AI Execution Gaps campaign (Global) | DXC Technology

Your AI isn’t scaling. Here’s why.

Most organizations struggle to move beyond AI pilots. DXC has helped enterprises scale AI across operations with our proven Xponential framework. Discover where you stand and what to do next.

       


Response of firms surveyed about the state of AI adoption in the enterprise :

53%

currently lack Agentic Governance to handle autonomous decision-making

30%

cite data quality and availability as a primary barrier to AI adoption

Only 37%

have reached high AI maturity


Obstacles to value

Across industries, organizations are investing heavily in AI, yet many struggle to realize meaningful results due to structural gaps and common patterns. AI initiatives are often launched in silos, with unclear ownership across business and IT. Data is often not ready for real-world use cases, and there are concerns about risk, cost and control. As a result, progress slows, business value is difficult to measure, and impact remains limited.

The issue is not the technology itself — it’s how AI is structured, governed and scaled across the enterprise.


Why AI initiatives fail to scale

These are the structural gaps that prevent AI from delivering measurable value at scale:

Ownership & Decision Rights

AI initiatives span multiple teams without clear ownership or decision authority. Decisions are slow, funding is fragmented, and outcomes are not tied to business performance. When addressed, AI becomes a business-owned capability with clear accountability, faster execution and measurable impact.

Trust & Governance

Concerns around reliability, compliance, cost and control limit adoption beyond low-risk use cases. This prevents AI from scaling into critical processes and creates uncertainty around risk and cost. When addressed, AI can be deployed confidently with full visibility, compliance and control.

Workforce & Operating Model​

AI is introduced without redesigning workflows, roles or responsibilities. Adoption remains low and expected productivity gains do not materialize. When addressed, work is structured around human and AI collaboration, enabling efficiency and scale.

AI-ready Data

Data is fragmented, inconsistent or not aligned to business use cases. This leads to poor model performance and slow implementation. When addressed, data becomes accessible, governed and usable in real-time contexts.

Scaling & Value Realization

AI initiatives remain in pilot phase without a path to scale. This results in no repeatability, no enterprise adoption and no clear ROI. When addressed, AI is deployed at scale with measurable business outcomes.



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In the regions

Across markets, organizations encounter similar execution challenges when scaling AI — with different priorities specific to the region:

ANZ

ASEAN

Europe