Leveraging AI for competitive advantage

Many types of AI models and approaches have been designed to tackle various specific challenges:

• Discriminative AI: Distinguishes between different classes or categories of data. It’s used for tasks like classification, regression and anomaly detection.
• Generative AI: Creates new content or data that’s similar to content produced by humans. It can generate realistic images, artwork and synthetic data for training machine learning models.
• Foundation Models: A ‘Jack-of-all-trades’ model undergoes training on extensive datasets spanning diverse domains (natural language processing, image understanding, audio processing, and more), empowering them to cultivate a broad spectrum of capabilities.
  
   

Large Language Models (LLMs) are a specialised category of foundation models tailored for understanding and generating human-like language. They’re trained on vast amounts of text data to understand and generate human-like language. ChatGPT is perhaps the most well-known type of LLM.

What sets LLMs apart from earlier AI models is their ability to learn from instructions and context. With just a few prompts or examples, LLMs can tackle new tasks without extensive fine-tuning. This capability is known as ‘in-context learning’ and has immense potential for organisations, allowing them to quickly deploy an AI solution tailored to their specific needs. For example, a bank could use an LLM for personalized customer interaction or to help assess the risk levels of various transactions or loan applications.

Because LLMs excel at understanding context, generating coherent responses and engaging in natural conversations, they can assist with a wide range of tasks; from answering customer queries and generating reports to analysing sentiment and extracting insights from unstructured data.

In banking, LLMs can power chatbots that provide personalised financial advice, analyse spending patterns and assist with transactions. In healthcare, they can help doctors quickly access relevant information from medical records, support clinical decision making and generate tailored treatment plans for each patient. AI is also advancing the publishing industry with personalization: in online newspapers and news channels, AI can suggest suitable additional articles based on reading habits.

LLMs can be an incredibly valuable tool for organisations looking to harness the full potential of their data. And as LLMs continue to evolve, they’re going to play an increasingly important role in shaping how organisations operate.

When implementing any new technology, organizations should seriously consider security and compliance implications. Particularly in industries like banking and healthcare, ensuring that AI systems are secure and compliant is paramount.

One way to address these concerns is to use an open-source AI model. Unlike proprietary systems such as ChatGPT, open-source LLMs are transparent about their training data and underlying algorithms (for example, how data is processed). Additionally, open-source models can be hosted within an organization’s infrastructure, allowing for greater control and customization to ensure it aligns with the organization’s security and compliance requirements.

For example, a healthcare provider could use an open-source LLM trained on medical data and host it on their servers. This would ensure that sensitive patient information remains within the provider’s ecosystem, minimizing the risk of data breaches and ensuring compliance with regulations like HIPAA.

When evaluating whether to use an open-source or proprietary LLM, organizations should first consider their specific needs and priorities:

 

Open-source models:
May be the better choice if transparency and security are of high importance. These types of models allow organizations to fine-tune the model using their own data, ensuring it behaves accordingly.

Proprietary models:
May be the better choice if speed and general versatility are most important. These models often run in the cloud, sharing limited details about their training data and how they process an organization’s data. Proprietary models typically don’t perform as well as customized open-source models due to a lack of fine-tuning options in many different domains and downstream tasks.

Ultimately, organizations should consult with AI experts to determine the most suitable approach for their specific goals and requirements.

AI engines are decision support engines, and the automation level can be defined case by case. While AI can automate many tasks and provide valuable insights, in some cases, it’s important to have human experts review and validate the outputs manually to ensure accuracy and compliance. For example, an LLM may flag a banking transaction as potentially fraudulent, but a human analyst will need to review the flagged transaction to determine whether it is indeed fraudulent or a false positive. Additionally, a human expert will need to handle any edge cases the AI may not cover.

For this reason, at Netcetera, we’ve designed our AI solutions with security and compliance at the forefront to ensure that sensitive data remains secure. Our experts work with our clients, including banking and healthcare organizations, to understand their specific security and compliance needs and implement AI solutions that adhere to relevant regulations and standards.

Experimenting with AI and conducting pilots is a good way to explore its potential, but the true value lies in its ability to drive tangible business outcomes. Therefore, organizations must move beyond simple tests and deploy production-grade AI applications that integrate with their systems. At Netcetera, our AI solutions are designed to be easily integrated into an organization’s existing systems.

In healthcare, our AI solutions are streamlining clinical workflows and helping improve patient outcomes. By automating routine tasks and providing decision support, our solutions are enabling healthcare professionals to focus on delivering high-quality patient care.

In banking, our AI solutions make customer interactions personalized and intuitive. They also make the underwriting process more efficient and improve the customer experience by reducing waiting times.

With Netcetera, organizations can move beyond the testing phase to develop and integrate scalable, production-grade AI applications that drive growth and innovation.

Every organization is unique, with its own set of challenges and goals. That’s why proprietary AI solutions often fail to address an organization’s specific needs, resulting in suboptimal results and missed opportunities. To counteract this at Netcetera, we take a customized and collaborative approach to developing AI solutions.

Our core aim is to deeply understand the business context of every client. Our AI experts work closely with our clients to learn about their specific requirements, pain points, and goals. By working collaboratively, we ensure that the proposed AI solution is suitable for each of our clients and can be seamlessly integrated into their existing systems and processes.

At Netcetera, our solutions can be customized to fit many different use cases. Whether clients want to use AI to assist with processing large volumes of unstructured data, extract insights from complex documents, or automate decision-making processes, we have the expertise and flexibility to deliver an effective, bespoke solution.