Strategic Priorities for 2026: Building AI-Ready Data Foundations

As enterprises prepare for large-scale AI adoption, their ability to deliver high-quality, contextual, and governed data to intelligent systems will define competitive advantage. This shift requires more than upgrading infrastructure—it demands a strategic rethinking of how data is engineered, accessed, and managed. The following five priorities outline how technical leaders should evolve their data architecture and operations in 2026 to support AI-native capabilities.

Invest in data engineering to support AI infrastructure

As AI adoption accelerates, data engineering is becoming a critical bottleneck. AI-native systems require clean, timely, and well-governed data delivered through robust infrastructure. This demands more than better tooling—it requires growing the data engineering function itself in both size and capability.

Recommended Action
Prioritize hiring, upskilling, and restructuring to ensure your organization has the data engineering talent required to support AI initiatives.

Approach

• Invest in expanding the data engineering team with professionals experienced in building scalable, real-time, and governed data systems.
• Upskill existing data engineers to work effectively with AI/ML teams, focusing on topics such as data product thinking, streaming architecture, and metadata management.
• Redefine team roles and workflows to align data engineering with AI development lifecycles, including support for reproducibility, lineage, and observability.
• Foster a strong collaboration model between data engineers, ML engineers, and business stakeholders to reduce turnaround time for data delivery and ensure quality inputs to AI systems.

Establish an API and metadata control plane

For AI agents to operate effectively within enterprise environments, they require structured, permissioned access to data through a controlled interface. A metadata control plane enables centralized governance, visibility, and access management for enterprise data assets.

Recommended Action
Abstract critical data systems behind secure, documented APIs and implement a control layer that unifies metadata, access policies, and lineage.

Approach

• Create standardized interfaces for interacting with datasets and models, exposing only approved entry points.
• Centralize metadata that describes dataset purpose, usage constraints, ownership, and schema evolution.
• Enforce access control, compliance rules, and policy enforcement at the interface layer rather than deep in systems.
• Enable metadata-driven discovery so that agents and applications can reason over available data assets dynamically.
  
  

Prioritize metadada harvesting and end-to-end lineage

AI systems depend on trust and context. Metadata and lineage are no longer optional—they are prerequisites for reliable automation, validation, and traceability.

Recommended Action
Capture metadata and lineage as a continuous part of every data flow. Ensure that context is consistently updated and accessible to downstream consumers and systems.

Approach

• Design pipelines to emit metadata about their structure, inputs, outputs, and dependencies as a native part of execution.
• Maintain a central system-of-record for lineage that supports both visual inspection and machine interpretation.
• Ensure that every transformation is traceable, versioned, and auditable—especially those used in model training or operational decision-making.
• Enable contextual queries over the metadata layer to support explainability, debugging, and compliance requirements.
  
  

Strengthen master and reference data foundations

Inconsistent or ambiguous definitions across domains degrade the performance and trustworthiness of AI systems. A reliable foundation of master and reference data is critical to ensure accurate and consistent interpretation across workflows.

Recommended Action
Implement strong governance, validation, and integration practices to ensure that core business entities and dimensions are stable, unified, and machine-readable.

Approach

• Standardize definitions of key business entities and dimensions across departments and systems.
• Automate the detection and remediation of duplicates, conflicts, and schema drift in reference data.
• Ensure that master data changes are versioned and traceable, with clear lineage to source systems.
• Integrate master data management into both operational systems and analytical environments.
  
  

Plan and pilot autonomous workflows

AI agents are best introduced in narrow, well-scoped scenarios where automation delivers immediate operational value without compromising risk posture. Pilot programs should focus on repeatable, low-variance tasks that demonstrate reliability and transparency.

Recommended Action
Identify high-frequency, low-risk workflows where automation can reduce human workload or improve responsiveness. Pilot AI agents with clear guardrails and measurable outcomes.

Approach

• Select workflows with structured inputs, defined rules, and observable outputs—such as report generation, data monitoring, or validation.
• Build feedback mechanisms that allow agents to learn from success/failure signals over time.
• Ensure each pilot includes safeguards for human review, escalation paths, and performance measurement.
• Use pilots to validate technical readiness and organizational alignment before broader rollout.
  
  

Next steps

Preparing for an AI-native future requires more than deploying models—it demands foundational shifts in how data is engineered, accessed, and governed. These five priorities serve as a blueprint for data leaders seeking to enable intelligent automation safely and at scale.

Forte Group works with organizations to build the architectural foundations, metadata systems, and operational practices required to support autonomous systems. By aligning technology strategy with business outcomes, we help our clients accelerate responsible AI adoption—securely, observably, and with confidence.

To learn more, explore our delivery capabilities or contact our data and AI strategy team at www.fortegrp.com/insights.