Generative AI (GenAI) is dominating headlines, celebrated as a groundbreaking technology with vast potential. Beyond the initial (and warranted) excitement, however, a more balanced perspective reveals the substantial – often overlooked – value of another AI technology: traditional machine learning (ML).
While generative AI garners attention for its creative capabilities, machine learning remains an unsung hero, utilizing data-backed algorithms to perform complex tasks such as predicting profitable customers or identifying a product in an image. ML can serve as a powerful catalyst for customer engagement that drives efficiency and innovation.
Distinguishing Machine Learning from Generative AI
The rush to adopt GenAI has led many organizations to view it as a one-size-fits-all solution. But it’s important to recognize that GenAI isn’t always the best fit for every use case.
Generative AI excels at creating content such as text and images, leveraging large language models (LLMs) trained on unstructured, text-based data. In contrast, ML models are trained on both structured and unstructured data, translated to numeric representations. ML offers solutions to more specific problems to predict outcomes or classify input rather than generating something entirely novel. In fact, various ML models and concepts are utilized as a foundational component to more complex AI, including GenAI – for example, determining the most frequent customer service requests in a call center that could ultimately be automated with GenAI.
Traditional machine learning better addresses many business challenges. ML enables leaders to extract valuable insights, automate tasks, and enhance operational processes. The integration of GenAI, machine learning, and human expertise will be key to maximizing business outcomes as workplace automation evolves.
When Machine Learning Outperforms Generative AI
Successfully embedding new technologies requires selecting the right tool for the job. Machine learning often proves more suitable, especially for tasks involving structured data with its robust analytical capabilities.
Moving beyond the misconception that GenAI is the ultimate AI solution, companies can unlock AI’s full potential by leveraging ML’s broader problem-solving abilities. This approach facilitates data-driven decisions that drive progress and innovation. It’s crucial for all levels of an organization – including the C-suite – to understand these differences.
One of my company’s clients initially sought to use GenAI for a categorical prediction task. After clarifying the distinctions and appropriate use cases, the client recognized the broader context and cost-benefit trade-off of machine learning, leading to a successful shift in approach.
Strengths of Traditional Machine Learning
Machine learning excels in applications involving real-world data and actionable insights, particularly when dealing with well-structured and labeled data like customer records or financial reports.
Examples of ML’s capabilities include:
- Categorical Predictions: Predicting specific outcomes such as customer churn, product recommendations, or identifying high-value customers.
- Trend Decomposition: Analyzing key metric changes to provide valuable business insights.
Beyond the “Wow” Factor
GenAI’s “wow” factor is compelling, and it undoubtedly drives significant value across multiple use cases. It can supplement our creativity, summarize research publications, and automate conversations. As with any new technology, it’s critical to identify the appropriate problems for its domain and remember that, even in the right domain, it can hallucinate dramatically and should always be supplemented by humans in the near term.
Conclusion
Many companies underutilize strong data analytics and machine learning. Mastering these areas can add more value than relying solely on GenAI. By understanding the distinct use cases and benefits of both technologies, businesses can establish a clear vision for implementation, driving greater ROI over time. Now is the time to learn, test, and scale machine learning solutions, which form the foundation of many essential products and business operations.