In March 2023, Bill Gates declared that GPT was "the most important technological advancement since the advent of graphical user interfaces, as important as the invention of microprocessors, personal computers, the Internet, and mobile phones." "Therefore, GPT will be the next new technology that companies and individuals need to pay close attention to."
Research by OpenAI found that 80% of employees in the U.S. can already use large language models (LLMs) like ChatGPT to influence or enhance 10% of their work content. Of those, one in five (19%) employees are likely to find that up to 50% of their work content is positively affected.
It can be seen that a new business model has arrived. So what does this mean for C-suite and management teams? Imagine if everyone in a business could be ten times more productive. Or a tenfold increase in creativity? Tenfold increase in value to the company? These will be unpredictable new creativity.
Now artificial intelligence (AI) is starting to pay huge dividends for businesses. Goldman Sachs estimates that over the next 10 years, generative AI alone will increase global wealth by 7% (nearly $7 trillion) and boost productivity by 1.5%.
Historically, a major constraint limiting wealth and productivity gains across industries has been lack of time. The fundamental reason why you give up countless ideas is mainly because you don't have the time or resources to think about the implementation of ideas. And this is where the real value of AI lies: not only automating monotonous tasks, but also providing new, more efficient ways of working. Increase efficiency and give people more time to be creative.
Artificial intelligence can improve the level of enterprise operations and transform the quality of enterprise services. Successful implementation of AI can create new revenue streams for businesses, improve profit margins and maximize return on investment.
As a business leader, AI is a big opportunity for you and for your business, and in the future, AI technology will completely rewrite the existing backward way of working.
C-suite leaders need a strategy to make AI work, that is, putting the technology into operation, establishing the framework, and embedding it into the enterprise. So, let's review the development of AI first.
Four convergence paradigms of AI technology
In the past few years, artificial intelligence technology has quietly undergone revolutionary changes. This advancement becomes apparent in highly complex Large Language Models (LLMs).
OpenAI's ChatGPT is probably the most famous example. However, without the continued development and aggregation of data, advanced GPU architectures, and the recent emergence of deep learning and Transformer frameworks, AI would not be where it is now.
In addition to deep learning, it focuses on training complex AI systems to learn and predict from large data sets. At the same time, Transformers also introduces an architecture that can better handle sequential data and capture complex relationships in these datasets. Deep learning and Transformers combine to pave the way for the development of today's advanced AI systems with context-aware capabilities.
Here are four main AI paradigms that executives should know about today, each implemented by deep learning and Transformers:
01 Supervised learning
Supervised learning is one of the best-known methods: AI models are trained on labeled examples. It creates accurate and reliable AI systems by learning known and preset outcomes in a labeled training set. However, supervised learning requires large amounts of labeled training data, which can be costly and time-consuming. But errors also occur when supervised models encounter new or complex situations that deviate from training.
02 Unsupervised learning
Unsupervised learning is when a model learns patterns and structures from unlabeled data without specific guidance. AI models trained in this way excel at revealing valuable insights and patterns, independent of labeled examples. However, the hands-off training process is governed by the reward system inside the model. As a result, unsupervised learning sometimes produces non-critical results when trying to solve a specific use case or business problem.
03 Reinforcement learning
AI models can be trained with human subject matter experts. This is called reinforcement learning. It is typically defined by an AI model that receives feedback from a human agent to produce the desired outcome. From a business perspective, this type of AI model training excels at creating accurate AI models tailored to specific use cases. Reinforcement learning generates reliable cognition that can be safely integrated with automation to deliver value. It is worth noting that reinforcement learning is equally time-consuming. However, the training process can be greatly shortened by active learning. This is where AI models prioritize the most informative sample data for human agents to label. Optimize performance with less manpower.
04 Generative artificial intelligence
Generative AI describes a set of algorithms that can generate new content from training data, including ChatGPT, Dall-E, Cohere, AI2, and DreamFusion. Generative AI can automate "creative" work, such as composing emails to customers from scratch, or generating images and music. However, generative AI models are difficult to customize to specific use cases. They are also prone to "errors" (i.e., incorrect, strange, or even unpleasant content). To get the most value from generative AI, it must get attention — that is, at the time of identification, each output should be reviewed before it is used by the rest of the business.
Drive innovation with AI-driven automation
These four AI paradigms have great implications for businesses. The vision of AI that can fully mimic human work is still far away, but now we have also easily implemented a universal technique for AI in enterprise processes.
The latest AI models mark a new era of enterprise automation. Businesses that get more value from automation projects and technologies can now integrate generative AI into their process automation stacks. Through AI-driven communication mining, automation will understand, extract important information (such as sentiment, tone, and intent) from customer emails or chat logs. At the same time, it is also possible to understand whether the message has reached a critical process. Generative AI and automation can be leveraged to write responses to customers, notify management of potential outcomes, and update core systems of record. This is true AI-driven automation.
We've reached a stage where employees can easily hand over important, customer-facing work to automated, integrated systems driven by artificial intelligence. Companies can now also use the technology to address the declining capacity and low productivity that have plagued the economy for decades. Addressing these issues means acquiring new operational capabilities to scale up their operations, avoid the costs associated with increasing labor, and effectively reduce business operating expenses.
Of course, before ChatGPT debuted, sports betting leader Flutter had already optimized user service with automation powered by generative artificial intelligence. Flutter was able to anticipate the user issues they would face and provide end-to-end service to users to control costs, improve service levels, and improve Net Promoter Score by more than 40 points. Oonagh Phelan, Flutter's head of AI and automation strategy, also told this story on the stage of FORWARD 5, which shows that the results of automation and AI integration and upgrading have begun to take shape. The benefits of generative AI and automation for enterprise transformation are being discovered.
Today, AI-driven automation has become an important part of many enterprise teams, and with AI+automation, your employees will have the freedom to think, create, and focus on the most complex business challenges and important users, unleash more of their employees' potential, and make business operations more intelligent and efficient.