What the Cloud Adoption journey has taught us about scaling AI

The rise of artificial intelligence (AI) over the past few years has sparked a familiar conversation in boardrooms. Although this is a new and disruptive technology that is full of potential, it is still a technology. Much like cloud computing over a decade ago, organisations spent years experimenting with cloud, running proofs of concept, and learning what worked before they committed to embedding it deeply into their technology and operations. Now, as AI moves from experimentation to strategic adoption, the lessons from cloud can help us to guide the journey.

When Cloud Computing arrived, early adopters dabbled with pilot projects, curious about the promise of scalability, elasticity and reduced infrastructure costs. But experimentation alone did not create sustainable value. Organisations that unlocked the true potential of cloud did so by investing in the foundations: establishing security guardrails, defining architectural patterns, building governance frameworks, and equipping teams with the right skills. Only then did cloud evolve from a set of experiments to a strategic platform that supported business transformation.

AI is following a similar path. In 2024 and 2025, many organisations embarked on proof of concepts (POCs) to showcase what AI could do. From automating routine tasks to supporting decision making with predictive insights. These pilots proved that the technology works. They also revealed a new set of questions:

·       How do we integrate AI responsibly?

·       How do we manage risk?

·       What security controls do we need in place?

·       How do we observe what decisions are being made by AI and why?

·       What organisational and technical foundations must be in place?

·       How do we maintain quality into production?

In 2026, the focus for enterprises is shifting from running POCs and pilots to scaling with confidence, so this year will be defined by strategy and laying scalable foundations. Organisations turning their learnings into frameworks, policies, and repeatable practices that enable AI to drive measurable value. That’s where the parallels with cloud adoption become most instructive.

Security and Responsible Use

When organisations looked to accelerate their cloud adoption, security wasn’t an afterthought, it became integral to every conversation. However, thinking had to evolve, there were new risks to deal with as the agility of cloud brought about a different way to think about security guard rails, data protection policies, and monitoring tools. With AI, the stakes are similarly high. Responsible AI use demands governance frameworks that address data privacy, model risk, bias mitigation and compliance with emerging regulations. Establishing these guardrails early will allow teams to innovate without exposing the business to undue risk.

Patterns and Best Practices

Cloud brought both flexibility and complexity. Organisations that standardised patterns for infrastructure provisioning, application deployment and cost management saw greater success mush faster. These patterns provided consistency, reduced technical debt, and accelerated delivery. AI adoption will benefit from the same discipline. Developing templates, workflows and integration patterns for common AI use cases such as document processing, conversational interfaces, or analytics augmentation will help organisations avoid reinventing the wheel with every project and taking advantage of ‘blueprints’ to get their solutions to market faster and with confidence.

People and Skills

One of the biggest lessons we can take from cloud adoption was that technology alone doesn’t drive transformation. People do. The organisations that succeeded, invested in training, certification and communities of practice. They helped engineers, architects and business leaders understand not just how cloud works, but how it changes the way we think, build and deliver solutions. With AI, this people-centric approach is equally important. Teams need to understand how to work with AI, evaluate outputs critically, and make sound decisions based on AI-driven insights. It’s also the people who best understand the processes and day to day work they do and therefore are best placed to recognise the use cases that would benefit from being augmented or replaced by AI, so educating them on recognising these use cases is also critical.

Governance and Operating Models

Cloud adoption forced organisations to rethink not just technology, but how decisions are made. Centralised governance, cross-functional oversight and well-defined operating models became essential. AI is forcing the same rethink. Who decides which use cases to pursue? How do we prioritise effort across the organisation? How do we measure impact? Establishing governance and operating models that reflect business priorities, ethical considerations and technical realities will be key to scaling AI responsibly. Also, once AI solutions have been implemented, the ongoing operational models to ensure performance, accuracy, cost optimisation and reliability are significant to get right.

Conclusion

Cloud adoption showed us that change was not about moving the fastest but about building the right foundations early and scaling with intent.

2026 is the year AI moves from isolated success stories to an enterprise capability. That shift demands the same discipline cloud required: strong guardrails, clear patterns, empowered people, and operating models that turn innovation into something repeatable and safe.

Just as cloud became the backbone of digital transformation, AI will become the backbone of modern decision making, automation, and insight. The opportunity now is to treat AI adoption not as a technology programme, but as a strategic transformation.