The new report, based on a survey of 1,100 technical executives and technologists from 19 countries including 100 respondents from Australia, comes as investment in AI is expected to grow to $1 trillion globally in the coming years

Databricks, the Data and AI company, today unveiled a new Economist Impact report, “Unlocking Enterprise AI: Opportunities and Strategies,” which examines the challenges businesses face in adopting and scaling AI, and the techniques they are using to drive greater value from these investments. The report found the vast majority of Australian enterprises (92%) are using generative AI (GenAI) in at least one function. But only 37% believe their GenAI applications are production-ready as respondents in Australia cite key hurdles including governance (50%), cost (38%), skills (38%) and quality (38%).

As demand for data intelligence grows worldwide, AI continues to be a major focus area for companies. According to IDC, Australia’s overall AI spending is forecast to reach US$8.3 billion by 2027, making it the second-highest AI spender in the APAC region. While more companies are investing in AI than ever before, struggles related to delivering business-specific, highly accurate, and well-governed results at a reasonable cost are preventing organisations from scaling their AI efforts and achieving more transformational results. 

“Australian organisations are increasingly focused on unlocking greater value from their data assets, and we’re seeing this firsthand with the growing commitment to AI adoption. But it’s clear many still face challenges in making these investments production-ready,” said Adam Beavis, Vice President and Country Manager at Databricks Australia. “At Databricks, we’re committed to helping our customers fully harness their data and AI investments through a unified, tailored platform that drives meaningful results. The potential for AI-driven data intelligence is immense, and organisations that can strategically integrate these capabilities won’t just differentiate themselves from the competition, but lead the transformation of their industries.”

“AI can lead to gains in productivity across the workforce. And for businesses just starting out on their AI journeys, it’s a logical way to measure initial progress,” said Senthil Ramani, Global Lead, Data and AI at Accenture. “However, organisations aiming to become the AI leaders of tomorrow will need to capitalise on the use of the technology to drive growth, enhance customer experience, manage risk and unleash enterprise knowledge. This holistic approach will not only boost efficiency but also open new business opportunities and can attract and retain talent.”

“A data and AI culture helps all parts of the business understand that we prioritise data-driven decisions, and that’s what will help us gain the insights that will improve performance,” said Gereurd Roberts, group managing director at Seven Digital, part of Australian media company Seven West Media. “[Our] priority is to train and upskill our teams and talent around GenAI, so that it becomes a process and a product that's internalised.”

The Economist Impact report surveyed 1,100 technical executives and technologists from 19 countries across Asia, Europe and the Americas and included additional insights from 28 C-Suite executives from 11 industries. Among the organisations represented are Accenture, CJ CheilJedang, Condé Nast, Dream Sports, Fanatics Betting & Gaming, Flo Health, Frontier, General Motors, HP, JetBlue, Mahindra Group, Mastercard, Molson Coors, Novartis, NTT Docomo, Opendoor, Providence, Rakuten Group, Repsol, Rivian, Seven West Media, Shell, Siam Commercial Bank, TD Bank Group, Thermo Fisher Scientific, Unilever, UPS and the United States Army. 

Additional key findings include:

Only 5% of Australian respondents believe AI is overhyped. In fact, 68% see the technology as crucial to their long-term goals. Despite the momentum, only 54% believe investment across technical and non-technical domains is sufficient.

By 2027, 98% of Australian respondents expect GenAI adoption across both internal and external use cases.

More than half of data scientists (63%) in Australia are still using a general-purpose large language model (LLM) without contextual enterprise data. Those models often struggle to provide the necessary quality, governance and the ability to evaluate outputs. 28% of data scientists in Australia have begun to augment their LLMs with proprietary data through retrieval augmented generation (RAG), and 60% of organisations in Australia see significant potential in combining LLMs with enterprise data to build data intelligence.

Organisations expect to mix and match different models and tools in their Agent Systems, spanning open source and proprietary technologies, to drive better performance. By 2027, 100% of Australian respondents plan to deploy open source AI models.

Just 11% of Australian respondents are confident their organisation can secure enough AI talent.

47% of Australian respondents acknowledge their organisation’s data and AI governance is insufficient.  Enterprises face challenges with fragmented data estates, complicating discovery, access permissions, data usage, audits and sharing. Governing AI models and tools is also vital to meet evolving AI regulations. To succeed, enterprises need a unified and open governance approach.

“From classic machine learning to generative AI, the business world’s obsession with AI isn’t letting up. But our findings show that, for many organisations, the real value comes when the technology is unleashed on their own proprietary data to develop data intelligence,” said Tamzin Booth, Editorial Director of Economist Impact. “That data intelligence is even more valuable in an increasingly unpredictable world. To drive the algorithm advantage they’re seeking, it’s clear enterprises must address significant challenges with producing high-quality outputs, identify ways to evaluate performance and governance with large AI models, and work out how to effectively connect AI to the workforce.” 

Read the full report here. To learn more about the Databricks Data Intelligence Platform, click here.

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