We have all heard the recent hype around the new artificial intelligence (AI) technology known broadly as Generative AI that is dominating headlines.

There are many parallels between the explosion of AI and the concept of digital transformation that was prevalent 15+ years ago. Companies realized that to stay competitive and vibrant they had to tech-enable all parts of their business. AI is the logical next step in the digital transformation era.

It can seem daunting for directors to come up the learning curve and decipher what is hype from what is an actionable business opportunity.

It is time for boards to think about this given how impactful this technology is and the potential dislocation to normal businesses.

Here is a general overview that will hopefully help the board community form an understanding of these exciting new developments and how they can apply to various business functions:

Generative AI: A type of artificial intelligence that can develop new content based on data rather than simply perceiving and classifying. Generative AI is multi-modal using text to create new outputs such as text, images, audio, videos, code, simulations, etc. It does this by learning from a large dataset of existing content. For example, a generative AI model that is trained on a dataset of text can be used to create new text, such as poems and stories. Generative AI systems are broadly categorized as a subset of machine learning. There are different Generative AI systems in various stages of completeness available on the market now such as ChatGPT and Bard. Generative AI is becoming increasingly popular as it can be used to develop realistic and engaging content.

Large Language Model (LLM): A type of artificial intelligence (AI) model that is trained on a massive dataset of text and code. LLMs can be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informed way. LLMs are trained using a process called deep learning. Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. Artificial neural networks are inspired by the human brain, they are able to learn complex patterns from data. LLMs are trained on massive datasets of text and code. These datasets can contain billions of words, and they can cover a wide range of topics. This allows LLMs to learn the statistical relationships between words and concepts.

ChatGPT & Bard: ChatGPT and Bard are both generative AI chatbots that are well-known in mainstream media. ChatGPT is developed by OpenAI while Bard is developed by GoogleGOOG AI. These chatbots are trained on massive datasets of text and code and can be used to generate text, translate languages, write different kinds of content, and answer questions in an informative way.

What’s the difference?

There are some key differences between these two popular chatbots. On a fundamental technical level, Microsoft’sMSFT ChatGPT and Google’s Bard employ different Large Language Models. ChatGPT uses the Generative Pre-trained Transformer 4 (GPT-4), while Google BARD relies on its bespoke Language Model for Dialogue Applications (LaMDA).

One key difference that is particularly relevant to everyday users is that Bard has access to the internet. Bard can draw its responses from the internet in real-time. ChatGPT relies on a dataset that only goes through 2021. This is a limitation where ChatGPT may not be able to provide real time up to date information.

One particularly differentiated aspect of Google’s Bard is the end-to-end sovereignty of data, training, models, etc. that will remain private to your business enabling complete control of your IP.

Generative AI Customer Use Cases

• Content creation: Can be used to create new content, such as social media posts.
• Customer service: Can be used to answer customer questions and provide support.
• Sales and marketing: Can be used to generate leads, qualify prospects, and close deals.
• Research and development: Can be used to generate new ideas, research new markets, and develop new products.

A Word of Caution

Some of the potential drawbacks of generative AI include:

• Bias: Generative AI models are trained on data, and if that data is biased, the model will be biased as well. This can lead to the model generating outputs that are discriminatory or unfair.
• Lack of control: Generative AI models can be difficult to control. Once a model is trained, it can generate outputs that are not what the user intended. This can be a problem if the model is used to generate sensitive content, such as medical or financial information.
• Cost: Generative AI models can be expensive to develop and maintain. This can be a barrier for small businesses or organizations with limited budgets.

A drawback that many everyday users are familiar with is the potential for misinformation. Generative AI models can be used to create hyper-realistic fake content known as “deep fakes”. This can be used to spread misinformation or to damage someone’s reputation or even a company’s reputation.

One well-known example of this is the recent viral photo of Pope Francis wearing a stylish white puffer jacket and a bejeweled crucifix. This image was created with the generative AI software Midjourney. This photo resulted in millions of views and left many social media users feeling duped. This is a relatively harmless example of how bad actors can spread misinformation using these tools.

Directors must be prepared to make thoughtful business judgments and provide well-informed oversight as the technology landscape is evolving more rapidly than ever.

I believe directors would be well served to come up the learning curve and immerse themselves in the world of generative AI. As directors, we all know our companies need to act fast to leverage these unprecedented opportunities. At the same time, we, as board members, need to ensure that we keep risk in check…. never easy but more important than ever in this new age of AI.

Businesses who are early adopters of enhancing the customer experience and tech enabling various facets of their business tend to be the winners. Laggards who are slow to innovate will not make it.

The  rapid advancements in AI merit serious consideration and research to see how it can be applicable to your specific industry. Perhaps begin integrating with small use cases that are “easy wins”. As this technology continues to evolve and improve your business will be well served to be familiar with its function and future potential.

Boards on which I serve are already inviting external experts (strategy consultants like Bain and AccentureACN or key vendors like Google) into the boardroom to share a tutorial and overview of generative AI and how it can relate to your company’s specific industry.

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