As artificial intelligence rapidly shifts from pilot projects to mission-critical operations, organizations are re-evaluating their data foundations and strategic direction. At the Gartner Data & Analytics Summit held in Mumbai, leading analysts unpacked how enterprises can harness AI’s promise—while avoiding its pitfalls—through intentional strategy, robust data practices, and emerging frameworks like retrieval-augmented generation (RAG).
Cracking the AI Value Conundrum
According to Ehtisham Zaidi and Aura Popa, both analysts at Gartner, the journey from AI pilot to production is often stymied by foundational issues—chief among them, data quality and lack of business alignment. Nearly 49% of organizations struggle to demonstrate the value of AI, and more than half report persistent issues with their data.
The solution, they argue, lies in closing the knowledge gap between business and data leaders, and in operationalizing data quality at scale. D&A leaders must ensure their strategies are clearly tied to business outcomes while navigating a landscape where AI hype often overshadows execution.
Building the Pillars of an AI-First Organization
Chirag Dekate, VP Analyst at Gartner, framed AI not just as a tool but as a potential industry disruptor. To unlock its business value, he recommended starting with a strategic vision that identifies where AI fits into an organization’s goals—and where the risks lie.
“Turn the promise of AI into reality by developing and executing your own AI strategy,” Dekate said. He emphasized creating a detailed roadmap for AI adoption across people, culture, governance, engineering, and data, while regularly recalibrating the strategy to stay aligned with evolving business needs.
Conquering the Top Five AI Challenges
In a separate session, Anirudh Ganeshan outlined the five most pressing challenges facing AI and analytics teams today—from establishing trust and demonstrating impact, to balancing responsibilities between business and IT. His advice: move beyond technology hype to create solution-oriented, adaptable systems supported by agile governance and strong cross-functional collaboration.
One standout insight: “Provide a heads-up of industry and technology trends to key stakeholders—focus on impact, not hype.”
Retrieval-Augmented Generation: The Future of GenAI Deployment
With GenAI continuing to dominate enterprise agendas, Prasad Pore, Sr Director Analyst at Gartner, turned attention to one of the most impactful enablers of its success: retrieval-augmented generation (RAG). This architectural pattern enhances LLM outputs by grounding them in organizational data—offering better explainability, traceability, and performance.
“Most LLMs aren’t equipped to solve specific business challenges out-of-the-box,” Pore noted. “But when combined with business-owned datasets through RAG, their effectiveness improves dramatically.”
To stay ahead, Pore advised enterprises to evolve their data management platforms into RAG-as-a-service ecosystems, prioritize robust metadata usage for security and transparency, and integrate core RAG technologies like vector search and graph-based systems.
Key Takeaways:
- 49% of organizations struggle to show AI’s business value, often due to poor data quality and weak business alignment.
- AI strategy must start with vision, value, and risk, followed by a roadmap for organizational adoption and maturity.
- Top challenges for D&A leaders include: trust-building, demonstrating benefits, moving beyond tech-first thinking, and defining business-IT responsibilities.
- RAG is emerging as a critical enabler for GenAI—it boosts LLM effectiveness by integrating enterprise data context.
- By 2028, 80% of GenAI business apps will be built on existing data platforms, reducing delivery time and complexity by half.