*Accenture Report: Generative AI Is Powering a New Reality for Business*
By Ghadeer Redler, Staff Writer, Workday
The release of OpenAI’s ChatGPT in late 2022 lit up the internet with astonishing AI-generated responses to a range of complicated questions and prompts. One especially colorful and memorable example? Instructions written in the style of the King James Bible for how to remove a peanut butter sandwich from a VCR.
Toss in AI-produced art using text-to-image tools and suddenly, the future of generative AI—and its potential value—feels very tangible. Not surprisingly, the AI race between the big tech firms has heated up in response.
But this new era of AI is about much more than improved search engines and word-processing software. Dig beneath the breathless headlines about ChatGPT and other new tools and one of the biggest step changes in AI history becomes clear: the availability of pretrained models that can be adapted to just about any task.
The recently unveiled content-generating AI tools are all based on “foundation models”—trained on massive quantities of raw data, such as language and images, that can be customized. These foundation models will revolutionize how and where businesses across industries use AI.
In fact, 96% of business executives are “either very or extremely inspired” by the new capabilities offered by AI foundation models, and 95% say it will usher in a new era of enterprise intelligence, according to Accenture’s report “When Atoms Meet Bits: The Foundations of Our New Reality.” But first, business leaders must marshal the right resources to realize AI’s potential. Below are some of the key findings from Accenture regarding the future of AI.
To seize the AI advantage, businesses must understand the distinctive qualities of foundation models that allow them to create novel capabilities and business value.
So, first things first: What exactly is a “foundation model”? Stanford Institute for Human-Centered Artificial Intelligence researchers coined the term in 2021 to describe large AI models trained on a vast quantity of data with significant downstream task adaptability.
A foundation model can be trained on one data modality (such as text) or several—such as text and images (such as ChatGPT), or even sound and video. Two prominent types of foundation models pushing generative AI forward are transformer machine learning (ML) models and large language models. Both are neural networks involving hundreds of millions, even trillions, of prediction-related parameters.
What makes these models uniquely powerful—and perhaps endlessly adaptable—is that their capabilities are not task specific. Because they are broadly trained across one or more data modalities, foundation models can learn new tasks with little to no additional training. As long as the task is within its domain, the AI can handle it.
What we’re seeing is this learning ability in action—and the race to harness its capabilities is on. Google, Microsoft, Baidu, and Meta have all created their own large language models while other companies, such as OpenAI, have created large multimodal models. DeepMind’s Gato may be the most advanced multimodal AI model yet: it can complete more than 600 tasks including chatting, captioning images, playing Atari video games, and stacking blocks with a robotic arm.
For now, most foundation models are fairly limited in the amount of data they’re trained on—mostly just natural language (text) and images. As models involve more and more varied data—video, 3D spatial data, protein structures, industrial sensor data—their potential uses and value will skyrocket. In fact, 97% of global executives in the Accenture report agree that AI foundation models will enable connections across data types, revolutionizing where and how AI is used, per our report.
Not surprisingly, the recent advances in AI foundation models have grabbed the attention of business leaders across the globe.
Companies are now experimenting with these models, adapting them for tasks that range from powering customer service bots to automated coding. And just as quickly as the models advance, organizations are discovering new ways to use them.
Take CarMax, for example. The company recently used OpenAI’s GPT-3 model to read and synthesize more than 100,000 customer reviews for every vehicle the company sells. The model then produced 5,000 summaries—a task the company says would have taken its editorial team 11 years to complete.
This example underscores an important point about how and why generative AI is poised to impact so many industries. Most companies won’t need to build their own foundation models. Instead, they can access existing models as platforms via open source channels or paid access. Just as companies lean on public cloud data centers, they will increasingly tap AI models created and offered by other companies. Thousands of applications have already been powered by OpenAI’s GPT-3, including copywriting, website building and, of course, chatbots.
97% of global executives in the Accenture report agree that AI foundation models will enable connections across data types.
There are two major benefits businesses should keep in mind. First, these models will completely transform human-AI interaction, whether that’s through natural language communications or coding. Google, for example, has already developed an AI code completion tool that has boosted software engineers’ productivity. Second, foundation models are making possible new AI applications and services that were too difficult—or even impossible—to build before. Again, this is because most businesses will be able to ramp up AI capabilities using pretrained models—no need to create their own model from scratch.
No surprise, then, that 98% of global executives agree AI foundation models will play a key role in their company strategies in the next three to five years, the research finds.
Because the frontier of AI models and their capabilities is moving so fast, companies will need to tread carefully to anticipate and avoid pitfalls. For example, a foundation model is only as good as the data it’s trained on—and many datasets are biased due to the historic exclusion of certain populations and demographics. In other words, there are real risks that all businesses should consider when deciding whether to leverage the power of a foundation model.
That said, a paradigm shift is now underway in the business of AI. There’s no question that these models will impact every industry, but what will set businesses apart is how they use AI and what problems they attempt to solve with it.
A paradigm shift is now underway in the business of AI.
