Proactive engagement and education are essential to shaping a human-centric AI economy. That was the resounding message at last week’s Gartner Data & Analytics Summit in London – echoed powerfully from keynote speakers, Aura Popa and Jørgen Heizenberg.
They compared today’s AI transformation to the space race of the 20th century. The mission to reach the moon was bold and ambitious, but along the way, it sparked unexpected breakthroughs – like water filtration, camera phones, and athletic shoes. These weren’t the original goals, but they became some of the most enduring innovations. The takeaway? It’s the journey, not just the destination, that delivers lasting value.
The same is true for AI. Artificial General Intelligence (AGI) may be the moonshot but the tools, models and mindsets we develop on the way there will shape our economic future.
Gartner defined this transformation through three interconnected journeys:
- The journey to business outcomes
- The journey to data and analytics capabilities
- The journey to cultural change
And the key to making progress on all three? Trust, adaptability and empowered people.
Trust is the foundation of AI readiness
A consistent theme across sessions, from data fabric to AI-powered automation, was this: your AI is only as strong as your data, and your data is only as valuable as the trust placed in it.
In a modern enterprise, trust means having confidence that your data is accurate, available, and ready for its intended purpose. It means knowing that the information fueling your AI workflows has context, lineage, and relevance—and that the people using it can rely on it to make important decisions.
Trust spans the entire data lifecycle. It’s embedded in how data is collected, how it’s governed, how it’s shared, and how it’s ultimately operationalized. From infrastructure to AI agents, trust is the connective tissue that makes it all work.
And critically, “AI-ready” doesn’t mean flawless data—it means fit-for-purpose data. As one keynote speaker put it:
- 90% accuracy might be acceptable for internal dashboards.
- 97% is the threshold for customer-facing AI.
- 99.9% is essential for financial automation.
That range—90 to 99.9%—illustrates the importance of tailoring your data strategy to the task at hand. Sometimes timeliness matters more than precision. Other times, the cost of a wrong answer outweighs the benefit of speed. Knowing the difference is what defines a mature, trustworthy data strategy.
But building trust is just the beginning. To create a truly AI-ready enterprise, organizations need a broader, more holistic approach—one that spans architecture, operations, and culture.
Three levers for driving a human-centric, AI-ready enterprise
1. Build trust to unlock business value
To deliver reliable business outcomes, organizations must:
- Establish clear trust models
- Monetize productivity improvements
- Communicate the tangible value of data and analytics
Trust is what transforms raw data into insight – and insight into impact.
2. Design for adaptability to scale AI effectively
Scalable AI doesn’t come from adding more tools. It requires a composable, modular foundation that:
- Reuses data products with active metadata
- Supports evolving AI use cases
- Enables agent-based workflows that extend across teams
This is what turns technical capability into enterprise-wide agility.
3. Empower people to sustain transformation
Technology can spark change – but people sustain it. To keep momentum, organizations must:
- Establish repeatable habits around data and AI use
- Embrace new roles and reskill around agent-centric workflows
- Foster collaboration across departments and disciplines
When teams are empowered, innovation becomes part of the culture.
Operational readiness in the real world: lessons from Enate
One of the most grounded—and let’s be honest, standout—takeaways from the Summit came from SnapLogic customer Enate (and yes, I’m a little biased).
Their message was simple but powerful: when you’re supporting enterprise customers like TMF, Infosys, and IHG, integration isn’t just an IT decision; it’s a business-critical one.
After a rigorous RFI process, comparing nine iPaaS vendors, Enate chose SnapLogic for its ability to handle complex integrations across Oracle, Salesforce, and SAP. But it wasn’t just the tech that stood out, it was the partnership. Their advice to the audience? “Work with people who you’re important to.” That says it all.
In a Summit full of talk around data strategy, AI readiness, and architectural frameworks, their session was a clear reminder: you can’t build toward AI if your operations aren’t already working smoothly underneath.
Enate’s story reinforced something we often say but don’t always spotlight enough: getting integration right is foundational. The tools you choose today don’t just serve internal teams. They shape the customer experience, the speed of innovation, and your ability to scale tomorrow.
Riflessioni finali
The Gartner Data & Analytics Summit reinforced something we’ve believed at SnapLogic for a long time: there’s no one-size-fits-all path to AI-ready data. It’s a journey, shaped by your use cases, business priorities, and your willingness to evolve the architecture underneath it all.
If you’re preparing to scale your AI initiatives, start by aligning your data strategy to the outcomes you want to achieve. And if you’re rethinking your architecture for the AI era and want to see what’s possible with a modern integration platform, get in touch with us to learn more.
