Stop Waiting for the Warehouse

Why enterprises struggle with data transformation

Most enterprise companies have made data transformation a corporate priority. Every organization faces data quality challenges and has important questions that need answering.

In this new era of AI, where AI is widely adopted by consumers, businesses are now waiting for the enterprise-grade version. New roles like Chief AI Officer (CAIO) are emerging to meet this demand.

The two competing responsibilities of CAIOs

These executives face two competing responsibilities:

1. Building AI-driven products for customers.
2. Using AI to improve internal efficiency.
   

But here’s the irony: these goals are at complete tension. Pulling top engineers off customer-facing work to solve internal data challenges? It doesn’t add up.

At a high level, the internal technical blueprint is nearly identical across companies: consolidate sales, marketing, and finance data into one place, clean and transform it, and make it queryable for leadership.

A real-world example

We recently spoke to a company still building their data warehouse (using a major vendor) with a target completion date of 2028. That’s three years away. In the meantime, they admit there’s no reliable source of truth.

Their data is scattered across dozens of CRM and ERP systems due to past mergers and acquisitions, and generating simple reports takes an army of people across finance, sales ops, and data teams.

In other words: no meaningful data decisions for years.

Why the warehouse has failed

The warehouse has failed because:

• Spreadsheets and dashboards exist, but CROs and CMOs still have unanswered questions.
• The traditional data warehouse is no longer the center of gravity.
• The modern data stack promised unified metrics and semantic clarity.
  

In practice? It’s become a backup system. It's fragmented, stale, and incomplete.

Across industries — SaaS, infrastructure, construction tech — the story is the same: the warehouse is a mirage. It takes too long, costs too much, and still doesn’t deliver.

The cost of legacy migrations

Startups struggle with data too, but large enterprises face deeper issues. Many are decades old and tangled in tech debt and conflicting assumptions.

Each department already has its own source of truth:

• Sales uses Salesforce.
• Marketing has HubSpot.
• Finance relies on NetSuite.
  

Forcing them into a monolithic warehouse slows everyone down and increases risk. Legacy warehouse migrations mean years of effort, millions of dollars, and still stale data.

Why AI makes the warehouse problem worse

AI won’t solve this. If anything, it makes the warehouse problem worse.

• You can’t train LLMs on broken or outdated data.
• You shouldn’t need 20% of headcount just to keep migration tools alive.
• Businesses don’t need a perfect global schema to answer a basic question like: What changed today?

What companies really need

They need AI that connects directly to the systems they already use — Salesforce, NetSuite, billing engines — and delivers trusted answers in seconds. Not months. Not after the next migration.

Instead of forcing every team into a centralized repo, we should meet them where they are: vertical-first, source-integrated, and real-time.

And yes, governance still matters, but not through bottlenecks. Real governance means traceability, freshness, and control at the source — not fragile pipelines and stale dashboards.

The takeaway

It’s simple: meet your data where it lives today. Not someday, right now.