Don’t Let Data Be Your AI Blocker

The Harsh Reality. Years Before You Can Even Start

Despite all the AI hype, most enterprises are stuck waiting, sometimes for years, before they can even access their own data, let alone innovate.

• Only 48% of digital initiatives deliver on business outcomes, according to Gartner [^1]. Many fail not due to lack of ambition, but because foundational issues, like data access, remain unresolved.

• 85% of big data and analytics projects fall short [^2]. These aren’t fringe cases, they’re systemic signals that the traditional model is broken.

• 87% of data science projects never reach production [^3]. This means the promise of AI often fizzles out before it ever reaches business impact.

  
  

At Data Tiles, we see this firsthand. Clients come to us after spending millions on cloud migrations and data platforms, only to realize they’re still waiting for tangible outcomes. The frustration is real.

We’ve written extensively about this in The Unstructured Data Bottleneck, where we show how GenAI and Latttice are turning inaccessible documents, PDFs, and raw data into governed data products in minutes.

“For too long, unstructured data sources like customer feedback forms, supplier PDFs, or embedded tables in slide decks have remained dark to analysis. With GenAI, we’ve finally cracked the bottleneck—and Latttice makes it repeatable and governed.”

• 80% of time in analytics is wasted on data prep [^4], not value creation.

• Over 30% of data professionals say data prep is their biggest time sink [^5], yet it’s rarely solved by cloud migrations or traditional platforms.

Stop Waiting. Migrations and IT Backlogs Are the Real Bottleneck

The standard data playbook still recommends:

1. Lifting everything into a central data lake or warehouse.

2. Embarking on a costly multi-year cloud migration.

3. Waiting for IT teams to build and prioritize data pipelines.

4. Hoping that usable, governed, AI-ready data appears at the end.

But that model is slow, expensive, and often demoralizing.

In our blog If Data Mesh Is the Future, What’s Stopping Enterprises from Adopting It?, we explore how outdated centralization strategies and technical dependencies kill transformation momentum.

“Many organizations claim they want to decentralize, but still hold onto centralized control structures that require technical handoffs at every step. That’s not Data Mesh. That’s just outsourcing the delay.”

Even with the best intentions, many organizations fall into the same pattern, waiting on IT, waiting on migrations, waiting on results.

And as we warned in Beware the Fake Data Mesh, many so-called modern platforms are just repackaged consulting-heavy models with no real empowerment for business teams.

“If your ‘self-service data mesh’ depends on 12 engineers building APIs and data pipelines just so a business team can ask a question, you don’t have a mesh, you have a bottleneck wearing a different hat.”

The AI Readiness Gap: Enthusiasm ≠ Execution

Employees are already using AI tools, 90% of them [^6], but only 13% of organizations are ready for AI adoption. This mismatch isn’t about intent. It’s about capability.

AI can’t thrive in an environment where data remains inaccessible, ungoverned, locked behind engineering bottlenecks, and lacking context. Without governed access, experimentation stays shadowed and unsupported.

We highlighted this gap in AI: Expectation vs. What’s Happening on the Ground, where we dive into the disconnect between AI hype and operational reality.

“AI isn’t magic, it’s fuelled by clean, contextual, accessible data. The disconnect isn’t due to bad models; it’s due to inaccessible foundations.”

Organizations want AI, but they’re still solving yesterday’s data problems with a continual revolving door of tools.

Our Position at Data Tiles

Don’t Let Data Be Your AI Blocker.

We created Latttice to solve this dilemma, bridging the gap between business ambition and technical execution.

Data + AI. Reimagined Together with Latttice

With Latttice, you don’t have to wait. You can:

• Access data in-place. No migration required.

• Build governed, trusted, and secure data products in minutes, not months.

• Empower business users. No code, no backlogs.

• Launch AI initiatives today, not three years from now.

  
  

This flips the old equation. Instead of data holding up AI, AI drives better data practices, now.

The Cost of Waiting

Gartner reports that poor data quality costs the average enterprise $12.9 million per year [^7]. That’s a cost of inaction - yet most firms still treat data readiness as a years-long engineering project.

But the world has changed. We’re in the era of data products, where governed, reusable assets drive decisions, and AI acts as co-pilot, not a backroom experiment.

You’ve Waited Long Enough

Still waiting to “finish” your warehouse or cloud migration before launching AI?

You’re already behind.

Break the cycle. Own your data. Start your AI journey now.

Latttice by Data Tiles: Giving every team the power to have a conversation with their data.

Let’s have a data conversation,

Lili Marsh.

Learn More from Data Tiles:

For deeper insights, explore more in our blogs

• The Unstructured Data Bottleneck

  
  

  “GenAI has removed the last obstacle to data access—unstructured sources. Now the bottleneck is mindset, not tooling.”

  
  

• If Data Mesh Is the Future, What’s Stopping Enterprises?

  
  

  “Data Mesh was never meant to be led by consultants or engineers—it’s a business-first approach to data ownership and outcomes.”

  
  

• Beware the Fake Data Mesh

  
  

  “We built Latttice to show that you don’t need theory to implement Data Mesh. You need practical, affordable tools for real users.”

  
  

• AI: Expectation vs. What’s Happening on the Ground

  
  

  “The AI gold rush is real, but most companies are panning with broken sieves—no structured access to the data that matters.”

External Reference List:

1. Gartner via Salesforce Ben, Tech Monitor, IT Pro – "Only 48% of digital initiatives meet intended business outcomes"

2. Gartner via Datamine.com – "85% of big data projects fail to deliver"

3. Gartner via Salesforce Ben & Designingforanalytics.com – "87% of data science projects never reach production"

4. McKinsey & Company – "80% of analytics time is spent on data prep"

5. TDWI via McKinsey & Salesforce Ben – "Over 30% of data pros say data prep is a major challenge"

6. McKinsey Global Survey – "90% of employees use GenAI, but only 13% of organizations are ready"

7. Gartner via Forbes & Mannara-Tech – "Poor data quality costs $12.9M annually"Bottom of Form