Why companies must build an AI-ready workforce

AI has become one of the most prized tools in today’s corporate landscape. Nearly every industry, from finance to healthcare, is adopting AI as an essential part to innovation, automation, and competitive positioning. And yet, for all its promise, one critical issue holds back progress – a shortage of qualified AI talent.

Companies are facing widening skills gaps that hinder project timelines, inflate hiring costs, and erode potential advantages. Because the global demand for AI specialists far exceeds the supply, it’s creating formidable challenges in hiring. But it’s also revealing an opportunity for harnessing the transformative potential of AI through talent. To address these challenges, a blended strategy is needed around bringing in qualified talent with AI know-how (whether it’s temporary or full-time) coupled with upskilling your existing workforce, as both are essential to building a successful, sustainable AI-ready workforce for the long term.

A demand boom and a talent shortfall

AI job postings have exploded, with roles requiring AI expertise growing 3.5 times faster than all other job postings since 2012 according to PwC’s 2024 AI Jobs Barometer. By sector, demand is highest in Information & Communication, where AI roles have increased fivefold, while Professional and Financial Services have seen a threefold and 2.8-time higher demand, respectively, compared to other sectors.

The growth is also global. Projections in Singapore, for instance, anticipate an 86% increase in demand for AI expertise over the next few years. However, nearly 40% of companies there report difficulties finding suitable candidates – a challenge mirrored worldwide. Since 2013, the demand for AI specialists has surged by 450%, and projections suggest a further 40% rise by 2025, particularly in data-reliant industries like healthcare, manufacturing, and finance.

The demand surge is underpinned by AI’s strategic benefits to companies, from optimizing supply chains to enhancing customer service. Yet, the talent required to design, train, and implement AI solutions is scarce, with specialized roles especially hard to fill.

The skills mismatch in AI roles

For companies seeking to build or expand AI capabilities, the problem is not only in finding talent, but finding people with the right skills. Machine learning engineers, NLP specialists, and computer vision experts are particularly sought after. From 2022 to 2032, machine learning engineering is projected to grow by 23% according to the US Bureau of Labor Statistics. NLP roles, crucial for applications like large language models (LLMs), have seen job postings grow year-over-year by 111%, while computer vision specialists are increasingly in demand as industries like automotive and healthcare adopt visual data processing.

From 2022 to 2032, machine learning engineering is projected to grow by 23% according to the US Bureau of Labor Statistics.

It's clear from the data that the skills companies need and those that candidates offer do not always align. Employers are looking for proficiency in specific programming languages like Python, required in 56.3% of postings, as well as deep learning frameworks like PyTorch (39.8%) and TensorFlow (37.5%). But despite these tools’ importance, many candidates lack the advanced knowledge necessary to apply them effectively in a real-world setting. Cloud computing skills – vital for deploying AI at scale – are often missing, with candidates falling short on essential platform proficiency like AWS and Microsoft Azure. This discrepancy highlights a broader gap between theoretical knowledge and practical, hands-on expertise, which directly impacts how quickly companies can integrate AI into their operations.

AI readiness: not all companies are prepared

Even when companies secure AI talent, many find themselves unable to fully capitalize on it. Achieving “AI readiness” goes far beyond hiring; it demands adequate infrastructure, high-quality data, skilled personnel, and strategic alignment. Infrastructure includes high-performance computing environments capable of handling complex AI workloads, while data – often fragmented or unstructured – must be clean, well-organized, and accessible. Skill gaps within existing teams further hinder progress, and without a strategic framework that ties AI efforts to business objectives, even well-staffed AI projects may fail to deliver value.

96% of organizations face significant barriers to AI integration, often due to infrastructure limitations, poor data quality, and insufficient skilled personnel according to a Fivetran study.

Companies vary widely in their readiness to adopt AI. Organizations fall into categories from “AI Unaware” to “AI Competent” according to the AI Readiness Index, where only a minority of companies have reached true “AI Maturity,” with the resources, strategies, and expertise to support comprehensive AI initiatives. In fact, 96% of organizations face significant barriers to AI integration, often due to infrastructure limitations, poor data quality, and insufficient skilled personnel according to a Fivetran study. AI readiness, then, isn’t just about recruitment; it requires a foundational investment in processes, tools, and culture.

Consequences for companies lagging in AI talent

The shortage of AI talent translates directly into delayed projects and competitive disadvantage. Companies unable to recruit or retain skilled professionals face roadblocks in implementing AI-driven solutions, slowing innovation, and stifling operational improvements. This presents risk as competitors capitalize on AI to personalize customer experiences, enhance automation, and gain insights through data analytics.

AI talent shortages also come with financial implications. As demand outstrips supply, AI professionals command salary premiums up to 25% higher than similar tech roles in certain markets. This increases budget constraints, especially for small to mid-sized companies. For large corporations, the impact is often measured in lost revenue and missed opportunities, with limited AI capabilities diminishing their ability to adapt and compete in an increasingly data-driven marketplace.

Strategic responses to the AI talent shortage

To tackle these challenges, many companies are investing in upskilling and reskilling programs. Amazon, for instance, has committed $1.2 billion to training 300,000 employees under its “Upskilling 2025” initiative, which includes courses in machine learning and software engineering. AT&T’s “Future Ready” program offers customized learning paths, and Cisco’s “Skills for All” platform is on a mission to train 10 million people globally over the next decade. These programs underscore a new approach – rather than relying solely on external hires, companies are building AI capabilities internally.

Collaborations with educational institutions are another way companies are bridging the AI skills gap. Amazon currently partners with universities to co-develop AI curricula that merge industry demands with academic research. The University of Florida’s partnership with NVIDIA aims to create an “AI University,” fostering AI research and providing training for underrepresented groups.

Meanwhile, MaivenPoint’s collaboration with Singapore University of Technology and Design (SUTD) focuses on continuing education for the local workforce. Such partnerships provide students with practical experience and offer companies a tailored talent pipeline.

These initiatives reflect a shift toward long-term AI talent strategies, where companies not only fill immediate roles, but also build sustainable frameworks for future hiring needs.

Building an AI-ready workforce

The demand for AI talent will only intensify as industries increasingly incorporate machine learning and data science into core operations. But with a substantial gap in skills, companies need more than competitive salaries to secure talent. A proactive approach to workforce development – one that includes investments in training, strategic partnerships with educational institutions, and a commitment to nurturing AI within existing teams – will become essential for companies aiming to maintain relevance in the digital age.

Ultimately, success in AI will belong to organizations that view talent acquisition not as a single transaction, but as an ongoing, integrated investment in capability-building. The transformative potential of AI cannot be overstated, but realizing it requires a deliberate, multifaceted approach to developing and deploying AI talent.