Qlik Embedded Analytics and AI Deep Dive

In their session at Tech Field Day Experience during Qlik Connect 2025, Luciane Ellis and Dave Channon explored the transformative impact of embedded analytics and AI on business applications. Emphasizing the increasing demand for real-time insights directly within workflows, the presenters highlighted how businesses are evolving past isolated dashboards to meet the expectations of both technical and non-technical users. The focus is now on integrating intelligence seamlessly into everyday tools like CRM or supply chain platforms, allowing users to get instant, context-aware insights without shifting applications. These embedded capabilities are proving valuable for independent software vendors (ISVs), who can now offer AI-augmented features such as predictive analytics and natural language interaction, translating into operational efficiencies and premium offerings for end customers.

As part of this transformation, Qlik has developed a suite of technology to support modern embedded analytics. Their Qlik Embed framework provides lightweight, cookie-free web integration and supports major frontend technologies, allowing applications to scale flexibly across multi-tenant SaaS architectures. Features like generative and predictive AI are natively integrated, enabling ISVs to deliver intelligent, hands-off experiences that vary based on user roles and access levels. These capabilities are not just limited to desktop environments but are essential for mobile use cases, such as logistics or retail operations, where quick, in-context insights are crucial. Moreover, Qlik emphasizes governance, security, and real-time user personalization, which are critical for enterprises and ISVs looking to deploy scalable, secure solutions.

To demonstrate the power and scalability of Qlik’s embedded platform, Dave Channon showcased a fictional ISV demo, Smart Chain. This end-to-end demo illustrated how quickly an isolated customer tenant can be spun up with full analytics, self-service dashboards, and AI-driven experiences wrapped within a single application workflow. The process included automated provisioning of data, users, apps, and assistants—all using Qlik’s rich API ecosystem and automation tools. The embedded analytics included customizable views, predictive insights, report generation, and visual interactivity—all accessible to users of varying roles and technical expertise. This approach, which combines centralized ISV-driven models with end-user-specific tuning, demonstrates Qlik’s flexibility and strength in delivering intelligent, user-centric, and scalable analytics solutions.