Conference Topic

Generative AI

Generative AI is transforming the business landscape, enabling companies to automate tasks, optimise operations, and unlock new avenues for growth. From generating realistic virtual prototypes to automating content creation, businesses are leveraging generative AI to enhance productivity, save costs, and improve customer experiences. With increased investments and advancements in the field, the market for generative AI is witnessing a surge, attracting both start-ups and established players. As businesses continue to explore the potential of this technology, the future holds immense opportunities for innovation, efficiency, and competitiveness.

Key questions businesses should ask themselves:

  • What specific business problem or process can Generative AI address? Identifying the areas where Generative AI can bring value is crucial to ensure a focused and effective implementation.
  • Do we have the necessary data? Generative AI models require substantial amounts of high-quality data to learn and Generate meaningful outcomes. Assessing the availability and quality of data is essential.
  • What are the potential risks and ethical considerations? Understanding the possible risks, biases, and ethical implications associated with Generative AI is important to ensure responsible and unbiased use of the technology.
  • Do we have the required expertise and resources? Implementing Generative AI often demands specialised skills and resources. Assessing the existing capabilities or considering partnerships or collaborations may be necessary.
  • How will Generative AI impact our workforce? Analysing the potential impact on employees and job roles is crucial to ensure a smooth transition and address any concerns or training needs that may arise.
  • What are the expected outcomes and ROI? Setting clear goals and evaluating the potential return on investment is essential to justify the implementation and measure its success.
  • How do I incorporate Generative AI capabilities into my existing operating model for data and analytics?


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