Data infrastructure and integration: The foundation for scalable enterprise AI

The short answer: Scalable AI depends on connected, governed, high-quality data 

AI models do not create enterprise value in isolation. They create value when they can use trusted data, act inside real workflows, and exchange information reliably across systems. 

The OMMAX AI Trends Report 2026 identifies integration complexity as the leading barrier to stronger AI impact, cited by 40% of 250 European decision-makers. Fragmented data foundations affect 30%, and data quality is the number one reason AI initiatives fail to scale, cited by 28%. These findings explain why many organizations can build compelling prototypes but struggle to reproduce their performance in production. 

At the same time, confidence is high: 67% of respondents believe their current infrastructure enables AI scaling. The tension between perceived readiness and observed barriers is a warning. Having cloud platforms, core systems, and data stores does not mean those assets are ready to support AI-driven workflows. 

The priority is not to perfect every data source before AI begins. It is to build data and integration around high-value use cases, while establishing reusable standards that make each subsequent deployment faster, safer, and more reliable. 

Why is integration the biggest barrier to AI impact? 

Enterprise workflows cross multiple systems. A customer-service agent may need account data from CRM, order history from ERP, product information from PIM, approved content from a knowledge base, and interaction history from a service platform. If the AI can access only part of that context, answers become incomplete. If data must be copied manually, the workflow remains slow. If systems disagree, trust declines. 

The report shows that core systems are widely used but unevenly integrated for AI. CRM is used by 63% of organizations and customer-service platforms by 55%. ERP and marketing automation systems are each used by 48%. Hybrid cloud is the dominant infrastructure model at 56%. 

Front-office platforms show relatively strong integration: 59% of CRM and 58% of ERP users report full integration for AI-driven workflows, while customer-service and marketing-automation systems are also above half. Data warehouses and lakes are a notable bottleneck. Among organizations using them, only 46% report full integration, while 53% are only partly integrated. 

That pattern is counterintuitive. The data platform is intended to consolidate enterprise information, yet it can remain disconnected from the operational context where AI decisions are made. A warehouse may contain historical data but lack real-time access, consistent semantics, or the permissions needed for an agentic workflow. 

What makes data "AI-ready"? 

AI-ready data is not simply available. It is suitable for a defined decision or task.

Five characteristics matter:

• Quality: The data is sufficiently accurate, complete, current, and consistent for the use case.
• Context: Definitions, lineage, and business meaning are clear enough for people and systems to interpret it correctly.
• Accessibility: Authorized AI products can retrieve the data at the required speed and granularity.
• Control: Classification, permissions, retention, and usage rules are enforceable.
• Feedback: Outcomes and corrections can flow back into the system to improve performance.

The acceptable threshold depends on the use case. A low-risk internal drafting assistant may tolerate more uncertainty than an agent that changes a price, makes a payment, or communicates regulated information. Data readiness should therefore be evaluated against the consequences of error.

How should enterprises sequence data, automation, and orchestration? 

The report's leadership priorities offer a practical sequence: integrate first, automate second, orchestrate third. 

Integrate first 

Connect the minimum set of authoritative sources required for the outcome. Resolve identity, permissions, semantics, and data quality for the workflow. Avoid building point-to-point connections that cannot be reused or monitored. 

Automate second 

Use AI to improve a defined task or decision inside the workflow. Establish quality evaluation, human oversight, and operational metrics. Confirm that the solution produces net value after data and integration costs. 

Orchestrate third 

Connect multiple tasks, systems, and agents into an end-to-end flow. Orchestration should happen only after the underlying components are observable and controlled. Otherwise, automation multiplies inconsistency across the value chain. 

This sequence does not require a multiyear data transformation before the first use case. It creates a use-case-led architecture in which each deployment improves the shared foundation. 

What can companies learn from the transport data excellence case? 

For one of the largest national transport providers, OMMAX implemented a holistic data excellence and governance program. The initiative achieved more than 99% data quality, enabled the automation of more than 25 processes, and contributed several million euros to EBIT through improved performance tracking. 

The case illustrates that data quality is not a technical hygiene metric. It is an economic capability. Better data can improve visibility, reduce manual reconciliation, enable automation, and strengthen management decisions. The resulting value is realized through operational performance, not through the data platform alone. 

Three lessons stand out.

First, quality needs an owner and a business definition. "99% data quality" is meaningful only when measured against agreed rules for the fields and processes that matter. 

Second, governance and automation can reinforce each other. Clear ownership, standards, and monitoring make automated processes more reliable; automation, in turn, creates faster feedback on defects. 

Third, value tracking should connect technical improvement to operational and financial outcomes. This protects data investment from being treated as infrastructure without a business case. 

How should architecture support enterprise and agentic AI? 

A scalable architecture should separate reusable capabilities from use-case-specific logic. 

Reusable capabilities include identity and access, model gateways, secure tool connections, data retrieval, content and prompt management, evaluation, observability, audit logging, cost controls, and human escalation. Use-case teams can then configure these components around a particular workflow rather than rebuild them. 

For agentic AI, integration becomes more consequential because the system may not only retrieve information but also take action. Architecture should apply least-privilege access, validate inputs and outputs, log tool calls, enforce transaction boundaries, and support rollback or interruption. The organization's control plane must be able to show which agent acted, which data it used, what decision it made, and what happened next. 

Hybrid cloud, used by 56% of surveyed organizations, can support this model, but it introduces design choices around data location, latency, vendor access, and monitoring. The correct pattern depends on the sensitivity and performance needs of each use case. 

How can enterprises manage data sovereignty without stopping adoption? 

The report finds that 80% of organizations share sensitive data with external AI providers: 39% fully and 41% partially. Most use a conditional, use-case-based sovereignty approach. Fifty-four percent report a flexible or partly restricted model, while 35% apply strict policies and requirements and 10% have no strict restrictions. 

This pragmatism can accelerate adoption, but inconsistency creates risk. If teams decide independently what data can be shared, with which provider, and under what controls, the organization may be unable to demonstrate compliance or respond to an incident. 

A practical sovereignty framework should define: 

• Data classification levels and examples
• Approved provider and hosting patterns
• Rules for training, retention, and onward use
• Geographic and legal requirements
• Encryption and access controls
• Contractual requirements and audit rights
• Human approval and exception processes
• Logging, monitoring, and incident response

The framework should be strict about principles and efficient in execution. Preapproved patterns can allow low-risk cases to move quickly while directing sensitive or high-impact cases to deeper review. 

Sector context matters. In the report, 44% of financial-services respondents follow strict regulations, compared with 27% in healthcare. A common enterprise framework should therefore allow requirements to vary by data type, jurisdiction, and use case without losing consistency. 

What is a use-case-led data roadmap? 

A use-case-led roadmap starts with the enterprise outcomes that AI must support. For each priority case, teams identify the required sources, quality gaps, integration dependencies, permissions, latency, and governance. Shared needs are then grouped into data products and platform capabilities. 

This approach creates a two-way link:

• Use cases justify and prioritize foundational investment. 
• Foundational components reduce the cost and risk of future use cases. 

For example, a customer-next-best-action portfolio may justify a governed customer identity layer, event streaming, consent enforcement, and CRM integration. Those capabilities can then support service, retention, personalization, and sales applications. 

The roadmap should include retirement and simplification. Adding another data store, integration tool, or AI platform can increase fragmentation if the operating model does not define what becomes authoritative and what will be decommissioned. 

Which metrics show that the AI foundation is improving? 

Useful measures combine technical and business performance: 

• Data quality against use-case-specific rules
• Percentage of priority sources connected and governed
• Time required to onboard a new source or tool
• Reuse of approved connectors and data products
• Production incident and exception rates
• Latency and availability for critical workflows
• Cost per transaction, task, or agent action
• Time from concept to production
• Realized value enabled by shared capabilities

The goal is not to maximize integration coverage. It is to make high-value workflows reliable while increasing the speed and safety of future delivery. 

Outlook: treat integration as a strategic value layer

The next phase of enterprise AI will connect models, agents, data, and operational systems across complete value chains. In that environment, fragmented architecture becomes a competitive constraint. Models may be interchangeable; trusted context and reliable action are not. 

The OMMAX AI Trends Report 2026 shows that European enterprises already possess much of the technical estate required for AI, but the connections remain uneven. Leaders should focus on the foundation that converts intelligence into action: high-quality data, reusable integration, enforceable sovereignty, and observable workflows. That foundation is not a prerequisite to value creation sitting in the background. It is part of the value product itself.

About the research

The OMMAX AI Trends Report 2026 is based on a quantitative online survey of 250 decision-makers conducted in April and May 2026 across France, Germany, Italy, the Netherlands, and the UK. Statista conducted the survey on behalf of OMMAX, Ibexa, and Make. Download the full report here.  

About OMMAX

OMMAX is a leading AI-first consultancy and AI-engineering platform specializing in AI strategy, business transformation, transaction advisory, and value creation in the age of AI. Founded in Munich in 2011, OMMAX serves large corporates, mid-sized companies, and private equity firms across Europe and the US, with more than 4,000 completed projects and a Net Promoter Score of 90.