If you’ve ever wondered how AI went from clunky virtual assistant conversations to sophisticated and versatile machines that can translate, write, and even code, here’s your answer: Transformer models. These are the supercharged versions of earlier AI models, capable of absorbing and understanding entire sections of texts at once and at great speed.

So how have these powerful multitaskers transformed industries across the board, and how are they reshaping the way we work?

Since their introduction in the seminal 2017 research paper, “Attention Is All You Need,” co-authored by Cohere founder and CEO Aidan Gomez, transformer models—also known as transformers—have propelled AI to new heights in natural language processing (NLP) and machine learning. We've seen this bear fruit in recent years through the rise of popular large language models (LLMs) with a range of applications.

For enterprises, transformer models’ ability to process multilingual and multimodal data can be game-changing. These AI systems are powerhouses for predictive analytics, knowledge discovery, customer service, and more, redefining businesses’ relationship with information.

A 2024 McKinsey Global Institute report suggests that generative AI, powered by foundational models built using transformers, could inject between $2.6 trillion and $4.4 trillion in economic value through enterprise use cases by 2040. There is no better time for business leaders to get acquainted with transformer models, understand how they work, and maximize their potential.

What is a transformer model?

A transformer model is a type of deep learning neural network that provides the foundation for some of the most advanced LLMs currently available.

Deep learning is a subset of machine learning that uses complex neural networks to identify patterns (or features) from large amounts of data. Interestingly, neural networks get their name from their loose mimicry of the structure of the human brain, passing information between nodes just like neurons transmit information in our minds.

This makes deep learning models exceptionally good at learning hierarchical patterns, such as understanding syntax in language or capturing complex relationships within large sets of unstructured data.

While all deep learning networks are fundamentally designed to process and identify patterns in data, transformer models excel at tracking even the most subtle context, and at understanding relationships within sequential data. For example, they can semantically understand the order and meaning of words in a sentence. They can also help differentiate those words that can have different meanings depending on context, such as “bow”—which could be the bow of a ship or a ribbon tied in a bow—or “bank” meaning a financial institution or the edge of a river.

This is key for applications like natural language understanding and machine translation, and why transformers excel in NLP tasks. Thanks to their speed and enhanced capabilities, transformers have become essential to generative AI applications spanning different ways to work with text, from text generation and summarization (creating written content) to text classification (categorizing customer support tickets based on sentiment analysis), and much more.

How does a transformer model work?

Transformer model architecture leverages the power of multiple layers of neural networks, diving deep into raw data to identify recurring patterns that inform its smart outputs.

However, not all deep learning models are created equal. Convolutional neural networks (CNNs), for instance, use filters to scan visual data and are typically used in image recognition. On the other hand, recurrent neural networks (RNNs) work by processing data sequentially. This is how they are able to capture and make conclusions based on the order and relationships within the sequence they are working on. Before the arrival of transformers, RNNs were among the most common machine learning architectures used in NLP.

Unlike RNNs, transformer models can track and contextualize “long-range dependencies"—that is, data elements that may appear out of sequence. As a result, transformers can understand more nuanced context and preserve coherence over longer text inputs. This can make them adept at more sophisticated and convincing natural language tasks.

What makes transformers so unique and powerful is a mechanism called “self-attention.” Self-attention helps transformers identify which parts of the input data (or "tokens") are most relevant and link them effectively. Specifically, tokens are individual units of data—characters, words, etc.—processed by the model to understand the input’s structure and meaning.

The self-attention mechanism is named after the way the model “pays attention” to each part of the input data in relation to every other part. The model compares the embeddings (or mathematical representations) of each token to every other token. These embeddings capture contextual and statistical information about the tokens, so that the model can recognize which components are most relevant to the task and generate a contextually informed output.

This ability to weigh and link input elements dynamically is why transformer models have been so important in the field of AI. They've also opened up a whole new dimension of possibilities in enterprise AI, spanning customer service virtual assistants and task automation to image synthesis and fraud detection.

Transformer models explained: the different types

Transformer models can vary significantly in structure, capabilities, and applications depending on their type. Some of the most common types of transformer models include the following.

Bidirectional transformers

Bidirectional transformer models, like Google’s BERT (Bidirectional Encoder Representations from Transformer), process text by considering both the words before and after each element in a sequence. This allows them to understand context in both directions simultaneously.

While bidirectional models can be on the smaller side and more resource-effective, models in general come in different sizes and this can vary depending on the specific task.

As perhaps the best-known example of a bidirectional transformer, BERT plays a critical role in Google’s search engine, helping it to interpret user intent and deliver relevant results, even for complex or conversational queries.

For instance, when a user searches for "pros and cons of traveling to New Zealand in February," BERT efficiently identifies that the query seeks a detailed comparison of advantages and disadvantages of traveling to New Zealand during that particular time, rather than offering generic travel advice.

Generative pre-trained transformers (GPTs)

Generative pre-trained transformers are a type of LLM and are among the best-known and most widely-used transformer models—particularly in consumer-facing applications. They are foundational to conversational AI systems.

As the moniker implies, GPTs are pre-trained on huge volumes of text data, often comprising books, articles, social media, and computer code, exposing them to a diverse set of data to learn from. This is a data-hungry process, but it enables them to produce highly convincing and contextually-aware natural language answers, making them useful for content generation tasks. Some do this by analyzing input text, breaking it down into tokens, and then predicting the next token based on patterns it learned during pre-training—that is, the initial training phase where the model is exposed to vast datasets to learn data patterns.

Multimodal transformers

Multimodal transformers are like multilingual experts for data: they can understand and process different kinds—or modalities—of input data at the same time. This can include various combinations of text, images, audio, video, and even data from external sensors like wearable tech.

This ability makes multimodal transformers useful for a range of tasks, including helping doctors to classify MRI images into disease subtypes, or a sales rep creating custom proposals based on a whole spectrum of insights stored across the text, images, and graphs of PowerPoint presentations.

One example of a multimodal transformer is Cohere's Embed 3, an industry-leading AI search model that is capable of generating embeddings from both text and images. Using it, businesses can build systems that accurately and quickly search important multimodal assets such as complex reports, product catalogs, and design files to boost workforce productivity.

Vision transformers (ViTs)

Vision transformers, or ViTs, are a newer type of transformer model introduced by researchers from Google Brain and Google Research in 2020.

Traditional image recognition models, such as CNNs, focus on small regions of an image in turn and then build up the bigger picture layer by layer. ViTs, meanwhile, split images into smaller patches and process them as tokens, using self-attention to analyze relationships between patches.

This helps ViTs understand details in individual patches while also seeing how those details fit together. It can make them effective for tasks like mapping geographic features in satellite imagery or identifying objects in crowded scenes.

How does this relate to LLMs?

Large language models are built on transformer architecture. However, while all LLMs are transformer models, not all transformer models are LLMs.

An LLM is a term used to describe transformer models that have been specifically designed for tasks involving natural language understanding and generation at scale. This includes generative pre-trained transformer models and other transformer-based architectures, such as encoder-decoder models used for translation or summarization tasks. On the other hand, a transformer model could be used for a task unrelated to language, such as image recognition, where the architecture works with various kinds of data without necessarily relying on natural language processing.

However, it should be noted that training an LLM is an expensive proposition, as they require huge volumes of data and lots of computing power.

Examples of transformer models: In practice

Transformer models are making an impact across many industries, helping enterprises tackle challenges that may previously have been too complex or impractical to solve.

Financial services

Transformer models can be used in the financial services sector to sift through unstructured data like transaction history, emails, bank statements, and chat logs to flag potentially-fraudulent activity.

Their ability to analyze different data types, alongside variables like customer behavior and geolocation, helps enable transformer-based AI systems to identify and alert organizations to suspicious patterns they might otherwise miss.

Transformers are also used by banks and financial services in areas like document retrieval and knowledge discovery. For example, in 2024 JPMorgan Chase introduced a transformer-based model called DocLLM to help professionals better understand complex business documents. The company also rolled out LLM Suite, a generative AI tool that acts as a virtual assistant to help employees with tasks like writing and document summarization.

Healthcare and life sciences

Healthcare organizations typically generate vast amounts of complex data, from electronic health records (EHRs) and clinical documentation to medicine information, scans, and genetic data. Transformer models can be applied to a variety of scenarios to help clinicians and researchers to manage and use that data.

Transformers could help a hospital’s back-office staff to automate repetitive administrative tasks like appointment scheduling and summarizing patient information, as well as supporting more groundbreaking applications like disease diagnosis and drug discovery.

In life sciences and medical research, the applications can even help drive discoveries. DeepMind AlphaFold, for instance, leverages transformer architecture to predict the three-dimensional structures of proteins from their amino acid sequences. This is a key step in understanding how proteins function and identifying targets for drug development. This type of technology has the potential to accelerate breakthroughs in treatments for diseases like cancer and Alzheimer’s.

In clinical settings, hospitals are increasingly using transformer-based NLP models to extract insights from unstructured clinical data. This can support clinical decision-making and help to reduce the documentation burden on healthcare staff.

Public services

Transformer models are also being introduced in the public sector for tasks requiring large-scale data analysis. The U.S. Department of Labor (DOL) uses a transformer-based text classification model, called the SOII Autocoder, to process and classify hundreds of thousands of work-related injury and illness cases from the Survey of Occupational Injuries and Illnesses (SOII).

In legal settings, governments and legal teams can use transformer-based NLP models to help prepare cases by automating research and document reviews.

Benefits of transformer model architecture

Transformer models are an exciting strand of AI technology. LLMs and similar transformer-based deep learning architectures are generating curiosity among numerous organizations for their range of benefits, like these:

Transformers are highly flexible. Businesses can use them to process customer feedback and operational logs, analyze lengthy legal documents and regulatory filings, or analyze any other source of unstructured text.
Transformer architecture can scale effectively with increased data and demand, allowing businesses to analyze growing datasets and expand capabilities without compromising performance. This helps them adapt to changing operational needs and market trends. However, even though transformers are efficient at processing large volumes of data, be aware that processing costs will rise in line with the data volume and complexity.
The self-attention mechanism in transformer models enables them to identify and keep track of relationships between distant data elements in the input. That can make them especially effective for tasks involving long text or information sequences—although resource use will be higher the longer the input sequence.
Techniques like retrieval-augmented generation (RAG) can make transformer models more practical for real-world use cases, combining their generative power with access to live data for up-to-date, contextually-rich outputs.
Pre-trained solutions can simplify language model adoption. Enterprises can often fine-tune LLMs for different applications, like customer service, without needing to extensively retrain them.

Challenges of transformer model architecture

All new technology comes with challenges, and transformers are no exception—particularly given their relatively nascent nature.

Adopting AI solutions typically requires changes to IT infrastructure, which can be costly and disruptive. It can be helpful to start with small, focused deployments that integrate with current workflows before scaling up.
You may need to decide whether to self-host your model or use third-party services, and each choice has its benefits. Self-hosting would mean purchasing the physical hardware for running a model, which can be very expensive, but it also means that your organization will gain full control over the models used and adds another layer of security. Cloud-based model providers are a relatively cheaper option; your organization accesses the provider's model via APIs, which helps with the cost but can introduce security risks.
Transformer models may not deliver value if they’re not designed to address specific operational needs. Business leaders should set clear objectives and involve the right stakeholders in the planning phases to ensure any new solutions align with business priorities.
A key challenge with transformer models is that they're bound by the limits of their training data, which can quickly become outdated. RAG helps to bridge this gap by pairing transformers with a real-time retrieval mechanism to pull relevant data from external sources, like a database or knowledge repository. This helps ensure responses are as contextually accurate and current as possible, especially if the model is connected to real-time information
Data quality can limit performance, and poor data often produces unreliable or biased results. A 2024 Accenture survey of 2000 CXOs (chief experience officers) found that 75% viewed high-quality data as essential to improving their generative AI capabilities. Organizations must put systems in place to clean and check data to help reduce hallucinations—instances when a models outputs incorrect or fabricated information—and ensure better accuracy.
Smaller datasets or highly-specific use cases can make transformers less effective or lead to "overfitting"—this is where a machine learning model becomes too adapted to its training data and struggles to apply what it has learnt to new information. Using pre-trained models can help mitigate this risk.

How to implement a transformer model

Implementing a transformer model in a business requires careful planning and investment across key areas. Enterprises must align their goals with the model’s capabilities and understand the resources needed for successful integration.

The following step-by-step guide is designed as a basic framework to guide business decision-makers and avoid common pitfalls:

Define your objectives. Clearly outline what the model will achieve and how it aligns with business goals. Focus on specific outcomes like automating customer service, analyzing large datasets, or generating performance reports.
Assess your infrastructure. Evaluate your existing IT systems to determine if they can support a transformer model. Consider things like computational power, storage requirements, hardware constraints, and network capacity. Additionally, there will be the cost of buying enough computing power (currently in the form of GPUs, or graphics processing units). In many cases, using a third-party model provider can be a more accessible option.
Gather high-quality data. Gather accurate and diverse datasets to train or fine-tune the model. Ensure data is cleaned, validated, and relevant to your business needs. Data quality is crucial for producing reliable and unbiased outputs. The data should be focused on the types of knowledge and tasks the model will need to process.
Allocate budget. Prepare a detailed budget that accounts for infrastructure, talent acquisition, data preparation, and ongoing maintenance. Include potential costs for pre-trained models or third-party tools to streamline development.
Build, train, or acquire talent. Identify any workforce skills you need, such as machine learning, data engineering, project management, or training in AI ethics. Invest in hiring qualified professionals or upskilling existing teams. Partner with external experts when needed.
Select the right model. Assess whether you really need to create a transformer model from scratch or whether using an existing, pre-trained model will suffice. Fine-tuning an existing model is typically faster and more cost-effective, especially for specific use cases like content generation or language translation.
Pilot and test. Deploy the model on a small scale to validate its performance and ensure it meets business requirements. Monitor outputs closely for accuracy and reliability.
Integrate and scale. If the pilot proves successful, you can consider integrating the model into broader workflows and scaling up its use across the organization. Make sure you train teams to use it effectively and monitor its impact over time.
Maintain and improve. Continuously evaluate the model’s performance and quickly address any issues that come up, such as model drift (when outputs become less reliable over time) or new data requirements. Regular updates and retraining are essential to maintaining relevance and effectiveness.

The future of transformer models

Transformer models are evolving quickly alongside the broader enterprise AI industry. Researchers are already exploring ways to reduce the computational demands of large transformer architectures and make them more scalable. This should help make the technology more practical and accessible for businesses—particularly those with limited resources or infrastructure.

Developers are also extending the capability of transformers to process multimodal data, integrating text, images, and other inputs. This is creating new opportunities across a number of industries, such as healthcare—where AI can help to detect and manage serious illnesses earlier—and logistics, where AI solutions can improve route planning and demand forecasting.

At the same time, organizations are also exploring a new frontier in generative AI with agentic systems. LLM-powered AI agents have the potential to transform workflows by evolving from content generators to action-takers. Agentic AI can not just help with ideas or information, but can actively execute complex, multi-step tasks and reasoning.

That said, energy consumption remains a key challenge, and reducing the environmental impact of AI systems will likely remain a key focus for AI developers. Stricter regulations, such as the European Union’s AI Act, are also driving organizations to prioritize transparency and accountability.

Whatever the future looks like, transformer models are likely to continue to shape how businesses operate and innovate for years to come.

Transformer models FAQs

What are the main components of a transformer model?

A transformer model is typically made up of two components: An encoder and a decoder. However, some models only use one component, depending on the task. For example, BERT uses only the encoder.

With older models, the encoder reads and processes input data to create a meaningful representation of it, such as the relationships between words in a sentence or patterns in an image. The decoder uses that representation to generate an output, such as translated text or a summary.

Arguably, transformers' most important feature is self-attention, which helps the model focus on the most relevant parts of the input while understanding relationships within the data.

Why are transformer models important in AI and NLP?

Transformers have quickly become a cornerstone of modern AI, especially in NLP. Their self-attention mechanism lets each token (data representation) directly reference all others, which helps them handle complex contexts more effectively than older models like RNNs or traditional NLP methods.

This can speed-up training while also delivering high performance in applications like language modeling, translation, summarization, and speech processing, and even image analysis. However, training a large transformer model still requires significant computational resources and time.

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