🦾How You Can Leverage:

Wanna up your chance at AI success? Get serious about data. 

Case in point? Consulting juggernaut Deloitte. 

Their US Chief Data and Analytics Officer, Ashish Verma, told us Deloitte runs a marketplace with 520 different data feeds. 

Most companies can't even catalog their internal spreadsheets properly, yet they’re still scrambling to implement multi-agentic AI swarms and whatever LLM-enfused buzzword of 2026 that’s about to pop off. 

Nahhh, shorties. That’s backwards. 

Here's what's really happening: while executives are busy buying the latest AI tools, their competitors are quietly building data infrastructures that make those same tools 10x more powerful.

The brutal truth?

The ceiling for your AI success is predetermined by your foundational data strategy.

Sorry…  "big data" ain’t a Google Sheet file with 87,000 rows.

The enterprises doing it right? 

Companies like Deloitte are spending HUNDREDS of millions on clean data sources because they figured AI is kinda worthless without it. 

So on today's show, we got the Data Strategy 101 from Ashish, a seasoned AI vet who’s helping propel the world’s largest company’s AI strategies forward. 

Listen up. 

Don’t miss this. 👇

That cozy little data warehouse you've been babying for years?

Cute.

Also completely useless for serious AI deployment.

Ashish dropped some knowledge about modern AI requirements: you need your internal data PLUS second-party data PLUS third-party data PLUS synthetic data to make anything meaningful happen.

The companies actually winning at AI?

They've built data marketplaces that work like Amazon for information.

Picture this: one central hub where 178,000 employees can discover, access, and combine multiple data sources without some poor IT person playing middleman and slowing everything down.

Ashish brought up an important point.

Their data marketplace is "contextual" - meaning it learns from user behavior to suggest relevant datasets before you even know you need them.

No more playing guessing games where you gotta know exactly what you're hunting for.

Welcome to AI that actually helps instead of frustrates.

Try This: 

Time for some brutal honesty about your data situation.

Map every single data source your company uses right now.

Yes, including that random Access database your accounting team refuses to abandon because "it just works."

Create four columns: Internal, Partner, Third-Party, and Synthetic.

Be real about what percentage falls into each bucket.

If 80% of your data is internal, your AI dreams are probably way too small for your actual business problems.

Next step?

Identify the three external data sources that could 10x your AI capabilities.

This might be industry datasets, government databases, or partner information you're currently ignoring.

Calculate the cost of acquiring these datasets versus the potential business impact.

Most executives get their minds blown when they discover a $50K annual data subscription could unlock MILLIONS in AI-driven insights.

Do the math.

Those PowerPoints and Word docs everyone's been ignoring?

Yeah, those are actually your secret competitive weapon.

Most organizations are literally sitting on goldmines of unstructured data hiding in documents, emails, and presentations that could transform their AI systems if they just processed and indexed them properly.

Ashish shared how Deloitte completely transformed their resource management game.

Get this: they contextualized and indexed their ENTIRE resume database.

We're talking 455,000 unstructured documents.

Previously?

Searchable only through basic keyword matching that sucked.

Now their system matches role descriptions to candidates in near real-time by actually understanding context, not just hunting for keywords.

The resource manager went from manually digging through resumes one by painful one to getting ranked, relevant results instantly.

That's the difference between AI that works and AI that makes everyone wanna quit.

Try This: 

Right now, identify your three largest repositories of unstructured data.

This is probably your shared drives, email archives, customer service tickets, or meeting transcripts.

Pick the repository that your team accesses most and causes the biggest headaches when searching.

Start small with a pilot of 100-500 files from this repository.

Use a tool like Azure Cognitive Search, Elasticsearch, or OpenAI's embedding API to create semantic indexes of this content.

Here's the fun part: run side-by-side tests.

Search for the same business-relevant query using your current keyword search versus the new contextual search.

Document the time savings and accuracy improvements like your promotion depends on it.

When you can show leadership that finding critical information went from 20 minutes of frustrated searching to 30 seconds of precise results, you'll have their attention AND their budget approval for scaling this approach.

Make the case impossible to ignore.

Excellent question.

Why are most data strategies failing spectacularly?

Because they're designed for yesterday's reporting dashboards, not tomorrow's autonomous agents.

Ashish emphasized the critical insight about walking with the end in mind - your data strategy should anticipate agentic AI capabilities, not react to immediate use cases.

Companies that wait until they need multi-agent orchestration to fix their data labeling and annotation processes will find themselves completely outmaneuvered by competitors who prepared their data infrastructure for probabilistic reasoning months earlier.

The annotation and labeling requirements for agent-driven systems are exponentially more complex than traditional analytics, especially when agents start working with external data sources beyond organizational boundaries.

Try This: 

Map out your AI roadmap for the next 18 months, then reverse-engineer what data labeling, annotation, and cataloging capabilities you'll need.

Start building those processes now, before you need them for agent deployment.