25 January, 2024

How To Tackle The AI Skills Gap in Software Development: Learn & Unlearn, Fast

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Monmayuri Ray, engineering manager – AI research at GitLab, is focused on making tech work for customers and solving problems at the intersection of AI, machine learning and DevOps. But she believes that there is a serious shortage of AI skills in the Australian market.

Australia’s IT skills shortage is not new – the lack of technology skills has been increasingly in the spotlight since 2016. The explosive growth of generative AI in 2023 however, indicates that we have reached a tipping point in the widening gap of necessary tech skills. According to ACS, 95 per cent of the workforce is expected to require reskilling, with high-growth areas like enterprise software development under threat unless there is a concerted effort to tackle the AI skills gap now.

Monmayuri Ray, engineering manager – AI research, GitLab

Recent data from GitLab found a discrepancy between organisations’ and practitioners’ satisfaction with AI training resources. Despite 75 per cent of respondents saying their organisation provides training and resources for using AI, a roughly equal proportion also said they are finding resources on their own, suggesting that the available resources and training may be insufficient.

Establish an AI strategy to determine reskilling requirements

The draw of implementing AI early in software development is undeniable as it offers a transformational opportunity to improve productivity and automation across every sector and role. However, determining the right reskilling approach in any organisation requires starting with a solid AI strategy from the beginning.

In our work with Australian organisations, AI-powered software development is often utilised with the intent of building point solutions to solve specific business needs versus system solutions. This approach offers a distinct advantage for them to consider how their reskilling will empower engineers and scientists to solve actual problems faced in our unique environment.

Organisations should adopt a top-down approach to AI reskilling by meticulously defining their AI strategy, prioritising requirements, and instituting a systematic framework to enable effective learning and development.

Cultivate an attitude of openness and flexibility for different learning types

When asked what types of resources are being used to build AI skills, the top responses in GitLab’s research were books, articles and online videos (49 per cent), educational courses (49 per cent), practice with open-source projects (47 per cent), and learning from peers and mentors (47 per cent). This suggests that no one format fits every learning type.

The reality is that for technology practitioners, the traditional path of structured, binary learning is no longer adequate for the fast-evolving field of AI. They will need the psychological flexibility to unlearn and navigate the challenges in the AI field. Unlearning is a crucial aspect for individuals to discard outdated tools, methods and practices to adapt to new and more effective approaches. People leaders should closely collaborate with their teams to consider individuals’ learning journeys and ensure training’s effectiveness and alignment with their organisation’s AI journey.

The end goal of AI reskilling should be to instil a lifetime of incremental learning, as understanding AI to optimise value from the technology is not a sprint but a marathon. Organisations need to wholly embrace the mindset of unlearning to cultivate a culture of continuous learning, where team members are open to acquiring new skills and knowledge throughout their careers.

Regardless of whether the organisation invests in structured learning through a short course or a four-year degree, it is important to remember that for the team member, this only marks the beginning of the learning journey. The application of the frameworks taught in those courses will be where moments of understanding and insight occur. Working in production-grade environments and in collaboration with other team members to solve real-world problems will be critical to seal the effectiveness of reskilling.

Curate a human-centred learning and development plan

While many technical skills are required for AI implementation, organisations cannot afford to sideline the need for team members to exercise good judgment, sound decision-making, and people skills. More than ever, engineers, developers and professionals alike will require active listening to understand the needs of customers, their challenges, and their goals. Successful AI applications will only come to fruition if they fit into the solution journey and add value for customers.

The promise of AI is exciting and there needs to be a broader and deeper understanding of where it fits in an organisation’s strategy and how to cultivate a mindset shift to empower individuals on their personal learning journeys. By combining a clear AI vision with a curated learning and development plan at a detailed level, organisations can better support team members to learn, unlearn and solve problems faster.

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