To unlock the power of AI, ensure your
data foundation is
rock solid.

Forrester Insight Brief

Enterprises are accelerating AI adoption, but many struggle to move beyond early initiatives. That's because the underlying data is impacted by inconsistency, fragmentation and siloing, while AI models and agents need consistent, clean, and continually refreshed data sources.

As Data Engineering and AI experts, in 2026, we commissioned a special Forrester Consulting study of 205 US IT and business decision-makers in banking and retail. The insights gleaned from the survey examine why data engineering is foundational to AI readiness and what leaders should prioritize to unlock the true potential of AI.

The AI Readiness Gap

Why AI Initiatives Stall Before They Scale

Most enterprises are moving fast on AI. The data foundations underneath were built for a different era — one defined by static reporting, siloed systems, and batch-driven analytics.

AI demands something more: accurate, contextual, governed, real-time data that can be trusted across teams, workflows, and decisions.

Common barriers to AI scale:

  • Data remains fragmented across legacy platforms
  • Metadata, lineage, and governance are incomplete
  • Pipelines are not ready for real-time AI workloads
  • Provider models struggle with flexibility, speed, and cost predictability

Early initiatives prove what is possible.
Data Engineering determines what is scalable.

The Blueprint For Success

Download the Forrester Insight Brief.

Learn why scalable AI depends on strong data engineering fundamentals - and what leaders should prioritize to move from fragmented initiatives, to production-ready AI.

Close the AI
Readiness Gap.

"AI models require accurate, contextual, and real-time data... without a strong foundation, AI programs stay fragmented, get stuck in pilot phases, and can be harder to scale."

Forrester Consulting study

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    The Execution Experts

    How Nineleaps turns the blueprint into production reality.

    Architectural theory can only take you so far. Closing the AI readiness gap requires specialized engineering execution. Explore how we partner with enterprise leaders to transform fragmented legacy systems into the clean, connected data foundations that make AI work at scale.

    Success Stories