87% of leaders say their infrastructure is AI-ready. 42% still cite it as a top challenge. What’s the disconnect?
New findings from 500+ global data and analytics leaders expose the disconnect between infrastructure confidence and integration reality — and what it means for CIOs and Enterprise Architects trying to scale AI.
Free report. See how your integration architecture compares to your peers.
THE CHALLENGEFragmented infrastructure slows AI at scale.
Enterprise AI depends on one foundational capability: the ability to move trusted data across systems, in real time, at scale. But most organizations operate across fragmented environments — cloud platforms, on-premises systems, and legacy applications — that weren’t built to work together. The result is a confidence gap: leaders believe their infrastructure is ready, while integration complexity keeps quietly blocking AI progress.
What the data shows
82-87%
of organizations prefer cloud/SaaS or hybrid licensing over on-premises for critical data management — reflecting how far modern environments have shifted.
KEY INSIGHT Scalable AI requires unified data architectures, and the returns on integration investment are measurable.
40% of organizations are expanding existing governance programs to cover AI — versus just 23% launching separate AI governance initiatives.
Organizations investing in data integration consistently report two outcomes CIOs and Enterprise Architects need to make the case for: improved data quality (45%) and better data access across systems (44%). As AI moves from experimentation to production, data architecture becomes a decisive factor in success — and the organizations best positioned to scale AI are those aligning integration strategy with governance, data quality, and readiness initiatives, not treating it as a standalone infrastructure problem.
WHAT’S IN THE REPORTSee where 500+ of your peers stand on integration architecture and AI readiness.
- Top integration challenges cited by data leaders in 2026
- Cloud and hybrid adoption benchmarks across global enterprises
- How leading organizations are aligning integration strategy with AI readiness
- 2026 data integrity priority rankings from data and analytics leaders worldwide
