THE FOUNDATION TO INNOVATION

A Strategic Guide to 
Data Readiness for AI

INTRODUCTION

The Readiness Gap

AI value depends on more than models, platforms and experimentation. It depends on data that’s connected, governed, secure and ready to move across the business.

Many enterprises believe they’re closer to AI readiness than they are — a major oversight. Leadership often benchmarks enterprise against its strongest department, while weaker functions remain hidden. This great divide between perception and reality leads to misalignment and leave AI projects stuck before they can scale.

95%

of AI projects fail to make it to production

consider their data completely ready for AI

92%

of data and AI leaders cite people and organizational change as the primary barrier

AI readiness isn’t a technology checkpoint. It’s a business capability. We set out to find where that gap begins and what it takes to close it.

Why Enterprises Overestimate Data Readiness

Most enterprises don’t lack ambition. But what they lack is a clear view of where readiness breaks down. There are many cautionary tales of companies launching initiatives without the structure needed to execute their vision effectively.

A strong data practice in one department can hide deeper issues across the enterprise. When leaders assume that maturity is consistent everywhere, AI initiatives start with the wrong scope, the wrong funding and the wrong operating model.

This slew of miscalculations leaves companies facing a hard truth: projects exceed budgets, miss targets, delay launches or require outside support that wasn’t originally planned.

The fix starts with a clear view of maturity across technology, data quality, structure, governance and security, aligning accountability and resources from the outset, to ensure AI initiatives can scale successfully.

WHAT MISALIGNMENT CREATES

Underfunded AI initiatives
Conflicting ownership across teams
Data quality gaps that surface too late
Delayed product launches
Higher costs from rework and outsourcing

The Five Roadblocks

Data readiness depends on maturity across five categories. Strength in one area can’t compensate for weakness in another. And weakness in one can crack the whole foundation. The way forward starts with an honest appraisal of your current state across technology, data quality, data structure, governance and security.

Technology

Data Quality

Data Structure

Governance

Security

Technology

Data Quality

Data Structure

Governance

Security

Aligning Technology to Strategy

Organizations routinely confuse a data strategy with a technology strategy, focusing on vendor and platform selection rather than the business problems that data should solve.

Technology sprawl creates both financial and operational drag: Enterprises waste 36% of SaaS licenses, or $19.8 million a year, while managing an average of 897 applications, only 28% of which are interoperable. At the same time, siloed teams and fragmented platforms create conflicting data structures, definitions and answers across the business. The root issue is ownership: AI is a business initiative, and its value comes from better decisions, redesigned processes and new ways of working, not from technology teams operating in isolation.

WHAT WORKS

Data strategy must be business-led, not distributed across different functions optimizing for their own needs. In every case, data discipline reflects how seriously enterprises treat data as a strategic question, rather than a technical one.

36%

annual enterprise SaaS license waste

$19.8M

average yearly cost of enterprise SaaS license waste

Re-Examining Data Processes

Poor data quality is typically framed as a technical problem of duplication, missing values and outdated records. But it’s fundamentally a human and process problem.

Poor data quality often starts with process friction. When clean data entry is slow, repetitive or unclear, teams take shortcuts. Everyday biases compound the problem. These behaviors distort the data, leading to flawed models and AI systems that confidently deliver the wrong answers. The deeper issue isn’t a lack of data, but the lack of a coherent data acquisition strategy that makes accurate data easier to capture and trust.

WHAT WORKS

A strong data acquisition and quality strategy ensures employees are incentivized to prioritize quality; that systems are intuitive enough to make clean entry effortless; and that data readiness is treated as a strategic goal rather than a process hurdle.

$5M

annual cost of poor data quality, per 25% of companies

92%

identify people and organizational change as the primary barrier

Assimilating Unstructured Data

Most enterprises hold vast amounts of unstructured data. AI cannot process this data in its raw form and classification and tagging remain top obstacles.

A common example of unstructured data that is being underutilized in business is performance feedback, exit interviews and grievance records. These materials are rich in detail but lack standardization, requiring human input to prepare them for AI use. The challenge goes beyond extraction: organizations need a shared, accurate view across departments so agents can work effectively. That takes more than technology.

WHAT WORKS

Organizations make progress by combining process redesign, targeting tools and human expertise to address gaps where automation falls short. That combination is not a workaround; it is the only approach that works at scale.

74%

of enterprises store more than five petabytes of unstructured data

62%

of organizations cite skill gaps in AI data management as a barrier

Defining Leadership Requirements

Governance is essential, but gaps in regulation, execution and automation continue to slow scalability and compliance.

This gap leaves companies vulnerable to non-compliance with laws like the EU AI Act, which imposes penalties of up to €35 million or 7% of global annual turnover. Unclear oversight and weak procedures only increase that risk. Adding roles like chief data officer or chief AI officer doesn’t solve the problem if authority remains fragmented, mandates stay unclear and efforts overlap. Without clear leadership, departments default to siloed priorities.

WHAT WORKS

Technology decisions should be made on strategic benefit, not cost. Likewise, data quality decisions shouldn’t depend on available resources, management pressure or delivering a false impression of competency.

86%

of organizations are aware of existing AI regulations

25%

of organizations have fully implemented governance programs

Redefining Security for the AI Era

Boards often ask the wrong question about data security. Instead of “Are we secure?” the focus should be on exposure — how it continuously evolves and how it’s managed. As systems grow, staff turnover occurs, and AI tools multiply, exposure becomes a moving target.

Exposure operates on multiple levels: systems, people and the space where both overlap. No database is fully secure, and even the largest organizations with extensive security infrastructure remain vulnerable. For example, one global company faced a breach affecting 200,000 devices across 79 countries, costing billions and exposing sensitive data. The issues run deeper than infrastructure. “Shadow AI” — where employees use unsanctioned tools to fill gaps — further compounds the risk.

WHAT WORKS

AI tools will only become more common and, for many roles, required. Trying to fully cut off access is short-sighted and insufficient. The better response is urgency: provide secure alternatives with clear policy before the gap fills itself.

20%

organizations reported a breach due to shadow AI

97%

of AI-breach victims lacked proper AI access controls

Four Actionable Strategies to Build AI Readiness

The challenges to data readiness rarely stay contained, as the gap in one department creates drag across others. However, the same is true of the fixes: Organizations that resolve even a single roadblock tend to find that the impact is felt beyond the original problem.

The four strategies that follow address the underlying causes of the above roadblocks, primarily around leadership, governance and accountability. Applying them consistently will close multiple gaps simultaneously.

Start narrow. Prove value. Scale what works.

Companies that try to mandate readiness everywhere at once often struggle to gain traction. Focused use cases create a blueprint for scaling and driving broader adoption organically.

Define Business Goals Before Technology Needs

The starting point for any data initiative should be a clear, specific business question: What problem needs solving, and what would it take to answer it reliably?

Start with a problem narrow enough to solve and important enough to matter, then map only the data sources, ownership and architecture needed for that scope. Trying to map the entire enterprise before acting often leads to failure, especially when leaders misdefine the problem or move before the business is ready. Success depends on sequencing people, process, governance and data before technology. One global consumer goods company proved the point by laying the groundwork first, then using interoperable agentic solutions to personalize marketing and customer service in real time, driving a 20% increase in retention.

PRACTICAL ACTIONS

Choose a high-value, solvable business problem
Map only the data sources needed for that scope
Assign clear ownership
Build the architecture for that use case first

Give Governance Freedom, Not Restriction

Governance works best when it enables safe action. Senior leaders must set the mandate, define ownership and make policies easy to follow through the architecture.

AI adoption stalls without a clear mandate from senior leadership. A CDO title means little without real authority, so governance strategy must come from the top and clearly explain what the organization is doing and why. Before teams build AI systems, they need to resolve data ownership, usage rights and access rules, then embed those policies into the architecture so compliance becomes automatic. The goal isn’t to restrict responsible use but to enable it: in our experience, one organization reduced shadow AI use by 60% by pairing a centralized AI platform with secure alternatives employees could use.

PRACTICAL ACTIONS

Secure visible support from senior leadership
Clarify data ownership before AI systems are built
Embed usage policies into workflows
Give teams approved tools they can actually use

Improve Data Quality Processes

Data quality improves when systems fit how people work. Treat clean data as a process and user experience challenge, not just a technical cleanup effort.

Many organizations know data matters but lack a clear vision for what good looks like or how to achieve it. Success depends on role-specific data skills, alignment with business goals and durable structures that make data literacy stick. Role-based education works better than generic training because it ties behavior change to daily work, while clear data ownership and a center of excellence help sustain standards over time. Automation can support quality at scale, but it works best when upstream processes produce clean data from the start. Another trend we saw was one organization underestimated legacy integration costs and saw a threefold increase in its data management budget before centralizing its approach.

PRACTICAL ACTIONS

Design data entry around daily workflows
Build role-specific data skills
Make quality a measurable responsibility
Create a center of excellence to maintain standards

Build Security by Design

Building security into data infrastructure from the start is essential so that it adapts as access, tools and risk change. And monitor these guardrails continuously.

AI-driven threats require engineered security platforms, not checklist governance. Data now moves across more people, tools and automated processes than legacy models were built to protect, making periodic review too slow for the risk. A zero-trust model fits this reality because it assumes breaches will happen and continuously verifies every user, system and agent instead of relying on a perimeter. Governance guardrails still matter, but in AI environments they must evolve through real-time monitoring, continuous model curation and end-to-end evaluation so organizations can move faster and safer within clear boundaries.

PRACTICAL ACTIONS

Adopt a zero-trust model
Continuously verify users, systems and agents
Monitor model drift and data access in real time
Give employees secure AI alternatives

What Data Readiness Makes Possible

When enterprises treat data readiness as a business capability, AI can move from isolated experimentation to measurable impact.

Scaled Data Literacy Across Regeneron Pharmaceuticals

Improved Data Access through a Modern Foundation for Zalando

Accelerated Enterprise Value and Secure AI Adoption for Altera

Faster Risk Assessment with Governance Guardrails for a Chemical Distributor

Build the Data Foundation that AI Needs to Scale

AI readiness starts with data readiness. The instinct to buy a solution for the data readiness gap often adds complexity without addressing the root problems. Platforms matter, but they are secondary to decisions about ownership, governance, process and security.

Enterprises can overcome the great readiness divide by addressing these five roadblocks with the four actionable strategies.

Those who succeed treat data readiness as a strategic business challenge. They prioritize the people working with data as much as the systems that store it.

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