The recent successes of generative AI models like ChatGPT and DALL-E have left savvy executives wondering how this new technology will revolutionize their industry. No one can predict the impact gen AI will have on an enterprise, but smart executives know that they must start adapting now. Here are some key considerations for business leaders navigating the evolving landscape of generative AI:
1. Generative AI isn’t just a “chatbot”
Gen AI’s abilities extend beyond conversational interfaces. It can also generate realistic images, design products, augment data and write code. Just as its ability to create text has left its mark on the creative economy, its other applications will transform most industries. Business leaders must learn the breadth of gen AI’s potential to drive innovation within their organizations.
2. Generative AI will impact every industry
Gen AI has already enhanced efficiency through automated content creation and decision-making processes in a variety of industries. Healthcare providers use gen AI to analyze images and help make diagnoses. Its ability to survey product reviews provides retailers with insights into customer behavior. It can even forecast finances and create budgets. Gen AI’s transformative potential is undeniable, and ABI research predicts that it will generate over $450 billion for businesses by 2030.
3. Now is a once-in-a-lifetime opportunity to shape gen AI integration:
In this pivotal moment, business leaders have the chance to shape the adoption of generative AI in their operations. As gen AI continues to evolve and mature, early adopters will be better positioned to adapt to future changes and developments. By becoming successful examples of gen AI integration, they could become key players in how their industries as a whole employ this new technology.
4. Leaders will need a strategy to integrate generative AI
Integrating generative AI tools is a more complicated process than just installing new software. Although the process for integrating a traditional technology may be complicated, adoption generally involves clear steps and a definitive endpoint. In contrast, generative AI constantly adapts to the needs of its users and may work differently among different teams within the same business. For example, the Harvard Business Review reports how the creation of a gen AI community within a business can avoid the duplication of work and promote the sharing of knowledge.
5. Leaders need to know the potential risks
Gen AI’s ability to collect, synthesize and generate vast amounts of data makes it a powerful tool that also presents possible risks. Gen AI models may inadvertently learn and replicate biases present in the training data, which could lead to discriminatory outcomes. If its training data is inaccurate or out-of-date, its resulting analyses will be as well. Gen AI tools also frequently have access to confidential business information, increasing the chances of a data breach. To prevent potential issues, business leaders must create a strategy for data vetting, bias detection and cybersecurity in their adoption of generative AI technologies.
6. Responsible AI
While enthusiasm for gen AI’s ability to increase efficiency and creativity is strong, concerns about its potential to cause harm persist. To mitigate potential risks and build public trust, corporations involved in AI development, such Microsoft, Google and OpenAI have each adopted responsible AI governance. Responsible AI practices entail the ethical development and deployment of AI systems to uphold human values, promote fairness and mitigate potential harm.
If you’re interested in learning about the full scope of generative AI and how to integrate it into your business, taking a class like MISY667: Introduction to Generative AI offered by Professor Harry Wang at the Alfred Lerner College of Business & Economics is a great place to start.