Not a single day goes by without us reading the latest and newest about AI. The technology has woven itself into everyday business decisions, with organisations actively using it to shape its product design, manage data and serve their customers. But while adoption races ahead, the supply of people who can truly make these systems work has yet to catch up.
In 2024 alone, demand for AI-related projects rose by more than 230%, making it the fastest-growing area of technology investment. What was once the domain of research and prototypes has become a business essential, and this acceleration is reshaping the job market.
Beyond adoption: the skills behind the shift
However, as organisations scale their use of AI, they are discovering that success depends on a different blend of expertise. If the focus previously has been on simply building AI chatbot with LLM, today business leaders are more worried about embedding AI safely and sustainably into wider systems.
Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) now sit at the centre of many enterprise strategies. Businesses are developing internal assistants, automating document retrieval and embedding conversational interfaces across departments. Demand for open-source LLMs such as Mistral has surged eightfold in the past year, driven by the desire to tailor AI capabilities to specific needs rather than rely on closed platforms.
At the same time, the renewed rise of low-code and no-code tools is changing how teams deploy technology. Platforms like Power Apps, n8n and Make have seen demand climb by 40% as non-technical teams begin to build their own automations. This decentralisation highlights a broader trend – AI is now becoming part of how every function, such as marketing, operations, HR and finance, accelerating efficiency and decision-making.
Security and governance take centre stage
With this rapid expansion, security has become one of the fastest-growing areas of demand. Projects related to compliance, auditability and governance have grown by around 35%, as companies realise that AI handling sensitive or proprietary data must be properly managed.
The focus has also shifted from experimentation to accountability. Employers are looking for people who understand not just how to deploy AI models, but how to govern them, setting parameters, tracking outputs and ensuring that systems meet regulatory and ethical standards. Specialists in data privacy, risk management and model audit are increasingly becoming important, particularly in sectors like healthcare, finance and telecoms where the cost of error is high.
The infrastructure behind AI is also under pressure. Cloud and data engineering roles continue to outpace supply, as enterprises build the scalable backbones that allow AI systems to run smoothly. Without robust data pipelines, security frameworks and governance processes, even the most advanced AI initiatives can stall.
Adaptability as the defining advantage
Employers are rethinking what kind of talent delivers the most value. As such, the definition of “technical talent” is changing fast. Companies still prize expertise, but they’re also looking for people who can bridge gaps between coding and compliance, design and data or between security and scale.
A data engineer who can design infrastructure with privacy in mind, or a cybersecurity specialist who understands cloud deployment, brings a more complete perspective than someone focused on a single discipline. This cross-functional fluency is what allows organisations to implement AI initiatives enterprise-wide much quicker and with fewer resources.
Meanwhile, areas that once drew early excitement are beginning to plateau. Generic chatbot projects or broad computer-vision experiments are giving way to vertical, use-case-driven applications that deliver measurable outcomes. Businesses are looking for specialists who can integrate AI directly into industry-specific processes such as predictive maintenance in manufacturing, patient analytics in healthcare or risk detection in financial services.
The widening skills mismatch
Despite record investment, around 40% of the most in-demand AI skills remain scarce in the market. Many organisations are prioritising cloud, data and security capabilities, while professionals are rushing toward newer specialisms in GenAI or model fine-tuning. This results in a growing mismatch between what companies need immediately and what job seekers are preparing for.
The imbalance is also shaping hiring strategies across the board. Some businesses are doubling down on internal training, while others are turning to external expertise. Between 2022 and 2024, demand for freelancers in data and technology rose by 70%. This is not a surprising statistic – freelancers offer the ability to quickly plug capability gaps, bringing both technical agility and real-world experience from other sectors.
Independent specialists often have an advantage in adapting to emerging trends. Working across projects exposes them to diverse tools and problem statements, allowing them to identify which technologies actually deliver value. That cross-pollination of insight gives them an edge and provides companies with immediate, practical expertise that can accelerate implementation.
How professionals can stay ahead
A career in AI, whether as a freelancer or within an organisation, is as much about perspective as it is about proficiency. Successful professionals know how to match technical expertise with an understanding of why the work matters and how it fits into wider goals. None of that counts for much, though, without dependable foundations such as robust cloud architecture, clean data and strong governance.
The next step is to connect those foundations with outcomes that matter. AI has to earn its place in a workflow by solving tangible problems such as automating compliance, improving speed, or supporting smarter decisions. To successfully turn AI from a promising concept into a measurable advantage, professionals need to understand both the technology and the business context.
Equally important is learning to think vertically rather than generically. The biggest breakthroughs are happening in specialised contexts where AI is designed to meet clear needs. That could mean applying natural language models to legal document review, using computer vision in precision manufacturing or integrating predictive analytics into supply chain management.
The AI boom is reshaping what it means to be “technical”, and it doesn’t mean mastering every new model. Success depends on understanding how to connect technology with data, people and purpose. And the market will continue rewarding those, who learn across boundaries and adapt early.
