Moving AI from pilot to production

Most organizations don't need convincing that AI has potential.

What I'm seeing instead is businesses trying to work out how to turn that potential into something that delivers real value.

Across the organizations we're working with, the conversation has shifted.

Business leaders are increasingly confident in AI's potential, but many are now focused on whether they're moving quickly enough to realize that value.

In my experience, the organizations making the fastest progress are those with clear use cases, the right foundations and the confidence to move successful pilots into production.

We're seeing this reflected in conversations with customers every day.

Organizations are moving beyond asking what AI could do and are now focused on how to scale it in a way that delivers measurable business impact.

The reality is that experimentation has shown what's possible.

The challenge now is deploying AI consistently, securely, intentionally and at scale across the organization.

Building the right foundations The past two years have rightly been characterized by experimentation as organizations explored new use cases, tested emerging technologies and began to understand where AI can make the biggest difference.

Getting an initial AI use case into production is an important milestone.

The harder task is repeating that success across multiple teams, business processes and environments while maintaining security , governance and performance.

Infrastructure is fundamental to successful AI adoption, and while the models, training, GPUs and applications are all important, networking is the critical part of that foundation.

Previously, many organizations defaulted to a cloud-first approach where almost every new workload was expected to live in the cloud .