As GenAI, large language models (LLMs), and agentic AI go mainstream, organizations are rethinking their data strategies—and realizing most of these strategies weren’t built for this.
While the foundational elements of data strategy still matter, today’s AI-powered world demands an evolved approach: that accounts for the scale, complexity, and speed at which organizations operate, innovate, and compete.
A successful AI strategy requires cohesive thinking across data, models, processes, platforms, and people. With new risks emerging as quickly as new opportunities, many organizations find themselves playing catch-up—not because they lack ambition, but because they haven't revisited the foundations with AI in mind.
Why the convergence of data and AI strategies matters
While most organizations have data and AI initiatives in flight, few have re-architected their strategies for what lies ahead. A fragmented landscape—spanning multicloud environments, data silos, point AI solutions, and ad hoc governance—can quickly limit scale and value. By contrast, unifying and AI strategies can unlock operational agility, responsible innovation, and differentiated customer experiences.
Three trends are driving this shift:
- The rise of GenAI and agent-based architectures: Traditional models of data integration and analytics aren’t sufficient for the fluid, real-time demands of generative applications and LLM operations (LLMOps). From retrieval-augmented generation (RAG) to custom agent frameworks, organizations need platforms and governance models built for these new AI-native workflows.
- Governance is no longer optional—it’s foundational: With AI-specific regulations such as the European Union's AI Act and evolving standards like the National Institute of Standards and Technology’s AI Risk Management Framework and ISO 42001, governance is shifting from compliance checkbox to design principle. Forward-thinking organizations treat AI governance, risk management, and cybersecurity as core enablers of scale and trust.
- “Land and expand” must be more than a sales tactic: The ability to start small (for example, by piloting a GenAI agent for internal use), and rapidly scale through reusable patterns, modular tooling and integrated governance separates the leaders from the laggards. A good strategy starts where you are but never stays there.
5 recommendations to get your data and AI strategies right
Here are our field-tested recommendations to get your data and AI strategies right in an AI-native world:
1. Make governance a design principle, not an afterthought
Start by embedding governance across the AI lifecycle, from data ingestion to model monitoring. This means establishing clear policies on data quality, lineage, labeling, access controls, and explainability. Use modular frameworks that integrate AI-specific guardrails (bias detection, model observability and prompt management) with broader data and security governance.
This isn’t just about compliance — it’s also about trust. And trust is what allows organizations to scale AI confidently, responsibly, and sustainably.
2. Invest in an enterprise-grade AI and data foundation
Successful AI at scale requires more than models. It requires platforms, pipelines, and processes that are built to last. This includes MLOps and LLMOps capabilities, reusable accelerators (like vector database integration and model evaluation tools), and a robust architecture that supports both centralized and federated operations.
Whether you’re using open-source, commercial, or hybrid solutions, the key is to orchestrate — not just assemble — your stack around flexibility, portability, and lifecycle accountability.
3. Operationalize early, not eventually
The most value comes from AI initiatives that go beyond proofs of concept and deliver real impact. That requires a delivery model rooted in agility, automation and cross-functional orchestration. Embed operationalization from the outset: design with monitoring, cost control (FinOps), human-in-the-loop governance, and cybersecurity controls in mind.
Think minimum viable product but with a clear runway to scale, powered by automation and repeatability. Tools like AI risk matrices, maturity assessments, and security test kits help reduce friction and accelerate time to value.
4. Bridge the talent, tools, and process gap
A unified data and AI strategy must address more than technology. It must also tackle cultural readiness, skills evolution, and process alignment. Create centers of excellence or AI offices that unify product management, engineering, data, and compliance functions. Encourage collaboration through shared platforms and transparent evaluation frameworks.
Most importantly, don’t underestimate change management. Driving adoption — both internally and externally — requires strong communication, trust-building, and a commitment to continuous learning.
5. Prioritize use cases by impact, risk, and readiness
With so much attention on GenAI use cases, the temptation is to jump straight into the most exciting ones. But not all are created equal — or equally ready. Establish a portfolio view that evaluates use cases based on business impact, technical feasibility, governance complexity, and security posture.
Use proven frameworks to define levels of use-case criticality and tailor your strategy accordingly. Some AI applications require red-teaming and penetration testing; others may be governed through lightweight controls. The point is, right-size your rigor.
New power — and new responsibility
While the fundamentals of good strategy still hold — alignment with business goals, scalability and adaptability — the “how” has changed. AI brings new power, but also new responsibility. The most resilient and impactful organizations are those that can integrate AI into the very fabric of their data, technology, and operating models—, securely, ethically, and at scale.
Get your data and AI strategy right, and you’re not just future-proofing your business. You’re building the foundation for leadership in the AI age.
What to do next
To learn more about how to design a unified, secure and governed data and AI strategy, explore NTT DATA’s comprehensive AI governance solutions.