Synopsis: Fletcher Keister, Chief Product and Technology Officer at GTT, lays out what he calls the three pillars of AI readiness: infrastructure, data, and skills. Without these, he argues, organizations risk repeating the same mistakes seen in earlier technology waves.
On infrastructure, Keister emphasizes that AI workloads demand far more than traditional IT systems were built to handle. Networks must be robust, low-latency, and capable of handling unprecedented traffic patterns. For enterprises still operating with legacy platforms, this becomes a gating factor for deploying AI at scale.
The second pillar is data. AI is only as good as the data it’s trained and run against. Keister points out that many organizations still struggle with fragmentation, governance, and quality. AI systems can’t deliver value if the data feeding them is incomplete or unreliable. Establishing strong pipelines, ensuring compliance, and maintaining security are now strategic priorities rather than afterthoughts.
Finally, skills may be the most overlooked challenge. AI projects can’t succeed without people who know how to build, train, integrate, and manage them. The shortage of data scientists, engineers, and architects continues to be a bottleneck. At the same time, employees across functions need to understand how to work alongside AI responsibly and effectively.
Keister stresses that these three elements are interconnected — weak infrastructure undermines data strategy, while gaps in skills prevent organizations from capitalizing on both. The good news, he says, is that companies can learn from past transformations in cloud and digital. With planning and investment, AI adoption can follow a smoother path.
The bottom line: AI readiness isn’t about chasing hype. It’s about building a foundation where infrastructure, data, and skills come together to make AI real, scalable, and sustainable.
