Australian organizations are keeping pace with global innovation in generative AI, according to Matthew Candy, global director of generative AI at IBM Consulting. A local legislative focus on regulating high-risk use cases could also foster the potential of AI in the local market, he said.
Candy recently visited Australia and the broader APAC region to meet with some of IBM Consulting's regional clients and partners, many of whom will transition from piloting generative AI to deploying models at scale during calendar year 2024.
Speaking to TechRepublic Australia, Candy predicted a move towards smaller AI models and the emergence of new digital products and services. She suggested that organizations looking at scale should focus on strategy and business value, as well as things like governance.
How does Australia compare to the world in generative AI?
IBM's 2023 Global AI Adoption Index identified Australia as a lagging market in AI adoption. It found that only 29% of Australian organizations were actively implementing AI in November 2023, far behind early adopter countries India (59%), China (50%), Singapore (53%) and the United Arab Emirates (58%).
SEE: Australian small and medium-sized businesses risk falling behind in AI
However, Candy said there is evidence that Australia is embracing generative AI. She said even organizations in traditional or regulated industries, as well as government agencies, are moving forward and that the local market showed a clear understanding of where value can be obtained.
“There is some pretty innovative work being done in Australia,” Candy said. “One of the teams I have spent some time with is creating innovative new digital products and services powered by generative AI models; “I definitely don't see anyone behind it just because they're in Australia.”
Generative AI use cases now being implemented in Australia
IBM Consulting has seen a lot of product pilot work in Australia, with a growing number of these projects now active beyond the pilot and experimentation phase that characterized 2023. From his time in the Australian market, Candy said some use cases for Interesting clients include:
- Companies in the asset-intensive utility industry are using AI-powered generative assistants to help executives make investment decisions around asset management and managing an asset portfolio, which could prove in a significant increase in savings.
- Utilities are wrapping generative AI assistants in complex knowledge bases, such as standard operating procedures, to enable network controllers to converse with complex document sets to remove friction from some tasks.
- Universities are using generative AI to help generate more personalized content to support student communications while allowing them to conversationally interact with the course content they are learning.
- The end-to-end software development lifecycle is being improved at at least one big bank, where IBM Consulting supports the use of generative AI to do things like translate project requirements into creative outputs, like user stories and code. .
- An Australian government agency is using large language models to create a new skills training platform.
'Value groups' and people among considerations when scaling AI
Candy was appointed to lead IBM Consulting's 160,000 global consultants into the era of generative AI in August 2023. She said Australian organizations looking to scale generative AI experimentation to deployment this year should keep a few things in mind .
Have a clear vision and strategy.
The foundation of success for generative AI is having the right type of strategy. This will be based on identifying a “North Star” or articulating a vision of what the new world that will utilize the potential of AI will look like. This vision and strategy will support the realization of the roadmap.
Align use cases with 'value groups'
Candy said it is important to find use cases aligned with “value groups.” Whether it's a bank, utility provider, or retailer, Candy suggests asking where in the organization there are heavy manual knowledge bases or document-intensive activities that slow down cycle times.
“An example is a contact center, where we see a lot of people focusing on generative AI,” Candy said. “How can you make your agents more effective by wrapping them in knowledge bases to improve call handling times or agent actions to provide a better customer experience?”
Launch an innovation engine for AI
Scaling AI requires an innovation engine for organizations to take use cases through validation, testing, piloting minimum viable products, and scaling. Candy said ways of working such as design-based and product-based could contribute to organizational success.
“You need that agile flywheel to build, deliver and scale,” Candy said.
Build a generative AI technology core
Candy said organizations need to be clear about the architecture and digital core layer they will use to manage their generative AI. This clarity is necessary because many organizations will use AI across multiple clouds, in addition to AI-enabled products like Salesforce and SAP.
Get up to speed on AI governance
Companies will need to manage issues such as bias, drift, and explainability across multiple AI models, as well as have the right processes in place for their people. They will also need to comply with regulations that are being created in multiple legal jurisdictions around the world.
Prioritize the people challenge
Candy maintains that around 70% of the challenge of implementing AI is a human challenge. This includes infusing employees with the skills necessary to have confidence in AI models and maximizing change management success by getting employees to broadly adopt AI at the grassroots.
The future of AI in Australia will balance use case regulation with innovation
Matthew Candy is “very excited” about the potential of generative AI. IBM Consulting is also implementing generative AI; It recently announced it would augment its 160,000 consultants with a variety of AI assistants to broaden expertise across a variety of roles in the organization.
SEE: AWS and IBM Consulting partner to expand generative AI training.
Candy said these AI assistants could encapsulate organizational knowledge and handle more repeatable parts of roles. These assistants will also be offered as products and services to IBM Consulting clients to help them scale better and faster through 2024.
Australian AI regulation has the right to focus on high-risk use cases first
Australia's announced regulatory approach to AI, which follows the European Union in focusing future regulation around the risks presented by specific AI use cases, was the “right approach”, Candy said.
“We believe in making sure there are adequate regulations and controls for different types of use cases; it’s really about regulating where the use case is,” Candy said.
IBM predicts the rise of smaller models, innovation and governance
Enterprise customers in Australia and around the world are thinking carefully about the best AI models to deploy. Candy predicts that smaller models will be attractive in 2024 due to advantages such as fewer trips and computing requirements, which will lead to lower costs.
Another trend expected in 2024 is the emergence of new innovative digital products and services that could change and transform existing business models, and Candy expects many great ideas and visions to emerge this year through excellence in generative AI.
With responsible AI becoming critical for organizations, Candy also predicts that there will be a continued focus on the foundations of AI governance, both from a technological standpoint and the integration with people and business processes. Australian organizations.
