The significant barrier of messy, legacy data is being overcome by AI. Snowflake is developing "agent-driven migrations" that automate the process of moving data from old systems onto modern platforms. This drastically reduces project timelines from multiple years to just a few weeks.

Beyond analyzing clean data, AI can play a crucial role in data remediation. It can be used to go back through historical datasets to perform automated quality checks and re-evaluate information, making legacy data valuable for modern analysis and modeling.

Waiting for perfectly clean data stalls AI adoption. Instead, deploy AI agents to execute tasks. Their diligence and consistency in handling information will progressively clean underlying systems of record as a byproduct of their work.

The vast majority of enterprise information, previously trapped in formats like PDFs and documents, was largely unusable. AI, through techniques like RAG and automated structure extraction, is unlocking this data for the first time, making it queryable and enabling new large-scale analysis.

A major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.

Enterprises are trapped by decades of undocumented code. Rather than ripping and replacing, agentic AI can analyze and understand these complex systems. This enables redesign from the inside out and modernizes the core of the business, bridging the gap between business and IT.

A key value of AI agents is rediscovering "lost" institutional knowledge. By analyzing historical experimental data, agents can prevent redundant work. For example, an agent found a previous study on mouse models that saved a company eight months and significant cost, surfacing data from an acquired company where the original scientists were gone.

Migrating ten years of data from a siloed system like Marketo into an environment where a single AI agent could access and act on it end-to-end resulted in a massive productivity leap. The ability for an agent to work with freed data in real-time proved more impactful than years of incremental improvements.

Peregrine applies long-horizon AI agents to the unglamorous but critical problem of data integration. These agents run for hours, analyzing customer databases and writing Python notebooks to automate 90% of the work. This internal tooling dramatically increases the efficiency and scalability of their deployment team.