Building a resilient data backbone for future orbits of AI

Enterprises have moved from debating AI’s potential to embedding it in production. Agent-based systems are now live in workflows, influencing decisions, and automating tasks. This shift introduces two enduring priorities: sustaining AI as technologies evolve and ensuring every deployment delivers measurable returns.

The pace of change reinforces these priorities. In the time it takes to approve your next AI budget, the technology landscape will have shifted again. With each shift measured in months, not years, technology roadmaps must be designed to adapt without rebuilding from scratch.

From data to dollars: A framework for sustained value

A resilient AI backbone starts with the data to dollars journey—the path from raw data to monetized insights embedded in business workflows. This journey operates across three interconnected pillars:

• Modernize: Upgrade the data stack to handle the full AI lifecycle, embedding generative and agentic AI into ingestion, processing, governance, and consumption.
• Maximize: Apply automation to reduce manual effort and resource needs while increasing data operations and lifecycle management throughput. In the Data Mastery report, organizations that embedded generative AI into data engineering reported significant reductions in cost and cycle time for adding new data, with top-performing enterprises achieving the highest efficiencies.
• Monetize: Treat data as a reusable product. Curate high-quality datasets, govern them for trust, and embed them into workflows to create measurable value streams. Leaders in the Data Mastery report highlight reusable data products as drivers of faster delivery and improved decision-making in targeted functions.

When executed together, these pillars transform data from a static asset into a compounding source of enterprise value.

The three lenses of AI in the enterprise

Within this framework, AI plays distinct roles:

• AI in engineering accelerates development with low/no-code platforms, co-pilots, and automation directly in the engineering stream.
• Data for AI ensures enterprise data is trustworthy, accessible, and ready for AI consumption through governance, annotation, security, and architectural fit.
• AI in business disrupts and redefines processes by integrating AI into value chains, from customer service to operational decision-making.

All three lenses are critical. Neglecting anyone limits AI’s potential to deliver consistent, enterprise-scale outcomes.

Data readiness: the foundation of trust and performance

In the current maturity stage, structured or unstructured data type matters less than its quality, trustworthiness, and accessibility. Advances in vector databases now make unifying insights from both forms feasible without costly graph database conversions. Agentic AI amplifies this capability by deploying specialized agents to act on combined datasets for targeted functions.

Architectural adaptability is key. Data mesh, data fabric, or data-as-a-product approaches should be selected based on the problem to solve and the enterprise’s operating model. The goal is a modular, governed architecture that can integrate new tools or models without disruption — a design principle that also serves as the foundation for orchestrating agentic AI at scale.

This modular, governed architecture also forms the operational blueprint for Virtusa Helio’s orchestration of agentic AI — aligning data, models, and task-specific agents under a unified control layer that ensures consistency, scalability, and measurable business outcomes.

Two dimensions of trust

In agentic environments, trust is built on two distinct but connected dimensions:

1. Quality of data: Determines the accuracy and reliability of insights
2. Quality of actions: Determines whether the decisions and outputs from LLMs and agents align with enterprise norms, compliance requirements, and expected outcomes

This requires embedding safeguards into the architecture from the start:

• Human-in-the-loop oversight during early stages.
• Truth table benchmarks to evaluate decisions.
• Reinforcement learning to refine agent behavior over time.

Trust is not a single checkpoint; it is a continuous operational standard.

Integration and ROI: the readiness benchmark

Technology adoption in isolation risks fragmentation. True readiness is measured by the ability to integrate AI capabilities into the existing enterprise ecosystem, creating measurable benefits without destabilizing operations.

This involves:

• Orchestrating AI with the current technology backbone.
• Articulating clear business benefits to justify integration.
• Ensuring flexibility so that evolving AI paradigms can be adopted without discarding prior investments.

For leadership teams, ROI is no longer theoretical. AI investments must be aligned to business outcomes from the outset, with value tracked over time. Virtusa helps enterprises define these value pathways, ensuring that AI integration delivers both immediate and sustained returns.

Sustainability through ownership

The long-term viability of AI increasingly depends on building rather than buying core platforms. Ownership ensures control over orchestration logic, operating models, and governance, enabling systems to evolve with business priorities and market changes.

External platforms can accelerate early adoption, but a build-first mindset allows enterprises to:

• Maintain strategic control over their data and AI ecosystems.
• Avoid repeated reinvestment as external tools change or sunset.
• Align platform evolution with organizational culture and processes.

This is not just a technical decision; it is a strategic one that defines how AI is sustained over years, not quarters.

Lifecycle-aware adoption

Every enterprise is at a different point in its AI lifecycle. The priority is to understand the current stage and define the next logical step—whether that means establishing governance, scaling deployment, or embedding AI deeper into business processes. This lifecycle-aware approach, central to Virtusa’s transformation programs and embedded in the Helio platform, ensures investments are targeted and momentum is maintained.

The path forward

AI will continue evolving, with new models, architectures, and delivery paradigms emerging quickly. The thriving enterprises will not react to every change but will have the resilience to absorb innovation without losing continuity.

By anchoring AI in a modular, governed, and data-centric foundation—modernizing the stack, maximizing automation, and monetizing data—organizations future-proof their capabilities, safeguard trust, and deliver sustained business impact. With deep expertise in building resilient AI backbones, Virtusa works alongside enterprises to translate these principles into actionable strategies, ensuring they deliver value today while remaining agile enough to adapt to the innovations of tomorrow.