Since the market explosion of enterprise-grade generative AI (GenAI) in 2023, organizations have harnessed the capabilities of foundational models from industry giants like OpenAI, Google DeepMind, and Mistral. These foundation models deliver massive acceleration in the AI development lifecycle, enabling enterprises to shrink the time to value from months to weeks.
However, many businesses have discovered that foundational models trained on vast public datasets often fail to meet specific operational needs. Enter customization—the practice of tailoring large language models (LLMs) to better align with a business's unique requirements by integrating proprietary data, teaching new skills, and optimizing prompt and data retrieval strategies.
The recent MIT Technology Review Insights report, "Customizing Generative AI for Unique Value," sponsored by Microsoft Azure, explores the landscape of customizing GenAI models and applications in depth. This report reveals how technology leaders are actively adapting trustworthy GenAI solutions within their organizations and the priority this customization holds in their enterprise-wide security and AI strategies.
Key findings from the report highlight the transformative potential of customization:
- Customization brings more than efficiency. While boosting efficiency remains a primary motivation—cited by half of all surveyed executives—it's not the only benefit. Customization also fosters unique solutions, with 49 percent of leaders pointing to competitive advantages gained through tailored applications. Equally important is the ability to customize to cite greater user satisfaction and drive creativity and innovation.
- RAG is the backbone of performance. Retrieval-augmented generation (RAG) is becoming a cornerstone of effective GenAI. Two-thirds of companies (67%) are employing or exploring this method, which enhances model outputs by leveraging internal and external data sources to ensure they are relevant and accurate.
- Automated evaluation gains traction. As organizations scale their GenAI efforts, automated evaluation methods are emerging as a critical tool. Currently, 26 percent of respondents are consistently utilizing automation, streamlining the assessment of model performance.
- Data integrity as a barrier. Despite the advantages of customization, ensuring data privacy and security remains the biggest challenge, with over half (52%) of leaders highlighting this concern. Addressing these issues is critical for organizations as they customize more actively.
- Empowering developers with advanced tools. Organizations are leveraging sophisticated tools to enhance developer efficiency and lifecycle management. Over half of the surveyed companies utilize telemetry tools to optimize performance and collaboration between engineers.
As a Microsoft Azure partner, this report offers invaluable insights into how 300 global technology executives are differentiating their businesses through customized GenAI solutions that leverage their own data and expertise.
Download the MIT report to explore top techniques and strategies to unlock your clients' unique value.