Moving AI from pilot to production

Date:
Thu, 20 Aug 2026 14:22:25 +0000

Description:
Why infrastructure, trust and leadership are key to scaling enterprise AI successfully.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter 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. Latest Videos From TechRadar Watch full video here: Rob Lay Social Links Navigation

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. You may like Why AI pilots stall and what organizations must fix to scale AI successfully Beyond Pilot Purgatory: What does it take to build AI that
works? The key steps that will enable organizations to scale Physical AI

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. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.

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. What to read next The AI
Scaling gap: why ambition is outpacing readiness Why connecting tech to operational reality will help businesses deliver on AI's promise How to move GenAI pilots from experiments to enterprise advantage

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. Building trust in AI 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. Make AI personal 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.

That's why leaders have an important role to play.

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's opportunity 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. Going forward 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. We've listed the best cloud backup solutions . This article was produced as part of TechRadar 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 TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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