TL;DR: Most enterprises are racing toward AI but stuck in slow, wasteful data delivery models. New 2026 Omdia research shows that organizations adopting structured, automated data products are seeing 20%-plus KPI improvements across the board. The question is no longer whether to make the shift; it’s how fast you can scale it.
Today, every enterprise wants to be AI‑driven. But there’s a major hurdle holding them back: delivering trusted data fast enough, or efficiently enough, to experience AI success.
New 2026 Omdia research, commissioned by Quest Software, makes one thing clear: While enterprises are racing to become AI‑driven, most are still struggling with trusting their data. Far too many organizations are constrained by slow, manual, and wasteful data delivery models, creating a growing gap between their actual reality and AI ambition.
And that gap is getting expensive.
The hidden cost of one‑off data delivery
According to the research which surveyed more than 250 North American data leaders, the typical enterprise data delivery project takes more than four months to deliver first insights and requires more than 800 person‑hours of effort.
At the same time, 38% of total data team effort is spent on non‑reusable, one‑off work, that may deliver value once that product is created.
The downstream impact is significant:
- 81% of organizations report duplicating work across data projects
- 28% of total data team workload is redundant effort
- Nearly 70% of projects require significant rework after delivery, often extending timelines by 30% or more
This delivery model was not designed for scale, and it was certainly not designed for AI.
Data products are becoming the standard approach for trusted data
Despite these challenges, there is hope. The research shows a decisive shift is already underway.
More than 90% of enterprises report that they are delivering data products in some form today. That’s the good news. Here’s the challenge – definitions and maturity levels of those data products vary widely. Many organizations still define data products as dashboards or standalone datasets rather than fully packaged, governed, reusable assets.
That distinction matters.
True data products include contextual and governance components such as data models, quality metrics, lineage, access controls, and other business context. These elements create trust and enable reuse. That unlocks the power of AI, leading to successful AI initiatives.
Many organizations may be building data products, but without the consistency and governance required to realize and safeguard their full value, those data products are one-offs, time consuming efforts that provide minimal value.
When data products mature, the results are impactful
Organizations that take a more structured and reusable approach to data products are not seeing incremental gains. They are seeing measurable, impactful improvements.
Nearly two-thirds of organizations report KPI improvements of 20% or more after adopting a more mature data products approach. Not dashboards. Not datasets. Real, tangible, value-adding data products. These gains span metrics that matter to both business and technology leaders:
- Faster time to insight
- Improved data accuracy
- Increased analyst productivity
- Better revenue attribution
These outcomes highlight a clear pattern. When data delivery shifts from one‑off projects to reusable products, data becomes the engine for scale rather than the bottleneck.
Automation accelerates scale and consistency
Standardization sets the foundation, but automation accelerates scale.
The research identifies three automation capabilities that consistently deliver the greatest business impact:
- Automated data modeling and schema design
- Real‑time data quality monitoring
- Automated data lineage tracking
Organizations that automate these three delivery steps report faster delivery, greater trust, and less rework, with more than 40% citing business impact improvements of over 15%.
By addressing the most common delivery bottlenecks such as quality, complexity, and governance overhead, automation enables teams to do more with the resources they already have.
AI‑powered automation as a force multiplier
Automation is not the endpoint. AI‑powered automation represents the next phase of scale.
AI‑powered capabilities will improve data quality and reliability, reduce human error across data product delivery, and increase scalability in data product creation. Further, 80% of organizations surveyed expect AI use in data modeling alone to increase scalability in data product creation by 10%. Many also anticipate that AI will help teams manage growing data product portfolios without increasing manual effort.
For organizations earlier in their automation journey, expectations of the impact of AI-powered automation are even higher. Nearly 40% expect to achieve a 35% or more improvement in data delivery-related metrics.
Why this research matters now
In 2026, the question isn’t whether to adopt data products. It’s how fast your organization can automate, standardize, and scale your delivery of trusted data.
This unlocks AI success sooner and also sets the foundation in motion for the future of agentic AI.
Use Omdia’s research findings as a blueprint to help you deliver the business case to accelerate initiatives that can close the gap between your own AI ambition and a trusted data reality.
You can review the full research in our Scaling AI and Business Value through Automated Data Products eBook. Inside the eBook, you’ll learn:
- Benchmarks on the true cost of one‑off data delivery
- How enterprises are defining and maturing data products today
- The KPIs leaders use to measure data product success
- Where automation delivers the highest ROI
- How AI‑powered automation is expected to accelerate scale
Ready to deliver trusted data for analytics and AI faster, reduce waste and improve reuse in data delivery? See what’s working for leading data teams in this latest research.
