Inside the infrastructure strategies propelling AI leaders
AI adoption is starting to translate into real-world returns. But as efforts accelerate, many organizations are running into the same problem: systems that are too expensive, too slow, and can’t scale. Among companies with disconnected data environments, 67% cited data storage, movement, and duplication as the largest recurring AI cost. This article explores three infrastructure considerations: delivering infrastructure at agentic speeds, streamlining data, and adopting infrastructure built for AI scale.
Inside the infrastructure strategies propelling AI leaders | Databricks Blog
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AI adoption is starting to translate into real-world returns. But as efforts accelerate, many organizations are running into the same problem: systems that are too expensive, too slow, and can’t scale.
Among companies with disconnected data environments, 67% cited data storage, movement, and duplication as the largest recurring AI cost, according to a recent survey of over 1,200 technology leaders by Economist Enterprise. For those with a unified data architecture, that number drops to just over half.
Now is the time to build the future-proof foundation for AI. But database migrations are expensive and a major source of frustration. The deeper organizations envelop themselves around legacy architecture, the harder it will be to get out. Open and AI-ready databases give companies more flexibility and control over how their data is used, and empower developers to quickly, securely, and efficiently reorient the business around AI.
“The art is distributing speed without distributing chaos,” Jose Manuel Silva, Vice President for Technology and Chief Digital Officer at Natura, said in the report.
This blog will go into the three considerations for enterprise infrastructure that can help speed-up AI innovation, minimize costs, and deliver AI agents that actually work.
Consideration one: Deliver infrastructure at agentic speeds
For 60% of companies, it takes up to 12 months to get AI workloads into production, according to the Economist Enterprise survey. Developers want to move at the speed of AI, but underlying infrastructure is stuck at an analog pace.
When code is created in seconds, databases can’t take minutes to provision. And as AI agents work autonomously to execute workflows, they need to be able to instantly spin up temporary, experimental environments separate from the larger IT landscape.
The combination of fast innovation, secure rollback, and instant restoration is what will propel organizations towards the outcomes they want — in much faster than 12 month cycles.
Consideration two: Streamline data
AI engines ingest data at speeds and volumes that many enterprises aren’t built to support.
All the rich information housed in transactional databases and other end sources around the business hold the critical context the AI systems need to deliver actionable intelligence and automate processes without interruptions. Often, this information is siloed in proprietary environments. Moving it requires building new pipelines and ETL workloads, adding complexity and costs.
An AI-ready database can unify operational and analytical data. All the data that developers need is always available, stored separately from the compute layer in low-cost cloud storage.
“If you can infuse AI on your data and it works, it means your data is really ready and follows the FAIR framework—findable, accessible, interoperable and reusable,” said Maria Macuare, Sr. Vice President and Global Chief Data Officer at Mondelēz International.
Consideration three: Adopt infrastructure built for AI scale
Legacy data architectures introduce a severe structural penalty to enterprise growth. Because legacy infrastructure scales rigidly, leadership is forced into a lose-lose compromise: overpaying for idle capacity just to survive peak demand, or under-provisioning and risking unresponsiveness when business spikes. This operational friction locks up premium engineering talent in routine maintenance, draining resources that should be funding competitive speed and strategic innovation.
With purpose-built AI databases, data lives in reliable, elastic, and cost-effective data lakes. Compute runs independently, which decouples cost from growth so companies can achieve greater operational flexibility. Developers can more freely experiment without burning through the budget. And systems can scale from high concurrency to zero in seconds to optimize spend. Costs are aligned with use to support unpredictable workloads and rapid AI agent activity. And with capabilities like instant recovery, developers can actually move fast without breaking things.
Read the full report from Economist Enterprise and learn the strategies that are pushing leaders to the front of the AI race.
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