HaoTech
A fresh report from Wiz Research, which scrutinized 150,000 cloud accounts, demonstrated a surge in cloud-based AI adoption. The report revealed that 70% of organizations have employed managed AI services.
To offer some context, this places AI nearly on par with managed Kubernetes services, utilized by 80% of the surveyed companies.
Key Highlights
- Over 70% of cloud-using firms have embraced managed AI services, making AI almost as prevalent as Kubernetes services (80%).
- Microsoft, specifically Azure AI Services and Azure OpenAI, emerges as a front-runner, spearheading AI deployment among major cloud service providers.
- Generative AI is making waves in cloud environments, with 53% of companies employing OpenAI technologies.
- PwC indicates that the primary challenge for business leaders is ‘extracting measurable value from AI’.
Microsoft was the unquestionable leader among the analyzed providers. Azure AI Services and Azure OpenAI topped the charts in AI deployment among major cloud service providers. Astonishingly, Azure Open AI usage witnessed a 228% jump within just four months.
These findings underscore a shift in AI adoption, with an increasing number of enterprises exploring the incorporation of AI workloads into their cloud portfolios.
AI Ushering in a New Era in Cloud Computing
A rising number of cloud providers are integrating generative AI into their offerings, with notable examples being Microsoft’s Azure AI Studio and Google Cloud’s support for generative AI on its Vertex AI platform. With the buzz around AI at its peak, it is evident that cloud computing is a pivotal technology enabling the creation of an efficient insight economy.
Despite hefty investments in AI by several firms, a substantial segment is still in the experimental phase with AI. For instance, less than ten instances have been deployed by 32% of companies.
This cautious approach is partly due to the exorbitant costs associated with training and fine-tuning generative AI models. Running these services is not cheap, with estimates suggesting that operating ChatGPT could cost up to $700,000 per day.
Consequently, many organizations are limiting the number of instances deployed in cloud environments to gauge AI’s value and consider further investment.
At present, the primary interest lies with OpenAI – 53% of cloud environments are using OpenAI or Azure OpenAI SDKs, compatible with services like GPT, DALL-E, and Whisper.
Wiz’s findings state, “Generative AI is a truly cloud-native technology, with training and inference remaining highly compute-intensive for most use-cases, encouraging organizations to leverage the computation and storage scaling offered by the cloud.”
The Significance of LLMs
Generative AI has piqued interest and gained traction based on its potential to boost productivity. MIT research shows that AI can enhance a worker’s performance by up to 40% compared to those not using it.
The versatility of language models like GPT-4 and Claude 2 allows a wealth of use cases, ranging from generating text and code to providing natural language summaries of isolated data signals, offering more context to human users.
Given that 60% of corporate data was stored in the cloud as of 2022, moving towards the AI cloud allows users to derive insights from a broader range of data signals than ever before.
Despite these prospects, many business leaders remain uncertain about the value provided by the technology. PWC reports that 88% of business leaders cite achieving measurable value from AI as one of their top challenges. 85% also list the cost of adoption as a hurdle.
Therefore, both end-user companies and cloud service providers will need to focus on enhancing the efficiency of training AI and machine learning models to realize their full potential. This could explain why many companies are leaning towards managed services – to curb overall costs.
In Conclusion
AI is here for the long haul. While generative AI solutions like GPT-4 are not flawless, they have managed to pique the interest of the enterprise market. Whether it is economically viable for an average company to run AI workloads in the cloud remains to be seen, but early indications are positive.
There is an evident desire to reap the productivity benefits of implementing AI, even if there is still a lack of clarity around the economics of doing so.
