As excitement continues to build around the capabilities of generative AI and employees begin to embrace it in their daily work, tech leaders and engineers are exploring ways for GenAI to have the biggest business impact.

Promises of increased productivity and efficiency are an effective lure for business leaders—but before they’ll fully buy-in and invest, those same leaders need to better understand what generative AI is, how it can be incorporated into their workflows, and what the tangible impact might be.

Many business decision-makers are surprised by both the breadth of tasks GenAI helps to solve, yet also the subtle ways in which AI tools can make mistakes.

Complexity grows fast when it comes to adjusting to different needs. There are myriad primary functions for generative AI, from conversational customer support to suggesting strategies for disaster response. Not all those who adopt a GenAI-driven strategy will use it in the same way.

So, what does this technology do? How does it work, and how can you use it to drive productivity in your business? And, most importantly, what are its strengths, its challenges, and what is the future of generative AI?

What is generative AI and how does it work?

Simply put, generative AI is a form of artificial intelligence that creates new content based on user input. For example, if the user input is a question, the output will be an answer.

Generative AI refers to an artificial intelligence large language model (LLM) that can create original content by learning from patterns in existing content. Each generative AI model is trained to analyze enormous quantities of source material (such as text, images, and audio files.) and create an “answer”. This yields outputs that are similar in style and composition to the input, but essentially new.

The efficacy and reliability of GenAI can vary depending on the model and the tool. Enterprise-grade AI models use sophisticated algorithms and require vast sets of training data to deliver reliably, regardless of whether they’re designed to solve complex problems or automate laborious tasks.

The output of a GenAI model is normally steered by a process known as “prompting”. During this, the user explains—typically in “natural language,” which is the way humans speak every day—what they're hoping to generate.

What is a prompt in generative AI?

Essentially, a prompt is some form of instruction, directive, or request for a response from a GenAI model. Prompts can take many forms, including:

Purely instructional input, such as telling the model what tone of voice to use
Examples of prior responses to direct the model to produce a similar or dissimilar response
Additional information the model should consider when generating a response
General directives, such as telling the model to format output in a specific way.

Any prompt should match the capabilities and training of the generative AI model in use While some AI models have been trained more simply, and on more specific tasks, others have had a broader training and, consequently, a broader understanding. There’s little point asking a specialized AI to complete an action that it hasn’t been trained for—say, for example asking a customer support virtual assistant to analyze some code.

It's also possible to narrow down a direction and push back against errors or misinterpretations through successive prompting. This is a process where the user meets the output of each prompt with a further prompt, effectively teaching the model how to find the right answers.

Generative AI explained: introducing transformer-based models

Very important to generative AI is a “transformer model”, which is a type of deep learning model first described in the 2017 paper co-authored by Cohere CEO and Co-Founder Aidan Gomez and titled “Attention is All You Need”. These models can translate text and speech in near-real-time, making them fundamental in natural language processing (NLP) and so helping GenAI to communicate in a more natural way. “Natural” in this context means more like humans speak and write every day; some GenAI output is barely distinguishable from human-written content.

A transformer is based on a neural network architecture, and leverages a technique known as “self-attention”. Transformers differ from earlier AI technology as they do not process data in a linear, sequential manner. Instead, a transformer empowers the model to process different parts of a prompt at the same time. This simultaneous handling leads to better performance on long-range dependencies—that is, those times where elements in a sequence, like words in a sentence or data points in a time series, significantly influence each other even when they are far apart in the sequence.

How does a transformer-based model work?

Transformers process prompts in a very different way from other, older GenAI models.

Tokenization and vectorization: The prompt is first converted into mathematical representations known as tokens, which splits the prompt into smaller units that are easier for the model to understand. The tokens then get converted into vectors known as embeddings. Embeddings represent the semantic meaning of each of the tokens. Lastly, positional encodings are added, and these are used by the model to understand the order of tokens used in this prompt.
Encoder-decoder: Transformers can have encoder-only architecture, decoder-only architecture, or both. An encoder processes the input and generates contextualized representations. A decoder takes encoded representations of previous outputs and generates new outputs.
Attention mechanisms: Transformers are innovative because they use self-attention to determine how the input tokens relate to each other. For each token, the model computes which other tokens in the input are the most relevant. This is done by using three vectors for each token (Query, Key, and Value). These vectors are used to measure the similarity of tokens, normalize the input, and produce output for a token.
Multi-head attention: Transformers use multiple attention heads–individual attention mechanisms working like “individual experts,” each looking at the input data from a different angle. Each head performs a similar self-attention process as defined above, and can uncover different relationships within inputs, resulting in a better capture of nuanced patterns.
Feedforward networks: Once inputs have passed through the attention mechanisms, the output is passed through feedforward layers—the parts of an AI model that help process information step by step—for processing.
Layer normalization: Transformers have inbuilt mechanisms keep training steady and reliable, such as maintaining track of previous data while processing new data (residual connections) and modifying values to maintain balance (normalizing layers)..
Output generation: For GenAI tasks such as producing text output, the decoder outputs responds token by token. Self-attention helps to ensure that the response is focused on the most relevant parts of the input. Tokens are converted back to natural language during the decoding process, where each token is mapped to its corresponding word or subword before the final output is produced.

Key advantages of transformer-based models:

Parallelization: Transformers are faster and more efficient because they process input tokens simultaneously.
Can handle long-range dependencies: Thanks to the attention mechanism, transformers are able to track relationships between tokens, including between parts of a sentence that are not directly next to one another.
Great for large models: Transformers scale well to being trained on huge datasets, and can support large models with billions of parameters.

With the combined power of the attention mechanism, transformer-based models, natural language processing, and strong prompts, generative AI can be put to various uses to create value for enterprise.

Use cases of generative AI

Generative AI has already made its mark across many industries, offering powerful tools for automation, creativity, and problem-solving. As people become more comfortable using it (and relying on it), they'll find many more applications.

Some applications of transformer-based models include:

Language translation: Transformer models can translate content from one language to another in no time, opening the global market to businesses and encouraging better communication with international clients.
Summarizing large texts: Having large amounts of data—like reports, presentations, spreadsheets, and other documents— to sift through can really slow down processes. Generative AI can help by summarizing text and data and extracting the necessary insights.
Performing sentiment analysis: Sentiment analysis tools are especially useful for companies wanting to make good use of their customer’s feedback, social media posts, or product reviews. Understanding public sentiment is the first step towards a better service or product.
Powering conversational assistants: AI tools can handle many tasks on the customer service side, helping customers live and around the clock, offering customized responses, handling inquiries, and improving user experience.
Processing images: Transformers trained to process and generate images are particularly useful in areas such as quality control and manufacturing, where they can identify issues in products before they proceed to production.

Examples of generative AI

The above covers some use cases of generative AI, so let’s now look at how those can be applied in different industries. What is a key feature of generative AI, after all, if not its wide range of potential real-world applications?

Healthcare and life sciences

Healthcare is arguably one of the industries that stands to gain the most from GenAI—and it's already seeing tremendous benefits from advances in medical diagnostics.

Medical professionals equipped with conversational GenAI models trained on vast bodies of medical research can help clinicians to make better decisions. AI-generated reports can help to save them time and potentially ease the burden on the healthcare system.

Likely more significant, however, is the progress that could be made in medical research. GenAI models have already been shown to increase the pace of drug modeling and synthesis, and they can also be put to work testing vast quantities of chemical combinations and predicting their interactions. In fact, the McKinsey Global Institute has estimated generative AI could create up to $110 billion a year in economic value for the pharma and medical-product industries.

On the patient care frontline, generative AI can help improve rehabilitation efforts, such as in physiotherapy. By accounting for user demographics, medical history, and health goals, a digital health and wellness assistant can personalize care plans to individual needs. Adjusting wellness plans with these considerations can see an increase in retention and engagement.

Regulatory technology (RegTech)

RegTech refers to the application of technology to assist financial services firms in managing and adhering to regulations. The advent of this application is especially needed as the complexity of financial regulations grows together with the increasing need for automation in the compliance process.

Like in many industries, GenAI helps financial institutions to automate manual tasks and keep operations running smoothly. One of its strengths in the field lies in its better handling of regulatory compliance and managing risks by automating critical processes like KYC (Know Your Customer) and AML (Anti-Money Laundering). Thanks to its ability to recognize patterns within data, GenAI models can identify transaction irregularities, highlight possible noncompliance, and produce more accurate regulatory reports.Additionally, GenAI can automate due diligence through the examination of online content, court documents, and news articles, as well as recognizing reputational threats before they become more serious.

Oil and gas

Few industries face as much danger as oil and gas; in the US alone the worker fatality rate is seven times higher than other industries. Generative AI can help these organizations gather and analyze crucial data—such as hazards, weather, and previous accidents and injuries in the workplace—to help improve safety.

Feeding this data to a model can help to analyze information to identify patterns and anomalies that illustrate when and why accidents occur, while the natural language processing of GenAI improves how you interact with this data. With AI’s rapidly-improving ability to collate vast amounts of information and suggest solutions, generative AI applications can also help oil and gas companies to predict future vulnerabilities.

Oil and gas workers could also benefit from having safety information in their pocket. A personal AI assistant could communicate risks in real time, warning of increased risks of leaks, or the location of a chemical spill alongside steps to resolve the issue safely.

Manufacturing and supply chain

GenAI can help in almost all stages of the manufacturing process, with opportunities to improve operations from planning to delivery and beyond.

In the planning stage, generative AI in manufacturing can create insights about the current inventory and notify when anomalies arise— like stocks running low sooner than expected. AI tools can then increase and decrease restock orders as needed automatically, helping to ensure there are no knock-on impacts on the efficiency of operations or the availability of products.

Issues with machinery can arise during production. GenAI can help predict the maintenance needs of critical equipment and reduce or eliminate downtime by intervening early. It also allows for root cause analysis, improving the process for mitigating potential future incidents.

Many manufacturing companies are also using GenAI for customer tracking updates in the delivery phases. Users can chat with a virtual assistant for real-time updates on deliveries, which can free-up labor to work on more meaningful tasks.

Retail

Retailers are turning to GenAI to enhance customer experience and streamline operations. For example, generative AI tools can be used to analyze customer behavior and preferences to deliver tailored suggestions, with personalized product recommendations becoming increasingly sophisticated.

Marketing is also being transformed, as generative models can help craft engaging product descriptions, promotional copy, and even video advertisements. Retailers can quickly test and iterate on these materials to see what resonates best with their audiences.

Beyond customer interaction, GenAI is improving inventory management by predicting trends in demand. This helps stores avoid overstocking unpopular items while ensuring they have enough of what customers want most.

Education

The education sector has begun embracing GenAI to make learning more accessible and personalized. AI-powered tutors can provide one-on-one assistance, explaining difficult concepts and adapting to each student's learning style. This reduces the workload on human educators while offering consistent and tailored support to students.

Content creation is another key area for generative AI in education. GenAI can generate practice questions, summarize dense academic texts, or create lesson plans tailored to different age groups or curriculums. This enables educators to focus more on teaching and less on administrative tasks.

Additionally, generative AI is making education more inclusive. For instance, it can produce accessible materials such as audio descriptions for visually impaired students or real-time translations for non-native speakers.

Public sector

In an era of tightening budgets, GenAI can be deployed to help lighten the load and enable public sector staff to handle more important and pressing issues.

Take AI security, for example: With more ransomware and denial-of-service attacks happening, cyber attack prevention is a pertinent issue in the public sector. GenAI can help with this by analyzing network traffic and identifying anomalies before they become a threat. This helps to strengthen the position of government agencies against attacks.

A more novel use for GenAI is road traffic flow management. A system could collate data from typical traffic patterns and identify congestion spots. Using this information, it can then suggest infrastructure improvements to benefit commuters and public transportation.

Disaster management is another key use for generative AI in the public sector. Real-time reports could be created to aid in response management, even suggesting the best course of action. AI could be deployed to predict the assumed outcome of a natural disaster, and could automatically notify residents about evacuation as well.

Benefits of generative AI

Generative AI has many use cases for various industries, but what are the benefits overall? How could a business take advantage of the opportunities at hand?

Productivity: Generative AI is great at producing useful output. From making budget reports to mocking up product variants, a GenAI tool can generate thousands of ideas, allowing employees to focus on the practical, more strategic details. This increased productivity can help to deliver the potential for quicker innovations.
Efficiency: Efficiency gains with generative AI can be remarkable. Instead of an individual having to manually sift through thousands of files to find information, these models can pull this data almost-immediately. This helps empower workers to sweep through tasks that are tedious and arduous to complete when done manually, and frees them to focus on more valuable tasks like strategy and forward-thinking.
Personalization: To help better engage prospects in the sales pipeline, GenAI can create unique content tailored to every individual, which makes them more likely to find the content useful—and ultimately to convert.
Multilingual/Multimodal Capabilities: Generative AI can quickly generate content in multiple languages and bridge language barriers. Additionally, thanks to multimodal inputs and outputs, which include processing both text and images, businesses can reach and interact with a wider, more varied audience across various media formats and languages.

Challenges of generative AI

As with any fast-evolving and maturing technology, there are some challenges associated with generative AI and its adoption. Understanding these is the best first step toward mitigating or avoiding the risks.

Inconsistency in results

GenAI results keep getting better, but they're still not 100% consistent and reliable—even hallucinating on occasion. Given that many industries demand extreme precision, this can make it hard to rely on GenAI alone without human oversight.

Potential solution

Retrieval-augmented generation (RAG) systems are capable of substantially mitigating this limitation. RAG uses an external database to find and retrieve content relevant to the prompt to refine generation to a more accurate output. It can also provide citations, which allow the user to follow the information trail to the original source, helping to improve trust in the results.

Processing cost

High-end GenAI models require significant computing power—especially during the training process. This can be both resource-intensive and expensive., making the more advanced uses of GenAI less accessible for those with no access to powerful hardware or large-scale computing systems.

Potential solution

Though cost will likely remain among the biggest pitfalls of generative AI, cloud-based services can reduce the need for costly infrastructure and make GenAI more accessible. Advances in model efficiency and hardware design over time may further lower costs, too.

Training data reliance

The performance of GenAI heavily depends on the quality of the model’s training data. If data has a bias, is incomplete, or has inaccuracies, the model's outputs will reflect those flaws. This makes data collection and curation critical.

Potential solution

Organizations should focus on building diverse, high-quality datasets, and regularly audit them to identify and address biases or inaccuracies.

Bias

As generative AI draws upon the patterns found in historical data, it tends to inherit the biases present in that data. This sets it up to produce fresh content that leans in the same direction.

Potential solution

Any training datasets used need to be as large and diverse as possible to minimize the risk of model bias being introduced at the training or fine-tuning stage. It may be impossible to gather a large enough dataset for some use cases; where this happens, GenAI can be used to produce synthetic training data to be included in the training dataset, filling in those gaps where real-world data is scarce or biased. Be sure to test your synthetic content and check it for biases, too.

Misuse

Given its ability to generate realistic content, GenAI can be misused to create images, audio files, or videos that distort the truth or propagate outright fabrications. Such content can be used for serious crimes, such as identity theft and fraud.

Potential solution

Effective measures—such as watermarking AI-generated content and implementing stricter AI security policies—can reduce the risk of misuse. When seeking generative AI providers to partner with, businesses should also research each company’s ethos to ensure responsible practices in their AI development processes.

Accessibility

As GenAI technology advances, it is important to ensure that it benefits everyone, especially under-represented groups, people with disabilities, and those from marginalized communities. In fact, these technologies have the power to become the drive towards more accessibility, with, for example, voice-to-text or real-time translation services making information more easily available for individuals with hearing or speech impairments. The use of multilingual generative AI with access to a broader array of languages also helps to make things more accessible to communities across the world, too. This move in the right direction, however, still needs some work.

Potential solution

The solution includes designing GenAI systems with accessibility in mind, involving under-represented groups in the development and testing stages, and developing standards and regulations to guarantee priority. For more on this topic, check out Cohere For AI.

What is the future of generative AI technology?

The future of generative AI looks promising. The ability to rapidly get answers rooted in real-time data with RAG can deliver increased capabilities and productivity, both personally and professionally.

However, it’s also important to acknowledge that this technology brings new challenges. Generative AI security is a prime example, as generated content has the potential to push past identity verification systems. GenAI also has the potential to cause damage if corrupted by unauthorized users. That's why it's important to only use companies that put an emphasis on safety measures and are committed to the ethical use of AI.

With the right planning, the right stakeholders, and the right approach to security and privacy in AI, generative AI tools can transform how an enterprise operates, communicates, and plans for its future.

FAQs about generative AI

What is one challenge in ensuring fairness in generative AI?

One of the main challenges in terms of AI fairness is mitigating bias in the training data. Since these models learn from vast datasets, any biases present in the data can influence the outputs, which leads to unfair or biased content.

Addressing this requires careful data curation, transparency in the model's decision-making processes, and continual monitoring to identify and rectify biases that may emerge as the model develops.

AI tools should serve all users without discrimination or unreasonable treatment, and there must be a committed effort by all parties involved to ensure this.

What is the main goal of generative AI?

The main goal of generative AI is to create new content based on patterns learned from existing content. This content should closely resemble the natural language used by humans.

GenAI can automate repetitive tasks, help solve complex problems, and aid innovation. This makes it useful in business operations across a wide range of industries. Given its sheer breadth of applications, the primary function of GenAI is simply to be useful.

What is the difference between generative AI and predictive AI?

The difference between generative AI and predictive AI is that the former creates new content while the latter makes predictions. However, both do this by analyzing existing data and finding historical trends and patterns.

As a result, GenAI has much broader applications as it can be valuable in a huge range of fields.

Predictive AI is still useful for helping businesses make data-driven decisions, and can still be deployed for forecasting data such as customer behavior, budgetary demands, or stock prices.

What type of AI is generative AI?

Generative AI fits within the machine learning category. Using models such as variational autoencoders (VAEs)—a type of model that takes complex data and simplifies it into a compressed version—and transformers to learn from huge sets of training data, generative AI produces outputs that follow the underlying patterns found in human creations.

Generative modeling builds on the predictive AI that preceded it by adding natural language processing (NLP) capabilities. Also, it improves awareness of contextual awareness, allowing it to engage in conversation with users, or with other machines.

How can generative AI impact the workforce?

Generative AI with tool use is already impacting the workforce by automating tasks that traditionally have required human input. For example, it can draft reports, automate inventory management, and mock-up user interfaces. This then frees up employees to focus on high-level tasks that benefit more from their unique skills, experiences, and preferences.

Maximizing worker productivity with GenAI requires carefully customized, secure solutions tailored for seamless execution.

What is a token in generative AI?

A token in generative AI is a unit of data representing a small piece of text. This may include a word or even part of a word. When a GenAI model receives a prompt, it breaks it down into tokens, allowing it to analyze and generate content more efficiently and effectively.

Transformer-based models also tag input words based on where they're positioned in sentences and how they relate to one another. It equips them to perform far better when parsing and generating natural language.

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