The Modern Enterprise Data and AI Stack.

A practical map of the layers that produce trusted, AI-ready data — and the structural gap most enterprises have not yet closed.

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Executive summary

Every enterprise data and AI stack has four jobs to do: define trust, store and process data, produce trusted data products, and consume them in decisions. The first two are well served by governance platforms and cloud data platforms. The fourth is well served by BI, applications and AI tools.

The third — turning governed business intent into reusable, AI-ready data products — is the layer most enterprises have not yet closed. That gap is why AI initiatives stall and why even mature data platforms struggle to deliver decision confidence.

Why it matters

Without the missing layer, governance stays passive, platforms stay underused and AI stays stuck in pilots. Closing it unlocks the value of every other investment in the stack.

What leaders should understand

The four jobs of the stack

Define trust, process data, produce trusted data products, consume them in decisions. Each needs its own discipline.

Where most spend has gone

Storage, processing, catalogs and BI. Useful, but they do not, by themselves, produce trusted data products.

Where the gap sits

Between governance and consumption — the layer that activates business intent into reusable, governed data products.

Why AI exposes it

AI consumes data continuously, at machine speed. It surfaces every weakness in trust, governance and reusability.

The Structural Gap

The missing layer between governance,
data platforms and AI.

Governance platforms define trust. Data platforms store and process data. AI platforms consume data. The missing layer turns governed business intent into executable, reusable, AI-ready data products.

Layer

Governance

Defines trust

Layer

Data Platforms

Store and process

Layer

Latttice

Activates and produces

Layer

AI & Decisions

Consume with confidence

The workflow

What Latttice actually does.

A single, governed lifecycle from business intent to trusted output — reusable across BI, APIs, applications and AI agents.

Step 01
Business intent
Step 02
Define data product
Step 03
Connect data sources
Step 04
Apply semantics and policies
Step 05
Execute logic
Step 06
Publish trusted output
Step 07
Consume through BI, APIs, applications and AI agents

How it fits into the Decision-Driven Enterprise

The Decision-Driven Enterprise treats the missing layer as a strategic capability. The Workbench produces the trusted data products; active governance enforces the rules; decision lineage closes the loop back to outcomes.