The rapid rise of artificial intelligence across business feels new, urgent and at times overwhelming. Yet for many technology and security leaders, there is a strong sense of déjà vu. The conversations happening today about AI adoption closely mirror those that surrounded early cloud adoption more than a decade ago. The same mixture of excitement, scepticism, regulatory anxiety and skills shortages is resurfacing, just with different technology at the centre.
When cloud platforms such as Amazon Web Services and Microsoft Azure first entered the enterprise space, organisations struggled to balance innovation with control. Shadow IT flourished, data moved beyond traditional perimeters, and security teams were asked to protect environments they did not design. AI is following a similar trajectory, but at greater speed and with higher stakes.
Understanding this parallel is critical and organisations that recognise the lessons of early cloud adoption are far better positioned to adopt AI safely, sustainably and in a way that delivers real business value.
One of the defining characteristics of early cloud adoption was the speed at which business units moved ahead of formal governance. Developers spun up workloads in minutes. Marketing teams adopted SaaS tools without consulting IT. Procurement processes lagged far behind innovation.
AI adoption is repeating this pattern almost exactly. Teams are already using generative AI tools to write content, analyse data, generate code and automate decisions. Many of these tools sit outside existing approval processes, creating blind spots around data handling, intellectual property and compliance.
Just as cloud forced organisations to rethink governance models, AI demands a shift from rigid control to enablement with oversight. Blanket bans tend to fail, while permissive adoption without guardrails creates long-term risk. The organisations that succeed are those that define clear principles for acceptable use, data boundaries and accountability, then allow innovation to happen within those limits.
Early cloud adoption broke many traditional security assumptions. Perimeter-based defences no longer made sense when data and workloads lived outside the corporate network. Identity became the new control plane, and shared responsibility models replaced fully owned infrastructure.
AI introduces a similar disruption. Models, prompts, training data and outputs all become part of the attack surface. Over-trusted automation, data leakage through prompts, and model manipulation are now real concerns. Security teams cannot simply bolt AI onto existing controls and hope for the best.
What cloud taught us is that security must be designed into the technology adoption strategy from the start. This means understanding where AI systems are used, what data they access, how outputs are validated, and how misuse is detected. It also means accepting that not every risk can be eliminated, but it can be managed intelligently.
During the early days of cloud, many organisations struggled to find people with the right mix of infrastructure, security and application expertise. Training lagged behind demand, and external support became essential.
AI is creating a similar skills challenge. Data scientists, AI engineers, governance specialists and security professionals who understand AI risk are all in short supply. Expecting existing teams to absorb this overnight is unrealistic.
The lesson from cloud is that capability building must be deliberate. This includes targeted training, realistic role definitions, and selective use of managed services and partners. Organisations that treat AI as purely a technology purchase often fail to realise its value. Those that invest in people and processes alongside tooling tend to mature far faster.
Cloud adoption initially outpaced regulation, creating uncertainty around data residency, privacy and accountability. Over time, frameworks and standards caught up, providing clearer guidance for organisations.
AI is on the same path. Regulations are emerging, but they are still evolving and uneven across regions. Waiting for perfect regulatory clarity before adopting AI is likely to leave organisations behind competitors who are already learning and adapting.
The more effective approach mirrors successful cloud strategies. Build AI programmes that are transparent, auditable and aligned with existing risk management practices. If governance is strong, regulatory change becomes an adjustment rather than a crisis.
AI is currently in its experimental phase for many organisations, but the shift to operational dependence is happening quickly. Decision support, customer interaction, fraud detection and security operations are all increasingly influenced by AI-driven systems.
This transition is where risk concentrates. As reliance grows, failures have greater impact. The cloud experience shows the importance of resilience, monitoring and contingency planning. AI systems must be understood, tested and governed as core business components, not novelty tools.
The similarity between AI and early cloud adoption is a guide to moving forward more intelligently. Organisations that learned from cloud now have a playbook.
Start by accepting that AI adoption is inevitable. Get the fundamentals right: identity, data, web, and workload security controls. Focus on visibility rather than restrictions. Define governance that enables safe use. Treat security as a design requirement, not an afterthought. Invest in skills and recognise where external expertise adds value. Finally, plan for AI to become embedded in critical processes.
AI, like cloud before it, will reward organisations that balance speed with discipline. Those that repeat past mistakes will rediscover familiar problems at a much larger scale.
If you want to explore how to adopt AI securely, responsibly and in line with your existing risk and governance frameworks, speak with our specialists to understand the practical steps your organisation should be taking now.