By Prof Daswin De Silva, Deputy Director, Centre for Data Analytics and Cognition
Artificial Intelligence (AI) is heralding a new era in workforce productivity, revenue growth and cost efficiencies across all industry sectors. Generative AI continues to lead this transformation, building exponentially on its foray into the public domain with the release of ChatGPT in late 2022.
ChatGPT was an overnight ‘success’ at disrupting several fields of work including higher education, media, creative arts, and health services. Generative AI is commonly defined by distinguishing it from what is now being termed Conventional AI; its capability to ‘generate new content that is non-trivial, human-like, precise and seemingly meaningful’, this content has its own acronym ‘AI-generated content (AIGC)’. On the other hand, conventional AI is task-oriented and well-defined for what can be aggregated as prediction, classification, association, and optimisation-type problems.
The uplift from Generative AI is gradually being recognised as a General-Purpose Technology due to workplace innovation and spillover market effects. Most knowledge work is highly exposed to Generative AI, with approximately 80 per cent of the workforce having a minimum of 10 per cent of work tasks automated and close to 19 per cent of occupations with a higher risk of 50 per cent exposure. Generative AI is highly competent at specific tasks, which means the overall work activity can be augmented instead of being automated by segmenting into tasks that are collaboratively assigned to AI agents and human workers.
As the hype settles in, governments and organisations have been strategising approaches, services, training and recruitment to capitalise on the opportunities of AI. Recognising AI as a focal technology of national interest, the Australian Government has invested in several initiatives such as the National AI Centre, AI Graduate Program and AI Adoption Centres, as well as the adoption of a risk-based approach that is similar to the EU AI Act for ethical and responsible AI.
However, where industry engagement is concerned, these initiatives are largely centralised, despite the fundamentally decentralised growth potential of AI (i.e. AI is not a one-size-fits-all technology). While commending and supporting these first steps, it is equally important to understand and unpack the missing elements.
Although the commercial variants of Generative AI are recent, AI research, technology development, teaching and training have been the mainstay of research centres and labs of Australian universities for many decades. Closer to home, La Trobe’s track record in AI spans across teaching, supervision, publications, grants, as well as deep industry engagement that delivers impactful outcomes, such as an Australian first Energy AI platform for net zero carbon emissions, bespoke AI micro-credentials training for Optus employees, an inclusive AI lifecycle approach and many others.
A simple yet effective decentralised approach for industry innovation would be to mobilise and incentivise ‘Australia’s better half’, the critical mass of AI expertise located in universities, for the co-development of responsible AI innovations, co-delivery of AI literacy skills training and co-creation of ethical AI guidelines.
AI also has a trust issue in the public eye, which the better half can approach through open and transparent means.
Unlike the United States and Europe, Australia lacks an active, risk-savvy startup culture that can invest and sustain AI innovators. This has resulted in a weak intersection between academics with ideas crossing paths with industries seeking innovation. Most often technology consultants are preferred over universities because they make time to understand and translate an industrial need into a technical solution with timely delivery. The academic AI community will benefit from training and mentoring to look beyond just the technical performance of an AI model into the practical value of a functional AI system. A similar approach should be adopted for AI literacy training, where extra effort should be expended to develop bespoke AI curricula that intertwine with the workplace setting, technology outfit and workforce competence. A noteworthy example here is the gradually disappearing need for programming skills and the fast-emerging need for prompt engineering when building and using Generative AI.
From an industry viewpoint, the challenging first steps of AI require a leadership mindset that encompasses ‘digital, data, analytics, AI’. An operational digital infrastructure with a centralised, all-data repository is a typical starting point for most organisations considering the move into AI. This data repository drives analytics dashboards, formal reporting, ad hoc querying, and serves as the enabling layer for the conventional AI capabilities of prediction, forecasting, etc. For instance, AI models for revenue forecasting require large volumes of high-quality training data of past transactions that should be reliably stored in the all-data repository, and computed in the next layer down, the digital infrastructure. In this linear approach, a mature conventional AI expertise will gradually inform the transition into Generative AI. An alternative to linear is to go ‘AI-first’ into Generative AI. For instance, starting with ‘Generative AI hackathons’ where all employees are provided initial training and paid, short-term access to Gen AI subscriptions and assistants to innovate their own workflows and work activities relative to baseline performance metrics. The first applications of this approach would be in content creation, meeting minutes, target marketing, and project management.
Just two indicators are sufficient to comprehend the transformative potential of AI, the number of new AI models being released and the number of new applications of existing AI models. Both indicators are on exponential trajectories. To compete alongside thriving AI economies elsewhere, all Australian industry sectors must take prompt action to synergise with the ‘better half’, the university AI communities, to initiate, inform, support and enable AI innovations for industry advancement.
La Trobe Industry Communications and Media Enquiries: industry.engagement@latrobe.edu.au
