Shelf Blog

OpenAI introduced artificial intelligence (AI) to the general public and their latest keynote emphasized their influence on the development of this new technology. Since Chat-GPT was unveiled in November 2022, every major tech company has announced their own AI initiative, but OpenAI remains the...

In our first entry in our series of future-proofing for artificial intelligence (AI), we discussed the inevitability of AI disruption and how your organization should prepare to manage change. In this second entry in the series, we focus on what your AI strategy needs to manage risk, evaluate ROI,...

Generative AI is revolutionizing search by delivering personalized and context-aware search experiences. This technology has revolutionized customer service with greater efficiency and accuracy. It has the potential to advance medical research by providing more relevant results for searches of prior studies and papers.

Customers have more ways to communicate with your organization than ever before, which presents a challenge for designing a seamless customer service journey. Multiple touchpoints, disconnected experiences, and increased costs can hinder customer satisfaction. Overcoming these challenges requires...

Your customers already want AI Customer Service. Artificial intelligence (AI) and Generative AI (GenAI) have created a new era of automating the most vital aspects of customer service. The results have shown boosted employee productivity, enhanced accuracy, and improved self-service containment rate.

Evaluating your organization’s tools, leadership, business model, and partnerships can identify areas of improvement needed to find success with your AI strategy. It’s not necessarily the case that AI will lead to automation of all business — and it may be your industry is one of the least-affected by this disruption — but these steps will ensure your organization is prepared for the future.

If you have been waiting for strong evidence before investing your organization’s time and resources into AI, the knowledge most organizations have already taken that step should be convincing. Now is the time to begin your journey into AI for your enterprise.

Large language models analyze datasets to derive patterns and rules as a method of learning and replicating human intelligence. As you can probably guess, the dataset used in a model can dramatically alter its understanding. We’ve used a number of analogies to explain the significance of this, but it boils down to the same principle: the inputs in LLMs greatly influence the outputs.

Large language models are a type of artificial intelligence (AI) infrastructure used to generate human-like text-based content based on the input they receive.

Harnessing the potential of AI will make the difference for competing organizations in the future. The opportunities made possible by this technology come with a number of new risks, but by following the steps outlined in this blog — and giving knowledge management the priority it deserves — business leaders can navigate the potential and risks of this new technology.

The accessibility and ease-of-use of your organization’s knowledge results in an agent who is immediately helpful at resolving customer questions. With one implementation, your support center can benefit from the service benefits of AI customer support including greater accuracy, better knowledge integration, and reduced onboarding costs.

Learn how Shelf’s AI-powered Answer Assist resolves one of the biggest pain points for support centers by creating entry-level experts.