How GenAI's Adoption Journey Is Mirroring Cloud Computing's Earlier Path

Jonathan LaCour
Mission

If you've been in the tech space for a while, you may be experiencing some deja vu. Though often compared to the adoption and proliferation of the internet, Generative AI (GenAI) is following in the footsteps of cloud computing.

Like cloud computing before it, GenAI is moving through recognizable adoption stages: early hype and skepticism evolving into grassroots implementation through unofficial channels, eventually giving way to formalized organizational adoption. Just as cloud technology required IT teams to transform their operations, GenAI tools will spur a big-picture rethinking of everyday work processes across sectors. Employees who integrate these powerful capabilities will benefit from enhanced productivity and results, but those resistant to change may find themselves at a competitive disadvantage.

GenAI and Cloud Computing: Early Doubts Evolved to Competitive Advantages

Cloud computing initially faced uncertainty from IT departments concerned about security risks, loss of control, and managing data in external environments. When faced with a transformative and disruptive technology, some organizations hesitated to entrust their systems to cloud providers justified by fear of change, potential but unfounded security concerns, and a fundamentally different cost model.

However, the competitive disadvantages of avoiding cloud adoption eventually forced technology professionals to evolve their skillsets. Today, cloud computing represents a $600+ billion market expected to grow at 21% annually through 2030. Early adopters gained substantial advantages as they embraced the cloud, advancing their careers to more prestigious Cloud Architect roles that paved the way for future-proofed professional success.

GenAI is following a remarkably similar but accelerated trajectory. Workers in potentially disrupted fields like software development and marketing initially resisted GenAI due to perceived threats. The reality is that GenAI isn't replacing jobs — it's making them better by allowing people to work smarter, not harder. Workers who embrace GenAI as an opportunity to enhance their existing work and skill sets will have a leg up over those who fear it.

Those who were willing to embrace GenAI early are already experiencing dramatic efficiency improvements that have started to drive widespread adoption. Dev teams are finding innovative problem-solving approaches and fundamentally reshaping their workflows. In the near future, developers may spend as much time guiding AI to build solutions as they do writing code themselves. While only 24% of application developers currently consider themselves GenAI experts, this percentage will only go up as more are exposed to GenAI's tangible benefits.

From Resistance to Regulation

Early cloud adoption faced organizational resistance, with some IT leaders implementing policies prohibiting or drastically limiting the adoption of cloud services, often negating many of the potential benefits and feeding a harmful cycle of reduced velocity. Engineering teams, frustrated by slow traditional infrastructure provisioning, defied these restrictions and embraced on-demand capabilities. This "shadow IT" movement further accelerated cloud acceptance as developers became advocates for API-driven infrastructure, eventually pushing resistant IT leaders to adapt or face the potential negative consequences for their business.

The adoption of GenAI has trickled upward in a remarkably similar way. Regardless of their organizational policies, many employees are using GenAI daily, and as these users repeatedly demonstrate GenAI's value, executive leadership is increasingly willing to formally invest. Just like how companies transitioned from unauthorized but prolific cloud usage to Cloud Centers of Excellence with standardized policies, organizations are now creating parallel structures for GenAI with AI ethics boards and policies that provide effective guardrails without stifling adoption.

The primary differences between GenAI and cloud have been the rate of change and adoption. The GenAI timeline has been accelerated, as many organizations have institutional memory of cloud transformation. GenAI governance frameworks are being implemented quickly to facilitate cross-organizational adoption, enabling an evolution from proof of concept to production.

Much as cloud expertise became indispensable for IT specialists, proficiency in AI systems and their governance has become a fundamental requirement for contemporary tech practitioners. Professionals who have taken historical lessons to heart and chosen to embrace GenAI instead of opposing it are poised to be at the forefront of whatever the next big tech disruption may be.

Jonathan LaCour is CTO of Mission

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How GenAI's Adoption Journey Is Mirroring Cloud Computing's Earlier Path

Jonathan LaCour
Mission

If you've been in the tech space for a while, you may be experiencing some deja vu. Though often compared to the adoption and proliferation of the internet, Generative AI (GenAI) is following in the footsteps of cloud computing.

Like cloud computing before it, GenAI is moving through recognizable adoption stages: early hype and skepticism evolving into grassroots implementation through unofficial channels, eventually giving way to formalized organizational adoption. Just as cloud technology required IT teams to transform their operations, GenAI tools will spur a big-picture rethinking of everyday work processes across sectors. Employees who integrate these powerful capabilities will benefit from enhanced productivity and results, but those resistant to change may find themselves at a competitive disadvantage.

GenAI and Cloud Computing: Early Doubts Evolved to Competitive Advantages

Cloud computing initially faced uncertainty from IT departments concerned about security risks, loss of control, and managing data in external environments. When faced with a transformative and disruptive technology, some organizations hesitated to entrust their systems to cloud providers justified by fear of change, potential but unfounded security concerns, and a fundamentally different cost model.

However, the competitive disadvantages of avoiding cloud adoption eventually forced technology professionals to evolve their skillsets. Today, cloud computing represents a $600+ billion market expected to grow at 21% annually through 2030. Early adopters gained substantial advantages as they embraced the cloud, advancing their careers to more prestigious Cloud Architect roles that paved the way for future-proofed professional success.

GenAI is following a remarkably similar but accelerated trajectory. Workers in potentially disrupted fields like software development and marketing initially resisted GenAI due to perceived threats. The reality is that GenAI isn't replacing jobs — it's making them better by allowing people to work smarter, not harder. Workers who embrace GenAI as an opportunity to enhance their existing work and skill sets will have a leg up over those who fear it.

Those who were willing to embrace GenAI early are already experiencing dramatic efficiency improvements that have started to drive widespread adoption. Dev teams are finding innovative problem-solving approaches and fundamentally reshaping their workflows. In the near future, developers may spend as much time guiding AI to build solutions as they do writing code themselves. While only 24% of application developers currently consider themselves GenAI experts, this percentage will only go up as more are exposed to GenAI's tangible benefits.

From Resistance to Regulation

Early cloud adoption faced organizational resistance, with some IT leaders implementing policies prohibiting or drastically limiting the adoption of cloud services, often negating many of the potential benefits and feeding a harmful cycle of reduced velocity. Engineering teams, frustrated by slow traditional infrastructure provisioning, defied these restrictions and embraced on-demand capabilities. This "shadow IT" movement further accelerated cloud acceptance as developers became advocates for API-driven infrastructure, eventually pushing resistant IT leaders to adapt or face the potential negative consequences for their business.

The adoption of GenAI has trickled upward in a remarkably similar way. Regardless of their organizational policies, many employees are using GenAI daily, and as these users repeatedly demonstrate GenAI's value, executive leadership is increasingly willing to formally invest. Just like how companies transitioned from unauthorized but prolific cloud usage to Cloud Centers of Excellence with standardized policies, organizations are now creating parallel structures for GenAI with AI ethics boards and policies that provide effective guardrails without stifling adoption.

The primary differences between GenAI and cloud have been the rate of change and adoption. The GenAI timeline has been accelerated, as many organizations have institutional memory of cloud transformation. GenAI governance frameworks are being implemented quickly to facilitate cross-organizational adoption, enabling an evolution from proof of concept to production.

Much as cloud expertise became indispensable for IT specialists, proficiency in AI systems and their governance has become a fundamental requirement for contemporary tech practitioners. Professionals who have taken historical lessons to heart and chosen to embrace GenAI instead of opposing it are poised to be at the forefront of whatever the next big tech disruption may be.

Jonathan LaCour is CTO of Mission

Related Links

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The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

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Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

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When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

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If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...