What happens when AI stops just answering questions and starts making decisions?

That’s the shift we’re entering right now.

And it’s exactly what the Agentic Analytics Summit 2026 is focused on.

I’ll be joining a panel at the event to break this down from a real-world perspective.

The shift no one can ignore

For years, analytics has been about helping humans make decisions.

Dashboards gave us visibility.
Self-service gave us access.
Copilots gave us speed.

Now we’re moving into something very different.

AI systems that can:

• Take a goal

• Break it into steps

• Execute workflows

• Adapt based on results

This is not just analytics anymore. This is execution.

Why most teams are not ready

Here’s what I keep seeing again and again.

Teams invest heavily in AI.
They build models.
They run pilots.

But they struggle to get to production.

Why?

Because the surrounding systems are not designed for AI that acts.

Things start breaking when you introduce agents:

• Data pipelines are not reliable enough

• Governance is not designed for autonomous decisions

• There is no clear ownership of outcomes

• Observability is missing

This is the gap.

What I’ll be discussing on the panel

In my session, I want to focus on what actually works.

We’ll go deep into:

From copilots to agents
What fundamentally changes when AI starts taking actions

The missing layer
Why orchestration and context are still the biggest challenges

Trust and control
How to enable AI without losing control of your systems

Real adoption
What companies are actually doing today

Final thought

We are entering a phase where AI is no longer just supporting decisions.

It is becoming part of the decision-making system itself.

And that changes everything.

If you’re working in data or AI, this is a shift worth paying attention to.

After 100+ conversations on The Ravit Show, one thing has become very clear to me. Building AI is no longer the hardest part. Scaling it is. Most teams I speak with already have a working solution. They have a clear use case and even some early traction. But then growth slows down. #ad

Not because the product is weak, but because adoption is hard. Enterprises are dealing with too many tools, long procurement cycles, and slow deployment processes. By the time everything is approved and implemented, the momentum is already lost.

This is the real bottleneck in AI today. From my perspective, Microsoft Marketplace is solving this exact problem. It’s not just a marketplace. It’s becoming a distribution and adoption engine for AI.

Here’s why this matters.

First, it simplifies decision making. Instead of evaluating vendors one by one, teams can explore 4,000+ AI apps and agents in a structured way, all in one place.

Second, it speeds up the buying process. Solutions are pre-vetted, contracts are standardized, and purchases can happen using existing cloud commitments. This removes a lot of friction that usually slows down enterprise deals.

Third, it accelerates adoption. These solutions integrate directly with Microsoft tools, making deployment faster and easier to manage. AI becomes part of the workflow instead of being tacked on to the side.

Fourth, it brings real scale. You get access to millions of Microsoft customers, reach 95% of the Fortune 500, and tap into a 500K+ partner ecosystem.
The impact is already visible. Companies are seeing faster deal cycles, larger deal sizes, and stronger growth driven by better distribution.

My takeaway is simple.
AI is no longer just a technology problem. It’s a distribution problem.
The teams that win will not just build better products. They will figure out how to get those products adopted more quickly.

That’s exactly where Microsoft Marketplace is making a difference.

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Best,
Ravit Jain
Founder & Host of The Ravit Show