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Most organizations know they need to scale AI. Far fewer know where the real obstacles hide. Data you cannot trust, infrastructure that was not built for inference, and security gaps that widen as AI workloads grow are often the factors that prevent organizations from moving from strategy to execution.
Drawing on GTT’s experience deploying AI infrastructure powered by NVIDIA and Dell Technologies, James Karimi, CIO & CISO at GTT, shares practical lessons for organizations looking to move from AI ambition to operational impact.
The work starts before the technology
Organizations are investing heavily in AI, yet many find themselves stuck at the starting line, held back by data they cannot fully trust, infrastructure that was not designed for AI workloads and security strategies that have not kept pace with an evolving threat landscape.
GTT’s approach started where most organizations skip ahead: Understanding the true state of your data before building, scaling or automating AI.
“Spend the four, six, or eight months really analyzing your data. That will give you a 50% advantage in being successful on this journey.”
James Karimi, CIO/CISO, GTT
Four lessons from GTT’s AI journey
Data readiness comes first
Before infrastructure is deployed or models are trained, organizations need a clear understanding of the quality, accessibility and governance of their data.
Focus on high-value cases
The most successful AI initiatives begin with a small number of prioritized business outcomes rather than attempting to solve every problem at once.
Build security into the foundation
As AI adoption expands, organizations must account for security, governance and compliance requirements from the outset.
Scale with purpose
Technology enables AI, but sustainable success comes from disciplined execution, organizational alignment and continuous refinement.
Originally recorded as part of the Insight On podcast series, this conversation explores the foundational decisions that helped GTT move from strategy to real-world implementation.
In this episode you’ll learn:
⦁ Why data readiness is the foundation of successful AI initiatives
⦁ Common reasons AI deployments fail before generating business value
⦁ How organizations can prioritize AI use cases for measurable outcomes
⦁ The role infrastructure, governance and security play in AI adoption
⦁ Lessons learned from GTT’s global AI deployment strategy
Frequently asked questions
Why does data governance matter before deploying AI?
AI models are only as reliable as the data they learn from. Organizations that invest time understanding data quality, lineage and accessibility are better positioned to scale AI successfully and generate meaningful outcomes.
How does network infrastructure impact AI success?
AI initiatives depend on secure, reliable connectivity between users, applications, data sources and AI platforms. A modern network foundation helps organizations scale AI while maintaining performance, visibility and security.
What infrastructure does GTT’s AI factory run on?
GTT’s AI Factory runs on NVIDIA and Dell infrastructure, deployed across four regional nodes in the US, UK and Europe to meet data sovereignty requirements.
What does GTT’s AI strategy mean for customers?
GTT’s AI strategy is designed to improve operational efficiency, strengthen security, and accelerate innovation across network and business operations. For customers, that translates into faster insights, improved service experiences and greater operational agility.
How should organizations prioritize AI investments?
Start with a small number of high-impact use cases tied to measurable business outcomes. This creates momentum, demonstrates value and helps establish a framework for broader AI adoption.
