AI Is Not Waiting
Your Data Products Can't Either
Executive Summary
AI adoption is accelerating faster than governance maturity.
Enterprise research from IDC, Gartner, Forrester, McKinsey, Deloitte, BARC, and TDWI consistently highlights the same structural gap: organizations invest in infrastructure and governance frameworks, but struggle to operationalize trusted, reusable data products at the business level.
That gap becomes critical during transformation programs and AI scale-out initiatives.
Latttice was built to close that gap.
Latttice is the Data Product Workbench. It activates governed data as reusable, business-owned data products. It does not replace governance platforms or cloud infrastructure. It activates them.
It can be introduced before, during, or after transformation, but its role does not change.
Activation.
The AI Acceleration Problem No One Talks About
AI strategy is no longer theoretical. It is budgeted. It is mandated. It is expected.
IDC's FutureScape predictions point to a near-term reality where AI governance, explainability, traceability, and operational accountability become mandatory enterprise capabilities. Forrester has described AI governance as shifting from optional oversight to board-level accountability. Gartner's research across AI governance and active metadata has reinforced the same message: governance cannot remain passive or retrospective if AI is to scale safely.
Yet McKinsey's research on next generation data architectures continues to show that many AI initiatives stall not because of model capability, but because underlying data is inconsistent, poorly owned, or weakly governed.
The problem is not model sophistication.
Advanced AI models are readily available and increasingly powerful.
The problem is productized data maturity.
Organizations lack the foundation of trusted, reusable data products.
AI magnifies whatever data it consumes. If governance is passive, unclear, or inconsistently applied, AI scales those weaknesses.
We have already seen the consequences.
Real World Consequences
The 2019 Science publication analyzing the Optum healthcare algorithm revealed embedded bias in widely deployed risk-scoring systems. The issue was not machine learning technique. It was biased data representation embedded in product logic and amplified at scale.
IBM publicly withdrew from certain facial recognition technologies in 2020 due to governance and ethical risk concerns.
Financial institutions such as Wells Fargo have faced sustained regulatory scrutiny tied to governance and compliance oversight failures.
1
Data Weakness
Biased, inconsistent, or poorly governed data enters systems
2
AI Amplification
Machine learning scales the underlying data problems exponentially
3
Enterprise Exposure
Organizations face regulatory, ethical, and operational failures
These are not isolated incidents. They demonstrate a consistent truth: AI failure is often data product failure.
The Investment Paradox
Over the past decade, enterprises have modernized aggressively.
Cloud platforms such as Databricks, Snowflake, and Amazon Redshift have become foundational. Orchestration and integration layers like AWS Glue Studio are often central to rebuilding pipelines and flows. Governance tools such as Collibra have matured. Data catalogs are populated. Lineage diagrams exist. Policies are defined.
BARC's work on Data Mesh and Data Fabric shows that organizations increasingly understand the need for domain ownership and decentralized responsibility. TDWI reinforces the same theme: central governance alone cannot scale operationally without domain accountability. Thoughtworks has repeatedly advocated product thinking in modern platform delivery: modernization has to translate into measurable outcomes, not just new infrastructure.
The theory is widely accepted.
The challenge is operationalization.
Business teams still depend on engineering queues to shape and publish reusable data assets. Governance policies exist, but enforcement often relies on manual controls, downstream auditing, or periodic review cycles.
1
Infrastructure Modern
Organizations with updated cloud platforms
2
Governance Documented
Enterprises with formal policies defined
3
Products Operationalized
Business-owned data products at scale
Infrastructure is modern. Governance is documented. AI ambition is high.
But business-owned data products remain scarce.
And when data products are scarce, AI is forced to run on fragile foundations.
The Migration Trap: Modernizing Yesterday's Confusion
This gap becomes most visible during large migration programs.
Organizations commit to a platform decision, lock in a roadmap, and mobilize teams. Databricks is a great example. Once a business commits to a lakehouse migration, the pressure to move fast becomes immense. But the most underestimated part of the program is not the new platform itself.
It is the data and its history.
Legacy definitions, inconsistent metrics, unclear ownership, undocumented transformations, inherited access patterns, and "everyone knows what this means" logic gets carried forward. If those issues are migrated into Databricks, they don't disappear.
They scale.
This visual represents the most expensive mistake in transformation: modernizing confusion instead of resolving it.
The same risk applies whether the target is Snowflake, Redshift, or another cloud data environment. And when orchestration layers like Glue Studio are used to rebuild pipelines quickly, the temptation is to translate existing complexity into a new technical format rather than resolve the underlying ambiguity.
Modern infrastructure does not resolve historical governance gaps. If unresolved confusion is migrated, it becomes modernized confusion.
That is one of the most expensive mistakes in transformation: spending millions to accelerate the same problems into a new platform, with AI now ready to amplify the consequences.
The Business Buy-In Problem: Why Transformations Lose Momentum
There is another failure mode that gets far less attention.
Business disengagement.
Many transformation programs turn into long periods where the business is told, "Once the platform is ready, you'll see the benefits." Meanwhile months pass while pipelines are rebuilt, models are redesigned, catalog fields are populated, and governance frameworks are negotiated.
The business experiences delay, not progress.
1
Month 1-3
Initial excitement. Business teams engaged and hopeful about transformation outcomes.
2
Month 4-8
Waiting period. Technical work continues but business sees no tangible value.
3
Month 9-12
Trust erosion. Shadow systems reappear. Confidence declines.
4
Month 12+
Disengagement. Business tolerates the program rather than champions it.
When business teams do not get early value, they lose confidence. Shadow spreadsheets reappear. Local extracts multiply. Definitions drift. Trust declines. And the transformation becomes an IT program that the business tolerates rather than a business program that leaders champion.
This is where Latttice changes the dynamic.
Why Latttice Exists
Latttice was not created to replace infrastructure.
It was created because infrastructure alone was not solving the activation gap.
Cameron Price, our Founder and CEO at Data Tiles, created Latttice after decades of delivering transformation programs across consulting, enterprise, and cloud environments. The recurring pattern was consistent:
Business domains defined their needs. Engineering assumed delivery. Complexity increased. Timelines expanded. Trust lagged.
The Core Problem
The issue was not engineering capability. It was ownership dislocation.
Business domains were separated from the process of productizing their own data. Governance existed, but it wasn't activated where the work actually happens. Over time, this created a structural bottleneck that no amount of infrastructure spend could resolve.
Latttice was designed to restore that balance.
Latttice is the Data Product Workbench.
It activates governed data as reusable, business-owned data products.
It does not replace.
It does not replace governance platforms. It does not replace cloud platforms. It does not replace BI tools.
It activates them.
Latttice brings together what you already have and makes it operational.
How Latttice Works
Latttice doesn't replace your tools. It activates them.
How It Works in Practice
- Connects directly to your existing data within your tools of choice (Databricks, Snowflake, Collibra, etc.)
- Business users design data products in plain language through the workbench.
- Governance rules and policies from your governance platforms are embedded automatically as products are created.
- Lineage, ownership, and accountability are tracked from the start.
- Works whether you're activating governance on existing systems or stabilizing during transformation.
- The result: trusted, reusable data products ready for AI and analytics without replacing your infrastructure.
Think of Latttice as the bridge that turns your existing investments into business-ready data products.
What Activation Actually Means
Activation means governance becomes operational at the point of data product creation.
Instead of relying on static catalog entries and downstream auditing alone, Latttice enables business domains to operationalize governed data products as part of day to day work. That includes productizing datasets in plain language, embedding governance rules as the product is created, and making lineage and accountability visible early, not after an incident.
Activation is how governance stops being a document and becomes a living control.
It also supports the practical requirement consistently emphasized in Data Mesh thinking and implementation guidance, including what Mike Ferguson describes as the need for productized data owned by domains, governed consistently, and made reusable across the enterprise.
Netflix's public discussions of domain-driven experimentation culture illustrate this principle in action: their data maturity did not emerge from infrastructure alone. It emerged from reusable, domain-owned assets that teams could trust and act on.
Latttice enables that outcome without requiring Netflix-scale engineering budgets.
Before, During, or After Transformation: The Role Does Not Change
This is where clarity matters most, because confusion kills adoption.
Is Latttice for migration? For AI? For governance? For transformation?
The answer is consistent.
Latttice is the activation layer.
01
Before Transformation
Productize and govern what you already have before migration accelerates.
02
During Transformation
Stabilize the business experience while technical teams evolve infrastructure.
03
After Transformation
Continuously activate cloud and governance investments with business-owned products.
Before Transformation
Before a migration to Databricks, Snowflake, Redshift, or similar platforms accelerates, Latttice can be used to productize and govern what you already have.
This is where it is most cost effective.
It helps prevent the most expensive mistake in modern transformation: modernizing yesterday's confusion. Instead of carrying forward ambiguous definitions, unclear ownership, and historical governance gaps, business domains can create trusted data products early and take ownership of them before they become the "new system's problem."
Traditional Approach
- Wait for platform completion
- Business experiences long delays
- Legacy problems migrate forward
- Trust erodes during waiting period
- Shadow systems proliferate
Latttice Approach
- Activate governed products immediately
- Business gets early satisfaction
- Clean, owned data from the start
- Confidence builds throughout journey
- Single source of truth maintained
Just as importantly, the business gets immediate satisfaction. They see progress early. They can participate. They can validate. They can trust.
During Transformation
During migration and rebuild, Latttice acts as a stabilization layer.
While technical teams evolve infrastructure, business domains can continue activating governed data products that are usable now. That reduces trust lag between platform completion and business adoption, and it reduces the temptation for shadow data to fill the vacuum.
It keeps transformation from becoming a waiting game.
This visual reinforces a critical insight: Latttice does not disappear at any stage. Its role remains constant throughout the entire transformation journey.
After Transformation
Even after modernization, infrastructure does not guarantee productization.
Many organizations end up with a modern platform and the same old bottleneck: only engineers can publish reusable assets, and governance remains partially passive.
Latttice continues to activate your cloud and governance investments by enabling business owned data products to be created, governed, reused, and improved continuously.
Before transformation
During transformation
After transformation
The role does not change. Activation.
Why This Matters Now
BARC research shows that many organizations attempting Data Mesh struggle with execution complexity and unclear ownership.
McKinsey continues to report that only a fraction of data transformation initiatives deliver sustained business value at scale.
IDC forecasts increasing scrutiny around AI explainability and governance controls.
The message is clear.
AI acceleration without productized governance maturity increases risk exposure.
Organizations that rush AI adoption without addressing data product foundations expose themselves to amplified failures.
Transformation requires sustained business confidence.
Long delays without visible progress erode trust and create shadow systems that undermine governance.
Infrastructure alone does not create data products.
Modern platforms are necessary but insufficient. Business domains need the ability to activate governed products themselves.
Latttice exists to stabilize that acceleration and to keep business confidence alive throughout transformation, not just at the end. It completes the structural layer between governance definition and business consumption.
It activates what you already have.
AI is not waiting. And your data products cannot either.
Join a Data Conversation,
Lili Marsh.
References
BARC (2024). Data Mesh and Data Fabric Survey.
Deloitte (2023). Data Governance in the Age of Generative AI.
Forrester (2023–2024). Research and commentary on AI governance and enterprise accountability.
Gartner (2022–2024). Research themes across AI governance, active metadata, data fabric, and data product thinking.
IDC (2023–2024). FutureScape: Worldwide Artificial Intelligence and Automation Predictions.
Intelligent Business Strategies – Mike Ferguson (2022–2024). Practical guidance and commentary on Data Mesh and data products.
McKinsey & Company (2022–2024). Research on next generation data architecture and data product operating models.
Obermeyer, Z. et al. (2019). "Dissecting racial bias in healthcare algorithms." Science.
References Continued
TDWI (2024). Data Mesh 101 and Domain Ownership Models.
Thoughtworks (2023–2024). Technology Radar and platform modernization insights related to product thinking and governance.
IBM (2020). Public statement and reporting regarding withdrawal from general-purpose facial recognition systems.
Wells Fargo (2020–2023). Public regulatory enforcement actions and reporting tied to governance and oversight failures.
Netflix (various). Public engineering discussions on domain-driven data architecture and experimentation culture.
Databricks, Snowflake, Amazon Redshift, AWS Glue Studio, Collibra (various). Public product and architectural materials referenced as commonly adopted platforms in enterprise transformation programs.