- By: Áine Byrne
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BUILDING SCALABLE AI: THE ROLE OF FOUNDATION MODELS - Part I
For business leaders navigating digital transformation, machine learning offers a vast toolkit for scaling intelligence, and foundation models are among the most impactful in certain domains. In our previous machine learning (ML) blog, we explored the machine learning pipeline and how ML models learn from task definition to feature engineering and validation.
This entry has proved to be a little longer than expected, so like in true mid-season cliffhangers, we’ve split this into two parts. The following part I looks at foundation models and their place within ML, while Part II covers how they have been deployed to help reshape business strategy.
Understanding Machine Learning, Foundation Models, and the Future of AI
Machine learning (ML) has rapidly become a central force in the field of artificial intelligence (AI). It has evolved from a niche technical capability into a strategic enabler. Today, it empowers businesses to perceive, recognise, predict, and generate in ways that were once unthinkable. From intelligent automation to predictive insights, ML is transforming how your company operates, innovates and ultimately competes.
Machine Learning enables companies to
in ways once thought unthinkable
ML offers a broad toolkit of techniques to enable you to stay one step ahead of your competitors, and among them, foundation models represent a powerful and scalable class of ML systems trained on vast, diverse datasets. They are driving today’s transformation by enabling smarter automation, deeper insights, and more efficient and scalable content creation across industries.
Revisit: Foundation models and their place in machine learning
In the evolving ML ecosystem, foundation models are driving a significant shift toward general-purpose intelligence. They are pre-trained on massive and diverse datasets, just think of the billions of text and documents, images, and videos available across the internet. Instead of relying on manually labelled data, they learn by predicting missing parts of natural data. This technique, known as self-supervised learning, allows models to teach themselves by solving tasks like inferring a missing word or completing an image.
“If a model can learn to predict the next word in a sentence, what else could it infer from patterns we haven’t even noticed?”
This is good news as foundation models are general-purpose and capable of being adapted to a wide range of tasks with minimal additional training. A big leap from the traditional ML models, which are often task-specific and rely on manually labelled data.
To put the leap from traditional ML to foundation models, into perspective, here’s a quick side-by-side comparison:
| Traditional ML | Foundation Models |
|---|---|
| Task-specific | General-purpose |
| Requires labelled data | Learns from unlabelled data |
| Manual feature engineering | Learns features automatically |
| Limited scalability | Highly scalable |
It’s important to note that the development of foundation models involves multiple layers of technical refinement, but once that’s done the benefit for business leaders is their ability to adapt, scale, and align with human goals. It’s also important to note that both traditional and foundation models have their place in the corporate world, just for different tasks and contexts.
These models are no longer just research tools, they’re strategic engines driving innovation, efficiency, and competitive advantage. To maximise their impact within your business it’s important you get the implementation correct. And that comes down to your blueprint, or architecture.
Foundation models’ true power is unlocked through thoughtful design and integration. At the heart of their effectiveness lies the architecture that enables them to process vast data, maintain context, and scale across tasks. To understand how these models deliver strategic value, we need to look under the hood, starting with the transformer architecture that powers them
The Blueprint of Modern AI: Why architecture matters
Most foundation models typically use transformer architectures, which enable them to process and understand complex patterns in language, images, and other data types with remarkable efficiency and scalability. Transformers use what we call attention mechanism to track relationships across sequences, enabling foundation models like ChatGPT to maintain context and generate coherent (well mostly!) responses.
“Ever wonder how ChatGPT seems to ‘remember’ what you said five messages ago? That’s the magic of attention mechanisms at work.”
We’ll explore transformer architecture and other architectures in more detail in upcoming blogs to help build a deeper understanding of how these models work under the hood.
Once your blueprint, or the model architecture is in place, the next challenge is aligning it with business goals.
Refining Intelligence: Aligning models with business needs
Training a foundation model is just the beginning. To make these models truly useful like ChatGPT for example, they undergo several stages of fine-tuning to adapt their capabilities to specific tasks and contexts.
Once a foundation model is pre-trained, it can be fine-tuned to align with specific business tasks and contexts.
One powerful fine-tuning method is Reinforcement Learning with Human Feedback (RLHF), where human feedback helps refine model responses to better align with business needs and user expectations. Over time, this iterative refinement improves the model’s accuracy, relevance and overall performance. This technique not only improves model performance, but it also helps align outputs with human values and expectations. This makes the model more reliable, context-aware, and suitable for real-world business applications, where trust, nuance, and clarity are essential.
“Fine-tuning is like mentoring a promising cadet or graduate. The raw potential is there, but feedback and guidance turn it into expertise.”
RLHF can be resource-intensive and may not be feasible for small businesses. For smaller businesses, leveraging pre-aligned models or using prompt engineering can offer a lightweight path to strategic alignment. However, in customer-facing applications for example, RLHF is especially valuable. It ensures responses reflect your brand’s tone and values. This alignment is critical for building trust and deepening customer engagement.
For business leaders exploring model alignment, RLHF stands out as a powerful technique, but it’s not the only approach. Other fine-tuning techniques such as supervised learning on curated datasets or rule-based alignment strategies also play important roles. But we’ll talk about those more in our upcoming blogs to help build a broader understanding of how models are adapted for real-world use.
Ultimately, it’s strategic leadership and human insight that ensures ML serves real-world needs, not just efficiently, but responsibly.
Final thoughts
And this is before we’ve managed to get to look at how foundation models in ML are being deployed for the benefit of companies. This juicy topic is the core to our Part II. Interested? Stay tuned as it will be dropping next week.
Missed the other blogs?
Catch up on our Machine Learning series below:
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