Qlik rolls out capabilities to cover more AI use cases

AI value requires full-stack support, and Qlik has responded. At Qlik Connect 2026, the company made announcements aimed at helping enterprises operationalise AI in the real world. 

- Qlik has partnered ServiceNow to bring trusted enterprise context into AI-powered workflows

- New capabilities across Qlik Answers, Discovery Agent, MCP Server, and new agents for prediction, automation, and analytics development

- New trust and governance capabilities for AI centred on data products

- Qlik further expanded its agentic execution strategy into data engineering

ServiceNow partnership

Qlik's new partnership with ServiceNow is designed to help enterprises bring trusted enterprise context and richer insight into the workflows and AI-driven processes where decisions turn into action. The collaboration hinges on how workflows and agents that act with richer context deliver better outcomes. Ultimately, enterprises enjoy greater data visibility inside ServiceNow as well as the ability to pair workflow execution with insight derived from a broader enterprise context. 

ServiceNow Workflow Data Fabric provides the workflow foundation, and the operational picture gets even stronger when paired with signals from enterprise resource planning (ERP), customer relationship management (CRM), billing, supply chain, support, and other systems. When those connections come together, teams can surface important relationships, respond faster, and act with greater confidence, Qlik said. 

Qlik and ServiceNow are building on this opportunity by strengthening the connection between governed enterprise
data, better insight, and workflow execution. Qlik brings the ability to combine ServiceNow Workflow Data Fabric
with broader enterprise context, use the Qlik Analytics Engine and AI to surface patterns and relationships across
systems, helping customers identify what should happen next. 

The partnership extends that power further by adding a robust governance and discovery layer inside ServiceNow through new Qlik metadata collectors for the ServiceNow Data Catalog. 

“Workflows and AI agents are being asked to do more than route work. They are being asked to interpret business
conditions and act with better judgment,” said James Fisher, Chief Strategy Officer, Qlik. 

“That takes more than system data on its own. It takes the ability to combine ServiceNow signals with broader enterprise context, apply analytics and AI, and feed that intelligence back into the workflow where action happens.”

The partnership reflects a broader shift underway in enterprise software, Qlik observed. Companies want AI that fits inside the systems where work already happens, but they also want those systems to operate with a fuller understanding of the business. That means connecting workflow execution to governed context, explainable insight, and signals drawn from across the enterprise, not just to a single application view. 

"The decisions people and agents make every day are only as good as the data behind them," said Pramod
Mahadevan, VP, Data & Analytics Product Ecosystem, ServiceNow. 

"Our partnership with Qlik connects those insights from third-party data directly to action inside ServiceNow, extending the reach of Workflow Data Fabric to the systems where critical data already lives. The result: people and agents that act on trusted, governed intelligence, and decision-ready data in the workflows where work gets done." 

What’s new: 

- Qlik metadata collectors for ServiceNow Data Catalog: ServiceNow Data Catalog now offers Qlik metadata collectors that integrate with a broad range of Qlik products, improving discovery, lineage visibility, and governance for shared data assets. 

- Analytics and AI that strengthen action: Qlik’s analytics engine sends insights directly into ServiceNow workflows and agents, improving decision-making with cross-system relationships, emerging patterns, and operational context. 

Prediction, automation, and analytics 

Qlik expanded its agentic analytics capabilities that brings together Qlik Answers, Discovery Agent, and MCP Server with new agents for prediction, automation, and analytics development to support a more complete path from question to action.

According to Qlik, the pressure on enterprise AI initiatives has changed. Teams are being asked to move faster, prove value sooner, and make outputs useable inside real operating workflows. This is not work that a simple question-answer bot can do, the company said: enterprises need AI systems that can surface what matters, reason in context, and drive action with traceability, control, and trust.

Qlik’s latest agentic analytics release is designed around this reality. Qlik is extending data products with shared, reusable business definitions — including measures, dimensions, and relationships — so Qlik Answers, analytics apps, and third-party assistants can work from more consistent business meaning across analytics and AI workflows.

- Qlik Answers brings together structured analytics and unstructured content in one governed experience, designed to support richer, more contextual responses and follow-up reasoning rather than isolated prompts.

- Discovery Agent monitors key data areas and helps surface important changes and anomalies early so users can investigate earlier and act faster.

- Automate Agent executes actions and workflows in downstream systems based on insight and agentic reasoning. Teams can trigger workflows directly using natural language.

- Predict Agent builds machine learning models, generates predictions, interpretes results, and helps answer forward-looking questions.

- Analytics Agent can now support analytics development tasks in addition to generating insights, helping teams move through creation workflows more efficiently.

- Broader AI access: MCP Server allows third-party AI assistants to use Qlik analytics to support decisions, bringing Qlik’s context-rich calculations into the assistants that teams already use while helping preserve value from existing Qlik investments.

Together, these capabilities create a more complete agentic experience: detect, investigate, predict, and act. They also reflect a broader Qlik view that analytics becomes materially more useful when answers are grounded in context, supported by explainable reasoning, and connected to execution.

 “The bar for enterprise AI is getting much higher,” said Mike Capone, CEO, Qlik.

“It is not enough to produce a fluent answer. AI has to understand the business in context, run on a trusted foundation, and connect insight to action in the systems teams already use. That is how organisations create value without adding more fragility, lock-in, or spend.”

Trust and governance 

Qlik also expanded its trust and governance capabilities for AI, centred on data products and the operational controls required to make them reliable for both human decision-making and AI-driven action.

As enterprises push AI deeper into workflows, the pressure lands on the data underneath every output, the company explained. Teams need to know which data products are reliable, whether conditions have changed, and when intervention is required before weak data turns into weak execution.

Qlik’s solution is to make trust operable. This release brings together data products, trust signals, operating standards, anomaly detection, and agent-assisted stewardship into a tighter set of capabilities designed to help teams monitor, govern, and improve the data products that feed analytics and AI.

“As AI moves from answers into decisions and actions, weak data stops being a reporting problem and becomes an execution problem,” said Capone.

“Data products need the same accountability as any other production asset, with clear signals for what humans and AI can safely rely on. That is how enterprises scale AI without scaling risk.” 

What’s new: 

- Data products in Qlik Analytics: Qlik is advancing data products as governed, AI-ready units of value that teams can create, manage, share, and reuse across analytics and AI workflows. This includes shared business definitions for measures, dimensions, and relationships, helping people and AI systems rely on more consistent business meaning.  

- Data Product Agent: Data Product Agent helps teams create, manage, and deliver data products using natural language. It is designed to evaluate data products for quality, generate Trust Scores, and help humans and AI systems understand where to go for data and how good it is.  

The Qlik Trust Score evaluates data products across dimensions such as accuracy, timeliness, diversity, and completeness. The goal is to help teams inspect readiness before decisions or automated actions depend on it. 

- Data contracts and operating expectations: Qlik is introducing a contract layer that helps teams define what a data product is expected to provide, giving producers a clearer operating standard and consumers a more explicit basis for trust.  

- Service levels, alerting, and anomaly detection: New service-level objectives, alerting, and anomaly detection help teams monitor whether data products continue to meet expectations over time, surfacing degradation and drift before issues compound into business risk.  

- Data Quality Agent: Qlik is extending agent-assisted operations into trust workflows with support for retrieving trust signals and data quality metrics, creating and editing rules, defining service levels, running calculations, and detecting anomalies through conversational interactions.  

- AI-enabled stewardship at scale: New stewardship capabilities help teams generate rules, improve glossary coverage, create field descriptions, and recommend remediations more efficiently.

Together, these capabilities are designed to help enterprises operationalise trust around data products, giving teams a more practical way to govern quality, understand reliability, and support AI usage with stronger signals and clearer accountability.

Agentic execution in data engineering

Source: Qlik.

The creation, evolution, and delivery of trusted data has been boosted with new capabilities as well to support data teams pressured to support more AI initiatives, move faster on new data demands, and do it all without adding fragility, backlog, or unnecessary cost. 

Qlik noted that the manual work still required to build pipelines, maintain transformations, troubleshoot issues, and keep data current enough for decisions and automation is holding these ambitions back.

The company's latest data engineering release is designed around that reality. It brings agentic execution into the engineering workflow itself, giving teams new ways to translate intent into working data assets, reduce repetitive effort, and speed delivery without stripping away the control required for production environments.

“Most companies do not struggle to imagine AI use cases. They struggle to deliver the trusted, current data those use cases depend on,” said Capone. 

“As demand rises, data engineering becomes the critical path. Qlik is helping teams reduce friction, protect trust, and keep pace with the business.”

What’s new: 

- Declarative pipelines: Qlik is introducing a more natural-language-driven way to help data engineers create and evolve pipelines in context with the pipeline canvas, offer next-step guidance, and lower the barrier to building trusted flows. This release also establishes the path toward broader pipeline agent capabilities over time.  

- AI assistant for Talend Studio: A new context-sensitive AI assistant inside the Talend Studio integrated development environment (IDE), planned for later this year, is designed to help developers request help, generate jobs, create documentation, and write SQL using natural language, shifting engineering work from manual coding toward higher-level orchestration.  

- Real-time routing for agentic data flows: Qlik is expanding Talend Studio to support real-time message routing for agentic processes, helping data engineers work with large language models, build domain-specific retrieval augmented generation (RAG) pipelines, and connect agentic systems through Model Context Protocol (MCP) components. The latest release also expands context and memory handling to support more complex enterprise-scale workflows.  

- Open Lakehouse streaming: Qlik has extended its Open Lakehouse with native streaming support so teams can unify continuous event data with batch and change data capture (CDC) workloads in one environment, reducing the need for separate tooling and helping keep AI and analytics closer to current business conditions.  

- A more complete engineering path for agentic workloads: Across declarative pipelines, real-time routing, Open Lakehouse Streaming, Talend Studio AI assistance, and the broader path toward Pipeline Agent capabilities, Qlik is positioning data engineering as a more intent-driven, agent-assisted function.

Taken together, the capabilities available now, along with the broader agentic experiences planned ahead, are designed to help data teams move from manual pipeline assembly toward a more agent-assisted operating model — one where engineering work becomes easier to initiate, easier to evolve, and better able to keep up with the freshness and reliability demands of AI, Qlik said.