As AI adoption increases the cost of moving, storing and securing information, organisations will shift towards trusted, reusable data foundations that reduce duplication and improve AI readiness.
The rapid expansion of enterprise AI is exposing an often-overlooked cost: the duplication, movement, storage and governance of corporate data. CryspIQ predicts that 2027 will mark the emergence of Enterprise Data Efficiency as a distinct priority, with organisations moving away from the traditional practice of copying data across multiple platforms towards fewer, better-governed and reusable data foundations.
Dan Peacock, CEO and Co-Founder of CryspIQ, argues that the next phase of enterprise data architecture will focus less on accumulating information and more on ensuring that the same trusted business meaning is available across reports, applications and AI agents.
For years, organisations have expanded their data estates through warehouses, data lakes and cloud platforms, often creating multiple copies to support different workloads. While these approaches improved storage capacity and accessibility, they have also increased the cost of moving, securing and governing information. CryspIQ expects enterprises to increasingly prioritise reducing this duplication while maintaining a consistent, governed view of business data.
“The next five years will be about needing fewer copies of our data.”
— Dan Peacock, CEO and Co-Founder, CryspIQ
AI is likely to intensify the challenge. As agents gain access to information across multiple enterprise systems, every query could trigger activity across several data sources and duplicated datasets. CryspIQ predicts unmanaged AI adoption could increase compute costs by 100% within five years, putting greater pressure on boards to assess returns on data and infrastructure spending.
The company also expects the data layer to become more important as AI makes it easier to build specialised applications. Rather than relying on large, all-in-one software suites, organisations may adopt smaller AI-powered applications for individual business functions. In this environment, a consistent data foundation could provide continuity as applications change.
Data efficiency will also have security and privacy implications. Fewer copies mean fewer locations containing sensitive customer information to protect, monitor and account for. Data minimisation could therefore deliver both cost savings and a reduced security burden.
Another significant shift will be the quality of data available to AI agents. While many enterprise agents currently summarise, draft and retrieve information, organisations remain cautious about allowing them to make decisions or take action. Traceable, quality-checked data with clearly defined business meaning will be essential to building confidence in autonomous systems.
CryspIQ expects data teams to evolve accordingly from building and maintaining pipelines towards defining business meaning, ensuring data quality and enabling consistent reuse across the enterprise. The objective is to establish trusted information foundations that remain relevant even as applications and AI technologies change.
