Enterprises are rushing to embrace generative AI as it becomes an essential priority for boards and executive leadership. However, the question of how to fund this transformative technology looms large, sparking a crisis of spending in the IT landscape. In this article, we explore the challenges CIOs face in financing generative AI initiatives, the potential repercussions of reallocating budgets, the critical role of staffing, and strategies for success in the cloud-based generative AI era.
Generative AI’s soaring budget demands
The push towards generative AI has caught many IT executives off guard, particularly regarding budgetary requirements. In 2023, generative AI budgets are expected to be 3.4 times greater than anticipated. While the urgency to invest in this technology is evident, only 15% of tech executives expect to fund this substantial uptick with entirely new spending.
Given the absence of substantial unallocated funds, 33% of tech executives are devising strategies to divert resources from other parts of the IT portfolio to support generative AI initiatives. This includes 37% of tech executives who plan to reallocate generative AI spending from their broader AI investment portfolio. However, this approach raises concerns about the potential consequences of underfunding critical areas.
The true cost of generative AI
Generative AI expenditure extends beyond cloud fees to include staffing costs, which can be substantial. The impact of generative AI on labor and cloud spending is expected to be far-reaching. Finding, training, and retaining skilled personnel to deploy generative AI systems will be costly, far surpassing the expenses associated with traditional IT systems.
CEOs must clearly understand how high-impact projects will utilize resources to budget adequately for associated costs. The risk of misallocation and underfunding in this transformative era is real, potentially harming both the bottom line and employee morale. Striking the right balance between investment and staffing is crucial for success.
Staffing challenges: The Achilles’ heel of generative AI
Staffing costs pose a significant challenge to generative AI adoption, with at least 20 open positions for every qualified candidate. While this talent shortage may improve over time as the market matures, companies need internal expertise to gain a competitive edge in cloud-based generative AI. Finding these experts can be a daunting task.
In-demand skills for generative AI
The skills in high demand for generative AI include data science, engineering, and design thinking. However, it’s not just about expertise with a single cloud provider; candidates must understand generative AI systems across various platforms to maximize their value.
As enterprises increasingly embrace AI in the cloud, the most significant risk to projects is not technology falling short of expectations but rather inadequate funding and a shortage of skilled talent. This situation could be exacerbated in the case of generative AI, which has unique demands and applications.
Considerations and solutions
Before diving headfirst into generative AI adoption, organizations should carefully assess whether these systems are genuinely needed. Misapplication of generative AI can lead to wasted resources. Additionally, businesses should explore optimization across all platforms, including data centers, to ensure the most efficient operation of generative AI solutions.
Navigating the challenges of generative AI requires strategic decision-making, much like previous technology trends such as client/server architecture, the internet, service-oriented architecture, and cloud computing. While generative AI holds enormous potential for businesses, it is vital to make objective decisions based on the specific needs and goals of the organization.
Despite the hurdles of funding and staffing, generative AI remains a technology with the power to transform enterprises. Successfully leveraging this technology will be a key differentiator for businesses, but doing so requires a thoughtful approach that balances budgetary constraints and staffing challenges. As generative AI continues to evolve, organizations must adapt and make informed choices to remain competitive in this ever-changing landscape.
