AI continues to shape cloud strategies, but AI implementation is going slower than most predicted. This is frustrating for technology providers who have made big bets on AI. What’s going on?

A recent survey conducted by Censuswide on behalf of Red Hat polled 609 IT managers across the United Kingdom and other major markets. More than 80% of IT managers reported an urgent AI skills shortage, mainly in areas such as generative AI, large language models (LLMs), and data science. This is up from 72% last year.

The need to sell AI, the need to consume AI, and the inability to do so lead to what I’m calling “AI stagnation,” a complex issue that is confounding many in the AI space, including yours truly.

AI at a near standstill

Technology providers continue to pour resources into AI development, creating advanced tools, platforms, and infrastructure. Tech giants’ and startups’ investments in AI are reaching unprecedented heights, with industry watchers predicting more than $120 billion in funding for AI startups in 2024 alone. The contributions of major players, such as Nvidia, OpenAI, and Anthropic, to a thriving AI market are reminiscent of the dot-com era. This type of capital influx is typically a positive indicator, signaling robust interest and faith in the potential for future returns.

However, while Microsoft, Google, Amazon, and other big providers are investing heavily in AI infrastructure, they also face increasing pressure to foster successful enterprise implementations. Their future growth hinges on capital infusion into cutting-edge technologies and their users’ ability to adopt these solutions effectively.

Moreover, as companies such as Nvidia grapple with operational hiccups in the rollout of innovative AI hardware, the risks surrounding this fast-paced technology become magnified. Performance and reliability issues can adversely affect the perception of AI products, leading potential adopters to hesitate further. Such struggles illustrate the fragile balance between technological aspiration and practical execution.

We need creative solutions to ensure sustained growth for technology providers, particularly the larger cloud companies heavily reliant on widespread AI adoption. The combination of high investment and low adoption could lead to an unsettling environment for technology providers. With AI capabilities becoming increasingly critical for cloud services, the stakes are higher than ever.

Supply that doesn’t follow demand

The story doesn’t end there. Enterprises’ inability to capitalize on these advancements due to a lack of qualified AI talent creates a bottleneck that some liken to an impending AI bubble. Today’s enterprises face an acute shortage of AI specialists—data scientists, machine learning engineers, and AI practitioners—who can drive meaningful initiatives. This talent gap is compounded by soaring salaries and competitive job markets, making it increasingly difficult to find skilled professionals.

This significant talent shortage means that enterprises cannot implement AI technologies. This stifles innovation. This disconnect between high levels of investment and the slowing pace of AI adoption underscores the need for a more strategic approach, bridging the gap between technological advancements and practical applications. This is what it will take to get us out of the AI stagnation intersection.

The ramifications of AI stagnation extend beyond mere numbers; they strike at the heart of competitive positioning within the technology sector. As enterprises delay AI implementations, cloud providers could be caught in a feedback loop where unmet expectations lead to disillusionment and reduced investment confidence. This dynamic could trigger a market reevaluation, placing even the most promising AI ventures under scrutiny.

What can we expect?

Implementing AI isn’t merely about acquiring advanced tools; it necessitates a comprehensive strategy that includes adequate training, culture shifts, and ongoing support. Organizations need to cultivate an environment where AI can thrive, assuring both leadership and the workforce that the investments will generate tangible returns. This is why addressing the talent shortage is not just a matter of filling roles but building capabilities that align with long-term objectives.

What lies ahead for AI adoption remains uncertain. Although pressures mount, most organizations will eventually navigate these initial roadblocks and realize significant long-term productivity gains. The key is to stay optimistic amidst short-term volatility, recognizing that the current challenges are not insurmountable.

Who is going to fix this?

What should everyone do to get AI started again and return major business value for the major investments? Technology providers and enterprises both have some work to do.

Technology providers should:

• Collaborate with educational institutions to offer training programs that teach employees AI and data science skills.
• Create user-friendly tools and support services that ease the integration of AI technologies into businesses.
• Form strategic alliances with universities and startups to share resources and create comprehensive AI solutions.
• Tailor AI offerings to meet the unique needs of various sectors, showcasing immediate value through relevant case studies.

Enterprises should:

• Build internal training programs to upskill staff and recruit diverse talent focused on AI.
• Encourage experimentation with AI technologies in a collaborative environment.
• Pilot AI projects to understand benefits and obstacles before scaling.
• Invest in data management practices to prepare for effective AI use.
• Align AI initiatives with business goals and establish metrics to track success.

The trouble now is that enterprises and technology providers are staring at each other, each hoping the other fixes the problem. Sadly, it doesn’t work that way.