Published on January 8, 2024, 1:41 pm

Generative AI has become a hot topic in the world of technology and business. According to a recent survey, 75% of Fortune 500 CEOs expect generative AI to improve operational efficiency, while over half believe it will drive growth. Data science leaders and their teams also share this optimism, with 90% believing that the hype surrounding generative AI is more than justified.

The question now is not whether generative AI will be transformative, but how we can leverage its potential to make money. To answer this question, we must first address the challenges associated with generative AI. Two key challenges stand out: identifying the right use cases and developing new business models, as well as operationalizing genAI models effectively.

Generative AI involves unlocking new possibilities by analyzing and generating unstructured data such as text, voice, images, and video. This means going after use cases that were previously untapped. For example, companies are exploring the use of chatbots that can help employees discover and summarize documents within their content management systems. However, since this is relatively uncharted territory, there is no one-size-fits-all approach to achieving success. It requires experimentation and innovation.

Another challenge lies in the operationalization of genAI models. These models are much larger than traditional AI models and require substantial resources to train and implement. However, three factors make this challenge temporary. First, infrastructure costs are constantly decreasing. Second, optimization techniques are evolving to reduce the footprint of these large-scale models. And third, companies are learning from experience and shifting towards smaller, specialized models that are fine-tuned for specific tasks.

While every company has the potential to make money with generative AI, many lack the specialized leadership and expertise required to identify the most promising use cases and develop corresponding applications. Skilled data scientists who understand both business context and technology strengths are essential for success.

Furthermore, most companies cannot rely on pre-built genAI models offered by tech giants, as these models may not meet specific enterprise needs. Instead, organizations need to implement their own capabilities for large language model operations (LLMOps), which allow them to fine-tune and govern their genAI models effectively.

Although some companies have already achieved success with generative AI, it will take time for most mainstream enterprises to see a significant impact on their bottom line. Implementing LLMOps capabilities and growing in-house expertise are crucial steps for organizations looking to leverage the advancements in generative AI.

In conclusion, generative AI presents immense opportunities for businesses. However, it requires strategic planning, investment in talent and technology infrastructure, and a deep understanding of use cases that align with business goals. Companies that have already invested in AI capabilities have a head start but those who have not must play catch-up if they want to stay competitive in the rapidly evolving landscape of generative AI.


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