Evolving AI Landscape and Trends 2024-25

AI Landscape Overview

Generative AI has captured the spotlight since the release of ChatGPT two years ago, with foundation models enabling powerful applications in content creation, automation, and knowledge work. While the rapid evolution of AI is transforming industries, they present their unique challenges: in building models, integrating them into applications, deploying and maintaining the applications in production.

For starters, building models remains complex, accessible mostly to those with significant AI skills. That was the case 15 years ago, when data scientists had to figure out which of a dozen types of models works best for a given task. In the mid 2010s the focus shifted to deep learning, and the challenge shifted to inventing and optimizing network architectures. The cost of doing so increased by an order of magnitude, compared to "classic" machine learning, both in terms of compute time and size of training data. Finally, in today’s world of LLMs and GenAI models that have dozens to hundred billions of parameters, only an elite group of companies with generous funding are able to push the boundary of model performance.

Moving from building to deploying models, bringing AI into production remains challenging due to operational complexity, cost, and the need for fine-tuning. Businesses are navigating this landscape by leveraging a mix of open-source tools, specialized platforms, and hyper scalers who provide a wide range of tooling, but at a higher cost. A bewildering set of decisions is typically required to properly architecture your AI system. That process has left many practitioners confused, and effectively created a high barrier for widespread adoption. - The complexity becomes apparent in the AI software stack shown below, with infrastructure and model components sitting on access to the data - the lifeblood of any AI system. 

A novel breed of AI platforms greatly simplies model training and deployment, eliminating the need for AI skills and big budgets. These platforms employ an embedding approach to building models on both structured and unstructured data (Learn more on the embedding approach to predictive analytics in this blog). Featrix is such a platform, and reduces the process of obtaining predictive models from a data science project requiring a budget to a task that most developers can handle. Featrix essentially bypasses most of the complexity in the infrastructure and model layers, "jumping" from your data data to the model serving layer (as indicated by the arrow in the graphic), all within a single platform and only a couple steps required. 

Next, let’s look at the top trends that currently garner a lot of interest in the AI ecosystem and the community is working to address.

Trends in AI for 2024-25

1. Cost-Effective Fine-Tuning of LLMs

The shift from deploying massive, generalized foundation models to fine-tuned, domain-specific LLMs is a pivotal trend. Advances in parameter-efficient fine-tuning techniques (e.g., LoRA) and access to open-source LLMs are making tailored models feasible and cost-effective. This approach improves performance in specific applications while reducing compute requirements.

2. Responsible AI: Ethics and Governance

As AI systems influence critical decisions, the need for ethical AI practices and robust governance frameworks is paramount. Companies are addressing bias, explainability, and data privacy concerns either with homegrown solutions or by using Enterprise AI platforms that have strong support for Governance. The ecosystem is also adapting to emerging regulations like the EU AI Act. Responsible AI development is becoming a competitive differentiator.

3. AI on Edge Devices

The demand for AI at the edge is growing, particularly in applications requiring real-time processing, such as IoT, robotics, and autonomous systems. While LLMs are too resource-intensive for edge deployment, traditional AI/ML techniques are thriving, enabling efficient on-device inferencing and reducing reliance on cloud infrastructure.

4. AI-Enhanced Knowledge Work

LLMs are revolutionizing knowledge work by serving as personalized assistants for tasks like summarization, research, and drafting. Tools like Google’s NotebookLM and AI copilots for coding and content creation are becoming indispensable for professionals, enhancing productivity and decision-making.

5. Generative AI in Industry-Specific Applications

Generative AI is moving beyond general-purpose use cases to industry-specific solutions such as legal document analysis, personalized education, and AI-driven RPA. This specialization is unlocking new value streams and driving deeper integration into enterprise workflows.

How Featrix Addresses Key AI Trends

Featrix offers practical solutions to today's AI challenges:

• No fine-tuning needed - build predictive models directly from your data
• Simplified governance and security by avoiding tool spread
• Lightweight models (millions vs. billions of parameters) suitable for edge computing
• Growing portfolio of industry-specific applications based on customer success

As AI continues to evolve, success depends on choosing solutions that balance innovation with practical implementation. Featrix offers a streamlined approach to predictive analytics that aligns with today's key trends while reducing complexity and cost.

Ready to learn more? Visit our product overview. If you’re ready to try Featrix on your data, sign up for our free trial.

And we'd love to hear your feedback and comments, reach out to hello@featrix.com.