Generative AI applications in business applications: a comparison between Salesforce and Microsoft’s digital platforms

Generative AI and its applications

Generative AI (GenAI) represents a significant shift from traditional AI models that involves moving from recognizing patterns to generating new content such as text, audio, video, and images. In the context of enterprise software, GenAI has various applications:

• Automation: Automates repetitive tasks, enhancing productivity and efficiency.
• Customer Service: Provides personalized responses and support through chatbots and virtual assistants.
• Data Analysis: Generates insights from large datasets, aiding decision-making and strategic planning.

Recent Trends and Advancements

We can note recent advancements in GenAI within the enterprise sector that include the development of so-called large language models (LLMs), such as OpenAI’s GPT series. LLMs can generate human-like text and have an exceptional understanding of natural language. Major tech companies like Salesforce and Microsoft are embedding GenAI into their platforms to innovate. This involves a significant increase in investments in AI technologies and efforts to forge new partnerships with GenAI startups to drive innovation and stay competitive.

Importance of Efficiency in AI Models

Efficiency in new AI models like LLMs is crucial to reduce innovation barriers. In fact, very few companies can afford to buy the computational resources needed to effectively run LLMs. In particular, efficiency of LLMs should be improved because of:

• Computational resources: Efficient models require fewer computational resources, making them more accessible and sustainable.
• Speed: Faster models can process and generate outputs quickly, improving user experience.
• Cost-effectiveness: Reducing the computational load lowers operational costs, making AI solutions more affordable (or feasible).

Evaluating the Efficiency of GenAI Models

To evaluate the efficiency of GenAI models, we must consider issues such as (Hugging Face, n.d.):

• Resource utilization: Measuring the computational power and memory required.
• Processing speed: Assessing the time taken to generate outputs.
• Cost: Analyzing the operational costs associated with running the models.
• Performance metrics: Evaluating the generated content’s accuracy, relevance, and quality.

Salesforce’s Platform and GenAI Integration

Salesforce, a leading CRM platform, has integrated GenAI into its various Cloud services, including Sales Cloud, Service Cloud, Marketing Cloud, and Commerce Cloud (see Haki et al., 2025). This integration was found to enhance personalized communication, improve process automation, and data-driven insights.

Bring Your Own Large Language Model (BYOLLM)

BYOLLM allows customers to use their LLMs within the Salesforce platform. This approach offers flexibility and ensures customers can leverage models that best fit their specific needs, potentially improving efficiency and performance.

Examples of Salesforce’s Adoption of Appropriately Sized LLMs

In addition to the BYOLLM approach, Salesforce’s AI strategy about ‘appropriately’ sized (or not so large) LLMs concerns efficiency and performance enhancement through (1) their own AI platform and (2) existing models:

1. Proprietary models: Salesforce has developed its models, like xLAM and xGen-Sales, which are optimized for specific tasks and industries.
2. Fine-tuned models: Salesforce uses fine-tuned versions of existing models like OpenAI’s GPT. These models are customized with Salesforce data to align with customer needs.

Such strategies (i.e., 1 and 2) ensure that Salesforce can deliver high-performance GenAI capabilities while effectively managing computational resources and costs (Haki et al., 2025).

Microsoft’s AI Platform and Integration of Generative AI Models

Microsoft’s AI platform, primarily powered by Azure, integrates generative AI models to enhance enterprise software applications, such as Microsoft Dynamics (CRM). Azure OpenAI Service allows businesses and institutions to leverage LLMs, including GPT-4, for various tasks, e.g., data analysis, customer support, and workflow automation. A key feature of this digital platform is that it includes a framework for integrating AI models with external tools and data sources (“Azure OpenAI Service”, n.d.), linking AI models and external services. The integration framework (or MCP) standardizes communication, which could enhance service integration, accessibility, and AI capabilities (Kaur, 2025).

Microsoft’s BYOLLM Approach

Microsoft’s Bring Your Own LLM (BYOLLM) approach allows businesses to use their LLMs on Azure. This approach can enhance efficiency by enabling organizations to customize their AI solutions based on their needs, which can lead to better performance and relevant results. For example, this allows businesses to perform fine-tuning, i.e., a particular machine learning technique (Roberts et al., 2020), which significantly reduces the time and computational resources required.

Examples of Appropriately Sized LLMs for Efficiency and Performance

Microsoft’s AI strategy to optimize LLMs involves (Jiang et al., 2023):

• LLMLingua: A technique that compresses prompts for LLMs, i.e., reduces the length of prompts while maintaining their effectiveness. This technique led to circa 20x reduction in prompt size that improved inference speed and reduced latency.

• On-Prem LLMs: Developing tools that allow businesses to deploy LLMs on their on-premise IT infrastructure, supporting increased data security and privacy. These tools helped optimize inference performance on existing infrastructure that maintained consistent latency and throughput.

• Scalable Fine-Tuning: Azure OpenAI Service supports scalable fine-tuning of LLMs, allowing businesses to adapt models to their specific tasks efficiently, as noted above.

These examples highlight Microsoft’s commitment to providing a resilient and robust digital platform for AI development (i.e., Azure OpenAI Service) tailored to the needs of individual businesses (Jiang et al., 2023).

Conclusion

Both Salesforce and Microsoft are actively embedding GenAI into their platforms, focusing on efficiency through strategies like BYOLLM and using appropriately sized or optimized models tailored to specific business needs. Both companies offer flexible and high-performance AI solutions for enterprises.

Their AI strategies differ slightly, however. Whereas Salesforce emphasizes proprietary and fine-tuned models integrated deeply within its CRM platform, Microsoft leverages its broad Azure infrastructure, offering advanced optimization techniques and flexible deployment options like on-premise solutions.

Feel free to leave your thoughts in the comments section below.

References

Azure OpenAI Service. (n.d.). Retrieved from azure.microsoft.com on April 14th 2025.

Haki, K., Safaei, D., Magan, A., & Griffiths, M. (2025). Integrating Generative AI Into Enterprise Platforms: Insights From Salesforce. Information Systems Journal. doi: 10.1111/isj.12593

Hugging Face. (n.d.). LLMs. Retrieved from huggingface.co/docs/ on April 15th 2025.

Jiang, H., Wu, Q., Lin, C.-Y., Yang, Y., & Qiu, L. (2023). LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023).

Kaur, S. (2025, March). Unleashing the Power of Model Context Protocol (MCP): A Game-Changer in AI Integration — Microsoft Community Hub.

Roberts, A., Raffel, C., & Shazeer Google, N. (2020). How Much Knowledge Can You Pack Into the Parameters of a Language Model? Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 5418– 5426.