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Next week, AI meets reality for engineering teams!!!!

Everyone loves talking about how AI is accelerating software.

Faster releases.
Smarter features.
More automation everywhere.

But here is the part teams tell me privately.

When AI speeds things up, operations gets harder.

More services appear.
Dependencies multiply.
Telemetry explodes.
And suddenly engineers are expected to respond to everything, instantly.

That is why I am paying close attention to what is happening next week at New Relic Advance.

This is not just another product event. From what we are hearing, this is expected to be the largest wave of releases New Relic has ever announced. That usually means one thing. The industry is shifting, and vendors are responding.

What makes this interesting to me is the bigger story behind it.

Observability is no longer just about looking at charts.
It is becoming about helping teams decide faster.
Prioritize better.
And act before small issues turn into outages.

In other words, moving from
seeing problems → to actually resolving them intelligently.

You will hear terms like agentic systems, AI-driven resolution, and autonomous workflows. But the real question I care about is simpler.

Will this actually give engineers their time back?
Will it reduce noise instead of adding to it?
Will it help teams ship faster without burning out?

That is what I will be watching closely next week.

If you want to follow along or join virtually,

I will share the real takeaways once the announcements land.

Because the future of AI in software is not just about what we build.

It is about whether we can actually run it.

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Glean made an announcement that says a lot about where enterprise AI is heading next

Not louder models.
Not more copilots.
But AI that actually helps people get work done.

I spend a lot of time speaking with data leaders, operators, and builders across companies. Almost everyone is experimenting with AI now. But very few feel it is truly embedded into their day-to-day work.

That gap between experimentation and real impact is the problem.

Glean’s latest update to its Assistant is trying to close that gap.

They are focusing on something many AI tools still miss, which is context. Enterprise work is not just prompts and documents. It is approvals, systems, brand rules, meetings, priorities, and security layers. If AI does not understand that environment, it stays a demo instead of becoming useful.

A few things from the announcement stood out.

First, real-time voice interaction. This sounds simple, but it matters. If AI can brief you while you move through your day instead of waiting for you to open another tab, it starts fitting into work naturally.

Second, content and slides that already follow company branding. Anyone who has worked inside an enterprise knows how much time is spent rewriting and reformatting outputs so they are usable. Removing that friction is more powerful than it sounds.

Third, agent sandboxes for deeper analysis. This is a sign that assistants are moving beyond summarization into actually doing work that involves reasoning over larger datasets and longer tasks.

And finally, actions across enterprise systems. This may be the most important shift. AI becomes valuable when it helps you move from insight to execution without breaking flow.

What I appreciate about Glean is that they consistently build for how enterprises actually operate. They do not just show what AI can do. They focus on what employees need to get through their workday.

If this direction continues, we may start to see enterprise AI move from a tool people try occasionally to something that quietly becomes part of how companies operate.

That is when adoption becomes real.

I will be watching closely how organizations respond to this over the coming months.

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Best,

Ravit Jain

Founder & Host of The Ravit Show