A recent survey commissioned by Red Hat was conducted by Censuswide among 609 IT managers in the UK and other major markets. As a result of the survey, more than 80% of IT managers answered that the shortage of AI technical personnel is an urgent issue, mainly in fields such as generative AI, LLM, and data science. This is an increase from 72% last year.
The need to sell AI, the need to consume AI, and the inability to do so give rise to so-called “AI stagnation,” a complex problem that has perplexed many in the field of AI.
The spread of AI has almost stopped
IT solution companies are pouring resources into AI development and creating advanced tools, platforms, and infrastructure. Investment in AI, including not only large companies but also startups, is reaching unprecedented levels, and industry experts predict that more than $120 billion will be invested in AI startups in 2024 alone. The contribution of major companies such as NVIDIA, OpenAI, and Antropic to the prosperity of the AI market is reminiscent of the dot-com era. This type of capital investment is generally a positive indicator, indicating strong interest and belief in the potential for future returns.
But even as big players like Microsoft, Google, and Amazon are investing heavily in AI infrastructure, they are also under increasing pressure to facilitate successful enterprise implementations. Their future growth depends on capital investment in cutting-edge technologies and users’ ability to effectively adopt these solutions.
Additionally, the risks surrounding rapidly changing AI technology are growing as companies such as NVIDIA experience operational difficulties in the process of launching innovative AI hardware. Performance and stability issues have a negative impact on the perception of AI products, making companies hesitant to adopt them. These difficulties demonstrate the fragile balance between technical aspirations and practical implementation.
Creative solutions are needed to ensure continued growth of IT solution companies, especially large cloud companies betting on widespread AI adoption. The combination of high investment and low adoption rates creates an unstable environment for these companies. As AI capabilities become increasingly important in cloud services, their importance is growing more than ever.
Supply not keeping up with demand
The story doesn’t end here. The lack of qualified AI talent creates bottlenecks when companies cannot take advantage of advanced technologies, and some liken this to an “AI bubble.” Companies today face an acute shortage of AI experts, including data scientists, machine learning engineers, and AI practitioners, who can drive meaningful initiatives. This talent gap is exacerbated by soaring salaries and a highly competitive job market, making it increasingly difficult to find skilled professionals.
Severe talent shortages mean companies are unable to implement AI technology. This hinders innovation. This disconnect between high levels of investment and the slow pace of AI adoption highlights the need for a strategic approach to bridge the gap between technological advancements and real-world adoption. This is exactly what is needed to escape the crossroads of AI stagnation.
The impact of AI stagnation goes beyond just numbers and goes to the heart of competitive advantage within the technology sector. As companies postpone AI implementation, cloud service providers can become caught in a vicious cycle where results that fall short of expectations lead to disillusionment and lower investment confidence. These dynamics can trigger a market reassessment that can make even promising AI startups question their viability.
What you need now
Implementing AI requires not just adopting cutting-edge tools, but a comprehensive strategy that includes appropriate training, cultural change, and ongoing support. Companies must create an environment where AI can thrive, giving both executives and employees confidence that their investments will generate tangible returns. That’s why solving the talent shortage isn’t just about filling roles, it’s about building capabilities that align with long-term goals.
The future of AI adoption remains uncertain. Although the pressure is mounting, most companies will eventually overcome these initial hurdles and realize significant productivity gains over the long term. The key is to remain optimistic amid short-term volatility and recognize that the current challenges are not insurmountable.
What needs to be done to restart AI and drive business value for key investments? Both AI solution companies and companies have work to do.
Here’s what AI solution companies need to do:
- We partner with educational institutions to provide educational programs that teach AI and data science skills.
- We develop user-friendly tools and support services that make it easy to integrate AI technology into your business.
- We form strategic partnerships with universities and startups to share resources and develop comprehensive AI solutions.
- AI products are tailored to meet the unique needs of different sectors, demonstrating immediate value through relevant case studies.
What companies need to do is as follows:
- Establish an internal training program to strengthen employee capabilities and recruit diverse talent focused on AI.
- Encourage experimentation with AI technologies in a collaborative environment.
- Pilot AI projects to identify strengths and obstacles before full-scale expansion.
- Prepare to use AI effectively by investing in data management practices.
- Align AI initiatives with business goals and establish metrics to track success.
The problem is that companies and AI solution companies are looking at each other and hoping that the other will solve the problem. Unfortunately, the problem cannot be solved that way.
editor@itworld.co.kr
Source: www.itworld.co.kr
