AI-Ready Data & AI Agents: Key Insights from the Gartner Data & Analytics Summit

The recent Gartner Data & Analytics Summit highlighted a critical industry inflection point: while organizations rush to implement AI, those succeeding understand that AI’s true power comes from its foundation. This year’s summit emphasized three interconnected themes: AI agents, AI-ready data, and trust and governance frameworks. As a SnapLogic representative at the summit, I was particularly excited to see our platform’s capabilities aligning with these emerging priorities.

The state of AI adoption

The enthusiasm around AI is undeniable, but the numbers tell a more nuanced story:

• Only 20% of organizations have successfully operationalized AI use cases consistently
• 49% of organizations cite demonstrating AI’s business value as their top barrier to adoption
• Data availability and quality remain the #1 obstacle to AI implementation

These statistics reveal a growing recognition: AI success depends less on algorithm sophistication and more on the quality, accessibility, and governance of underlying data assets. The session went on to highlight the keys to success – establish trust through transparent governance, scale through modular and adaptable (composable) architecture, make data AI-ready and reusable, and drive team transformation through AI literacy and upskilling.

AI agents: the next evolution of intelligent systems

One of the summit’s most thought-provoking sessions was “AI Agents: Are You Ready to Set Your AI Free?” presented by Gartner analyst Ben Yan. The session provided a clear definition that resonated with many attendees:

“AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments.”

According to Gartner, AI agents represent a fundamental transformation in how AI systems operate:

• From requiring strong supervision to acting autonomously
• From deterministic task flows to undeterministic ones
• From static behavior to adaptable behavior
• From centralized to decentralized architectures

Importantly, not everything marketed as an “agent” qualifies. Large language models themselves, automated procedures, conversational assistants, and RPA workflows don’t inherently constitute AI agents – a distinction that helps cut through market hype.

When to consider AI agents: identifying prime opportunities

For organizations evaluating where to implement AI agents, Gartner identified several characteristics that make solutions prime candidates:

• Dynamic and unpredictable environments with constantly changing conditions and high uncertainty
• Complex decision-making needs involving multiple factors and long-term goals
• Requirements for continuous adaptability through ongoing learning and improvement
• Potential for autonomy in systems that need 24/7 availability and independently actable tasks
• Strong integration capabilities with interoperability, extensibility, and scalability

These characteristics help organizations prioritize use cases where AI agents can deliver genuine transformation rather than incremental improvements.

The session showcased Lenovo’s success in building a product configuration agent. They have a wide variety of customers from large enterprises to consumers who often had to describe their requirements to representatives who then configured and sent back proposals. Their multi-agent architecture with specialized agents for planning, summarization, recommendations, pricing, analysis, and configuration, all interacting with knowledge bases and toolkits, allows customers to describe and configure products themselves using natural language.

Why GenAI projects fail

Perhaps the most sobering insight came from Gartner analyst Arun Chandrasekaran, who predicts that by 2025, at least 50% of generative AI projects will be abandoned after proof of concept. The primary reasons include:

1. Lack of business value – Prioritize high-impact use cases with clear success metrics
2. Over-reliance on AI alone – Use AI alongside traditional automation and rule-based systems
3. Unprepared data infrastructure – Strengthen data governance, metadata capture, and vector embeddings
4. Vendor lock-in risks – Adopt a modular, composable AI architecture
5. Insufficient experimentation – Implement AI sandboxes, A/B testing, and monitoring frameworks
6. Weak risk & compliance controls – Embed responsible AI frameworks and bias monitoring
7. Scaling challenges – Plan for enterprise-wide scalability from the outset
8. AI talent shortage – Invest in AI upskilling and low-code tools
9. High costs & low ROI – Optimize AI workloads and continuously measure ROI
10. Unclear AI governance – Assign clear ownership and ethics policies

These pitfalls highlight why organizations need integration platforms that can bridge the gap between AI aspirations and practical implementation.

The integration imperative

As we navigate the evolving AI landscape, one thing is clear: the organizations that will lead in the AI era aren’t necessarily those with the most advanced algorithms, but those with the most robust, accessible, and well-governed data ecosystems. Integration platforms like SnapLogic that can seamlessly connect data sources, enable AI-ready data pipelines, and support emerging agent architectures will be critical enablers of this transformation.

The summit reinforced that sustainable AI success hinges on four pillars:

1. Trust through transparent governance – Ensure ethical, secure, and responsible AI deployment
2. Make data AI-ready and reusable – Create a strong data foundation
3. Scale through modular, open (composable) architecture – Build flexible infrastructures that grow with organizational needs
4. Team transformation through AI literacy and new skills – Empower rather than replace human expertise

For organizations looking to achieve competitive advantage through AI, the message is clear: focus first on your data foundation and integration capabilities. The rest will follow.

Ready to become a data-driven, composable, agentic enterprise?

The journey toward effective AI and AI agent implementation begins with modernizing your integration platform which allows you to get your data AI-ready, create a composable architecture, and enable you to democratize agent creation. SnapLogic’s cloud-native integration platform unifies data and application integration, API management, and agent creation, greatly simplifying your journey to becoming a modern agentic enterprise.