Generative AI: Taking the Leap While Navigating Its Risks

“Don’t let fear hold you back; take a leap of faith and see where it leads.” — Curious George

We are at the start of an incredible technological advance called Generative Artificial Intelligence (GAI). We don’t know where it will lead us. Every day brings us more enhancements of this technology and more stories about how it is good or bad. Sometimes, it feels like we are on a precipice. It is an exciting time to be leading — you get to shape the future of the organization you are leading and take advantage of all that GAI has to offer while minding the challenges that come with it.

Leaders need to understand what it is and how it can be used in decision-making.

What is GAI? 

“Generative artificial intelligence is artificial intelligence capable of generating text, images, videos, or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.” — Wikipedia.

If you are a novice, there are a few excellent resources you can get started with:

• Coursera’s “AI for Everyone” by Andrew Ng is meant for non-technical users with applications.
• Udacity offers a “Generative AI for Business Leaders” training Course that covers the foundations of generative AI, its business applications, implementing generative AI projects, building the right team, developing a generative AI strategy, and the underlying architectures and data needs. It includes a project to create a 100-day roadmap for bringing generative AI into an organization.
• “Generative AI for Leaders” from Coursera is a beginner-level course that offers a comprehensive journey into understanding, applying, and mastering generative AI as a tool to amplify leadership capabilities. It covers topics like using generative AI as a thought partner for leadership planning, building agendas, job descriptions, and proposal analysis.

GAI in Business Operations and Decision-Making 

“AI has the potential to automate mundane tasks, freeing us for work that requires uniquely human traits such as creativity and critical thinking — or, possibly, managing and curating the AI’s creative output.” — Ethan Mollick, Co-Intelligence

In the early 2000s, “Big Data” gained momentum in business decision-making, noting the ability of decision-making tools to handle large amounts of data available from information systems. For example, the company I cofounded, Retail Solutions, provided analytics to retailers and CPG companies based on retail data such as point-of-sale, distribution, and inventory, which helped them make informed decisions about what to promote, how much inventory to carry, and how to prevent out-of-stock.

In the last decade, machine learning, a subset of artificial intelligence, became part of the arsenal of tools businesses use. Their use has allowed enterprises to harness the power of the data to make operations more efficient and derive valuable insights about customer behavior. An example of the use of machine learning can be found in the recommendation engines used by businesses like Netflix. The algorithms learn from the vast amount of customer data to understand each customer’s watching behavior and be able to suggest what to watch next based on it, and also based on customers whose tastes are similar.

Today, businesses can mine even more data with GAI, such as call center interactions, email texts, and financial reports. GAI affords businesses quick summarization of the vast amount of internal and external data. A semantic search of information available across documents, product catalogs, and knowledge bases has been made possible by the power of the large language models (LLMs) which enable GAI. My previous article, GenAI Unleashed: A Leader’s Guide for Maximizing Global Impact in Talent Management, Content Creation, and Customer Support, described several business areas that can benefit from GAI.

All this power comes with some downside. The technology is in the early stages, and the LLMs tend to “hallucinate” and makeup falsehoods. Leaders also need to be mindful of the bias in the underlying data (which, by the way, reflects the bias of humans who generated the data). The accuracy of the GAI solutions needs improvement. However, as I mentioned in a previous article, Riding the Wave of Generative AI: Tips for Enterprise Leaders, there are three things a leader can do to get started, namely, understand where the technology is, identify how GAI can help your business, and set up experiments.

Collaboration and Augmentation 

“The key to success in the AI era will be to understand how to leverage AI to augment human capabilities.” — Unknown

Keep the words “augment” and “collaborate” in your mind in the many ways you can use GAI. Approach GAI as a tool that can work alongside humans to increase productivity.

Today, GAI is reasonably capable of generating 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 mid-level professional writing tasks substantially increased productivity. It says,

“ChatGPT could increase workers’ productivity in two ways. On the one hand, it could substitute for worker effort by quickly producing output of satisfactory quality that workers directly submit, letting them reduce the time they spend on the task. On the other hand, it could complement workers’ skills: humans and ChatGPT working together could produce more than the sum of their parts, for example if ChatGPT aids with the brainstorming process, or quickly produces a rough draft and humans then edit and improve on the draft.”

It is essential to consider the collaboration parameters when using GAI in decision-making. Richard Benjamins, former Chief Responsible AI Officer at Telefonica and founder of its “AI for Society and Environment” area, proposed a “Choices Framework” for considering ethical and responsible choices when using GAI. He defines an “Ethics Continuum and Impact on Society.” It has “Use AI for good” at one end of the continuum and “Malicious use of AI” at the other end, with “Do not use AI if effects cannot be mitigated, “Best Effort to avoid the negative impact of AI,” and “Negative effect of AI is considered collateral damage” in between. He says organizations need to decide, based on their norms and values, where they want to be in a continuum of ethics.

Embrace GAI with Caution 

The advent of Generative Artificial Intelligence (GAI) can be compared to historical 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 benefits, and its shortcomings would be tremendously advantageous for an enterprise. The practice of holding opposable ideas in mind is precious in understanding the continuously changing world of GAI. With many voices expressing opposing views on the advances in GAI, one has to think for oneself. Understand the diverse points of view, and then decide for yourself. And, as Curious George said, don’t let fear hold you back.

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