Since its launch in November 2022, OpenAI’s ChatGPT has captured the imagination of both consumers and enterprise leaders by demonstrating the potential generative AI has to dramatically transform the ways we live and work. As the scope of its impact on society continues to unfold, business and government organizations are still racing to react, creating policies about employee use of the technology or even restricting access to ChatGPT.

The most prudent among them have been assessing the ways in which they can apply AI to their organizations and preparing for a future that is already here. The most advanced among them are shifting their thinking from AI being a bolt-on afterthought, to reimagining critical workflows with AI at the core.

How generative AI—like ChatGPT—is already transforming businesses

The global generative AI market is approaching an inflection point, with a valuation of USD 8 billion and an estimated CAGR of 34.6% by 2030. With more than 85 million jobs expected to go unfilled by that time, creating more intelligent operations with AI and automation is required to deliver the efficiency, effectiveness and experiences that business leaders and stakeholders expect.

Generative AI presents a compelling opportunity to augment employee efforts and make the enterprise more productive. But as C-Suite leaders research generative AI solutions, they are uncovering more questions: Which use cases will deliver the most value for my business? Which AI technology is best suited for my needs? Is it secure? Is it sustainable? How is it governed? And how do I ensure my AI projects succeed? 

Having worked with foundation models for a number of years, IBM Consulting, IBM Technology and IBM Research have developed a grounded point of view on what it takes to derive value from responsibly deploying AI across the enterprise. 

Differences between existing enterprise AI in enterprises and new generative AI capabilities 

As the name suggests, generative AI generates images, music, speech, code, video or text, while it interprets and manipulates pre-existing data. Generative AI is not a new concept: machine-learning techniques behind generative AI have evolved over the past decade. The latest approach is based on a neural network architecture, coined “transformers.” Combining transformer architecture with unsupervised learning, large foundation models emerged that outperform existing benchmarks capable of handling multiple data modalities.  

These large models are called foundational models, as they serve as the starting point for the development of more advanced and complex models. By building on top of a foundation model, we can create more specialized and sophisticated models tailored to specific use cases or domains. Early examples of models, like GPT-3, BERT, T5 or DALL-E, have shown what’s possible: input a short prompt and the system generates an entire essay, or a complex image, based on your parameters.  

Large Language Models (LLMs) were explicitly trained on large amounts of text data for NLP tasks and contained a significant number of parameters, usually exceeding 100 million. They facilitate the processing and generation of natural language text for diverse tasks. Each model has its strengths and weaknesses and the choice of which one to use depends on the specific NLP task and the characteristics of the data being analyzed. Choosing the correct LLM to use for a specific job requires expertise in LLMs. 

BERT is designed to understand bidirectional relationships between words in a sentence and is primarily used for task classification, question answering and named entity recognition. GPT, on the other hand, is a unidirectional transformer-based model primarily used for text generation tasks such as language translation, summarization, and content creation. T5 is also a transformer-based model, however, it differs from BERT and GPT in that it is trained using a text-to-text approach and can be fine-tuned for various natural language processing tasks such as language translation, summarization and responding to questions. 

Acceleration and reduced time to value 

Being pre-trained on massive amounts of data, these foundation models deliver huge acceleration in the AI development lifecycle, allowing businesses to focus on fine tuning for their specific use cases. As opposed to building custom NLP models for each domain, foundation models are enabling enterprises to shrink the time to value from months to weeks. In client engagements, IBM Consulting is seeing up to 70% reduction in time to value for NLP use cases such as call center transcript summarization, analyzing reviews and more. 

Deploying foundation models responsibly 

Given the cost to train and maintain foundation models, enterprises will have to make choices on how they incorporate and deploy them for their use cases. There are considerations specific to use cases and decision points around cost, effort, data privacy, intellectual property and security. It is possible to use one or more deployment options within an enterprise trading off against these decision points. 

Foundation models will dramatically accelerate AI adoption in business by reducing labeling requirements, which will make it easier for businesses to experiment with AI, build efficient AI-driven automation and applications, and deploy AI in a wider range of mission-critical situations. The goal for IBM Consulting is to bring the power of foundation models to every enterprise in a frictionless hybrid-cloud environment. 

For more information, see how generative AI can be used to maximize experiences, decision-making and business value, and how IBM Consulting brings a valuable and responsible approach to AI.

Register for webinar, “What does ChatGPT mean for business? Learn what AI and watsonx can do for your business
Was this article helpful?

More from Artificial intelligence

Building trust in the government with responsible generative AI implementation

5 min read - At the end of 2023, a survey conducted by the IBM® Institute for Business Value (IBV) found that respondents believe government leaders often overestimate the public's trust in them. They also found that, while the public is still wary about new technologies like artificial intelligence (AI), most people are in favor of government adoption of generative AI.   The IBV surveyed a diverse group of more than 13,000 adults across nine countries including the US, Canada, the UK, Australia and Japan.…

6 benefits of data lineage for financial services

5 min read - The financial services industry has been in the process of modernizing its data governance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. How can banks, credit unions, and financial advisors keep up with demanding regulations while battling restricted budgets and higher employee turnover? The answer is data lineage. We’ve compiled six key reasons why financial organizations are turning to lineage platforms like Manta to get…

IBM Tech Now: February 26, 2024

< 1 min read - ​Welcome IBM Tech Now, our video web series featuring the latest and greatest news and announcements in the world of technology. Make sure you subscribe to our YouTube channel to be notified every time a new IBM Tech Now video is published. IBM Tech Now: Episode 92 On this episode, we're covering the following topics: IBM watsonx Orders EDGE3 + watsonx G2 Best of Software Awards Stay plugged in You can check out the IBM Blog Announcements for a full…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters