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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.

CTO and Solutions Engineering Director for Cisco UK and Ireland.

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.

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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.

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. Today, business leaders are becoming more intentional, carefully considering the purpose of each AI workload and where it resides.

For many organizations, modernizing existing infrastructure is a higher priority than replacing it. The focus is on ensuring current environments can support growing AI workloads while continuing to deliver the resilience, security and agility the business depends on.

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Infrastructure alone, however, isn't enough. Organizations also need to be clear about the problems they're trying to solve.

I've found it's far more effective to begin with focused, modular, use case-driven deployments than with large-scale AI programs that attempt to transform the organization overnight. Whether its helping customer service teams resolve queries faster, improving fraud detection, supporting engineers with technical knowledge or helping employees automate repetitive tasks, organizations build confidence much faster when people can see AI solving real operational problems.

When organizations can demonstrate tangible business benefits and measurable return on investment from one deployment, it becomes much easier to move successful pilots into production and expand AI into other parts of the business.

As organizations become more ambitious with AI, trust becomes every bit as important as capability.

Trust is the cornerstone of AI adoption. Without trust, adoption stalls because users and stakeholders are less likely to engage with new technologies, regardless of their potential benefits.

At the same time, AI workloads introduce a level of operational dynamism that many existing security approaches were not designed to manage. As organizations connect more data, deploy larger models and begin introducing autonomous agents into operational workflows, they need security capabilities that evolve alongside them.

Security, governance and transparency aren't obstacles to innovation. They're what enable organizations to deploy AI confidently and responsibly.

That means observability, security and governance need to be embedded into every layer of the AI environment, from infrastructure and networks through to applications, models and autonomous agents. Organizations must manage AI agents and ensure they remain secure while operating within clearly defined business guardrails, in many cases applying the same principles of control, management and security as they have done to human workers for years.

The challenge is that organizations are no longer governing a single AI application. They're increasingly managing multiple models, tools and services across different environments. Creating governance that keeps pace with that complexity requires security to become part of day-to-day operations rather than an afterthought.

Ethical, transparent and responsible use of AI also builds the confidence employees, customers and stakeholders need before AI can be adopted at scale. Without robust security and governance, organizations are likely to remain cautious, limiting both adoption and the value AI can deliver.

Organizations make much faster progress when people can see how AI helps them solve a real problem. In our experience, people are much more likely to use AI once they've seen it solve a problem they recognize.

When leaders share practical examples, demonstrate how AI helps them in their own roles and encourage experimentation, they create the confidence others need to do the same.

The UK has a significant opportunity to become a global leader in enterprise AI adoption by helping organizations move beyond experimentation and deploy AI securely, responsibly and at scale.

In my experience, successful AI adoption starts with strong foundations - investing in infrastructure that can support AI workloads, becoming more intentional about where those workloads run, embedding security and governance into every layer of the AI environment, and focusing on practical use cases that demonstrate measurable business value before scaling further.

AI is increasingly becoming a business imperative. The opportunity now is to help more organizations deploy AI consistently, securely and at scale.

Those that take a deliberate, long-term approach to infrastructure, trust and adoption will be better placed to realize the full potential of AI across the business.

If we can help more organizations make that transition from pilot to production, the UK will be well placed to lead through successful AI adoption.

This article was produced as part of Tech Radar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of Tech Radar Pro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

CTO and Solutions Engineering Director for Cisco UK and Ireland.

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Get full access to premium articles, exclusive features and a growing list of member rewards