Key Insights
Key takeaways from A new frontier in artificial intelligence report published by Deloitte
CFTE summarised “A new frontier in artificial intelligence” report by Deloitte. The report delves into enterprise and consumer use cases, shift focus to how players across the market can build sustainable business models, and wrap up with some considerations and bold predictions for the future of Generative AI.
Key Aspects
- Key aspects include the emergence of Generative AI with applications like ChatGPT, DALL-E, and Lensa, and the potential for profound economic impacts and relationship changes between humans and technology.
Table of Contents
- Decoding the Generative AI magic trick
- Consumer and enterprise use cases for Generative AI
- Commerce and competition in Generative AI
- Adopting and commercializing Generative AI
Key Findings and Insights
This report will give you an insight into:
- Generative AI Tech Stack: The report details the three-layered tech stack of Generative AI: infrastructure, platform (or model layer), and applications. The infrastructure layer, considered the most stable, includes compute, networking, and storage solutions optimised for AI workloads, like NVIDIA’s GPUs and Google’s TPUs. The model layer, rapidly evolving, consists of foundation models that are pre-trained on broad datasets. These models are accessible to developers via APIs, allowing them to fine-tune models for specific use cases. The application layer, the user-facing end of the stack, is where Generative AI applications are developed and deployed.
- Consumer and Enterprise Use Cases: Generative AI has sparked a range of consumer applications, categorised into efficiency, instruction, creation, and entertainment. On the enterprise side, the use cases are primarily horizontal (general purpose) but are expected to evolve into more specialized, industry-specific vertical applications. These horizontal use cases target well-established automation centers and focus on cost optimization and productivity improvement. Vertical use cases, in contrast, require domain-specific knowledge and are often more sustainable in terms of value creation.
- Commerce and Competition in Generative AI: The competitive landscape in Generative AI is described as spanning multiple fronts, with pure-play providers operating within a single layer and integrated providers playing in multiple layers. The report also notes emerging competitor archetypes and potential monetization strategies, including subscription and consumption-based models, and emphasizes the role of data security and privacy in vendor selection.
- Adopting and Commercializing Generative AI: The final section of the report discusses the transformation that Generative AI can bring to business models, processes, and value dynamics. It highlights specific sectors and functions that could benefit from Generative AI, such as software development and customer support. The report also outlines potential benefits and considerations for businesses adopting Generative AI, including the importance of data quality, transparency, fairness, safety, and robustnes
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