How AI in Business Gives You a Competitive Edge

A patient with a history of Type 2 diabetes and hypertension schedules a routine checkup with their primary care physician. The patient has recently begun wearing a special device to monitor physical activity and blood glucose levels. They’ve also visited a specialist recently and have had several lab tests. The primary care physician has access to a comprehensive overview of this patient’s health status, so they can provide effective care.

A customer emails a support team expressing frustration over a delayed product delivery. The AI system instantly analyzes the email’s sentiment and identifies the customer’s frustration level. After the request is categorized as a high-priority shipping issue, it’s routed directly to the logistics support team. This team, already informed about the customer’s sentiment, prioritizes the inquiry, quickly addressing the issue and promptly communicating a resolution to the customer. The rapid, informed response helps to mitigate the customer’s frustration and prevents potential negative feedback or public complaints on social media.

Another compelling use case example of AI’s impact can be observed in the banking and finance industry, particularly in the domain of risk assessment and fraud detection. The integration of AI in these areas not only enhances the accuracy of fraud detection but also streamlines risk assessment processes, ultimately contributing to more secure and efficient banking operations.

Banks handle huge volumes of transactions daily, making it challenging to manually monitor and identify fraudulent activities. AI-powered systems that use machine learning algorithms can analyze patterns in transaction data in real time to detect anomalies that may indicate fraud. These systems learn from historical transaction data, enabling them to identify fraudulent transactions with high levels of accuracy. For instance, if an AI system notices an unusually large transaction from an account that typically exhibits low transaction volumes, it can flag this as suspicious for further investigation.

The AI process in this case involves several key steps, including risk modeling and pattern recognition that detects deviations from the norm. AI algorithms also analyze a wide range of data, such as credit and transaction history, and employ adaptive learning, in which AI systems continuously learn from new transactions and customer interactions. By leveraging machine learning and data analytics, banks can protect against fraud more effectively and make more informed decisions about credit risk, ensuring a safer banking environment for all customers.

An AI system analyzes a customer’s transaction history and notices that their card is being used simultaneously in multiple countries, a strong indicator of potential fraud. The system immediately flags these transactions and temporarily freezes the account to prevent further activity. Simultaneously, it alerts the bank’s fraud
team and the customer to verify the transactions. This rapid response mechanism significantly reduces the impact of fraudulent activities on both the customer and the bank.

With so many tools and software programs available, the best way to narrow down your options is to let your teams play around with the technology. This experimentation not only helps familiarize everyone with the AI model’s functions but also allows them to witness its strengths and weaknesses firsthand. Ntiva CEO Steven Friedkin stresses the importance of exploring unfamiliar AI functionalities with an open mind, even for just 15 minutes per day.

Prompt injection attacks involve entering a text prompt into a chatbot that enables the user to perform unauthorized actions, such as gaining access to sensitive data that the AI platform
may have access to or been previously trained on. Be cognizant of what information and systems your team grants AI platforms access to and even what information they enter into tool interfaces such as AI chatbots. As of yet, AI systems don’t truly understand concepts such as data sensitivity or obsolescence. Explaining such concepts to an AI is extremely challenging, if not impossible by today’s standards. Remember that sharing sensitive, proprietary, or confidential data, even with a “secured” AI, can put that data—and the organization—at risk.

Properly evaluate all potential AI vendors and implementation partners before working with them. Confirm data sources, content validity, industry experience, and standardization procedures before closing the deal. Consider software vendors that offer a trial period so you can test the waters before committing.

Your chosen AI solutions need to reliably grow and evolve alongside your business. For example, a flexible, proactive, and scalable AI solution has limited hardware constraints and a robust infrastructure based on collaboration and business alignment.

To protect against the negative consequences of shadow AI, some industry experts recommend only allowing work within sanctioned or preauthorized AI systems. Offering an official list of approved applications can certainly give IT teams more control over what happens in a business’s tech stack. However, a centralized approach also comes with its own potential drawbacks. For example, when people feel monitored, they’re often less likely to experiment and innovate as freely as they would otherwise. The nuances of these decisions require an experienced and strategic perspective.