Yes, it is becoming easier for larger enterprises to build custom generative AI applications. As always, that will not necessarily mean it is a wise use of time and resources to build "all" GenAI apps and features "custom." In many cases it will make more sense simply to use the features existing or new suppliers can support.
A new survey of 1,300 CEOs by TCS suggests 72 percent already are retooling their firms to support use of artificial intelligence.
Some 51 percent of surveyed firms are planning to build their own generative AI implementations. Or at least that is what the CEOs believe.
Whether those beliefs are ultimately borne out in reality or not is not so clear, but it would be reasonable to suggest that where it does make sense, it will most often be in cases where a particular enterprise has some highly-specific tasks of identifiable importance and a particular set of existing software systems that must be supported.
In principle, executives might push in this direction if unusual security or privacy needs exist, or when such custom development offers the chance of gaining a competitive advantage over competitors.
In most cases, those “custom” implementations are likely to use small language models more often than not, some might argue, as the cost and complexity are more manageable. Also, there are lots of plausible reasons why a respondent might agree with a question “are you planning to create your own enterprise-specific LLMs for use in Generative AI implementations?”
Not all “yes” answers might mean full intention to create a custom language model. Many respondents might interpret the question more in the sense of “are you planning to customize a LLM for your own company’s use?” Most respondents could respond affirmatively in that sense, at least in the sense of using proprietary company data.
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