Benefits of Custom Enterprise AI Models
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There are compelling reasons to develop custom enterprise models:
- Enhanced privacy and security.
- Improved performance through fine-tuning with proprietary internal data.
- Easier integration by tailoring AI tools to the characteristics of company workflows.
In particular, privacy and security risks of generative AI are major concerns for enterprises. An IT/cloud manager in the finance, banking, and insurance industries described existing generative AI tools as "too much of a security risk" in response to an ESG survey.
"Many available AI technologies are free or enabled by existing vendors—organizations have no opportunity to proactively review the risks of the technology based on data privacy, security, compliance, confidentiality, and intellectual property considerations," said another respondent, a business vice president in the telecommunications industry.
When it comes to internal data used to train models, fine-tuned proprietary models can provide better oversight for security-conscious organizations. With internal models, organizations can maintain control over sensitive data rather than sharing access with third parties.
Models tailored to a company's specific tasks and data may also produce more relevant outputs and fewer hallucinations. This can alleviate some organizations' concerns about using third-party models to obtain accurate, fair, and representative outputs.
"There are concerns about the accuracy and completeness of AI reports," said a consultant in the healthcare and health services industry in response to the ESG survey. "How do we confirm and validate data sources? Additionally, there is the issue of algorithmic bias."
The process of training models on target datasets (here, information about the organization and its industry) is called fine-tuning, which can yield more accurate results for relevant tasks. AI tools customized to address specific business problems and workflows can improve efficiency and reduce integration issues. In short, this means custom models may require less extensive oversight while producing outputs that better align with business needs.