Artificial intelligence is rapidly transforming how businesses operate. From automating workflows and enhancing customer experiences to accelerating software development and modernizing legacy systems, AI has become a strategic priority for organizations worldwide. Yet despite significant investments in AI technologies, many enterprises continue to struggle when moving from experimentation to production.
One of the biggest reasons AI initiatives fail is not the technology itself—it is the data foundation supporting it.
According to Niels Zeilemaker, Global Chief Technology Officer at Xebia, organizations often focus heavily on building intelligent AI agents while overlooking the infrastructure those agents depend upon. As businesses rush to adopt agentic AI systems, many discover that even the most advanced models cannot perform effectively when the underlying data ecosystem is fragmented, poorly documented, or inaccessible.
The reality is simple: AI agents are only as effective as the data they can access and understand.
Without a strong foundation, AI systems may misinterpret information, connect unrelated datasets, generate inaccurate conclusions, or fail to deliver meaningful business outcomes. These problems are often blamed on the AI itself, but the root cause frequently lies elsewhere.
For organizations seeking to implement AI successfully, the first step is not selecting a model or building an agent. It is ensuring that enterprise data is structured, governed, accessible, and optimized for AI consumption.
The Growing Importance of Agentic AI
The rise of agentic AI represents a major shift in enterprise technology.
Traditional AI systems typically perform specific tasks based on predefined instructions. Agentic AI goes further by enabling systems to make decisions, execute actions, collaborate across workflows, and operate with a higher degree of autonomy.
These intelligent agents can:
- Analyze business data
- Automate operational processes
- Support software development
- Improve customer interactions
- Accelerate decision-making
- Assist with enterprise transformation initiatives
However, agentic AI introduces new requirements.
Unlike human employees who can compensate for missing information through experience, conversations, or intuition, AI agents rely entirely on the data available to them.
This makes data quality more critical than ever before.
Why Great AI Agents Still Fail
Many organizations assume that deploying a powerful large language model automatically guarantees success.
According to Zeilemaker, this assumption is flawed.
A company may build what appears to be a sophisticated AI agent, yet still experience poor results because the agent lacks access to accurate and reliable information.
Common issues include:
- Incomplete datasets
- Poorly structured records
- Missing metadata
- Inconsistent documentation
- Fragmented information systems
- Conflicting data sources
When these problems exist, AI agents struggle to understand context.
They may connect fields that should never be connected, misinterpret business rules, or provide inaccurate recommendations.
Importantly, these failures are not always the fault of the AI model.
Instead, they reflect weaknesses within the organization’s data architecture.
If the foundation is not prepared for AI, even the most advanced agent will struggle to deliver value.
Data Catalogues Become Essential for AI Success
One area that deserves special attention is data cataloguing.
While data catalogues have existed for years, their importance increases dramatically in agentic AI environments.
In traditional business settings, humans can often overcome documentation gaps.
For example, if an employee encounters unclear data, they can:
- Contact a colleague
- Ask a manager
- Consult internal experts
- Review historical knowledge
Humans have access to informal communication channels that help fill information gaps.
AI agents do not.
An AI agent depends entirely on what is documented and available within the system.
If a data catalogue contains incorrect descriptions, incomplete definitions, or outdated information, the AI will likely make incorrect assumptions.
This can lead to:
- Faulty analysis
- Incorrect decisions
- Poor customer experiences
- Operational inefficiencies
As agentic AI adoption grows, maintaining comprehensive and accurate data catalogues becomes a critical business requirement rather than a best practice.
AI Agents Need Clear Context
Context is one of the most valuable assets in artificial intelligence.
Human employees naturally understand organizational structures, business processes, and industry-specific terminology.
AI systems require this context to be explicitly documented.
For example, an agent analyzing customer data must understand:
- Field definitions
- Data relationships
- Business rules
- Compliance requirements
- Operational constraints
Without context, the AI may interpret information incorrectly.
This creates significant risks, particularly in industries where accuracy is essential.
Organizations preparing for AI transformation must ensure that contextual information is embedded throughout their data ecosystem.
Xebia’s Approach to AI Transformation
Xebia has positioned itself as a company focused on helping organizations move beyond AI strategy and into production-ready implementation.
The organization’s mission centers on turning AI ambitions into measurable business outcomes.
According to Zeilemaker, one of Xebia’s defining characteristics is its commitment to knowledge sharing.
The company actively participates in industry events and technology communities to exchange insights and stay ahead of emerging trends.
This culture of continuous learning allows Xebia to adapt quickly to changing market conditions and identify areas where it can establish leadership.
Among those areas, data and AI have become particularly important.
By combining deep technical expertise with practical implementation experience, Xebia helps organizations navigate the complexities of AI adoption while avoiding common pitfalls.
Building an AI-Ready Data Foundation
At industry events such as AI & Big Data Expo, Xebia has emphasized the importance of establishing a strong AI foundation before deploying advanced AI systems.
Many enterprises face challenges caused by fragmented data environments.
Over time, organizations often accumulate multiple databases, legacy systems, cloud platforms, and departmental data repositories.
These disconnected systems create barriers for AI initiatives.
Before AI agents can deliver value, businesses must unify and modernize their data landscapes.
This involves:
- Consolidating data sources
- Improving governance
- Enhancing metadata management
- Standardizing documentation
- Establishing data quality controls
A strong foundation enables AI agents to access the information they need while reducing the risk of errors.
Introducing the Agentic Data Foundation (ADF)
To address these challenges, Xebia developed what it calls the Agentic Data Foundation (ADF).
The framework extends traditional data platforms by creating an environment specifically designed to support AI agents.
Rather than treating AI as a separate layer, ADF integrates intelligent agents directly into the data ecosystem.
This enables organizations to deploy agents for:
Customer-Facing Applications
AI agents can improve customer experiences by:
- Providing personalized recommendations
- Supporting self-service interactions
- Automating support processes
- Enhancing engagement
Internal Business Operations
Within the enterprise, agents can:
- Automate workflows
- Accelerate reporting
- Support decision-making
- Reduce manual effort
- Improve operational efficiency
By embedding AI directly into the data platform, organizations gain a scalable foundation capable of supporting future AI initiatives.
Accelerating Data Platform Migration
Many businesses continue to operate legacy systems that limit their ability to leverage modern AI technologies.
Migrating to modern data platforms has traditionally been a lengthy and complex process.
Historically, organizations relied on:
- Manual migration efforts
- Large consulting teams
- Extended project timelines
- Significant financial investments
More recently, large language models have helped accelerate portions of this work through code generation and automation.
Xebia is taking this concept further.
Rather than using AI solely as an external tool, the company integrates AI capabilities directly into the migration process itself.
This approach enables organizations to leverage contextual knowledge from the data platform to accelerate migrations more effectively.
The result is faster, more reliable modernization efforts with reduced risk.
How Experience Shaped Xebia Axis
Years of experience working on enterprise migrations and AI implementations ultimately led to the creation of Xebia Axis: Agentic Data Foundation.
This solution is designed to help enterprises prepare their data environments for AI adoption faster than traditional approaches.
Xebia Axis focuses on:
- Data readiness
- AI integration
- Governance
- Scalability
- Operational efficiency
By addressing foundational challenges early, organizations can accelerate AI initiatives while reducing implementation risks.
AI-Native Software Engineering with Xebia ACE
Beyond data modernization, Xebia is also transforming software development through its AI-Native Software Engineering framework known as Xebia ACE.
Software engineering is one of the areas experiencing the most significant impact from generative AI.
Developers increasingly use AI tools to:
- Generate code
- Create documentation
- Review changes
- Identify bugs
- Improve productivity
However, organizations often struggle to balance AI-driven speed with governance and quality requirements.
Xebia ACE aims to solve this challenge.
The framework integrates AI across the entire Software Development Lifecycle (SDLC) while maintaining enterprise standards.
Delivering Faster Results Without Sacrificing Quality
According to Xebia, organizations implementing ACE can achieve substantial benefits.
Potential outcomes include:
- Up to 40% faster software delivery
- Up to 70% lower legacy transformation costs
- Increased developer productivity
- Improved consistency
- Enhanced scalability
These improvements allow enterprises to realize AI-driven efficiencies while preserving the quality controls required for production environments.
The Challenge of Vibe Coding
The growing popularity of “vibe coding” demonstrates how accessible AI-assisted development has become.
Today, almost anyone can use AI tools to create applications.
However, building software is not the same as deploying enterprise-grade solutions.
Many organizations hesitate to push AI-generated applications into production because of concerns related to:
- Quality assurance
- Governance
- Security
- Compliance
- Maintainability
This is where frameworks like Xebia ACE become valuable.
Rather than relying entirely on automated code generation, ACE provides a structured methodology that combines AI acceleration with proven engineering practices.
Organizations gain the benefits of AI without sacrificing control.
Governance Remains Critical
As AI-generated code becomes increasingly common, governance remains a top priority for enterprise leaders.
Large organizations often have established development processes designed to ensure reliability and compliance.
Replacing these processes entirely with AI-driven automation introduces risks.
Xebia ACE enables organizations to integrate AI into existing workflows while preserving governance structures.
This approach reduces the likelihood of uncontrolled software development and helps maintain organizational standards.
AI and Software Security
Security represents another major concern.
As more code is generated automatically, organizations must ensure that vulnerabilities are not introduced into production systems.
The software industry is still determining the best ways to secure AI-generated code.
However, new solutions are emerging.
One notable development is the introduction of AI-powered pull request reviewers.
These systems analyze code changes before deployment and provide additional oversight.
Rather than replacing human reviewers, AI acts as an additional layer of quality assurance.
AI as a Senior Reviewer
Zeilemaker sees significant potential in this approach.
AI-powered code review tools effectively add another experienced reviewer to the development process.
These systems can:
- Identify vulnerabilities
- Detect coding errors
- Highlight compliance issues
- Improve code quality
- Reduce deployment risks
Organizations may soon treat AI reviewers as virtual senior engineers participating alongside human teams.
This hybrid model combines human expertise with AI scalability.
The Future of Enterprise AI
The enterprise AI landscape continues to evolve rapidly.
Organizations are moving beyond experimentation and seeking practical ways to deploy AI at scale.
Success depends on more than selecting the latest model or implementing the newest technology.
Businesses must establish the foundations necessary to support intelligent systems.
This includes:
- High-quality data
- Strong governance
- Accurate documentation
- Modern infrastructure
- Scalable platforms
Without these elements, AI agents will struggle regardless of their sophistication.
Conclusion
The excitement surrounding agentic AI is justified. Intelligent agents have the potential to transform operations, accelerate software development, improve customer experiences, and drive significant business value.
However, as Xebia’s Global CTO Niels Zeilemaker emphasizes, successful AI adoption begins long before organizations deploy their first agent.
The true foundation of AI success is data readiness.
AI agents depend on accurate, accessible, and well-documented information. Poor data quality, fragmented systems, and inadequate cataloguing can undermine even the most advanced AI implementations.
Through initiatives such as Agentic Data Foundation (ADF), Xebia Axis, and Xebia ACE, organizations can modernize their data environments, accelerate migrations, strengthen governance, and confidently scale AI initiatives.
As enterprises continue their AI journeys, one lesson becomes increasingly clear: building smarter agents is important, but building the right foundation is what ultimately determines success.
Read Also:
- Google Job Cuts Reflect AI-Driven Shift in Big Tech
- Visa Integrates ChatGPT for AI-Powered Shopping
- Siri AI Finally Arrives: Apple Rebuilds Siri with Google Gemini
Discover more from AiTechtonic - Informative & Entertaining Text Media
Subscribe to get the latest posts sent to your email.