AI adoption across enterprises is accelerating, yet many organizations struggle to scale beyond pilot use cases. A recent CIO and ComputerWoche study highlights a clear gap between AI ambition and actual business outcomes. The root cause is not the AI technology itself, but insufficient data readiness, weak governance, and a lack of scalable platforms. This is precisely where T-Systems enables measurable impact.
The latest “AI-ready Data Platforms 2026” study shows that over 70% of companies already consider their platforms AI-ready, yet only a fraction achieve measurable value1. This paradox highlights a structural issue: AI adoption is not a challenge, but operationalization is.
Organizations prioritize efficiency gains (55%), cost reduction (49%), and improved data access (48%) as primary goals. However, the expected ROI often remains unrealized due to fragmented execution across business and IT1.
The implication is clear: AI maturity is not defined by tools or pilots, but by the ability to integrate data, scale use cases, and embed AI into core business processes.
Companies have high confidence in AI strategies; however, they face persistent data challenges. The study identifies poor data quality (30%), fragmented data landscapes (28%), and slow IT processes (25%) as the top barriers1.
This aligns with market observations: AI systems are only as effective as the data foundation behind them. Even more critically, only about one-quarter of organizations can fully access all relevant data for AI use cases1.
The result is a disconnect between leadership expectations and operational reality. Business units struggle with limited access, while IT overestimates readiness—creating a gap that slows transformation.
To unlock AI at scale, enterprises must move from isolated initiatives toward an integrated approach. This requires a combination of:
T-Systems addresses this challenge with a holistic AI and data portfolio, combining consulting, AI, data professional services, and operations into a unified, end-to-end stack. The approach is modular—ranging from quick-start workshops to enterprise-scale AI deployments—ensuring measurable outcomes at every stage.
T-Systems combines industry expertise with a complete AI and data stack, covering consulting, data foundations, AI services, and infrastructure.
Key strengths include:
The portfolio is designed to address the exact gaps highlighted in the study, transforming fragmented data environments into scalable AI-driven business platforms.
The study confirms a major trend: Organizations are shifting investments from experimentation toward AI automation and architecture modernization.
T-Systems helps organizations prepare data for AI through a structured, step-by-step approach called the data journey roadmap.
Gain a clear view of current data setup, including how data is managed, governed, and used.
This step highlights what works, what doesn’t, and where improvements are needed.
Identify where data can create business value and what capabilities are required.
Build a clear roadmap with priorities, initiatives, and a plan to move forward.
Put the strategy into action by organizing data, defining roles and rules, and designing a scalable data architecture.
This creates a strong foundation for advanced AI use cases.
With a solid data foundation, organizations can eliminate data silos, improve access to information, and make faster, data-driven decisions.
T-Systems’ end-to-end AI and data expertise helps organizations move beyond pilot projects and scale AI across the enterprise, delivering measurable, long-term impact.
1 AI-ready Data Platforms 2026 Study, CIO/ComputerWoche, 2026, published by Foundry
