“Don't let your fears get the better of you. Take the plunge and see where it leads.” — Curious George
We are at the beginning of an amazing technological advancement called Generative Artificial Intelligence (GAI). We don't know where it will lead us. Every day, this technology gets stronger and stronger, and there are more stories about its good and bad. At times, it feels like we are on the edge of a cliff. It's an exciting time to be a leader. You can shape the future of the organizations you lead and take advantage of all GAI has to offer while being mindful of the challenges that come with it.
Leaders need to understand what it is and how it can be used to inform decision-making.
What is GAI?
“Generative artificial intelligence is artificial intelligence that can generate text, images, videos, or other data using a generative model, often in response to prompts. A generative AI model learns the patterns and structure of the training data you input and generates new data with similar characteristics.” — Wikipedia.
If you're just starting out, there are some great resources to help you get started.
- Coursera's “AI for Everyone” by Andrew Ng is geared towards non-technical people using the application.
- Udacity offers a training course called “Generative AI for Business Leaders” that covers the fundamentals of generative AI, its business applications, implementing a generative AI project, building the right team, developing a generative AI strategy, and the underlying architecture and data needs. The course also includes a project to create a 100-day roadmap for adopting generative AI in your organization.
- Coursera's “Generative AI for Leaders” is a beginner-level course that provides a comprehensive journey to understand, apply, and master generative AI as a tool to enhance leadership capabilities. It covers topics such as using generative AI as a thought partner for leadership planning, creating agendas, job descriptions, and proposal analysis.
GAI in business operations and decision making
“AI has the potential to automate mundane tasks, freeing us up for work that requires uniquely human traits, like creativity and critical thinking. Or we can manage and curate the creative output of AI.” — Ethan Mollick, Co-Intelligence
In the early 2000s, “big data” gained momentum in business decision-making as attention shifted to the ability of decision-making tools to process large amounts of data coming from information systems. For example, Retail Solutions, which I co-founded, provides retailers and CPG companies with analytics based on retail data like POS, distribution, and inventory, helping them make informed decisions about what to promote, how much stock to carry, and how to prevent out-of-stocks.
Over the past decade, machine learning, a subset of artificial intelligence, has become part of the arsenal of tools used by businesses. Using machine learning, companies can harness the power of data to make their operations more efficient and derive valuable insights into customer behavior. An example of the use of machine learning can be seen in the recommendation engines used by companies such as Netflix, where algorithms learn from vast amounts of customer data to understand each customer's viewing behavior and based on that, suggest what to watch next or make suggestions based on customers with similar tastes.
Today, businesses can use GAI to mine much more data, including call center interactions, email text, and financial reports. GAI allows businesses to quickly summarize vast amounts of internal and external data. Semantic search of information available across documents, product catalogs, and knowledge bases is made possible by the power of large language models (LLMs) that make GAI possible. In our previous article, GenAI Unleashed: A Leader's Guide to Maximizing Global Impact in Talent Management, Content Creation, and Customer Support, we discussed several business areas that can benefit from GAI.
These powers also have some drawbacks: the technology is still in its early stages, and LLMs are prone to “hallucinating” and inventing falsehoods. Leaders should also be mindful of bias in the underlying data (which, incidentally, reflects the bias of the humans who generated the data). There is room for improvement in the accuracy of GAI solutions. However, as I mentioned in my previous article, Riding the Generative AI Wave: Tips for Enterprise Leaders, there are three things leaders can do to get started: understand the current state of the technology, identify how GAI can help your business, and set up experiments.
Collaborate and Extend
“The key to success in the age of AI is understanding how to use AI to augment human capabilities.” — Unknown
As you think about the different ways GAI can be used, keep the words “augment” and “connect” in mind. Approach GAI as a tool that can work with humans to improve productivity.
Currently, GAI is quite capable of making some decisions, but humans must decide whether and how to use it.
A 2023 research paper, “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence,” found that using ChatGPT for intermediate-level professional writing tasks significantly increased productivity.
“ChatGPT can improve worker productivity in two ways. First, it can replace the worker's efforts by quickly producing output of satisfactory quality that the worker can submit directly, allowing them to spend less time on a task. Second, it can complement the worker's skills, meaning that humans and ChatGPT working together can produce results that are greater than the sum of their parts. For example, ChatGPT can assist in the brainstorming process, or quickly produce drafts that humans can edit and improve.”
When using GAI for decision-making, it is essential to consider collaboration parameters. Richard Benjamins, former Chief Responsible AI Officer at Telefonica and founder of the company's “AI for Society and the Environment” area, proposed a “choice framework” to consider ethical and responsible choices when using GAI. He defines a “continuum of ethics and impact on society.” At one end of the continuum is “using AI for good,” at the other end is “using AI for bad,” and in between are “not using AI if we cannot mitigate the impact,” “making every effort to avoid the negative impacts of AI,” and “considering the negative impacts of AI as collateral damage.” He says that organizations need to decide where they want to be on the continuum of ethics based on their norms and values.
Embrace GAI with caution
The emergence of artificial general intelligence (GAI) is comparable to historic technological and scientific breakthroughs that transformed society, such as the Industrial Revolution. Generative AI is not a panacea for all problems. Therefore, understanding what it is, its advantages and disadvantages can be of great advantage to your business. The habit of keeping opposing ideas in mind is valuable in making sense of the ever-changing world of GAI. There are many voices expressing opposing views about the progress of GAI, so you need to think for yourself. Understand diverse perspectives and make your own judgment. And as Curious George said, don't let fear stop you.
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