Closing the AI skills gap is not just a hiring problem

Today, organisations are increasingly moving AI use cases out of the pilot phase and scaling it throughout their organisation. They are looking to the technology to improve productivity, strengthen customer experience and open up new forms of growth. But there is a constraint sitting underneath that momentum, and it will ultimately determine how far and fast organisations can go.

Confluent’s 2025 Data Streaming Report found that just over one in five (22%) Australian organisations have established agentic AI-based solutions, while 33% are in pilot or early deployment stages. Yet many businesses struggle to move faster, with 59% of Australian organisations saying their workforce lacks the skills and expertise to manage AI projects and workflows.

This is the gap businesses need to address. If more organisations are going to scale advanced AI solutions successfully, they can’t treat skills as something they hire in or build up later. A more integrated approach is needed.

‘Shift-left’ data and AI skills development

Closing the AI skills gap is often talked about as a hiring problem, but that perspective is too narrow. Yes, Australia needs more specialists, but no market can rely on hiring its way out of a capability problem that is changing this quickly.

That is where a ‘shift-left’ mindset becomes useful. In engineering, it means addressing problems earlier, before they become harder and more expensive to fix. In data architecture, it means building in governance, quality and processing closer to the source so systems are more reliable and effective from the start. We need the same mindset in workforce skills development. In education, this means introducing real-time data and AI skills earlier in the curriculum and ensuring students enter the workforce with practical exposure to realistic data sets and scenarios. In the workplace, this means embedding ongoing learning into everyday work, so capability grows in step with technology rather than behind it.

Below are three ways organisations can shift left to ensure talent, skills and technology are aligned and can move forward together.

1. Embed real-time data and AI skills into core education

If capability needs to be built earlier, education is the best place to start. Beyond simply producing more AI specialists, this is about recognising that data and AI literacy are becoming baseline skills for a much broader range of roles across the workforce.

That said, even as AI becomes embedded across organisations, not everyone will need to understand every technical detail, but they do need to understand how data is generated, governed and used. Yet too many programs still treat data engineering, real-time processing and applied AI as specialist electives. In reality, these capabilities sit behind some of the most important business use cases, from fraud detection and personalised customer experiences to better operational decision-making.

This goes well beyond computer science and engineering degrees. Students in business, finance, sustainability and other disciplines will increasingly work alongside AI-driven systems in some form. So, while they don’t need to become technologists, they do need to be able to ask the right questions and interpret outputs sensibly to make decisions with confidence.

2. Expand hands-on learning through collaboration

Understanding concepts isn’t the same as applying them in live environments, where systems must operate at scale and under pressure.

This is where stronger collaboration between industry, academia and government becomes important. Universities and training providers can give students access to real datasets, sandboxed environments and projects that reflect actual business problems. Meanwhile, government bodies can support AI labs, offer research partnerships and provide shared testing environments that let people apply what they have learned in a practical setting.

Internships, traineeships and industry-led programs also play a role in bridging the divide between theory and practice. By creating more pathways for hands-on experience, organisations can help people build the judgement, confidence and practical fluency they need to move from classroom learning to real workplace demands.

3. Align certifications with real-world roles

Lastly, learning can’t stop once students have left the classroom. The technology keeps changing, and people need clear ways to build on their skills throughout their careers.

Certifications still have a place, especially when they are tied to real roles and practical responsibilities. Too often though, training sits too far away from the work itself. People complete a course and gain a credential, but don’t have a clear path to apply that learning in practice.

Employers can close that gap by making development part of career progression from the start. That means building in apprenticeships, rotations and structured learning pathways as part of professional and organisational objectives, especially for emerging roles like Data Streaming Engineers and Agentic AI Specialists, and giving people regular chances to update their skills as tools and expectations evolve. It also means treating development as something that continues across every stage of a career, rather than just at the beginning of it.

The organisations making the most progress with AI are those investing in capability from within. Rather than relying solely on external hiring in an increasingly competitive market, they are creating opportunities for employees to build experience, deepen expertise and move into new roles as the technology evolves.

Looking ahead, Australia’s AI ambitions are clear. Investment is growing, rates of adoption are rising, and the potential for economic impact is significant. But realising that potential will depend on more than technology alone. A shift-left approach provides a practical path forward. By building capability earlier and embedding continuous learning into how organisations operate, we can close the gap between AI ambition and execution, and ensure we have AI and data skills that will matter most tomorrow.

Top image credit: iStock.com/M.photostock