As GenAI, LLMs, and AI agents go mainstream, organizations are rethinking their data strategies—and realizing most weren’t built for this. While the foundational elements of data strategy still matter, today’s AI-powered world demands an evolved approach: one that accounts for the scale, complexity, and speed at which organizations operate, innovate, and compete.
A successful (Data) and 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 more than ever
While most companies 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, a unified (Data) and AI strategy 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, companies 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 EU AI Act and evolving standards like NIST’s AI RMF and ISO 42001, governance is shifting from compliance checkbox to design principle. Forward-thinking companies 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 (e.g., pilot 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 strategy right
Here are 5 battle-tested recommendations to get your (Data) and AI strategy right in today’s 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 around data quality, lineage, labeling, access controls, and explainability. Leverage modular frameworks that integrate AI-specific guardrails (bias detection, model observability, prompt management) with broader data and security governance.
This isn't just about compliance—it’s 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/LLMOps capabilities, reusable accelerators (like vector database integration or 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 PoCs 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 MVP—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 modern (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—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.
Final thoughts
While the fundamentals of good strategy still hold—alignment with business goals, scalability, 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.
- To learn more about how to design a responsible, secure and governed (data) and AI strategy, Explore NTT DATA’s comprehensive AI governance solutions.
- To learn more about NTT DATA and Gen AI, discover NTT DATA’s enterprise GenAI capabilities