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AI Agents Will Not Save Your Data Strategy
And Governance Alone Will Not Either
Executive Summary
A Clear Perspective on Data Strategy
AI agents are powerful tools, but they do not fix unusable data. Governance defines policy, but it does not activate data for business use. The missing layer is operational data activation, the mechanism that turns governed assets into decision-ready products that business teams can actually consume.
Despite years of data programs, cloud migrations, and governance initiatives, most organizations still struggle with a fundamental challenge: their people cannot access, understand, or trust the data they need to make decisions. Gartner research consistently highlights that many AI initiatives struggle to scale due to weak data foundations. McKinsey analysis in "How to Unlock the Full Value of Data? Manage It Like a Product" demonstrates that sustained business value requires alignment between data, ownership, and decision making. Harvard Business Review research in "If Your Data Is Bad, Your Machine Learning Tools Are Useless" shows that AI initiatives fail when foundational data quality and accessibility are not addressed.
This article explains why governance alone does not deliver business value, why the AI Agent narrative is premature without usable data, and why enabling operational access through business-owned data products is the real foundation for sustainable AI adoption, particularly in regulated environments and large transformation programs.
Key Insights
  • Governance defines rules but does not create usable assets
  • AI Agents cannot fix broken data foundations
  • Access to trusted data remains the critical missing piece
  • Business owned data products bridge the execution gap
The Foundation: Access, Trust, and Clarity
Organizations need more than technology. They need a clear path from governed data to business value. This requires understanding the relationship between governance frameworks, data access mechanisms, and the business teams who depend on reliable information to make critical decisions.
Governance
Defines policies, rules, and controls
Access Layer
Activates governed data into usable products
Business Value
Enables confident, data driven decisions
The Relationship Between AI, Governance, and Data Access
The success of AI initiatives depends on a foundation of governed, accessible data. Without the middle layer that translates governance into business ready data products, organizations remain stuck between policy and value. This visualization demonstrates why all three elements must work together, with data access serving as the critical activation layer.
Do We Actually Understand Our Data?
This is the question we rarely stop to ask.
After decades of platform upgrades, cloud migrations, and large consulting engagements, many organizations are still no closer to answering basic business questions without delay or dependency. Data has moved. Spend has increased. But for many business teams, operational usability has not improved in a meaningful way.
What is most concerning is that this has been normalized. Complexity is accepted as maturity. Limited access is framed as necessary governance. And being data driven becomes something organizations discuss in strategy documents, rather than something they experience in daily operations.
Research consistently shows that while organizations continue to invest heavily in data and AI, only a small proportion achieve sustained business value, largely due to misalignment between data, ownership, and decision making. Moving data, it turns out, is not the same as enabling its use. (McKinsey, 2023)
Reality Check: What the Evidence Shows
Industry Research Findings
The current enthusiasm for AI Agents is colliding with an unresolved access problem that organizations have struggled with for years.
Industry research shows that many agent based AI initiatives are expected to be abandoned, not because the technology lacks promise, but because organizations are attempting to automate on top of weak data foundations and unclear business outcomes.
Industry studies consistently demonstrate that the majority of AI proof-of-concepts do not scale into production environments, with weak data foundations identified as among the leading causes of program failure. McKinsey research in "The State of AI in 2023: Generative AI's Breakout Year" indicates that while organizations are rapidly adopting AI, only a small proportion achieve meaningful business impact. Harvard Business Review analysis shows that AI initiatives fail to scale when organizations lack proper data quality, ownership structures, and integration into core business processes. The pattern is clear: AI cannot compensate for unusable data.
Agent Initiatives
Many expected to be abandoned due to weak data foundations
AI Scaling Failures
Root cause: accessibility, trust, and ownership issues
The Challenge
Intelligence alone cannot overcome structural data problems

These findings challenge the assumption that intelligence alone can overcome structural data problems. It cannot.
Automation Already Exists: AI Is Not Needed Everywhere
Another part of the AI Agent narrative deserves scrutiny. Most enterprises already operate extensive automation across finance, operations, supply chain, and customer workflows. Rules based systems, workflow engines, and analytics platforms have delivered value for years.
Existing Automation
Rules based systems and workflow engines delivering proven value
Historical Gains
Largest productivity gains occurred before generative AI
AI Enhancement
AI delivers value when layered onto well understood processes and trusted data
Research shows that AI delivers value when layered onto well understood processes and trusted data, not when positioned as a wholesale replacement. The idea that AI Agents must now replace everything is not only unnecessary, it is misleading. In many cases, the real issue was never automation failure. It was that people could not access, understand, or trust the data feeding those processes.
Why Governance Alone Still Leaves Organizations Stuck
Governance: The Foundation
Governance is essential. It defines rules, protects risk posture, and establishes accountability. Many organizations believe that once data is cataloged, classified, and access policies are defined, they have solved their data challenge. In reality, they have defined the framework. They have not created consumable business assets.
The Missing Execution Layer
Governance tells us what data is, who can access it, and under what conditions. What it does not do is turn that data into decision-ready products that business teams can use confidently. This is the execution gap. Gartner research notes that governance frameworks must be paired with activation mechanisms to deliver business value. Without activation, governance can become documentation rather than execution.
What governance provides: Rules about what data is, who can access it, and under what conditions
What governance does not provide: Trusted, consumable assets that business teams can use confidently to make decisions
The Result
This is why so many organizations appear well governed on paper, yet still rely on tickets, extracts, and shadow spreadsheets in practice. Governance was never meant to be the destination. It was meant to be the foundation.
Without an execution layer that activates governed data into business-owned data products, governance alone cannot deliver value to people or to AI.
Why AI Agents Amplify What Already Exists
Into this unresolved challenge comes the AI Agent wave, promising autonomous operation and business transformation. However, Gartner commentary on agent maturity cycles reveals that many agentic AI projects are early stage experiments, with organizations often underestimating the operational readiness required for successful deployment. The fundamental issue is this: AI agents amplify what already exists. They increase speed, not clarity.
01
Business Teams Lack Foundation
If teams do not clearly know what data they can access, what it means, or how trustworthy it is, agents cannot operate safely
02
Agents Require Stable Foundations
AI agents need clear data definitions, trusted sources, and established governance to function effectively
03
Agents Cannot Fix Foundations
AI agents do not resolve ambiguity, invent meaning, or repair broken data architectures
04
They Amplify Existing Weaknesses
Agents accelerate whatever already exists—including gaps, inconsistencies, and access barriers
If business teams still struggle with basic data access and trust, how can AI Agents be expected to perform better?
The technology cannot compensate for structural problems in how organizations manage and provide access to their data assets.
Solution
Latttice: The Missing Access and Activation Layer
This is where the conversation needs to shift. Latttice was built to solve the problem that decades of data programs have not: how business teams actually access and use trusted data.
Access Data Where It Resides
Access data where it already resides without moving or duplicating it, reducing complexity and risk
Create Business Data Products
Turn governed data into business owned, AI ready data products that teams can confidently use
Embed Governance by Design
Embed governance by design rather than as an afterthought, ensuring compliance from the start
Put Ownership with Business
Put ownership with the people who understand the data and rely on it every day
The Data Product Workbench
Rather than replacing existing investments, Latttice acts as the data product workbench, the execution layer where governed data becomes usable. This approach preserves your current governance investments while finally delivering what has been missing: operational activation.
Why Data Products Change Everything
Data products introduce ownership. Business teams take responsibility for the data they create and maintain, rather than treating it as someone else's problem.
Data products introduce measurable accountability. When data is packaged as a product, quality, timeliness, and usability become trackable metrics rather than abstract goals.
Data products make governance executable. Policies move from documentation into runtime enforcement, embedded directly into how data is accessed and consumed.
Data products enable AI readiness. When data is trusted, accessible, and well-defined, AI systems can consume it safely and effectively.
This is the turning point. This is where governance becomes operational. This is where strategy becomes execution.
What This Means for Regulated Industries
For regulated industries, the stakes are substantially higher. Organizations in banking, insurance, healthcare, energy, and government cannot afford uncontrolled data access, undocumented transformations, or opaque AI decision making. Gartner and Forrester research consistently identifies premature AI deployment in regulated environments as a significant risk factor when foundational data governance and access controls are not properly established.
Yet many regulated organizations face the same paradox: data is heavily governed, but still difficult to use. Compliance teams enforce restrictions. Business teams work around them. Value remains trapped.
Auditability
Every data access and transformation must be traceable for regulatory review
Runtime Governance Enforcement
Policies must be enforced at the point of use, not just documented
Lineage
Complete visibility into data origin, transformation, and consumption
Controlled Access Without Duplication
Data must remain in place while access is governed and monitored
Latttice enables governed access without increasing risk, allowing regulated organizations to move faster while maintaining trust, auditability, and control.
What This Means for Consulting-Led Transformations
Large transformation programs often focus on architecture, platforms, and operating models, but struggle to demonstrate how value reaches the business. Strategy documents describe future states. Architecture diagrams show data flows. But without an execution layer that turns strategy into consumable data products, transformation remains theoretical.
Harvard Business Review research demonstrates that transformation initiatives stall when data ownership, operating model alignment, and business process integration are treated as secondary concerns rather than foundational requirements. The gap between strategy and execution widens when business teams cannot directly consume the data they need.
The risk is that AI agents become another abstraction layer, positioned as the solution before the usability problem is solved. Business teams need to see progress through adoption, not through documentation.
1
Show Real Outcomes Early
Business teams can access and use data products within weeks, not quarters
2
Avoid Rip and Replace Disruption
Work with existing governance and infrastructure investments
3
Prove Progress Through Adoption
Measure value through business consumption, not just technical completion
This shifts the conversation from what will be built to what is being used.
Conclusion
The More Honest Conclusion
The Reality
AI Agents will play a role. Governance will remain essential. But neither will succeed if organizations continue to avoid the harder question: can your people actually access, understand, and trust the data they need?
The Question
Before asking what AI Agents can do, leaders need to ask whether their people can actually consume, understand, and trust the data they already have.
1
1
Accessible
Data that teams can reach without barriers
2
2
Owned
Business teams take responsibility for their data
3
3
Trusted
Confidence in data quality and lineage
4
4
Shaped into Products
Transformed into decision-ready assets
5
5
Foundation for AI
Enables effective AI deployment
This is not a technology problem. It is a business problem. And it starts with execution. Organizations that succeed in the AI era will not be those who adopt agents first. They will be those who make their data usable first.
Summary: The Core Message
Organizations face a critical choice. They can continue investing in AI Agents and governance frameworks separately, hoping that one or the other will finally unlock data driven decision making. Or they can acknowledge the missing piece: an access and activation layer that turns governed data into business ready products.
The evidence is clear. Years of platform investments have not solved the access problem. Governance initiatives have defined policies but not enabled use. And the rush toward AI Agents risks automating on top of broken foundations. What is needed is an execution layer that bridges governance and business value, enabling access without increasing risk, and activating data without requiring wholesale platform replacement.
The Familiar Narrative
Walk into nearly any executive briefing today, or sit through almost any consulting led strategy presentation, and you will hear a familiar story. AI Agents will automate decisions. Intelligence will scale itself. Complexity will finally disappear.
In many of these decks, AI Agents are positioned as the breakthrough that will fix what years of data investment could not. It is an attractive narrative, offering the promise of transformation without the hard work of addressing fundamental data access problems.
"AI Agents will automate our decision making processes end to end."
"Intelligence will scale across the organization without manual intervention."
"The complexity we have struggled with for years will finally disappear."
But this narrative avoids a more uncomfortable truth, most organizations still do not clearly understand their data, or what real access to it looks like.
Until that fundamental problem is solved, AI Agents cannot deliver on their promise.
The Question We Rarely Ask
After Decades of Investment
After decades of platform upgrades, cloud migrations, and large consulting engagements, many organizations are still no closer to answering basic business questions without delay or dependency.
Data has moved from one system to another. Spend has increased significantly. Infrastructure has been modernized. Yet for many business teams, access has not improved in a meaningful way.
Data is still hard to reach, requiring tickets and approval processes
Insights are still slow, taking weeks instead of minutes
Access is still mediated through intermediaries rather than enabled directly
What is most concerning is that this has been normalized. Complexity is accepted as maturity. Limited access is framed as necessary governance. And being data driven becomes something organizations talk about in strategy documents, rather than something they experience in daily operations.
Research Shows a Persistent Gap
Evidence consistently demonstrates that while organizations continue to invest heavily in data and AI, only a small proportion achieve sustained business value. The root cause is not technology failure, but misalignment between data, ownership, and decision making. (McKinsey, 2023; Harvard Business Review)
Achieving Value
Only a small proportion of organizations achieve sustained business value from data investments
Access Challenges
Majority of organizations struggle with basic data accessibility for business teams
Trust Issues
Business users lack confidence in the data they can access
Moving data from one platform to another, it turns out, is not the same as enabling its use. Organizations can have the most modern cloud infrastructure, the most sophisticated data lakes, and the most comprehensive catalogs, and still fail to put trusted data in the hands of the people who need it.
Industry Evidence on AI Agent Challenges
Industry research presents findings that should give pause to organizations rushing to implement agent based solutions. Gartner analysts have observed that many agentic AI initiatives face significant challenges, not because the technology lacks promise, but because organizations are attempting to automate on top of weak data foundations and unclear business outcomes.
Why Agent Initiatives Fail
Industry research shows that many agent based AI initiatives are expected to be abandoned in the coming years. This is not because the technology lacks promise or capability.
Rather, organizations are attempting to automate on top of weak data foundations and unclear business outcomes. The agents themselves may work perfectly well, but they cannot compensate for fundamental data access and trust problems.
The Scaling Problem
Studies repeatedly show that most AI initiatives fail to scale beyond pilot projects. Again, the issue is not that models are inadequate or that data science teams lack skill.
Organizations struggle with data accessibility, trust, and ownership at the foundational level. These structural problems prevent AI from scaling, regardless of how sophisticated the models become.
Gartner analysts have cautioned that some vendors are overstating agent capabilities in early-stage offerings, with many agentic AI projects remaining experimental rather than production-ready.

These findings challenge the assumption that intelligence alone can overcome structural data problems. It cannot. Intelligence amplifies existing capabilities, including existing weaknesses.
Automation Has Been Working for Years
Another part of the AI Agent narrative deserves scrutiny. Most enterprises already operate extensive automation across finance, operations, supply chain, and customer workflows. Rules-based systems, workflow engines, and analytics platforms have delivered value for years.
Rules Based Systems
Established automation handling structured processes reliably
Workflow Engines
Orchestrating complex multi step business processes
Analytics Platforms
Providing insights and supporting decision making
AI Enhancement
New layer on proven foundations where appropriate
MIT Sloan Management Review research in "Achieving Individual and Organizational Value with AI" and McKinsey analysis in "The State of AI in 2023" indicate that the largest productivity gains from automation occurred before generative AI, with AI delivering value when layered onto well-understood processes and trusted data, not when positioned as a wholesale replacement. Forrester research on enterprise AI adoption highlights the risks of inflated expectations when organizations introduce AI without operational readiness.
The idea that AI Agents must now replace everything is not only unnecessary. It is misleading.
In many cases, the real issue was never automation failure. It was that people could not consume, understand, or trust the data feeding those processes.
The Governance Paradox
Well Governed on Paper
Governance is essential, but it is often mistaken for progress. Gartner research consistently highlights that a significant proportion of AI initiatives fail to meet value expectations due to governance misalignment and fragmented execution.
In reality, they have only defined the rules and established the framework. They have not created something the business can act on or use to make decisions.
Stuck in Practice
This is why so many organizations appear well governed on paper, yet still rely on tickets, extracts, and shadow spreadsheets in practice. The governance layer exists, but the execution layer does not.
Business teams work around the official systems because those systems, while governed, do not provide the access they need to do their jobs effectively.
Governance Defines
What data is, who can access it, under what conditions
Missing Layer
Turns governed data into consumable business products
Business Teams Need
Trusted, accessible assets they can use confidently
Governance was never meant to be the destination. It was meant to be the foundation.
Without an execution layer that activates governed data into business owned data products, governance alone cannot deliver value to people or to AI systems.
The Premature Agent Wave
Into this unresolved execution gap comes the AI Agent wave, promising autonomous operation and business transformation. However, Gartner commentary on agent maturity cycles reveals that many agentic AI projects are early stage experiments, with organizations often underestimating the operational readiness and data foundations required for successful deployment. But there is a fundamental contradiction here that deserves careful examination.
1
Business Teams Lack Clarity
Teams still do not clearly know what data they can access, what it means, or how trustworthy it is
2
AI Agents Require Clarity
Yet AI Agents are expected to operate safely and effectively on behalf of these same teams
3
Agents Cannot Fix Foundations
AI Agents do not resolve ambiguity, invent meaning, or fix access problems by operating above them
4
They Amplify Weaknesses
They consume what they are given and accelerate whatever weaknesses already exist in the data layer
If business teams still do not clearly know what data they can access, what it means, or how trustworthy it is, how can AI Agents be expected to operate safely or effectively?
The answer is they cannot. And attempting to deploy them anyway creates risk rather than value.
Latttice: Solving the Access Problem
This is where the conversation needs to shift. Latttice was built to solve the operational challenge that decades of data programs have not: how business teams actually consume and use trusted data.
Latttice is the access layer. The activation layer. The data product workbench.
The Access Layer
Access data where it resides without moving or duplicating it. No rip and replace. No migration risk.
The Activation Layer
Turn governed data into business-owned, AI-ready data products. Governance defines policy. Latttice executes it.
The Governance Enabler
Embed governance by design rather than as an afterthought. Runtime enforcement, not just documentation.
The Ownership Model
Put ownership with the people who understand the data and rely on it every day. Business teams, not just IT.
Latttice works naturally alongside governance platforms such as Collibra. Governance defines what data is and who can access it. Latttice activates it, turning governed assets into consumable products that business teams and AI systems can actually use.
Working Alongside Governance Platforms
This is why Latttice works naturally alongside governance platforms such as Collibra. The relationship is complementary, not competitive. Each plays a distinct and necessary role in the data value chain.
Governance Platforms Define:
  • What data exists and what it means
  • Who should have access and under what conditions
  • Policies, classifications, and compliance requirements
Latttice Activates:
  • Turns governed data into consumable business products
  • Enables access without data movement or duplication
  • Enforces governance at runtime, not just in documentation
  • Puts ownership with business teams who understand context
Together, they create the complete picture: governance that defines trust, and access that delivers value. One without the other leaves organizations stuck between policy and progress.
Critical for Regulated Industries
For regulated industries, the stakes are substantially higher. These organizations cannot afford uncontrolled data access, undocumented transformations, or opaque AI decision making. Compliance, auditability, and explainability are mandatory requirements enforced by regulators.
Regulatory Compliance
Meet all regulatory requirements without exception while enabling business agility
Complete Auditability
Maintain full data lineage and access trails for regulatory review and internal governance
Explainability
Ensure complete transparency in data transformations and AI decision making processes
Controlled Access
Enable safe, governed data use within strict compliance boundaries and security policies
Yet many regulated organizations face the same paradox: data is heavily governed, but still difficult to use.
Latttice enables governed access without increasing risk, allowing regulated organizations to move faster while maintaining the trust and control that regulators demand.
Let's Talk About Your Data Strategy
The challenges discussed in this article are not theoretical. They are real obstacles that organizations face every day as they try to become more data driven and leverage AI effectively.
If you recognize these challenges in your own organization, or if you are responsible for data and AI adoption and want to explore how to bridge the gap between governance and business value, we invite you to join the conversation.
For Business Leaders
Understand how to translate data investments into tangible business outcomes
For Data Officers
Explore practical approaches to activating governed data for business teams
For Technology Leaders
Discover how to bridge governance and access without replacing existing platforms
Connect with Jessie Moelzer
Reach out to discuss your data strategy challenges and explore how Latttice can help your organization.
Whether you are navigating a large transformation program, working to demonstrate value from governance investments, or preparing for AI adoption, the conversation starts with understanding your current state and identifying the specific gaps between governance and business use.
The Path Forward: Execution Enables Everything
The path forward is clear, even if it requires confronting uncomfortable truths about current data strategies. AI Agents will play a valuable role in the future of business operations. Governance will remain essential. But neither will succeed if organizations continue to avoid the harder question of data activation.
First Question
Can your people actually access, understand, and trust the data they already have?
Then Ask
What AI Agents can realistically accomplish on top of that foundation?
Finally Build
The access layer that turns governed data into business ready products
Because AI does not fix broken data strategies. It exposes them. The real breakthrough comes when data becomes accessible, owned, trusted, and shaped into products designed for decisions. That is the foundation AI needs, and it starts with access.
This is not a technology problem waiting for a technology solution. It is a business problem that requires business thinking, supported by technology that enables rather than constrains. The organizations that understand this distinction will be the ones that successfully bridge the gap between data investment and business value.
Join a Data Conversation,
Jessie Moelzer.
References
Gartner
  • Gartner. (2023). Top Strategic Technology Trends for 2023.
  • Gartner research and analyst commentary on AI readiness, governance foundations, and AI project value realization.
McKinsey & Company
  • McKinsey & Company. (2023). The State of AI in 2023: Generative AI's Breakout Year.
  • McKinsey & Company. (2022). How to Unlock the Full Value of Data? Manage It Like a Product.
Harvard Business Review
  • Redman, T. (2018). If Your Data Is Bad, Your Machine Learning Tools Are Useless. Harvard Business Review.
  • Harvard Business Review. Articles on AI scaling challenges, data ownership alignment, and operating model readiness.
MIT Sloan Management Review and Boston Consulting Group
  • MIT Sloan Management Review & Boston Consulting Group. (2023). Achieving Individual and Organizational Value with AI.
Forrester Research
  • Forrester. (2023–2024). Research on enterprise AI adoption, automation expectations, and data maturity challenges.
Penn Wharton Budget Model
  • Penn Wharton Budget Model. (2023). The Economic Effects of Generative AI.