Why the right team matters more than ever to achieve AI goals

However, most companies lack the AI expertise necessary to meet the demands of their use cases.

Although most businesses are now adopting generative AI (genAI) for specific use cases, the scarcity of skilled AI practitioners is driving many organisations to rely on in-house resources. However, experts warn that without a clear understanding of the skills required for successful genAI implementation—or how to acquire them—many companies are treading a precarious path.
Without the right team in place, achieving your AI ambitions will be a significant challenge.
While employers believe that AI could increase productivity by 51 per cent if effectively integrated into operations, a recent AWS Study on AI Skills in APAC revealed that 75 per cent of organisations struggle to find the AI talent they need. Moreover, 79 per cent admit they lack the knowledge to train their workforce to develop those skills internally.

“The field is evolving rapidly, and while companies recognise the importance of being AI-ready, many are unsure where to begin,” explained Leif Pedersen, APAC cloud and AI product manager at Lumify Work, a training organisation formerly known as Dimension Data Learning Services (DDLS).
Lumify Work provides a variety of AI-focused training programmes, including its 8-week CloudUp and AWS Generative AI Accelerator boot camps, AWS Skill Builder on-demand digital training, and a selection of AI practitioner certifications.

“What we’re observing in the Australian market is a lot of uncertainty,” Pedersen continued. “Businesses are asking, ‘Where do I start?’ and ‘How can I understand what this technology means for my organisation?’”

“While some companies are doing impressive work, the majority are still new to the technology. It’s very much a tentative approach in this region, and the rapid pace of change makes it difficult to keep up.”

An AI Skills Roll Call

For businesses venturing into the AI landscape, the assumption that existing technologists and developers can effortlessly adapt to AI capabilities often leads to disappointment.
A recent Gartner survey of over 700 companies across the US, UK, and Germany revealed that 87 per cent already have dedicated AI teams in place, with two-thirds creating new roles specifically for generative AI (genAI).

“The deployment of AI and genAI within organisations requires specialised skills,” said Jorg Heizenberg, Gartner VP Analyst. “For data and analytics teams, investment in new talent is not only essential but also urgent.”

Heizenberg added that determining the size of data and analytics (D&A) teams is complex, with numerous dependencies to consider. “Chief data analytics officers (CDAOs) must assess how many roles are necessary to ensure their teams are both effective and successful.”

The Expanding Spectrum of AI Roles

Many might be surprised at the diversity of roles required within contemporary AI teams. Essential positions include machine learning engineers, data engineers, data scientists, prompt engineers, AI ethicists, and data-to-analytics translators.

Project teams also need strategic leaders to craft AI strategies and oversee their lifecycle. Roles such as AI architect, AI risk and governance specialist, AI product manager, analytics engineer, UX designer, and AI developer are vital for meeting business requirements.

With genAI increasingly shaping sensitive, customer-facing interactions—where privacy and ethics are paramount—companies must also incorporate emerging roles like knowledge engineers, model validators, and decision engineers into their teams.

Equally important is involving the broader workforce in this transformation. Gartner reported that 39 per cent of organisations have introduced genAI literacy programmes to help employees analyse and make informed decisions using data. This step is critical, as AI tools are ultimately implemented to benefit those workers.

Failing to invest in AI skills development now could lead to long-term repercussions. Heizenberg warned that a quarter of workforce attrition could be attributed to managers’ lack of data literacy. “Business leaders must prioritise data and AI literacy,” he advised. “The two are interconnected; data literacy underpins AI literacy, and vice versa.”

Bootstrapping the Internal AI Team

Rather than attempting to build a fully-fledged AI team from the outset, Peter Vandaele, an AWS technical trainer, recommends starting with a single use case and keeping it simple.

To enhance developers’ AI skills, he suggests working with tools like the AWS Bedrock AI foundation. Developers can begin with foundational models for tasks such as human speech and natural language processing and gradually progress to more complex, business-oriented applications.

“If you start small,” Vandaele explained, “you can get going quite easily with tools like Bedrock.” He highlighted its features, which support every stage of the machine learning lifecycle, from data preparation and infrastructure setup to model training.

As businesses refine their AI use cases, they gain a clearer understanding of their skills gaps, enabling targeted training to maximise benefits for both the organisation and its AI teams.

“It ultimately comes down to your goals,” said Leif Pedersen, APAC Cloud and AI Product Manager at Lumify Work. “Do you simply need to understand how AI can improve your processes, or are you looking to build advanced AI models and engines capable of handling vast datasets while addressing challenges like bias, ethics, and AI hallucinations?”

“With the right use case, there’s a good chance we’ve provided training in that area,” Pedersen added. “You can be as technologically advanced as your needs require.”

News Source: IT News Australia