“We Built 1,000 Dashboards… And No One Used Them.”

That BlueCoat Guy — Podcast

I did not expect this answer.

When I asked Divya Krishna about the biggest gap in how companies use data today, I was ready for something technical.

Better models. Better infrastructure. Better AI.

She said something far simpler.

“The last mile of decision-making is still missing.”

And that one line changes how you look at everything happening in AI right now.

🎥 Watch the full conversation here

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The uncomfortable truth about data today

We’ve spent the last decade doing one thing really well:

Building dashboards.

Thousands of them.

Beautiful ones. Accurate ones. Expensive ones.

And yet…

“There were thousands of dashboards… but no one was using them.”

That’s not a tooling problem.

That’s a thinking problem.

Because somewhere along the way, we confused information with action.

From “data teams” to “decision systems”

Divya’s journey explains this perfectly.

She started in hardcore engineering environments like Bosch and Collins Aerospace. Then moved into data science. And somewhere in between, she saw a pattern:

• Data is generated
• Stored
• Cleaned
• Transformed
• Visualized

…and then?

Nothing happens.

“We built really good models… but without an outcome, it still doesn’t hold value.”

This is where most companies are stuck.

They optimized for accuracy. Not for decisions.

The shift no one is talking about enough

We are quietly moving from:

👉 Systems that inform to 👉 Systems that act

Divya calls this the rise of a decision intelligence layer.

A layer that sits on top of your data and answers the only question that matters:

“What should I do next?”

Not:

• What happened
• Why it happened
• What might happen

But:

What action should be taken right now

The 4 shifts that will define 2026

This is where the conversation got interesting.

Not hype. Not buzzwords. Actual shifts already happening.

1. Agentic AI: From insights to action

We are entering a world where AI doesn’t just analyze.

It executes.

But there’s a catch.

“Enterprises are in a trust transition phase.”

They trust AI to suggest.

Not to decide.

So what’s happening?

• AI agents are being deployed in low-risk environments first
• Guardrails are becoming more important than models
• Humans still control the “what”, AI handles the “how”

This hybrid phase is where most companies will live for the next few years.

2. Decision Intelligence > Dashboards

Photo by 1981 Digital on Unsplash

Dashboards were built for visibility.

Decision systems are built for movement.

The difference is subtle but powerful:

• Dashboards tell you what’s happening
• Decision systems tell you what to do

And in a world of overwhelming data…

Humans alone can’t keep up anymore.

3. Smaller models will quietly win

Everyone is obsessed with massive AI models.

But inside enterprises, something else is happening.

“Smaller models are more specific, portable, and easier to deploy.”

Instead of one giant brain, companies are building:

👉 Multiple small, specialized agents 👉 Each solving a specific problem 👉 Orchestrated together

Think less “one super AI” More “team of focused experts”

4. Action pipelines will replace reporting

This one is the biggest shift.

We are moving from:

👉 Reporting pipelines to 👉 Action pipelines

Systems that don’t wait for humans to act.

They:

• Trigger workflows
• Execute decisions
• Run 24/7

“These pipelines can work for you continuously.”

You don’t check dashboards anymore.

The system moves without you.

So… will AI replace data scientists?

Short answer: No.

Better answer: It will replace the wrong definition of data science.

“The ‘how’ will be automated. The ‘why’ remains human.”

What AI will take over:

• Data cleaning
• Transformations
• Pattern detection

What remains human:

• Problem framing
• Context understanding
• Decision intent

The real job is shifting from:

👉 “How do I build this model?” to 👉 “Why does this data matter?”

The most underrated shift: Human-centered data

This part stuck with me.

Today, data sits with central teams.

Tomorrow, it moves to the edges.

• Sales teams use their own data
• Finance teams own their insights
• Operators make real-time decisions

“Data will be in the hands of people who understand it best.”

That’s what human-centered data science actually means.

Not more tools.

More ownership.

The bigger picture

This isn’t just about AI.

It’s about how decisions get made.

For years:

• Humans made decisions
• Data supported them

Now:

• Systems make decisions
• Humans define intent

And the companies that get this right…

Won’t just move faster.

They will redefine how organizations operate.

Final thought

Divya said something towards the end that stayed with me:

Technology doesn’t replace humans. It expands them.

We’ve already automated physical work.

Now we’re automating parts of thinking.

Which leaves us with a bigger question:

What should humans focus on next?

If this made you think…

I break down conversations like this every week.

Not summaries. Not hype.

Real insights from people building the future.

👉 Subscribe if you want to stay ahead of where AI, data, and systems are actually going.

And if this resonated, forward it to someone who’s still building dashboards.