The Emergence of Domain-Specific Small Models
Like any enterprise technology, the real value of Generative AI (Gen AI) and its ROI can be distilled into two fundamental outcomes:
Driving Business Growth – Increasing revenue through AI-driven products and services.
Enhancing Operational Efficiency – Reducing costs by automating tasks and streamlining workflows.
While AI advancements generate excitement, their true impact lies in strategic, results-driven applications. As AI use cases continue to evolve, the buzz created by early foundation model developers has played a crucial role in educating non-technical audiences on tools like ChatGPT. But the question remains—what’s next?
The Future: Domain-Specific AI Models and Applications
Many early enterprise AI adopters have deployed such models and applications, but with the advent of LLMs, this approach is gaining renewed attention.The future of enterprise AI is shifting towards smaller, domain-specific models (including SLMs - Small Language Models versus LLMs - Large Language Models) designed for specialized tasks, enhancing accuracy and efficiency. Here’s why:
Data Limitations – Large AI models have already leveraged most publicly available datasets, making additional gains less impactful.
Intellectual Property (IP) Protection – Enterprises need to safeguard proprietary data, requiring more controlled AI environments. Concerns around compliance and security plays critical role as well.
Cost Efficiency – Training and maintaining large-scale models is expensive, making them impractical for smaller context aware, domain specific applications and use cases.
If you’re an enterprise leader navigating conversations around Generative AI, agentic software, compliance, security risks in AI training data, and/or the potential impact of AI on SaaS GTM models, understanding these emerging trends is essential for evolving your AI strategy.
One of the biggest benefits of the AI boom has been the rapid advancement of toolsets—including open-source solutions. Just a few years ago, AI adoption required expensive data science teams and proprietary platforms. Today, organizations can train smaller models at a fraction of the cost using techniques like the Teacher-Student paradigm, model distillation, advanced reasoning and other traditional ML algorithms(like RL) where large language models (LLMs) help train smaller, more efficient domain specific models with larger context window. The emergence of DeepSeek-R1 and a range of newer, more affordable models shows this paradigm shift, showcasing these principles in action. Additionally, targeted AI investments in foundation models tailored for specific industries further illustrate this trend—Abridge and Hippocratic AI serve as notable examples within the healthcare sector.
2025: The Year Practical AI Transforms Enterprise Domains
In 2025, AI has already shifted from theoretical promise to practical domain specific enterprise applications. Organizations are prioritizing:
Building and/or fine-tuning domain-specific AI models or using domain specific out-of-box solutions tailored to their unique application needs.
Developing AI engines and workflows while maintaining full control over their intellectual property.
Turning data into business value by delivering AI-powered insights and features to customers.
Improving operational margins by using AI to eliminate inefficiencies and reduce costs.
If you’re looking to implement AI-driven solutions that deliver tangible results, our team brings real-world experience in developing and executing AI strategies. Reach out if you are interested in understanding “how” part of building domain specific powerful small models.
Credits
1)Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes - https://arxiv.org/abs/2305.02301
2)DeepSeek-R1 - https://github.com/deepseek-ai/DeepSeek-R1
3) TorchRL - https://github.com/pytorch/rl