When you open up your search engine and enter a query, whether it’s Google, Bing or DuckDuckGo, you’ll receive a list of hundreds of thousands of websites that may or may not match what you’re looking for. Your search engine doesn’t truly know your intent — it gauges what’s relevant based on specific keywords in your query.
This same dynamic is present when searching internal company knowledge, resulting in customer frustration, high hold times and general inefficiency for banks and credit unions.
That’s where generative artificial intelligence comes in.
By leveraging AI techniques, such as natural language processing, machine learning and deep learning, generative search tools have the potential to generate highly relevant, personalized and informative results. In fact, Google has even recently announced it’s testing a new AI search update to deliver better search results with greater context.
But what does this look like in the context of financial services? By providing employees with quick and efficient access to information, this can streamline workflows, reduce average handle time and elevate the overall customer experience.
So how does this type of tool work exactly?
Let’s say you’re looking for an easy dinner recipe with whatever is left in the fridge. Using a generative AI search tool, you can type in “show me an easy dinner recipe using chicken, spinach and mushrooms,” and the generative search engine would provide a simple step-by-step recipe to follow.
First, it interprets the context of your query to understand that you’re looking for an easy recipe using those three ingredients. Next, using machine learning and natural language processing, it would identify specific websites that have recipes containing those ingredients and ensure they are relevant to your query. To display the final, unique result, the generative AI search engine would incorporate the top results, including your ingredients, to offer a directed response.
What might a world of generative AI-enabled search engines look like compared to the traditional keyword-based results we see today?
Let’s take a look at a few of the primary differences, such as:
Without understanding how keywords work, many people struggle to find the results they need from their search engines. This is because it’s not natural for people to ask for information using highly specific, predefined words or phrases, and the engines themselves often overlook contextual nuances.
On the other hand, generative AI search engines allow users to ask complete (and often complex) questions, and natural language processing enables more conversational interactions. Because these AI models are able to understand a user’s intent and consider a broader context behind each input, they’re capable of providing highly directed responses.
Speaking of context, keyword search engines might save your history, but they don’t understand how those queries string together. In other words, your searches are siloed, which can cause frustration when you’re following up on related questions.
Generative AI search excels in this area, maintaining context between questions and answers to create a more natural, conversational interaction that builds on prior context.
Keyword searches often employ a more general vocabulary that often leads to results that overlap with various topics or industries. We’ve all been conditioned by conventional search tools to cast a wide-net. This can make it challenging to retrieve specific information, especially in niche fields or industries.
With generative AI, users can articulate more direct, context-specific inputs that make it easier to find exactly what they need. For example, the keyword query “broken bike chain” might yield broad results with repair shops, products and blog posts, while asking “How do I fix a bike chain that fell off?” would offer specific tips and instructions.
Traditional search engine results are also ranked by a variety of factors — and the publisher’s budget plays a large role in their placement. If you’ve ever found unhelpful or biased information at the top of a search engine results page, that’s likely why.
Generative AI search engines can avoid this by intelligently surveying multiple results to provide a single best answer.
Finally, keyword searches often require a literal match with a word or phrase in your inquiry. While this can be helpful, it can also limit results — especially when you need to use homonyms or you’re trying to exclude a specific keyword.
For instance, if you’re searching for a “bass shop near me,” a keyword-based search engine might send you to the Bass Pro Shop instead of the Guitar Center. And let’s say you’re in the market for a new phone, but you’d prefer one without a telephoto lens. By searching for, “smartphones without telephoto lens,” you might get some results worth viewing, but they will also include phones with this feature due to the keyword “telephoto lens.”
Meanwhile, generative AI searches are capable of understanding your question at a deeper level — the literal semantic meaning — to provide results that are directly related to your intent. That way, you receive a list of phones that use a periscope camera lens instead.
As an innovative pioneer in AI solutions for financial services, Posh empowers your institution to boost agent and customer satisfaction while saving time for high-value projects.
Knowledge Assistant is a game-changing, generative AI search solution designed to provide your agents with quick and easy access to the exact information they need — when they need it.
When your frontline staff don’t know the answer to a customer question, Knowledge Assistant speeds things along by providing agents the relevant information through a conversational interface. As an AI-enabled knowledge management platform, Knowledge Assistant helps you cut search time by 93%, saving your staff time while improving customer satisfaction.
Learn more about what AI solutions like Knowledge Assistant can do for you.