I’m sitting in the front row at Qlik Connect, and one thing is very clear:

We are moving past the era of AI demos. What Qlik just announced is a structured push toward making AI operational inside the business. Not just insights. Not just copilots. But systems that can detect, reason, predict, and take action on top of trusted data. This came through across three major announcements:
Agentic Analytics, Agentic Data Engineering, and Agentic Trust.

Let’s break it down.

1. Agentic Analytics: Closing the gap between insight and action

For years, analytics has stopped at dashboards or answers. Qlik is trying to remove that boundary. They are building a connected system of agents:

• Qlik Answers as the entry point

• Discovery Agent to continuously monitor and surface signals

• Predict Agent to answer forward-looking questions

• Automate Agent to trigger workflows

• Analytics Agent to accelerate development

This creates a full loop:

detect → investigate → predict → act

What stands out is that this is not stitched together loosely. It is grounded in a shared semantic layer, meaning the system understands business context consistently across analytics and AI workflows.

That matters.

Because most AI tools today generate answers without grounding. Here, Qlik is pushing toward systems that can reason in context and then execute.

The bigger shift is this:
analytics is no longer the end of the workflow. It becomes the starting point for action.

2. Agentic Data Engineering: Fixing the real constraint

Every AI conversation sounds exciting until you hit the data layer.

That is where most initiatives slow down. Qlik is bringing agentic capabilities into data engineering to address exactly that:

• Declarative pipelines that move from coding to intent

• AI assistance embedded in Talend for generating jobs, SQL, and documentation

• Real-time routing to support agentic workflows and RAG pipelines

• Open Lakehouse Streaming to unify batch, CDC, and streaming data

The focus is not on writing code faster.

It is on reducing the friction across the entire lifecycle:

• building pipelines

• modifying them

• keeping them current

• operating them at scale

This is critical because AI systems depend on fresh, reliable data. If data pipelines lag, everything on top breaks. What Qlik is doing here is positioning data engineering as an agent-assisted function, where teams spend less time on repetitive work and more time on architecture and business impact.

3. Agentic Trust: Making data reliability operational

This is where Qlik is taking a strong and practical stance.

Trust is no longer treated as governance overhead. It is being built into the system as an operational layer.

Key capabilities include:

• Data Products as the core unit for analytics and AI

• Trust Scores to evaluate readiness of data

• Data contracts to define expectations

• Service levels and alerting to monitor performance

• Anomaly detection to catch issues early

• Agent-driven workflows for data quality and stewardship

This changes how teams think about data.

Instead of asking, “Do we trust this dataset?”
the system continuously answers that question.

And more importantly, it does it before AI takes action.

Because once AI starts triggering workflows or decisions, weak data is no longer a reporting issue. It becomes an execution risk.

The bigger picture: One connected architecture

What makes these announcements interesting is not each piece individually.

It is how they connect.

• Agentic Analytics drives decisions

• Agentic Data Engineering powers the data

• Agentic Trust ensures reliability

Together, this creates a system where:

• signals are detected automatically

• reasoning happens in context

• predictions are generated

• actions are executed

• trust is continuously evaluated

This is what it actually takes to run AI inside real business workflows.

My takeaway from being here

Most AI conversations today are still centered around what the technology can show.

What I’m seeing at Qlik Connect is a shift toward what AI can reliably run.

That is a much harder problem.

It requires:

• strong data foundations

• consistent business context

• operational trust

• and systems that can move beyond answers into execution

Qlik is clearly betting that this is where the market is going.

And based on the conversations happening here, that shift is already underway.

More coming soon from Qlik Connect.

Everyone is building AI right now. But almost no one is talking about the harder part. How do you actually sell it? I’ve been noticing a pattern.

A lot of teams can:
- Build solid AI products
- Create impressive demos
- Even get early pilots

But then things slow down.
Because going from

idea → product → revenue

is not just a technical problem. It’s a distribution problem.

You need:
- Access to the right buyers
- Partnerships that help you scale globally
- A way to get bigger, stickier deals and simplify sales

And most teams are not set up for that. That’s why this shift is interesting. Instead of trying to figure out distribution from scratch, more companies are plugging into platforms like Microsoft Marketplace.

Where buyers are already searching. Where enterprise deals already happen. Where scale is built into the system. This changes the game. Because now, you are not just building a product. You are building with distribution in mind from day one. And in today’s market, distribution is the real advantage.

The AI Trust Summit by Bigeye is one of those events that feels very timely right now. Everyone is building AI, shipping models, and pushing things into production. But the real question that keeps coming up in my conversations is simple. Can we actually trust what we are building?

That is exactly what this summit is focused on. Not the hype, not just the models, but the foundation that everything depends on. Data quality, reliability, and visibility. The speakers are people who are dealing with these challenges every day, building real systems, and seeing where things break.

Check out the upcoming event which has all star lineup

I have seen firsthand how Bigeye is changing this space. They are not just helping teams monitor data. They are helping teams build trust in their entire data and AI stack. From catching data issues early to giving clear visibility into pipelines, this is the layer most teams realize they need only after something goes wrong.

This is why this event matters. Because AI is moving fast, but trust is not keeping up. And if that gap is not solved, everything else becomes fragile.

This is the conversation the industry needs right now.

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Best,

Ravit Jain

Founder & Host of The Ravit Show