Shelf Blog
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The healthcare business is now experiencing an increase in staffing requirements. With a huge amount of requests, it is nearly hard to give competent service. In such situations, there is a need to implement conversational AI in healthcare. By 2026, AI patient engagement will reduce the no-show...
When a company chooses a bot, it expects high-quality results. And when the results fall short of expectations, many assume the model is flawed. But almost always, the problem isn’t the model itself. A model may understand language flawlessly, yet it can generate a massive amount of text,...
Despite the rapid development of artificial intelligence, which is making headlines everywhere, many people still think that conversational AI is just a regular chatbot for FAQs. But let’s get one thing straight: if you think that, you’re stuck in 2019. Because in 2026, conversational AI use cases...
Generative AI for customer service has moved from the experimental stage to core infrastructure. By 2026, 85% of CX leaders plan to launch a customer-facing GenAI solution, and companies are seeing an average return of $3.50 for every $1 invested. But there’s one caveat that’s rarely discussed...
Conversational AI for customer service has advanced significantly, from programmed menus to autonomous agents. And advancement has been so quick that by 2026, enterprise conversational AI will be classified into three formats: rule-based chatbots, LLM-backed systems, and completely agent-based AI....
As AI becomes embedded across virtually every business function, keeping track of the terminology grows increasingly challenging. Some people talk about conversational AI, others insist on generative AI, and you may have even read that agentic AI is the best option. But is there actually a...
Every business owner knows how important it is to track Return on Investment (ROI). And of course, when it comes to implementing agentic AI, you want a clearer understanding of whether the investment will really pay off. Traditional automation, including RPA and classic chatbots, has already led...
Let’s go back just three years and recall that choosing knowledge management solutions used to be fairly straightforward: look at the search functionality, the editor, and access rights; compare prices; and make a decision. But today, the logic has changed, and if you choose based on the...
AI chatbots were actively implemented in 2024. By 2025, business owners realized that 90% accuracy wasn’t enough when a customer was facing a real problem. In 2026, contact centers went even further, and now the question is no longer “Do we have AI?” but “Is our AI capable of working independently...
The “build or buy” decision for an enterprise AI chatbot is no longer a binary choice. In 2026, the real question is: at what level of the AI stack is proprietary logic needed – and where is an off-the-shelf solution sufficient? The enterprise chatbot market grew to $10.3 billion in 2025 and...
Sooner or later, any AI implementation in customer support faces one question from management: Does it actually work? Some see no measurable impact at all, while others fall short of the results they anticipated. But the real answer lies in three metrics: average handling time, first-contact...
Let’s look at a real-life scenario: A team launches an AI chatbot, the deflection rate reaches 70%, and everyone is thrilled. But just three months later, the situation changed completely. Churn rises, CSAT falls, the most valuable customers leave and the source of each problem is the same chatbot...