The patterns of technology adoption continue to be predictable, with GenAI following the same path cloud computing blazed before it. Just as cloud architecture became a critical skill for IT professionals, AI literacy and governance expertise are becoming essential for today’s technology leaders.
Enough time has passed since Generative AI’s explosive debut that we can say one thing for certain: GenAI is one of the most disruptive technologies of the modern era. Some have compared the rapid ascent of GenAI to that of the nascent internet, but I think it harkens back to the origins and trajectory of cloud computing.
Like cloud computing before it, GenAI is following a familiar adoption pattern: initial resistance from specialists and bottom-up adoption through shadow IT, followed by top-down governance frameworks. Just as IT departments were forced to adapt and evolve with the advent of public cloud, entire workforces will need to reimagine their day-to-day work in the context of powerful AI-driven technologies. Those who embrace the disruption will demonstrate their ability to be more effective and efficient, and those who do not will risk being left behind.
Initial adoption reluctance for cloud and GenAI
When cloud computing first emerged, many IT departments approached this new tech with a heavy layer of skepticism due to concerns about data security, lack of control over infrastructure, and a perceived lack of understanding about how to manage data in a cloud environment. Many organizations were reluctant to hand over control of their data and IT systems to a third party because they worried about potential vulnerabilities and disruptions to their operations.
It quickly became clear that those who failed to embrace the cloud were going to be left behind, pushing all technology professionals to adapt their skill sets to more effectively manage cloud services. Now, over a decade later, cloud computing is a massive market valued at over $600 billion in 2023 and projected to grow at a CAGR of over 21% until 2030. Those who bought in early were at an advantage. Many legacy network engineers who rapidly adopted cloud networking are now finding themselves with more significant roles as Cloud Architects better set up for long-term professional success.
GenAI adoption has followed a similar pattern so far. Workers with roles likely to experience disruption, such as developers and marketing professionals, were initially skeptical of GenAI because of the perceived threat to their jobs. While some are predicting that GenAI will be able to entirely replace certain roles, the reality is much murkier. Rather than being fearful that GenAI will take their jobs, workers who look at GenAI as an opportunity will use the technology to augment the work they’re already doing and find entirely new ways to contribute.
Early adopters will find their efficiency and capabilities rapidly improve, and their discovery will not go unnoticed – adoption will skyrocket. Developers will identify new ways of solving problems, which will significantly alter their workflows. I can see a world in the not-too-distant future where developers spend as much time prompting and guiding AI to build solutions as they do programming.
Though recent research showed that only about a quarter (24%) of application developers feel like they’re GenAI experts at this time, that number will continue to tick up as more and more developers use GenAI and see valuable outcomes.
See also: 10 Executives on Why and How GenAI is Here to Stay
From shadow IT to top-down governance
In the early days, many IT leaders were resistant to cloud, putting in place policies that prohibited employees from using services like Amazon EC2 and S3. Engineering teams are forced to wait for traditional infrastructure to be provisioned rebelled, taking advantage of AWS’ on-demand infrastructure in spite of restrictive policies. This “shadow IT” movement accelerated the cloud’s disruption as developers fell in love with API-driven infrastructure, and before long, laggard IT leaders were forced to alter their ways or risk causing harm to their businesses.
GenAI has mirrored this bottom-up adoption strategy. At this point, many engineers are using GenAI every day, whether their organization wants them to or not. However, because engineers and other users have proven its utility time and time again, leaders at the top are increasingly inclined to invest in GenAI tools.
The enterprise adoption patterns of cloud computing and generative AI share striking parallels, particularly in how their governance frameworks have evolved. Since both technologies initially saw rapid, bottom-up adoption driven by individual teams operating outside official channels, they eventually forced organizations to develop comprehensive governance structures to manage risks and standardize usage. Just as companies moved from ad-hoc cloud usage to establishing Cloud Centers of Excellence with standardized policies, they’re now creating similar frameworks for GenAI, including AI ethics boards and formal usage policies.
The key difference is the accelerated timeline of GenAI governance adoption as organizations apply lessons learned from their cloud journey. They recognize that proactive governance — balancing innovation with control — is essential for sustainable enterprise-wide adoption. This involves implementing similar principles: centralized oversight with distributed execution, clear policies and procedures, and robust monitoring and compliance mechanisms.
The patterns of technology adoption continue to be predictable, with GenAI following the same path cloud computing blazed before it. Just as cloud architecture became a critical skill for IT professionals, AI literacy and governance expertise are becoming essential for today’s technology leaders. Those who learned from the past to proactively embrace and help shape these changes rather than resist them will likely find themselves leading the next wave of digital transformation.
