Guest Post by Melissa Incera, S&P Global Market Intelligence
This blog is the first in our series on emerging Generative AI use cases, a companion to the 451 Research report, “Generative AI: Balancing Innovation and Privacy.”
Generative AI continues its formidable march within the enterprise. And while most organizations are in experimentation phases, a shift toward production at scale is underway. With market and organizational maturity growing, a balanced perspective of generative AI’s strengths and weaknesses is enabling organizations to mitigate risks while prioritizing safety and data privacy, paving the path for use cases that are as secure and safe as they are transformative. Over this series of blog posts, we will dive deeper into several of these use cases, exploring their benefits and how organizations are effectively applying them.
An evolution in generative AI use cases
As approaches and environments for Gen AI grow in sophistication and security, so do the areas where organizations are applying it. Early on, organizations adopted generative AI for tasks such as data visualization and information summarization — lower-order work where domain knowledge was not a requirement. In the next 12 months, however, survey respondents project the biggest value gains in functions that require more organizational and workflow-specific context, such as code generation, customer experience, advanced information retrieval and secure content generation. Agentic AI is another area of rapidly emerging interest and is expected to drive improvements in process optimization and task automation.
Spotlight on secure content generation
Content creation is a fundamental application of generative AI, and a net new opportunity created by generative models’ unique capabilities. It has seen rapid uptake in the enterprise given its ability to enhance productivity and automate common production tasks. Text generation in particular has captured the minds of users for its broad applicability and remains the topmost purchased generative AI modality. Increasingly, businesses are also experimenting with other modalities including images, 3D renderings, audio and video, with many targeting content workflows that cross modalities, such as marketing workflows that pair generation of product imagery with campaign copy, or customer service use cases that pair audio and text.
GenAI content modalities in use and in plan
Q. Has your organization purchased or developed a generative AI capability that is being used to create any of the following outputs? This question looks specifically at technologies that have been PURCHASED or DEVELOPED. Base: All respondents (n=1,006). Source: 451 Research’s Voice of the Enterprise: AI & Machine Learning, Use Cases 2025.
A common challenge, however, when employees use public generative AI tools or foundation models, is a lack of organizational specificity. Fine-tuning models for brand alignment in a secured environment is one effective approach to generate content that complies with style guidelines and is authentic to a brand. This, combined with retrieval-augmented generation, which enables LLMs to draw from or reconstitute existing assets, enables organizations to produce highly relevant content at an increased pace and frequency, driving productivity payoffs.
Looking ahead
As organizations transition to higher-value and more complex Gen AI use cases, a focus on privacy and security is essential for harnessing the technology’s transformative potential. With use cases such as content generation, which often involves intellectual property and sensitive information, the choice of AI platform and environment is especially critical. Sharing content with public AI services can expose organizations to potential data breaches and intellectual property theft, as input prompts and generated responses may be stored, analyzed and accessed by other entities. By operating in their own secure environments, businesses can maintain greater control over security protocols and data, maximizing the benefits of generative AI without compromising on safety and security standards.
About the Author
Melissa Incera is a research analyst on the 451 Research Data, AI & Analytics team within S&P Global Market Intelligence. Her focus lies in generative AI, its emergent use cases in enterprise applications and the foundation models that underpin it. Specifically, she tracks the text-generation landscape and its offshoots in enterprise search, AI assistants, marketing and writing tools. Prior to joining the AI team, Melissa spent a few years covering technology M&A and financial markets looking at high-level trends, noteworthy deals and investments, and forecasting activity across technology subsectors. She also did some work with the Workforce Productivity & Collaboration team, where she researched the future of work and emerging applications to enhance the employee experience. Melissa holds a bachelor’s degree in English Literature from Boston College and an MBA from ESADE, Ramon Llull University in Barcelona.
