Generative AI: Buying off-the-shelf apps versus building custom models from scratch
Artificial Intelligence (AI) has revolutionized the way businesses operate, but it's important to ensure that it's solving real issues. In this age of digital transformation, tailoring AI solutions to identify pain points can help businesses focus their resources on applications that are likely to have the highest impact. Afterward, the most important decision remains whether to either procure a generative AI-enabled software or perhaps build custom models from scratch. This decision will depend on a few key factors which are shared below.
The diagram from Gartner below outlines five distinct approaches for deploying generative AI within applications, each with a varying degree of investment in terms of buying or building the underlying technology.
The Five Approaches explained below
1. Consume Generative AI embedded in apps: This approach is the most direct and involves utilizing applications that already have generative AI capabilities built into them. It's a consumer-oriented model where businesses can leverage AI without direct involvement in development, which reduces complexity and the need for specialized AI expertise.
2. Embed Generative AI APIs in a custom app frame: Here, businesses can integrate generative AI functionalities into their custom applications by using APIs provided by AI platforms. This is a step towards customization, allowing the company to maintain its proprietary application interface while harnessing the power of generative AI.
3. Extend Generative AI models via data retrieval: In this method, existing generative AI models are extended by integrating them with data retrieval systems. This approach can enrich AI functionalities by providing them with access to a broader dataset, potentially improving the AI's performance and tailoring it more closely to specific use cases.
To either procure a generative AI-enabled software or perhaps build custom models from scratch will depend on a few key factors.
4. Extend Generative AI models via fine-tuning: This strategy involves taking pre-existing generative AI models and fine-tuning them with company-specific data. This allows for a more customized AI solution than the previous methods, as it adapts the model to reflect the nuances of the company's data and operational context.
5. Build Custom models from scratch: The most investment-heavy option, involves creating generative AI models from the ground up. This approach provides the highest level of customization and potential competitive advantage but requires significant expertise, resources, and time commitment.
These approaches represent a spectrum from buying off-the-shelf AI-enabled applications to investing in the development of bespoke AI solutions. The choice among these approaches would depend on a company's strategic priorities, technical capability, resource availability, and the specific business challenges they aim to address with AI.
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