Uncover the secrets to successful machine learning (ML) projects in enterprises, from avoiding common pitfalls to driving significant business value.
After nearly two years of hype, speculation, use case analysis, pilots, and scaling deployments, generative artificial intelligence in the enterprise has some results to show for the multi-billion dollar investments made by the big tech vendors. Explore success stories and key lessons from Australian enterprises.
ICT policies often leave even seasoned executives uneasy, perceived as lengthy, endless compliance exercises yielding little reward beyond periodic reviews.
Generative artificial intelligence (AI) has delivered significant return on investment (ROI) for software development teams and is recognised as more of an opportunity than a threat. With big tech and venture capital firms continuing to invest in AI-assisted software development tools, this trend is going to accelerate. However, concerns around IP and security are not yet fully resolved.
Generative (gen) artificial intelligence (AI) risks span data, model, deployment, and governance dimensions, with different concerns coming to the forefront at each adoption stage.
To present AI effectively to the board, emphasise its role in augmenting rather than replacing existing capabilities.
Uncover the potential of Google’s Spanner database with graph processing for advanced AI applications and data management strategies.
The growth of digital content has made it increasingly challenging for organisations to manage their information assets effectively. What is IT’s critical role in developing and implementing strategies to manage content chaos and information hyperinflation?
Kapil Mahajan, the company’s Group and Global CIO, effectively harnessed AI and machine learning, along with computer vision technologies, to enhance logistics planning, improve demand forecasting, and streamline cash collection processes.
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