To maximize the business impact of artificial intelligence (AI), it’s worth understanding the different types of AI, what problems each solves best and the challenges and risks of utilizing them.

Historically, building and implementing machine learning (ML) models was a formidable undertaking, requiring the combined efforts of data scientists, data engineers and domain experts. However, as startups and less-technical enterprises began to adopt AI, these technologies have become much more accessible and adaptable.

Large language models and generative AI ushered in a new era. Today, AI solutions are more readily available and offer numerous benefits relevant to many businesses and industries.

But how do you know where to start?

What's the difference between traditional ML and generative AI?

Traditional ML involves making specific decisions. For instance, one common application of ML involves the automated approval or rejection of loan applications based on structured data, like financial records and credit scores. In this scenario, ML focuses on algorithms to detect patterns and reach decisions.

Generative AI, like OpenAI’s ChatGPT, is a language model that takes a request and works to generate an answer or expand on that request. It acts as a language model that interprets natural language questions and prompts, providing complex responses or expanding on a topic. In essence, its primary function is to interpret and generate human-like text.

Where generative AI truly shines is in handling unstructured data, such as text documents or images. It can process the context provided and generate coherent and contextually relevant responses. It excels in scenarios where the information is not neatly organized and relies on natural language understanding to provide meaningful outputs.

If you've interacted with chatbots or virtual assistants, it's generative AI that enables those bots to respond in a manner that closely resembles human conversation. Unlike traditional ML, which adheres to predefined decision-making rules, generative AI uses its language comprehension and creative abilities to engage users in a more human-like and contextually aware manner.

How can AI and ML expand your opportunities to grow?

AI's ability to enhance customer experiences and improve operational efficiency can have a transformative impact on virtually any business. It can lead to greater customer satisfaction, increased profitability and the discovery of new business opportunities.

Here are seven examples:

• AI can analyze vast amounts of data – including historical sales data, customer behaviors, social media trends and economic indicators – to identify emerging patterns and trends. This information should allow companies to adapt their strategies, product offerings and marketing campaigns more swiftly than competitors who don’t use AI.
• Chatbots and virtual assistants can provide instant and personalized customer support or suggest products or services tailored to individuals based on analysis of their preferences and data analytics.
• AI can be used to provide sales teams with better leads and the insights they need to bring the right products to the right customers.
• With the help of AI, companies can anticipate customer needs, helping develop innovative products or services that address emerging market trends. This innovation can open new revenue streams and growth opportunities.
• AI can be leveraged to make credit scoring more accurate, allowing lenders to reach new customer segments or offer more favorable terms.
• AI can help in identifying investment opportunities and manage portfolios with reduced risk, potentially leading to higher returns.
• AI and ML are relatively mature in another key area: fraud detection and anti-money laundering processes. Together, they help reduce the number of false positives generated by rule-based systems. They can also complete real-time data processing using advanced models to identify high-risk transactions efficiently.

Above all, AI is changing the way firms are doing business and becoming a key tactic for many businesses, including for revenue growth. We’ve seen the analytical power of ML; generative AI is even more adept at identifying trends and making predictions about the market. From giving lenders an edge in reaching new markets to helping investment managers play it smart, AI’s analytical capabilities open doors.

Meanwhile, the integration of chatbots is not merely technological innovation: It creates seamless customer interactions and optimizes efficiency, from improving customer support to expediting financial operations.

However, integrating AI comes with its own technological, legal and ethical challenges. Financial services firms, for example, must navigate complex regulatory frameworks to ensure compliance. Ethical concerns revolve around issues, such as data privacy, bias in algorithms and the potential impact on employees. This is why many firms always keep “a human in the loop,” approaching AI as an assistant to humans and not a replacement. This strategy should increase productivity but also help maintain customer trust.

Striking a balance between reaping the benefits of AI and addressing these legal and ethical challenges is crucial for firms to responsibly deploy AI’s power in their operations. When wielded effectively, the array of AI tools available today can accelerate business growth and delight customers with innovative solutions and personalized experiences.