Implementing AI during a global talent shortage

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C-suite demands for enterprise-wide proliferation of AI are often complicated by the lack of available talent and skills required to strive for such deployments. Budget is rarely the limiting factor, especially for larger organizations. What is missing are people with the practical knowledge and skills to test and institute AI across an entire organization.

When the right machine learning (ML) models are combined with the right use cases, AI can improve customer service, perform administrative tasks, analyze huge datasets, and perform many other functions organizational data in huge volume and with low error rates. Business leaders know this. Yet they are prevented from acting on that knowledge.

A new study by SambaNova Systems has shown that, globally, only 18% of organizations are deploying AI as a large-scale, enterprise-wide initiative. Similarly, 59% of IT managers in the UK say they have the budget to hire additional resources for their AI teams, but 82% said hiring on those teams was a challenge.

Every hour of repetitive tasks that can be reduced by automating or increased with AI is an hour employees can spend driving value through higher-order lateral thinking tasks. Companies are watching their competitors find a competitive advantage as they test, iterate and deploy AI programs at scale, seeking all the AI ​​and ML expertise they can attract in the meantime.

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This skills crisis is neither new, nor surprising, nor easy to solve. It's been a problem across the tech industry for years, if not decades. In 2011, a PwC study found that over 56% of CEOs were concerned about the lack of talent to fit into digital roles. And more than a decade later, 54% of technology leaders ranked talent acquisition and retention as the top threat to business growth.

The age of AI has compounded this problem: the pace of change exceeds what has come before.

The skills crisis is exacerbated by the rapid pace of change in AI models

The challenge for anyone working in AI who wants to keep their skills up to date is twofold. First, the pace of change is breathtaking and seems to be accelerating all the time. Second, as models get bigger, they become less accessible to software engineers because large models require large budgets to operate.

Probably the hottest topic in AI is large language models (LLMs). The first Generative Pre-Trained Transformer (GPT) model was released by OpenAI in 2018 – which, as a general-purpose learner, is not specifically trained to perform the tasks it is good at. The model takes advantage of deep learning and is able to perform tasks such as summarizing text, answering questions, and generating text output at a human level. The first model came out four years ago, but it's only ...