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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

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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

Hot Topics

The Latest

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

Image
Pagerduty

 

Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...