Large language models (LLMs) have revolutionized the business landscape, with every company now exploring the potential of Generative AI. While tools like ChatGPT showcase immense power, the challenge lies in effectively leveraging this technology to drive value.

For many businesses, incorporating AI often translates to developing chatbots, AI co-pilots, agents, or assistants. However, as the initial hype around these solutions subsides, organizations are grappling with the complexities of building systems around LLMs.

One of the primary hurdles is the inherent unpredictability of LLMs, surpassing even traditional machine learning systems. This unpredictability makes it challenging to deploy these models reliably to solve specific problems.

To address issues like hallucination, the suggestion is to employ “judge” LLMs to evaluate system responses for accuracy and appropriateness. However, scaling up the number of LLMs escalates costs, complexity, and uncertainty within the system.

Despite the hurdles, Generative AI presents lucrative opportunities for businesses. AI has already propelled numerous companies to success, and the trend is unlikely to wane.

The crux lies in problem-solving rather than merely utilizing AI. The true potential of AI unfolds when businesses pinpoint the right problems to address, such as Netflix’s personalized recommendations, UPS’s delivery route optimization, Walmart’s inventory management, and similar success stories.

While identifying the “right problem” sounds simple in theory, its execution proves challenging. To assist in this endeavor, here are 3 AI use cases focused on a crucial business aspect – sales. These examples aim to ignite your creativity and demonstrate practical implementations.

Feature Engineering involves extracting features from text to train machine learning models or conduct analysis. For instance, extracting job titles, years of experience, and industry from a set of LinkedIn profiles and representing them numerically.

Traditionally, feature engineering involved manual creation or purchasing features from third parties. However, LLMs introduce a new approach to this process.

Imagine qualifying leads for a SaaS offering that protects mid-market corporations against cyber threats. The goal is to filter leads to include IT leaders only. Various strategies can address this issue.

Leveraging LLMs to extract specific details from resumes allows for a cost-efficient, automated, and detailed approach to lead qualification.

Structured data offers convenient rows and columns, whereas unstructured data poses challenges. Harnessing NLP and deep learning advancements can facilitate the transition of unstructured data into an analytics-friendly format.

Continuing from the previous scenario, extracting text embeddings can distill resumes into meaningful numerical representations, facilitating comparisons with past customers and aiding in lead prioritization.

Text embeddings bridge the gap between unstructured and structured data, allowing for a more straightforward assessment of lead similarities and differences.

Finally, lead scoring involves evaluating lead quality based on predictors like job title, revenue, and behavior. Recent AI advancements enable better parsing of unstructured data for enhanced lead scoring models.

Utilizing text embeddings for lead prioritization entails training predictive models to categorize potential customers based on profiles. These models assign grades to leads, aiding in classification and prioritization.

For technical enthusiasts, detailed examples and code for these use cases are available in the associated GitHub repository.

AI indeed offers vast potential for businesses, necessitating a targeted approach to problem-solving. By exploring alternative AI strategies beyond chatbots, companies can unlock new avenues for innovation and growth.