Generative artificial intelligence (AI) is a buzzword around the world. Industries have been forced to adapt to the changes this disruptive technology brings, which many people label as one of the biggest threats and challenges to the modern business environment.
It is true that generative AI is a disruptive force and will have a more significant impact on some industries rather than others. An example of this is that its effects on education and communications (journalism and media) will be far more disruptive than its impact on manufacturing and supply chain management/logistics.
However, like all technological changes, no industry is immune to this change. Therefore, no industry can escape the impact of generative AI. The question becomes, do I view this as a treat, or do I view this as a positive disruption that may add significant downstream benefits to my business? CEOs need to know a bit more about this technology to make this decision.
More than a chatbot
The article points out that generative AI can be used to automate, augment, and accelerate work. For the purposes of this article, we focus on ways generative AI can enhance work rather than on how it can replace the role of humans.
While text-generating chatbots such as ChatGPT have been receiving outsize attention, generative AI can enable capabilities across a broad range of content, including images, video, audio, and computer code. And it can perform several functions in organizations, including classifying, editing, summarizing, answering questions, and drafting new content. Each of these actions has the potential to create value by changing how work gets done at the activity level across business functions and workflows.
How generative AI differs from other kinds of AI
The article adds that, as the name suggests, the primary way in which generative AI differs from previous forms of AI or analytics is that it can generate new content efficiently, often in “unstructured” forms (for example, written text or images) that aren’t naturally represented in tables with rows and columns (see sidebar “Glossary” for a list of terms associated with generative AI).
The underlying model that enables generative AI to work is called a foundation model. Transformers are key components of foundation models—GPT actually stands for generative pre-trained transformer. A transformer is a type of artificial neural network that is trained using deep learning, a term that alludes to the many (deep) layers within neural networks. Deep learning has powered many of the recent advances in AI.
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However, the article points out that some characteristics set foundation models apart from previous generations of deep learning models. To start, they can be trained on extremely large and varied sets of unstructured data. For example, a type of foundation model called a large language model can be trained on vast amounts of text that is publicly available on the internet and covers many different topics. While other deep learning models can operate on sizable amounts of unstructured data, they are usually trained on a more specific data set. For example, a model might be trained on a specific set of images to enable it to recognize certain objects in photographs.
In fact, other deep learning models often can perform only one such task. They can, for example, either classify objects in a photo or perform another function such as making a prediction. In contrast, one foundation model can perform both of these functions and generate content as well. Foundation models amass these capabilities by learning patterns and relationships from the broad training data they ingest, which, for example, enables them to predict the next word in a sentence. That’s how ChatGPT can answer questions about varied topics and how DALL·E 2 and Stable Diffusion can produce images based on a description.
The article adds that given the versatility of a foundation model, companies can use the same one to implement multiple business use cases, something rarely achieved using earlier deep learning models. A foundation model that has incorporated information about a company’s products could potentially be used both for answering customers’ questions and for supporting engineers in developing updated versions of the products. As a result, companies can stand up to applications and realize their benefits much faster.
However, because of the way current foundation models work, they aren’t naturally suited to all applications. For example, large language models can be prone to “hallucination” or answering questions with plausible but untrue assertions (see sidebar “Using generative AI responsibly”). Additionally, the underlying reasoning or sources for a response are not always provided. This means companies should be careful of integrating generative AI without human oversight in applications where errors can cause harm or where context is needed. Generative AI is also currently unsuited for directly analysing large amounts of tabular data or solving advanced numerical optimization problems. Researchers are working hard to address these limitations.
Putting generative AI to work
The article adds that CEOs should consider the exploration of generative AI a must, not a maybe. Generative AI can create value in a wide range of use cases. The economics and technical requirements to start are not prohibitive, while the downside of inaction could be quickly falling behind competitors. Each CEO should work with the executive team to reflect on where and how to play. Some CEOs may decide that generative AI presents a transformative opportunity for their companies, offering a chance to reimagine everything from research and development to marketing and sales to customer operations. Others may choose to start small and scale later. Once the decision is made, there are technical pathways that AI experts can follow to execute the strategy, depending on the use case.
Much of the use (although not necessarily all of the value) from generative AI in an organization will come from workers employing features embedded in the software they already have. Email systems will provide an option to write the first drafts of messages. Productivity applications will create the first draft of a presentation based on a description. Financial software will generate a prose description of the notable features in a financial report. Customer-relationship-management systems will suggest ways to interact with customers. These features could accelerate the productivity of every knowledge worker.
But generative AI can also be more transformative in certain use cases. Following, we look at four examples of how companies in different industries are using generative AI today to reshape how work is done within their organization. The examples range from those requiring minimal resources to resource-intensive undertakings.
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A specific point of view
Viewing disruption as a positive or negative force is one of the biggest coping mechanisms companies can use to define their future.
Since the beginning of time, companies have faced risks that have been disruptive forces. Certain technological advancements, such as the printing press and the invention of automobiles, have changed the course of history. Humans have learned to adapt to the changes associated with these advancements; generative AI will be no different.
I am an outspoken supporter of technology. It can add significant value to companies by allowing them to achieve greater precision in their systems and processes, resulting in a better-finished product.
However, there are concerns about how this will impact jobs. I will take you back to a quote made by Steven Hawking in 2018. “Ever since the start of the Fourth Industrial Revolution, there have been fears of mass unemployment as machines replace humans. Instead, the demand for human-based goods and services has risen in line with the increased capabilities of machines. There is a great danger from AI if we allow it to become self-designing” Human oversight is a critical component of technology. Generative AI may impact the job market as the technological advancement of the printing press impacted the media and communication industry; it is up to humankind to reskill themselves to fit into a future world of work where technology is a significant influencer.
The Mystery Practitioner is an industry commentator focusing on the shifting dynamics and innovative thinking that BRPs and turnaround professionals must embrace to achieve business success.
