The accelerating demand for intelligent, autonomous analytics highlights a critical shift in how organizations seek to leverage data, moving from static reports to dynamic, proactive solutions. Qlik's new agentic experience directly addresses this by providing an AI framework where specialized agents reason, plan, and initiate actions, offering not only built-in capabilities but also the flexibility for customers to integrate their own custom agents. Embracing such advanced, AI-driven frameworks is becoming essential for businesses aiming to overcome data silos, accelerate decision cycles, and achieve greater agility in today's complex market.